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Research methods--quantitative, qualitative, and more: overview.

Quantitative Research
Qualitative Research
Data Science Methods (Machine Learning, AI, Big Data)
Text Mining and Computational Text Analysis
Evidence Synthesis/Systematic Reviews
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About Research Methods

This guide provides an overview of research methods, how to choose and use them, and supports and resources at UC Berkeley.

As Patten and Newhart note in the book Understanding Research Methods , "Research methods are the building blocks of the scientific enterprise. They are the "how" for building systematic knowledge. The accumulation of knowledge through research is by its nature a collective endeavor. Each well-designed study provides evidence that may support, amend, refute, or deepen the understanding of existing knowledge...Decisions are important throughout the practice of research and are designed to help researchers collect evidence that includes the full spectrum of the phenomenon under study, to maintain logical rules, and to mitigate or account for possible sources of bias. In many ways, learning research methods is learning how to see and make these decisions."

The choice of methods varies by discipline, by the kind of phenomenon being studied and the data being used to study it, by the technology available, and more. This guide is an introduction, but if you don't see what you need here, always contact your subject librarian, and/or take a look to see if there's a library research guide that will answer your question.

Suggestions for changes and additions to this guide are welcome!

START HERE: SAGE Research Methods

Without question, the most comprehensive resource available from the library is SAGE Research Methods. HERE IS THE ONLINE GUIDE to this one-stop shopping collection, and some helpful links are below:

SAGE Research Methods
Little Green Books (Quantitative Methods)
Little Blue Books (Qualitative Methods)
Dictionaries and Encyclopedias
Case studies of real research projects
Sample datasets for hands-on practice
Streaming video--see methods come to life
Methodspace- -a community for researchers
SAGE Research Methods Course Mapping

Library Data Services at UC Berkeley

Library Data Services Program and Digital Scholarship Services

The LDSP offers a variety of services and tools ! From this link, check out pages for each of the following topics: discovering data, managing data, collecting data, GIS data, text data mining, publishing data, digital scholarship, open science, and the Research Data Management Program.

Be sure also to check out the visual guide to where to seek assistance on campus with any research question you may have!

Library GIS Services

Other Data Services at Berkeley

D-Lab Supports Berkeley faculty, staff, and graduate students with research in data intensive social science, including a wide range of training and workshop offerings Dryad Dryad is a simple self-service tool for researchers to use in publishing their datasets. It provides tools for the effective publication of and access to research data. Geospatial Innovation Facility (GIF) Provides leadership and training across a broad array of integrated mapping technologies on campu Research Data Management A UC Berkeley guide and consulting service for research data management issues

General Research Methods Resources

Here are some general resources for assistance:

Assistance from ICPSR (must create an account to access): Getting Help with Data , and Resources for Students
Wiley Stats Ref for background information on statistics topics
Survey Documentation and Analysis (SDA) . Program for easy web-based analysis of survey data.

Consultants

D-Lab/Data Science Discovery Consultants Request help with your research project from peer consultants.
Research data (RDM) consulting Meet with RDM consultants before designing the data security, storage, and sharing aspects of your qualitative project.
Statistics Department Consulting Services A service in which advanced graduate students, under faculty supervision, are available to consult during specified hours in the Fall and Spring semesters.

Related Resourcex

IRB / CPHS Qualitative research projects with human subjects often require that you go through an ethics review.
OURS (Office of Undergraduate Research and Scholarships) OURS supports undergraduates who want to embark on research projects and assistantships. In particular, check out their "Getting Started in Research" workshops
Sponsored Projects Sponsored projects works with researchers applying for major external grants.
Next: Quantitative Research >>
Last Updated: Apr 3, 2023 3:14 PM
URL: https://guides.lib.berkeley.edu/researchmethods

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Knowledge Base
Methodology

Research Methods | Definition, Types, Examples

Research methods are specific procedures for collecting and analysing data. Developing your research methods is an integral part of your research design . When planning your methods, there are two key decisions you will make.

First, decide how you will collect data . Your methods depend on what type of data you need to answer your research question :

Qualitative vs quantitative : Will your data take the form of words or numbers?
Primary vs secondary : Will you collect original data yourself, or will you use data that have already been collected by someone else?
Descriptive vs experimental : Will you take measurements of something as it is, or will you perform an experiment?

Second, decide how you will analyse the data .

For quantitative data, you can use statistical analysis methods to test relationships between variables.
For qualitative data, you can use methods such as thematic analysis to interpret patterns and meanings in the data.

Methods for collecting data, examples of data collection methods, methods for analysing data, examples of data analysis methods, frequently asked questions about methodology.

Data are the information that you collect for the purposes of answering your research question . The type of data you need depends on the aims of your research.

Qualitative vs quantitative data

Your choice of qualitative or quantitative data collection depends on the type of knowledge you want to develop.

For questions about ideas, experiences and meanings, or to study something that can’t be described numerically, collect qualitative data .

If you want to develop a more mechanistic understanding of a topic, or your research involves hypothesis testing , collect quantitative data .

You can also take a mixed methods approach, where you use both qualitative and quantitative research methods.

Primary vs secondary data

Primary data are any original information that you collect for the purposes of answering your research question (e.g. through surveys , observations and experiments ). Secondary data are information that has already been collected by other researchers (e.g. in a government census or previous scientific studies).

If you are exploring a novel research question, you’ll probably need to collect primary data. But if you want to synthesise existing knowledge, analyse historical trends, or identify patterns on a large scale, secondary data might be a better choice.

Descriptive vs experimental data

In descriptive research , you collect data about your study subject without intervening. The validity of your research will depend on your sampling method .

In experimental research , you systematically intervene in a process and measure the outcome. The validity of your research will depend on your experimental design .

To conduct an experiment, you need to be able to vary your independent variable , precisely measure your dependent variable, and control for confounding variables . If it’s practically and ethically possible, this method is the best choice for answering questions about cause and effect.

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Your data analysis methods will depend on the type of data you collect and how you prepare them for analysis.

Data can often be analysed both quantitatively and qualitatively. For example, survey responses could be analysed qualitatively by studying the meanings of responses or quantitatively by studying the frequencies of responses.

Qualitative analysis methods

Qualitative analysis is used to understand words, ideas, and experiences. You can use it to interpret data that were collected:

From open-ended survey and interview questions, literature reviews, case studies, and other sources that use text rather than numbers.
Using non-probability sampling methods .

Qualitative analysis tends to be quite flexible and relies on the researcher’s judgement, so you have to reflect carefully on your choices and assumptions.

Quantitative analysis methods

Quantitative analysis uses numbers and statistics to understand frequencies, averages and correlations (in descriptive studies) or cause-and-effect relationships (in experiments).

You can use quantitative analysis to interpret data that were collected either:

During an experiment.
Using probability sampling methods .

Because the data are collected and analysed in a statistically valid way, the results of quantitative analysis can be easily standardised and shared among researchers.

Quantitative research deals with numbers and statistics, while qualitative research deals with words and meanings.

Quantitative methods allow you to test a hypothesis by systematically collecting and analysing data, while qualitative methods allow you to explore ideas and experiences in depth.

In mixed methods research , you use both qualitative and quantitative data collection and analysis methods to answer your research question .

A sample is a subset of individuals from a larger population. Sampling means selecting the group that you will actually collect data from in your research.

For example, if you are researching the opinions of students in your university, you could survey a sample of 100 students.

Statistical sampling allows you to test a hypothesis about the characteristics of a population. There are various sampling methods you can use to ensure that your sample is representative of the population as a whole.

The research methods you use depend on the type of data you need to answer your research question .

If you want to measure something or test a hypothesis , use quantitative methods . If you want to explore ideas, thoughts, and meanings, use qualitative methods .
If you want to analyse a large amount of readily available data, use secondary data. If you want data specific to your purposes with control over how they are generated, collect primary data.
If you want to establish cause-and-effect relationships between variables , use experimental methods. If you want to understand the characteristics of a research subject, use descriptive methods.

Methodology refers to the overarching strategy and rationale of your research project . It involves studying the methods used in your field and the theories or principles behind them, in order to develop an approach that matches your objectives.

Methods are the specific tools and procedures you use to collect and analyse data (e.g. experiments, surveys , and statistical tests ).

In shorter scientific papers, where the aim is to report the findings of a specific study, you might simply describe what you did in a methods section .

In a longer or more complex research project, such as a thesis or dissertation , you will probably include a methodology section , where you explain your approach to answering the research questions and cite relevant sources to support your choice of methods.

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Effective Use of Statistics in Research – Methods and Tools for Data Analysis

Remember that impending feeling you get when you are asked to analyze your data! Now that you have all the required raw data, you need to statistically prove your hypothesis. Representing your numerical data as part of statistics in research will also help in breaking the stereotype of being a biology student who can’t do math.

Statistical methods are essential for scientific research. In fact, statistical methods dominate the scientific research as they include planning, designing, collecting data, analyzing, drawing meaningful interpretation and reporting of research findings. Furthermore, the results acquired from research project are meaningless raw data unless analyzed with statistical tools. Therefore, determining statistics in research is of utmost necessity to justify research findings. In this article, we will discuss how using statistical methods for biology could help draw meaningful conclusion to analyze biological studies.

Table of Contents

Role of Statistics in Biological Research

Statistics is a branch of science that deals with collection, organization and analysis of data from the sample to the whole population. Moreover, it aids in designing a study more meticulously and also give a logical reasoning in concluding the hypothesis. Furthermore, biology study focuses on study of living organisms and their complex living pathways, which are very dynamic and cannot be explained with logical reasoning. However, statistics is more complex a field of study that defines and explains study patterns based on the sample sizes used. To be precise, statistics provides a trend in the conducted study.

Biological researchers often disregard the use of statistics in their research planning, and mainly use statistical tools at the end of their experiment. Therefore, giving rise to a complicated set of results which are not easily analyzed from statistical tools in research. Statistics in research can help a researcher approach the study in a stepwise manner, wherein the statistical analysis in research follows –

1. Establishing a Sample Size

Usually, a biological experiment starts with choosing samples and selecting the right number of repetitive experiments. Statistics in research deals with basics in statistics that provides statistical randomness and law of using large samples. Statistics teaches how choosing a sample size from a random large pool of sample helps extrapolate statistical findings and reduce experimental bias and errors.

2. Testing of Hypothesis

When conducting a statistical study with large sample pool, biological researchers must make sure that a conclusion is statistically significant. To achieve this, a researcher must create a hypothesis before examining the distribution of data. Furthermore, statistics in research helps interpret the data clustered near the mean of distributed data or spread across the distribution. These trends help analyze the sample and signify the hypothesis.

3. Data Interpretation Through Analysis

When dealing with large data, statistics in research assist in data analysis. This helps researchers to draw an effective conclusion from their experiment and observations. Concluding the study manually or from visual observation may give erroneous results; therefore, thorough statistical analysis will take into consideration all the other statistical measures and variance in the sample to provide a detailed interpretation of the data. Therefore, researchers produce a detailed and important data to support the conclusion.

Types of Statistical Research Methods That Aid in Data Analysis

Statistical analysis is the process of analyzing samples of data into patterns or trends that help researchers anticipate situations and make appropriate research conclusions. Based on the type of data, statistical analyses are of the following type:

1. Descriptive Analysis

The descriptive statistical analysis allows organizing and summarizing the large data into graphs and tables . Descriptive analysis involves various processes such as tabulation, measure of central tendency, measure of dispersion or variance, skewness measurements etc.

2. Inferential Analysis

The inferential statistical analysis allows to extrapolate the data acquired from a small sample size to the complete population. This analysis helps draw conclusions and make decisions about the whole population on the basis of sample data. It is a highly recommended statistical method for research projects that work with smaller sample size and meaning to extrapolate conclusion for large population.

3. Predictive Analysis

Predictive analysis is used to make a prediction of future events. This analysis is approached by marketing companies, insurance organizations, online service providers, data-driven marketing, and financial corporations.

4. Prescriptive Analysis

Prescriptive analysis examines data to find out what can be done next. It is widely used in business analysis for finding out the best possible outcome for a situation. It is nearly related to descriptive and predictive analysis. However, prescriptive analysis deals with giving appropriate suggestions among the available preferences.

5. Exploratory Data Analysis

EDA is generally the first step of the data analysis process that is conducted before performing any other statistical analysis technique. It completely focuses on analyzing patterns in the data to recognize potential relationships. EDA is used to discover unknown associations within data, inspect missing data from collected data and obtain maximum insights.

6. Causal Analysis

Causal analysis assists in understanding and determining the reasons behind “why” things happen in a certain way, as they appear. This analysis helps identify root cause of failures or simply find the basic reason why something could happen. For example, causal analysis is used to understand what will happen to the provided variable if another variable changes.

7. Mechanistic Analysis

This is a least common type of statistical analysis. The mechanistic analysis is used in the process of big data analytics and biological science. It uses the concept of understanding individual changes in variables that cause changes in other variables correspondingly while excluding external influences.

Important Statistical Tools In Research

Researchers in the biological field find statistical analysis in research as the scariest aspect of completing research. However, statistical tools in research can help researchers understand what to do with data and how to interpret the results, making this process as easy as possible.

1. Statistical Package for Social Science (SPSS)

It is a widely used software package for human behavior research. SPSS can compile descriptive statistics, as well as graphical depictions of result. Moreover, it includes the option to create scripts that automate analysis or carry out more advanced statistical processing.

2. R Foundation for Statistical Computing

This software package is used among human behavior research and other fields. R is a powerful tool and has a steep learning curve. However, it requires a certain level of coding. Furthermore, it comes with an active community that is engaged in building and enhancing the software and the associated plugins.

3. MATLAB (The Mathworks)

It is an analytical platform and a programming language. Researchers and engineers use this software and create their own code and help answer their research question. While MatLab can be a difficult tool to use for novices, it offers flexibility in terms of what the researcher needs.

4. Microsoft Excel

Not the best solution for statistical analysis in research, but MS Excel offers wide variety of tools for data visualization and simple statistics. It is easy to generate summary and customizable graphs and figures. MS Excel is the most accessible option for those wanting to start with statistics.

5. Statistical Analysis Software (SAS)

It is a statistical platform used in business, healthcare, and human behavior research alike. It can carry out advanced analyzes and produce publication-worthy figures, tables and charts .

6. GraphPad Prism

It is a premium software that is primarily used among biology researchers. But, it offers a range of variety to be used in various other fields. Similar to SPSS, GraphPad gives scripting option to automate analyses to carry out complex statistical calculations.

This software offers basic as well as advanced statistical tools for data analysis. However, similar to GraphPad and SPSS, minitab needs command over coding and can offer automated analyses.

Use of Statistical Tools In Research and Data Analysis

Statistical tools manage the large data. Many biological studies use large data to analyze the trends and patterns in studies. Therefore, using statistical tools becomes essential, as they manage the large data sets, making data processing more convenient.

Following these steps will help biological researchers to showcase the statistics in research in detail, and develop accurate hypothesis and use correct tools for it.

There are a range of statistical tools in research which can help researchers manage their research data and improve the outcome of their research by better interpretation of data. You could use statistics in research by understanding the research question, knowledge of statistics and your personal experience in coding.

Have you faced challenges while using statistics in research? How did you manage it? Did you use any of the statistical tools to help you with your research data? Do write to us or comment below!

Frequently Asked Questions

Statistics in research can help a researcher approach the study in a stepwise manner: 1. Establishing a sample size 2. Testing of hypothesis 3. Data interpretation through analysis

Statistical tools in research can help researchers understand what to do with data and how to interpret the results, making this process as easy as possible. They can manage large data sets, making data processing more convenient. A great number of tools are available to carry out statistical analysis of data like SPSS, SAS (Statistical Analysis Software), and Minitab.

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Research Methods in Psychology - 4th American Edition

(40 reviews)

Carrie Cuttler, Washington State University

Rajiv S. Jhangiani, Kwantlen Polytechnic University

Dana C. Leighton, Texas A&M University, Texarkana

ISBN 13: 9781999198107

Publisher: Kwantlen Polytechnic University

Language: English

Formats Available

Conditions of use.

Learn more about reviews.

Reviewed by Beth Mechlin, Associate Professor of Psychology & Neuroscience, Earlham College on 3/19/24

This is an extremely comprehensive text for an undergraduate psychology course about research methods. It does an excellent job covering the basics of a variety of types of research design. It also includes important topics related to research... read more

Comprehensiveness rating: 5 see less

Content Accuracy rating: 5

I did not notice any errors in this text.

Relevance/Longevity rating: 5

The content is very relevant. It will likely need to be updated over time in order to keep research examples relevant. Additionally, APA formatting guidelines may need to be updated when a new publication manual is released. However, these should be easy updates for the authors to make when the time comes.

Clarity rating: 5

This text is very clear and easy to follow. The explanations are easy for college students to understand. The authors use a lot of examples to help illustrate specific concepts. They also incorporate a variety of relevant outside sources (such as videos) to provide additional examples.

Consistency rating: 5

The text is consistent and flows well from one section to the next. At the end of each large section (similar to a chapter) the authors provide key takeaways and exercises.

Modularity rating: 5

This text is very modular. It is easy to pick and choose which sections you want to use in your course when. Each section can stand alone fairly easily.

Organization/Structure/Flow rating: 5

The text is very well organized. Information flows smoothly from one topic to the next.

Interface rating: 5

The interface is great. The text is easy to navigate and the images display well (I only noticed 1 image in which the formatting was a tad off).

Grammatical Errors rating: 5

I did not notice any grammatical errors.

Cultural Relevance rating: 5

The text is culturally relevant.

This is an excellent text for an undergraduate research methods course in the field of Psychology. I have been using the text for my Research Methods and Statistics course for a few years now. This text focuses on research methods, so I do use another text to cover statistical information. I do highly recommend this text for research methods. It is comprehensive, clear, and easy for students to use.

Reviewed by William Johnson, Lecturer, Old Dominion University on 1/12/24

This textbook covers every topic that I teach in my Research Methods course aside from psychology careers (which I would not really expect it to cover). read more

This textbook covers every topic that I teach in my Research Methods course aside from psychology careers (which I would not really expect it to cover).

I have not noticed any inaccurate information (other than directed students to read Malcolm Gladwell). I appreciate that the textbook includes information on research errors that have not been supported by replication efforts, such as embodied cognition.

Many of the basic concepts of research methods are rather timeless, but I appreciate that the text includes newer research as examples while also including "classic" studies that exemplify different methods.

The writing is clear and simple. The keywords are bolded and reveal a definition when clicked, which students often find very helpful. Many of the figures are very helpful in helping students understand various methods (I really like the ones in the single-subject design subchapter).

The book is very consistent in its terminology and writing style, which I see as a positive compared to other open psychology textbooks where each chapter is written by subject matter experts (such as the NOBA intro textbook).

Modularity rating: 4

I teach this textbook almost entirely in order (except for moving chapters 12 & 13 earlier in the semester to aid students in writing Results sections in their final papers). I think that the organization and consistency of the book reduces its modularity, in that earlier chapters are genuinely helpful for later chapters.

Organization/Structure/Flow rating: 4

I preferred the organization of previous editions, which had "Theory in Research" as its own chapter. If I were organizing the textbook, I am not sure that I would have out descriptive or inferential statistics as the final two chapters (I would have likely put Chapter 11: Presenting Your Research as the final chapter). I also would not have put information about replicability and open science in the inferential statistics section.

The text is easy to read and the formatting is attractive. My only minor complaint is that some of the longer subchapters can be a pretty long scroll, but I understand the desire for their only to be one page per subchapter/topic.

I have not noticed any grammatical errors.

Cultural Relevance rating: 3

I do not think the textbook is insensitive, but there is not much thought given to adapting research instruments across cultures. For instance, talking about how different constructs might have different underlying distributions in different cultures would be useful for students. In the survey methods section, a discussion of back translation or emic personality trait measurement/development for example might be a nice addition.

I choose to use this textbook in my methods classes, but I do miss the organization of the previous American editions. Overall, I recommend this textbook to my colleagues.

Reviewed by Brianna Ewert, Psychology Instructor, Salish Kootenai College on 12/30/22

This text includes the majority of content included in our undergraduate Research Methods in Psychology course. The glossary provides concise definitions of key terms. This text includes most of the background knowledge we expect our students to... read more

Comprehensiveness rating: 4 see less

The content I have read is accurate and error-free.

The content is relevant and up-to-date.

The text is clear and concise. I find it pleasantly readable and anticipate undergraduate students will find it readable and understandable as well.

The terminology appears to be consistent throughout the text.

The modular sections stand alone and lend themselves to alignment with the syllabus of a particular course. I anticipate readily selecting relevant modules to assign in my course.

The book is logically organized with clear and section headings and subheadings. Content on a particular topic is easy to locate.

The text is easy to navigate and the format/design are clean and clear. There are not interface issues, distortions or distracting format in the pdf or online versions.

The text is grammatically correct.

Cultural Relevance rating: 4

I have not found culturally insensitive and offensive language or content in the text. For my courses, I would add examples and supplemental materials that are relevant for students at a Tribal College.

This textbook includes supplemental instructor materials, included slides and worksheets. I plan to adopt this text this year in our Research Methods in Psychology course. I expect it to be a benefit to the course and students.

Reviewed by Sara Peters, Associate Professor of Psychology, Newberry College on 11/3/22

This text serves as an excellent resource for introducing survey research methods topics to undergraduate students. It begins with a background of the science of psychology, the scientific method, and research ethics, before moving into the main types of research. This text covers experimental, non-experimental, survey, and quasi-experimental approaches, among others. It extends to factorial and single subject research, and within each topic is a subset (such as observational research, field studies, etc.) depending on the section.

I could find no accuracy issues with the text, and appreciated the discussions of research and cited studies.

There are revised editions of this textbook (this being the 4th), and the examples are up to date and clear. The inclusion of exercises at the end of each chapter offer potential for students to continue working with material in meaningful ways as they move through the book and (and course).

The prose for this text is well aimed at the undergraduate population. This book can easily be utilized for freshman/sophomore level students. It introduces the scientific terminology surrounding research methods and experimental design in a clear way, and the authors provide extensive examples of different studies and applications.

Terminology is consistent throughout the text. Aligns well with other research methods and statistics sources, so the vocabulary is transferrable beyond the text itself.

Navigating this book is a breeze. There are 13 chapters, and each have subsections that can be assigned. Within each chapter subsection, there is a set of learning objectives, and paragraphs are mixed in with tables and figures for students to have different visuals. Different application assignments within each chapter are highlighted with boxes, so students can think more deeply given a set of constructs as they consider different information. The last subsection in each chapter has key summaries and exercises.

The sections and topics in this text are very straightforward. The authors begin with an introduction of psychology as a science, and move into the scientific method, research ethics, and psychological measurement. They then present multiple different research methodologies that are well known and heavily utilized within the social sciences, before concluding with information on how to present your research, and also analyze your data. The text even provides links throughout to other free resources for a reader.

This book can be navigated either online (using a drop-down menu), or as a pdf download, so students can have an electronic copy if needed. All pictures and text display properly on screen, with no distortions. Very easy to use.

There were no grammatical errors, and nothing distracting within the text.

This book includes inclusive material in the discussion of research ethics, as well as when giving examples of the different types of research approaches. While there is always room for improvement in terms of examples, I was satisfied with the breadth of research the authors presented.

This text provides an overview of both research methods, and a nice introduction to statistics for a social science student. It would be a good choice for a survey research methods class, and if looking to change a statistics class into an open resource class, could also serve as a great resource.

Reviewed by Sharlene Fedorowicz, Adjunct Professor, Bridgewater State University on 6/23/21

The comprehensiveness of this book was appropriate for an introductory undergraduate psychology course. Critical topics are covered that are necessary for psychology students to obtain foundational learning concepts for research. Sections within the text and each chapter provide areas for class discussion with students to dive deeper into key concepts for better learning comprehension. The text covered APA format along with examples of research studies to supplement the learning. The text segues appropriately by introducing the science of psychology, followed by scientific method and ethics before getting into the core of scientific research in the field of psychology. Details are provided in quantitative and qualitative research, correlations, surveys, and research design. Overall, the text is fully comprehensive and necessary introductory research concepts.

The text appears to be accurate with no issues related to content.

Relevance/Longevity rating: 4

The text provided relevant research information to support the learning. The content was up-to-date with a variety of different examples related to the different fields of psychology. However, some topics such as in the pseudoscience section were not very relevant and bordered the line of beliefs. Here, more current or relevant solid examples would provide more relevancy in this part of the text. Bringing in more solid or concrete examples that are more current for students may have been more appropriate such as lack of connection between information found on social media versus real science.

The language and flow of the chapters accompanied by the terms, concepts, and examples of applied research allows for clarity of learning content. Terms were introduced at the appropriate time with the support of concepts and current or classic research. The writing style flows nicely and segues easily from concept to concept. The text is easy for students to understand and grasp the details related to psychological research and science.

The text provides consistency in the outline of each chapter. The beginning section chapter starts objectives as an overview to help students unpack the learning content. Key terms are consistently bolded followed by concept or definition and relevant examples. Research examples are pertinent and provide students with an opportunity to understand application of the contents. Practice exercises are provided with in the chapter and at the and in order for students to integrate learning concepts from within the text.

Sections and subsections are clearly organized and divided appropriately for ease-of-use. The topics are easily discernible and follow the flow of ideal learning routines for students. The sections and subsections are consistently outlined for each concept module. The modularity provides consistency allowing for students to focus on content rather than trying to discern how to pull out the information differently from each chapter or section. In addition, each section and subsection allow for flexibility in learning or expanding concepts within the content area.

The organization of the textbook was easy to follow and each major topic was outlined clearly. However, the chapter on presenting research may be more appropriately placed toward the end of the book rather than in the middle of the chapters related to research and research design. In addition, more information could have been provided upfront around APA format so that students could identify the format of citations within the text as practice for students throughout the book.

The interface of the book lends itself to a nice layout with appropriate examples and links to break up the different sections in the chapters. Examples where appropriate and provided engagement opportunities for the students for each learning module. Images and QR codes or easily viewed and used. Key terms are highlighted in relevant figures, graphs, and tables were appropriately placed. Overall, the interface of the text assisted with the organization and flow of learning material.

No grammatical errors were detected in this book.

The text appears to be culturally sensitive and not offensive. A variety of current and classic research examples are relevant. However, more examples of research from women, minorities, and ethnicities would strengthen the culture of this textbook. Instructors may need to supplement some research in this area to provide additional inclusivity.

Overall, I was impressed by the layout of the textbook and the ease of use. The layout provides a set of expectations for students related to the routine of how the book is laid out and how students will be able to unpack the information. Research examples were relevant, although I see areas where I will supplement information. The book provides opportunities for students to dive deeper into the learning and have rich conversations in the classroom. I plan to start using the psychology textbook for my students starting next year.

Reviewed by Anna Behler, Assistant Professo, North Carolina State University on 6/1/21

The text is very thorough and covers all of the necessary topics for an undergraduate psychology research methods course. There is even coverage of qualitative research, case studies, and the replication crisis which I have not seen in some other... read more

There were no issues with the accuracy of the text.

The content is very up to date and relevant for a research methods course. The only updates that will likely be necessary in the coming years are updates to examples and modifications to the section on APA formatting.

The clarity of the writing was good, and the chapters were written in a way that was accessible and easy to follow.

I did not note any issues with consistency.

Each chapter is divided into multiple subsections. This makes the chapters even easier to read, as they are broken down into short and easy to navigate sections. These sections make it easy to assign readings as needed depending on which topics are being covered in class.

Organization/Structure/Flow rating: 3

The organization was one of the few areas of weakness, and I felt that the chapters were ordered somewhat oddly. However, this is something that is easily fixed, as chapters (and even subsections) can be assigned in whatever order is needed.

There were no issues of note with the interface, and the PDF of the text was easy to navigate.

The text was well written and there were no grammatical/writing errors of note.

Overall, the book did not contain any notable instances of bias. However, it would probably be appropriate to offer a more thorough discussion of the WEIRD problem in psychology research.

Reviewed by Seth Surgan, Professor, Worcester State University on 5/24/21

Pitched very well for a 200-level Research Methods course. This text provided students with solid basis for class discussion and the further development of their understanding of fundamental concepts. read more

No issues with accuracy.

Coverage was on target, relevant, and applicable, with good examples from a variety of subfields within Psychology.

Clearly written -- students often struggle with the dry, technical nature of concepts in Research Methods. Part of the reason I chose this text in the first place was how favorably it compared to other options in terms of clarity.

No problems with inconsistent of shifting language. This is extremely important in Research Methods, where there are many closely related terms. Language was consistent and compatible with other textbook options that were available to my students.

Chapters are broken down into sections that are reasonably sized and conceptually appropriate.

The organization of this textbook fit perfectly with the syllabus I've been using (in one form or another) for 15+ years.

This textbook was easy to navigate and available in a variety of formats.

No problems at all.

Examples show an eye toward inclusivity. I did not detect any insensitive or offensive examples or undertones.

I have used this textbook for a 200-level Research Methods course run over a single summer session. This was my first experience using an OER textbook and I don't plan on going back.

Reviewed by Laura Getz, Assistant Professor, University of San Diego on 4/29/21

The topics covered seemed to be at an appropriate level for beginner undergraduate psychology students; the learning objectives for each subsection and the key takeaways and exercises for each chapter are also very helpful in guiding students’ attention to what is most relevant. The glossary is also thorough and a good resource for clear definitions. I would like to see a final chapter on a “big picture” or integrating key ideas of replication, meta-analysis, and open science.

Content Accuracy rating: 4

For the most part, I like the way information is presented. I had a few specific issues with definitions for ordinal variables being quantitative (1st, 2nd, 3rd aren’t really numbers as much as ranked categories), the lack of specificity about different forms of validity (face, content, criterion, and discriminant all just labeled “validity” whereas internal and external validity appear in different sections), and the lack of clear distinction between correlational and quasi-experimental variables (e.g., in some places, country of origin is listed as making a design quasi-experimental, but in other chapters it is defined as correlational).

Some of the specific studies/experiments mentioned do not seem like the best or most relevant for students to learn about the topics, but for the most part, content is up-to-date and can definitely be updated with new studies to illustrate concepts with relative ease.

Besides the few concepts I listed above in “accuracy”, I feel the text was very accessible, provides clear definitions, and many examples to illustrate any potential technical/jargon terms.

I did not notice any issues with inconsistent terms except for terms that do have more than one way of describing the same concept (e.g., 2-sample vs. independent samples t-test)

I assigned the chapters out of order with relative ease, and students did not comment about it being burdensome to navigate.

The order of chapters sometimes did not make sense to me (e.g., Experimental before Non-experimental designs, Quasi-experimental designs separate from other non-experimental designs, waiting until Chapter 11 to talk about writing), but for the most part, the chapter subsections were logical and clear.

Interface rating: 4

I had no issues navigating the online version of the textbook other than taking a while to figure out how to move forward and back within the text itself rather than going back to the table of contents (this might just be a browser issue, but is still worth considering).

No grammatical errors of note.

There was nothing explicitly insensitive or offensive about the text, but there were many places where I felt like more focus on diversity and individual differences could be helpful. For example, ethics and history of psychological testing would definitely be a place to bring in issues of systemic racism and/or sexism and a focus on WEIRD samples (which is mentioned briefly at another point).

I was very satisfied with this free resource overall, and I recommend it for beginning level undergraduate psychology research methods courses.

Reviewed by Laura Stull, Associate Professor, Anderson University on 4/23/21

This book covers essential topics and areas related to conducting introductory psychological research. It covers all critical topics, including the scientific method, research ethics, research designs, and basic descriptive and inferential statistics. It even goes beyond other texts in terms of offering specific guidance in areas like how to conduct research literature searches and psychological measurement development. The only area that appears slightly lacking is detailed guidance in the mechanics of writing in APA style (though excellent basic information is provided in chapter 11).

All content appears accurate. For example, experimental designs discussed, descriptive and inferential statistical guidance, and critical ethical issues are all accurately addressed, See comment on relevance below regarding some outdated information.

Relevance/Longevity rating: 3

Chapter 11 on APA style does not appear to cover the most current version of the APA style guide (7th edition). While much of the information in Chapter 11 is still current, there are specifics that did change from 6th to 7th edition of the APA manual and so, in order to be current, this information would have to be supplemented with external sources.

The book is extremely well organized, written in language and terms that should be easily understood by undergraduate freshmen, and explains all necessary technical jargon.

The text is consistent throughout in terms of terminology and the organizational framework (which aids in the readability of the text).

The text is divided into intuitive and common units based on basic psychological research methodology. It is clear and easy to follow and is divided in a way that would allow omission of some information if necessary (such as "single subject research") or reorganization of information (such as presenting survey research before experimental research) without disruption to the course as a whole.

As stated previously, the book is organized in a clear and logical fashion. Not only are the chapters presented in a logical order (starting with basic and critical information like overviews of the scientific method and research ethics and progressing to more complex topics like statistical analyses).

No issues with interface were noted. Helpful images/charts/web resources (e.g., Youtube videos) are embedded throughout and are even easy to follow in a print version of the text.

No grammatical issues were noted.

No issues with cultural bias are noted. Examples are included that address topics that are culturally sensitive in nature.

I ordered a print version of the text so that I could also view it as students would who prefer a print version. I am extremely impressed with what is offered. It covers all of the key content that I am currently covering with a (non-open source) textbook in an introduction to research methods course. The only concern I have is that APA style is not completely current and would need to be supplemented with a style guide. However, I consider this a minimal issue given all of the many strengths of the book.

Reviewed by Anika Gearhart, Instructor (TT), Leeward Community College on 4/22/21

Includes the majority of elements you expect from a textbook covering research methods. Some topics that could have been covered in a bit more depth were factorial research designs (no coverage of 3 or more independent variables) and external... read more

Nothing found that was inaccurate.

Looks like a few updates could be made to chapter 11 to bring it up to date with APA 7. Otherwise, most examples are current.

Very clear, a great fit for those very new to the topic.

The framework is clear and logical, and the learning objectives are very helpful for orienting the reader immediately to the main goals of each section.

Subsections are well-organized and clear. Titles for sections and subsections are clear.

Though I think the flow of this textbook for the most part is excellent, I would make two changes: move chapter 5 down with the other chapters on experimental research and move chapter 11 to the very end. I feel that this would allow for a more logical presentation of content.

The webpage navigation is easy to use and intuitive, the ebook download works as designed, and the page can be embedded directly into a variety of LMS sites or used with a variety of devices.

I found no grammatical errors in this book.

While there were some examples of studies that included participants from several cultures, the book does not touch on ecological validity, an important external validity issue tied to cultural psychology, and there is no mention of the WEIRD culture issue in psychology, which seems somewhat necessary when orienting new psychology students to research methods today.

I currently use and enjoy this textbook in my research methods class. Overall, it has been a great addition to the course, and I am easily able to supplement any areas that I feel aren't covered with enough breadth.

Reviewed by Amy Foley, Instructor/Field & Clinical Placement Coordinator, University of Indianapolis on 3/11/21

This text provides a comprehensive overview of the research process from ideation to proposal. It covers research designs common to psychology and related fields. read more

This text provides a comprehensive overview of the research process from ideation to proposal. It covers research designs common to psychology and related fields.

Accurate information!

This book is current and lines up well with the music therapy research course I teach as a supplemental text for students to understand research designs.

Clear language for psychology and related fields.

The format of the text is consistent. I appreciate the examples, different colored boxes, questions, and links to external sources such as video clips.

It is easy to navigate this text by chapters and smaller units within each chapter. The only confusion that has come from using this text includes the fact that the larger units have roman numerals and the individual chapters have numbers. I have told students to "read unit six" and they only read the small chapter 6, not the entire unit for example.

Flows well!

I have not experienced any interface issues.

I have not found any grammar errors.

Book appears culturally relevant.

This is a great resource for research methods courses in psychology or related fields. I am glad to have used several chapters of this text within the music therapy research course I teach where students learn about research design and then create their own research proposal.

Reviewed by Veronica Howard, Associate Professor, University of Alaska Anchorage on 1/11/21, updated 1/11/21

VERY impressed by the coverage of single subject designs. I would recommend this content to colleagues. read more

VERY impressed by the coverage of single subject designs. I would recommend this content to colleagues.

Content appears accurate.

By expanding to include more contemporary research perspectives, the authors have created a wonderful dynamic that permits the text to be the foundation for many courses as well as revision and remixing for other authors.

Book easy to read, follow.

Consistency rating: 4

Content overall consistent. Only mild inconsistency in writing style between chapters.

Exceptionally modular. All content neatly divided into units with smaller portions. This would be a great book to use in a course that meets bi-weekly, or adapted into other formats.

Content organized in a clear and logical fashion, and would guide students through a semester-long course on research methods, starting with review content, broad overview of procedures (including limitations), then highlighting less common (though relevant) procedures.

Rich variety of formats for use.

No errors found.

I would appreciate more cultural examples.

Reviewed by Greg Mullin, Associate Professor, Bunker Hill Community College on 12/30/20, updated 1/6/21

I was VERY pleased with the comprehensiveness of the text. I believe it actually has an edge over the publisher-based text that I've been using for years. Each major topic was thoroughly covered with more than enough detail on individual concepts. read more

I did not find any errors within the text. The authors provided an unbiased representation of research methods in psychology.

The content connects to classic, timeless examples in the field, but also mixes in a fair amount of more current, relatable examples. I feel like I'll be able to use this version of the text for many years without its age showing.

The authors present a clear and efficient writing style throughout that is rich with relatable examples. The only area that may be a bit much for undergraduate-level student understanding is the topic of statistics. I personally scale back my discussion of statistics in my Intro to Research Methods course, but for those that prefer a deeper dive, the higher-level elements are there.

I did not notice any shifts with the use of terminology or with the structural framework of the text. The text is very consistent and organized in an easily digestible way.

The authors do a fantastic job breaking complex topics down into manageable chunks both as a whole and within chapters. As I was reading, I could easily see how I could align my current approach of teaching Intro to Research Methods with their modulated presentation of the material.

I effortlessly moved through the text given the structural organization. All topics are presented in a logical fashion that allowed each message to be delivered to the reader with ease.

I read the text through the PDF version and found no issue with the interface. All image and text-based material was presented clearly.

I cannot recall coming across any grammatical errors. The text is very well written.

I did not find the text to be culturally insensitive in any way. The authors use inclusive language and even encourage that style of writing in the chapter on Presenting Your Research. I would have liked to see more cross-cultural research examples and more of an extended effort to include the theme of diversity throughout, but at no point did I find the text to be offensive.

This is a fantastic text and I look forward to adopting it for my Intro to Research Methods course in the Spring. :)

Reviewed by Maureen O'Connell, Adjunct Professor, Bunker Hill Community College on 12/15/20, updated 12/18/20

This text edition has covered all ideas and areas of research methods in psychology. It has provided a glossary of terms, sample APA format, and sample research papers. read more

This text edition has covered all ideas and areas of research methods in psychology. It has provided a glossary of terms, sample APA format, and sample research papers.

The content is unbiased, accurate, and I did not find any errors in the text.

The content is current and up-to-date. I found that the text can be added to should material change, the arrangement of the text/content makes it easily accessible to add material, if necessary.

The text is clear, easy to understand, simplistic writing at times, but I find this text easy for students to comprehend. All text is relevant to the content of behavioral research.

The text and terminology is consistent.

The text is organized well and sectioned appropriately. The information is presented in an easy-to-read format, with sections that can be assigned at various points during the semester and the reader can easily locate this.

The topics in the text are organized in a logical and clear manner. It flows really well.

The text is presented well, including charts, diagrams, and images. There did not appear to be any confusion with this text.

The text contains no grammatical errors.

The text was culturally appropriate and not offensive. Clear examples of potential biases were outlined in this text which I found quite helpful for the reader.

Overall, I found this to be a great edition. Much of the time I spend researching outside material for students has been included in this text. I enjoyed the format, easier to navigate, helpful to students by providing an updated version of discussions and practice assignments, and visually more appealing.

Reviewed by Brittany Jeye, Assistant Professor of Psychology, Worcester State University on 6/26/20

All of the main topics in a Research Methods course are covered in this textbook (e.g., scientific method, ethics, measurement, experimental design, hypothesis testing, APA style, etc.). Some of these topics are not covered as in-depth as in other Research Method textbooks I have used previously, but this actually may be a positive depending on the students and course level (that is, students may only need a solid overview of certain topics without getting overwhelmed with too many details). It also gives the instructor the ability to add content as needed, which helps with flexibility in course design.

I did not note any errors or inaccurate/biasing statements in the text.

For the most part, everything was up to date. There was a good mix of classic research and newer studies presented and/or used as examples, which kept the chapters interesting, topical and relevant. I only noted the section on APA Style in the chapter “Presenting Your Research” which may need some updating to be in line with the new APA 7th edition. However, there should be only minor edits needed (the chapter itself was great overview and introduction to the main points of APA style) and it looks like they should be relatively easy to implement.

The text was very well-written and was presented at an accessible level for undergraduates new to Research Methods. Terms were well-defined with a helpful glossary at the end of the textbook.

The consistent structure of the textbook is huge positive. Each chapter begins with learning objectives and ends with bulleted key takeaways. There are also good exercises and learning activities for students at the end of each chapter. Instructors may need to add their own activities for chapters that do not go into a lot of depth (there are also instructor resources online, which may have more options available).

This is one of the biggest strengths of this textbook, in my opinion. I appreciate how each chapter is broken down into clearly defined subsections. The chapters and the subsections, in particular, are not lengthy, which is great for students’ learning. These subsections could be reorganized and used in a variety of ways to suit the needs of a particular course (or even as standalone subsections).

The topics were presented in a logical manner. As mentioned above, since the textbook is very modular, I feel that you could easily rearrange the chapters to fit your needs (for example, presenting survey design before experimental research or making the presenting your research section a standalone unit).

I downloaded the textbook as an ebook, which was very easy to use/navigate. There were no problems reading any of the text or figures/tables. I also appreciated that you could open the ebook using a variety of apps (Kindle, iBook, etc.) depending on your preference (and this is good for students who have a variety of technical needs).

There were no grammatical errors noted.

The examples were inclusive of races, ethnicity and background and there were not any examples that were culturally insensitive or offensive in any way. In future iterations of the replicability section, it may be beneficial to touch upon the “weird” phenomena in psychology research (that many studies use participants who are western, educated and from industrialized, rich and democratic countries) as a point to engage students in improving psychological practices.

I will definitely consider switching to this textbook in the future for Research Methods.

Reviewed by Alice Frye, Associate Teaching Professor, University of Massachusetts Lowell on 6/22/20

Hits all the necessary marks from ways of knowing to measurement, research designs, and presentation. Comparable in detail and content to other Research Methods texts I have used for teaching. read more

Hits all the necessary marks from ways of knowing to measurement, research designs, and presentation. Comparable in detail and content to other Research Methods texts I have used for teaching.

Correct and to the point. Complex ideas such as internal consistency reliability and discriminant validity are well handled--correct descriptions that are also succinct and articulated simply and with clear examples that are easy for a student reader to grasp.

Seems likely to have good staying power. One area that has changed quickly in the past is the usefulness of various research data bases. So it is possible that portion could become more quickly outdated, but there is no predicting that. The current descriptions are useful.

Very clearly written without being condescending, overly casual or clunky.

Excellent consistency throughout in terms of organization, language usage, level of detail and tone.

Imho this is one of the particular strengths of the text. Chapters are well divided into discrete parts, which seems likely to be a benefit in cohorts of students who are increasingly accustomed to digesting small amounts of information.

Well organized, straightforward structure that is maintained throughout.

No problems with the interface.

The grammar level is another notable strength. Ideas are articulated clearly, and with sophistication, but in a syntactically very straightforward manner.

The text isn't biased or offensive. I wish that to illustrate various points and research designs it had drawn more frequently on research studies that incorporate a specific focus on race and ethnicity.

This is a very good text. As good as any for profit text I have used to teach a research methods course, if not better.

Reviewed by Lauren Mathieu-Frasier, Adjunct Instructor, University of Indianapolis on 1/13/20

As other reviews have mentioned, this textbook provides a comprehensive look at multiple concepts for an introductory course in research methods in psychology. Some of the concepts (i.e., variables, external validity) are briefly described and glossed over that it will take additional information, examples, and reinforcement from instructors in the classroom. Other sections and concepts, like ethics or reporting of research were well-described and thorough.

It appeared that the information was accurate, error-free, and unbiased.

The information is up-to-date. In the section on APA presentation, it looks like the minor adjustments to the APA Publication Manual 7th Edition would need to be included. However, this section gives a good foundation and the instructor can easily implement the changes.

Clarity rating: 4

The text is clearly written written and provides an appropriate context when terminology is used.

There aren't any issues with consistency in the textbook.

The division of smaller sections can be beneficial when reading it and assigning it to classes. The sections are clearly organized based on learning objectives.

The textbook is organized in a logical, clear manner. There may be topics that instructors choose to present in a different manner (non-experimental and survey research prior to experimental). However, this doesn't generally impact the organization and flow of the book.

While reading and utilizing the book, there weren't any navigation issues that could impact the readability of the book. Students could find this textbook easy to use.

Grammatical errors were not noted.

There weren't any issues with cultural sensitivity in the examples of studies used in the textbook.

Reviewed by Tiffany Kindratt, Assistant Professor, University of Texas at Arlington on 1/1/20

The text is comprehensive with an effective glossary of terms at the end. It would be beneficial to include additional examples and exercises for students to better understand concepts covered in Chapter II, Overview of the Scientific Method,... read more

The text is accurate and there are minimal type/grammatical errors throughout. The verbiage is written in an unbiased manner consistently throughout the textbook.

The content is up-to-date, and examples can be easily updated for future versions. As a public health instructor, I would be interested in seeing examples of community-based examples in future versions. The current examples provided are relevant for undergraduate public health students as well as psychology students.

The text is written in a clear manner. The studies used can be easily understood by undergraduate students in other social science fields, such as public health. More examples and exercises using inferential statistics would be helpful for students to better grasp the concepts.

The framework for each chapter and terminology used are consistent. It is helpful that each section within each chapter begins with learning objectives and the chapter ends with key takeaways and exercises.

The text is clearly divided into sections within each chapter. When I first started reviewing this textbook, I thought each section was actually a very short chapter. I would recommend including a listing of all of the objectives covered in each chapter at the beginning to improve the modularity of the text.

Some of the topics do not follow a logical order. For example, it would be more appropriate to discuss ethics before providing the overview of the scientific method. It would be better to discuss statistics used to determine results before describing how to write manuscripts. However, the text is written in a way that that the chapters could be assigned to students in a different order without impacting the students’ comprehension of the concepts.

I did not encounter any interface issues when reviewing this text. All links worked and there were no distortions of the images or charts that may confuse the reader. There are several data tables throughout the text which are left-aligned and there is a large amount of empty white space next it. I would rearrange the text in future versions to make better use of this space.

The text contains minimal grammatical errors.

The examples are culturally relevant. I did not see any examples that may be considered culturally insensitive or offensive in any way.

As an instructor for an undergraduate public health sciences and methods course, I will consider using some of the content in this text to supplement the current textbook in the future.

Reviewed by Mickey White, Assistant Professor, East Tennessee State University on 10/23/19

The table of contents is well-formatted and comprehensive. Easy to navigate and find exactly what is needed, students would be able to quickly find needed subjects. read more

The table of contents is well-formatted and comprehensive. Easy to navigate and find exactly what is needed, students would be able to quickly find needed subjects.

Content appears to be accurate and up-to-date.

This text is useful and relevant, particularly with regard to expressing and reporting descriptive statistics and results. As APA updates, the text will be easy to edit, as the sections are separated.

Easy to read and engaging.

Chapters were laid out in a consistent manner, which allows readers to know what is coming. The subsections contained a brief overview and terminology was consistent throughout. The glossary added additional information.

Sections and subsections are delineated in a usable format.

The key takeaways were useful, including the exercises at the end of each chapter.

Reading the book online is a little difficult to navigate page-by-page, but e-pub and PDF formats are easy to navigate.

No errors noted.

Would be helpful to have a clearer exploration of cultural factors impacting research, including historical bias in assessment and research outside of research ethics.

Reviewed by Robert Michael, Assistant Professor, University of Louisiana at Lafayette on 10/14/19

Successfully spans the gamut of topics expected in a Research Methods textbook. Some topics are covered in-depth, while others are addressed only at a surface level. Instructors may therefore need to carefully arrange class material for topics in... read more

The factual content was error-free, according to my reading. I did spot a few grammatical and typographical errors, but they were infrequent and minor.

Great to see nuanced—although limited—discussion of issues with Null Hypothesis Significance Testing, Reproducibility in Psychological Science, and so forth. I expect that these areas are likely to grow in future editions, perhaps supplementing or even replacing more traditional material.

Extremely easy to read with multiple examples throughout to illustrate the principles being covered. Many of these examples are "classics" that students can easily relate to. Plus, who doesn't like XKCD comics?

The textbook is structured sensibly. At times, certain authors' "voices" seemed apparent in the writing, but I suspect this variability is unlikely to be noticed by or even bothersome to the vast majority of readers.

The topics are easily divisible and seem to follow routine expectations. Instructors might find it beneficial and/or necessary to incorporate some of the statistical thinking and learning into various earlier chapters to facilitate student understanding in-the-moment, rather than trying to leave all the statistics to the end.

Sensible and easy-to-follow structure. As per "Modularity", the Statistical sections may benefit from instructors folding in such learning throughout, rather than only at the end.

Beautifully presented, crisp, easy-to-read and navigate. Caveat: I read this online, in a web-browser, on only one device. I haven't tested across multiple platforms.

High quality writing throughout. Only a few minor slip-ups that could be easily fixed.

Includes limited culturally relevant material where appropriate.

Reviewed by Matthew DeCarlo, Assistant Professor, Radford University on 6/26/19

The authors do a great job of simplifying the concepts of research methods and presenting them in a way that is understandable. There is a tradeoff between brevity and depth here. Faculty who adopt this textbook may need to spend more time in class going in depth into concepts, rather than relying on the textbook for all of the information related to key concepts. The text does not cover qualitative methods in detail.

The textbook provides an accurate picture of research methods. The tone is objective and without bias.

The textbook is highly relevant and up to date. Examples are drawn from modern theories and articles.

The writing is a fantastic mix of objective and authoritative while also being approachable.

The book coheres well together. Each chapter and section are uniform.

This book fits very well within a traditional 16 week semester, covering roughly a chapter per week. One could take out specific chapters and assign them individually if research methods is taught in a different way from a standard research textbook.

Content is very well organized. The table of contents is easy to navigate and each chapter is presented in a clear and consistent manner. The use of a two-tier table of contents is particularly helpful.

Standard pressbooks interface, which is great. Uses all of the standard components of Pressbooks well, though the lack of H5P and interactive content is a drawback.

I did not notice any grammar errors.

Cultural Relevance rating: 2

The book does not deal with cultural competence and humility in the research process. Integration of action research and decolonization perspectives would be helpful.

Reviewed by Christopher Garris, Associate Professor, Metropolitan State University of Denver on 5/24/19

Most content areas in this textbook were covered appropriately extensively. Notably, this textbook included some content that is commonly missing in other textbooks (e.g. presenting your research). There were some areas where more elaboration and more examples were needed. For example, the section covering measurement validities included all the important concepts, but needed more guidance for student comprehension. Also, the beginning chapters on 'common sense' reasoning and pseudoscience seemed a little too brief.

Overall, this textbook appeared to be free from glaring errors. There were a couple of instances of concern, but were not errors, per se. For example, the cut-off for Cronbach's alpha was stated definitively at .80, while this value likely would be debated among researchers.

This textbook was presented in such a way that seemed protect it from becoming obsolete within the next few years. This is important for continued, consistent use of the book. The authors have revised this book, and those revisions are clearly summarized in the text. Importantly, the APA section of the textbook appears to be up-to-date. Also, the use of QR codes throughout the text is a nice touch that students may appreciate.

Connected to comprehensiveness, there are some important content areas that I felt were lacking in elaboration and examples (e.g. testing the validity of measurement; introduction of experimental design), which inhibits clarity. Overall, however, the topics seemed to be presented in a straightforward, accessible manner. The textbook includes links to informative videos and walk-throughs where appropriate, which seem to be potentially beneficial for student comprehension. The textbook includes tools designed to aid learning, namely "Key Takeaways" and "Exercises" sections at the end of most modules, but not all. "Key Takeaways" seemed valuable, as they were a nice bookend to the learning objectives stated at the beginning of each module. "Exercises" did not appear to be as valuable, especially for the less-motivated student. On their face, these seemed to be more designed for instructors to use as class activities/active learning. Lastly, many modules of the textbook were text-heavy and visually unappealing. While this is superficial, the inclusion of additional graphics, example boxes, or figures in these text-heavy modules might be beneficial.

The textbook appeared to be internally consistent with its approach and use of terminology.

The textbook had a tendency to 'throw out' big concepts very briefly in earlier modules (e.g. sampling, experimental/non-experimental design), and then cover them in more detail in later modules. This would have been less problematic if the text would explicitly inform the student that these concepts would be elaborated upon later. Beyond this issue, the textbook seems to lend itself to being divided up and used on module-by-module basis.

The organization of the chapters did not make intuitive sense to me. The fact that correlation followed experimental research, and that descriptive research was the second-to-last module in the sequence was confusing. That said, textbook is written in such a way that an instructor easily assign the modules in the order that works best for their class.

Overall, the interface worked smoothly and there were few technical issues. Where there were issues (e.g. inconsistent spacing between lines and words), they were negligible.

The text seemed to be free from glaring grammatical problems.

Because this is a methodology textbook, it does not lend itself to too much cultural criticism. That said, the book did not rely on overly controversial examples, but also didn't shy away from important cultural topics (e.g. gender stereotypes, vaccines).

Reviewed by Michel Heijnen, Assistant Professor, University of North Carolina Wilmington on 3/27/18

The book covers all areas related to research methods, not only for the field of psychology, but also to other related fields like exercise science. Topics include ethics, developing a research questions, experimental designs, non-experimental... read more

Reviewed content is accurate and seems free of any personal bias.

The topic of research methods in general is not expected to change quickly. It is not expected that this text will become obsolete in the near future. Furthermore, for both the field of psychology as well as other related fields, the examples will continue to have an application to explain certain concepts and will not be outdated soon, even with new research emerging every day.

The text is written so an undergraduate student should be able to understand the concepts. The examples provided in the text greatly contribute to the understanding of the topics and the proposed exercises at the end of each chapter will further apply the knowledge.

The layout and writing style are consistent throughout the text.

Layout of the text is clear, with multiple subsection within each chapter. Each chapter can easily be split into multiple subsection to assign to students. No evidence of self-refers was observed, and individual chapters could be assigned to students without needed to read all preceding chapters. For example, Chapter 4 may not be particularly useful to students outside of psychology, but an instructor can easily reorganize the text and skip this chapter while students can still understand following chapters.

Topics are addressed in a logical manner. Overall, an introduction to research is provided first (including ethics to research), which is followed by different types of research, and concludes with types of analysis.

No images or tables are distorted, making the text easy to read.

No grammatical errors observed in text.

Text is not offensive and does not appear to be culturally insensitive.

I believe that this book is a great resource and, as mentioned previously, can be used for a wider audience than just psychology as the basics of research methods can be applied to various fields, including exercise science.

Reviewed by Chris Koch, Professor of Psychology, George Fox University on 3/27/18

All appropriate areas and topics are covered in the text. In that sense, this book is equivalent to other top texts dealing with research methods in psychology. The appeal of this book is the brevity and clarity. Therefore, some may find that, although the topics are covered, topics may not be covered as thoroughly they might like. Overall, the coverage is solid for an introductory course in research methods.

In terms of presentation, this book could be more comprehensive. Each chapter does start with a set of learning objectives and ends with "takeaways" and a short set of exercises. However, it lacks detailed chapter outlines, summaries, and glossaries. Furthermore, an index does not accompany the text.

I found the book to be accurate with content being fairly presented. There was no underlying bias throughout the book.

This is an introductory text for research methods. The basics of research methods have been consistent for some time. The examples used in the text fit the concepts well. Therefore, it should not be quickly dated. It is organized in such a way that sections could be easily modified with more current examples as needed.

The text is easy to read. It is succinct yet engaging. Examples are clear and terminology is adequately defined.

New terms and concepts are dealt with chapter by chapter. However, those things which go across chapters are consistently presented.

The material for each chapter is presented in subsections with each subsection being tied to a particular learning objective. It is possible to use the book by subsection instead of by chapter. In fact, I did that during class by discussing the majority of one chapter, discussing another chapter, and then covering what I previously skipped,

In general, the book follows a "traditional" organization, matching the organization of many competing books. As mentioned in regard to modularity, I did not follow the organization of the book exactly as it was laid out. This may not necessarily reflect poorly on the book, however, since I have never followed the order of any research methods book. My three exams covered chapter 1 through 4, chapters 5, 6, part of 8, and chapters 7, the remainder of 8, 9, and 10. Once we collected data I covered chapters 11 through 13.

Interface rating: 3

The text and images are clear and distortion free. The text is available in several formats including epub, pdf, mobi, odt, and wxr. Unfortunately, the electronic format is not taken full advantage of. The text could be more interactive. As it is, it is just text and images. Therefore, the interface could be improved.

The book appeared to be well written and edited.

I did not find anything in the book that was culturally insensitive or offensive. However, more examples of cross-cultural research could be included.

I was, honestly, surprised by how much I liked the text. The material was presented in easy to follow format that is consistent with how I think about research methods. That made the text extremely easy to use. Students also thought the book was highly accessible Each chapter was relatively short but informative and easy to read.

Reviewed by Kevin White, Assistant Professor, East Carolina University on 2/1/18

This book covers all relevant topics for an introduction to research methods course in the social sciences, including measurement, sampling, basic research design, and ethics. The chapters were long enough to be somewhat comprehensive, but short enough to be digestible for students in an introductory-level class. Student reviews of the book have so far been very positive. The only section of the text for which more detail may be helpful is 2.3 (Reviewing the Research Literature), in which more specific instructions related to literature searches may be helpful to students.

I did not notice any issues related to accuracy. Content appeared to be accurate, error-free, and unbiased.

One advantage of this book is that it is relevant to other applied fields outside of psychology (e.g., social work, counseling, etc.). Also, the exercises at the end of chapter sections are helpful.

The clarity of the text provides students with succinct definitions for research-related concepts, without unnecessary discipline-specific jargon. One suggestion for future editions would be to make the distinctions between different types of non-experimental research a bit more clear for students in introductory classes (e.g., "Correlational Research" in Section 7.2).

Formatting and terminology was consistent throughout this text.

A nice feature of this book is that instructors can select individual sections within chapters, or even jump between sections within chapters. For example, Section 1.4 may not fit for a class that is less clinically-oriented in nature.

The flow of the text was appropriate, with ethics close to the beginning of the book (and an entire chapter devoted to it), and descriptive/inferential statistics at the end.

I did not notice any problems related to interface. I had no trouble accessing or reading the text, and the images were clear.

The text contained no discernible grammatical errors.

The book does not appear to be culturally insensitive in any discernible way, and explicitly addresses prejudice in research (e.g., Section 5.2). However, I think that continuing to add more examples that relate to specific marginalized groups would help improve the text (and especially exercises).

Overall, this book is very useful for an introductory research methods course in psychology or social work, and I highly recommend.

Reviewed by Elizabeth Do, Instructor, Virginia Commonwealth University on 2/1/18

Although this textbook does provide good information regarding introductory concepts necessary for the understanding of correlational designs, and is presented in a logical order. It does not, however, cover qualitative methodologies, or research... read more

There does not seem to be any errors within the text.

Since this textbook covers a topic that is unlikely to change over the years and it's content is up-to-date, it remains relevant to the field.

The textbook is written at an appropriate level for undergraduate students and is useful in that it does explain important terminology.

There does not seem to be any major inconsistencies within the text.

Overall, the text is very well organized - it is separated into chapters that are divided up into modules and within each module, there are clear learning objectives. It is also helpful that the textbook includes useful exercises for students to practice what they've read about from the text.

The topics covered by this textbook are presented in an order that is logical. The writing is clear and the examples are very useful. However, more information could be provided in some of the chapters and it would be useful to include a table of contents that links to the different chapters within the PDF copy, for reader's ease in navigation when looking for specific terms and/or topics.

Overall, the PDF copy of the textbook made it easy to read; however, there did seem to be a few links that were missing. Additionally, it would be helpful to have some of the graphs printed in color to help with ease of following explanations provided by the text. The inclusion of a table of contents would also be useful for greater ease with navigation.

There does not seem to be any grammatical errors in the textbook. Also, the textbook is written in a clear way, and the information flows nicely.

This textbook focuses primarily on examples from the United States. It does not seem to be culturally insensitive or offensive in anyway and I liked that it included content regarding the avoidance of biased language (chapter 11).

This textbook makes the material very accessible, and it is easy to read/follow examples.

Reviewed by Eric Lindsey, Professor, Penn State University Berks Campus on 2/1/18

The content of the Research Methods in Psychology textbook was very thorough and covered what I would consider to be the important concepts and issues pertaining to research methods. I would judge that the textbook has a comparable coverage of information to other textbooks I have reviewed, including the current textbook I am using. The range of scholarly sources included in the textbook was good, with an appropriate balance between older and classic research examples and newer more cutting edge research information. Overall, the textbook provides substantive coverage of the science of conducting research in the field of psychology, supported by good examples, and thoughtful questions.

The textbook adopts a coherent and student-friendly format, and offers a precise introduction to psychological research methodology that includes consideration of a broad range of qualitative and quantitative methods to help students identify and evaluate the best approach for their research needs. The textbook offers a detailed review of the way that psychological researchers approach their craft. The author guides the reader through all aspects of the research process including formulating objectives, choosing research methods, securing research participants, as well as advice on how to effectively collect, analyze and interpret data and disseminate those findings to others through a variety of presentation and publication venues. The textbook offers relevant supplemental information in textboxes that is highly relevant to the material in the accompanying text and should prove helpful to learners. Likewise the graphics and figures that are included are highly relevant and clearly linked to the material presented in the text. The information covered by the textbook reflects an accurate summary of current techniques and methods used in research in the field of psychology. The presentation of information addresses the pros and cons of different research strategies in an objective and evenhanded way.

The range of scholarly sources included in the textbook was good, with an appropriate balance between older, classic research evidence and newer, cutting edge research. Overall, the textbook provides substantive coverage of the science on most topics in research methods of psychology, supported by good case studies, and thoughtful questions. The book is generally up to date, with adequate coverage of basic data collection methods and statistical techniques. Likewise the review of APA style guidelines is reflects the current manual and I like the way the author summarizes changes from the older version of the APA manual. The organization of the textbook does appear to lend itself to editing and adding new information with updates in the future.

I found the textbook chapters to be well written, in a straightforward yet conversational manner. It gives the reader an impression of being taught by a knowledgeable yet approachable expert. The writing style gives the learner a feeling of being guided through the lessons and supported in a very conversational approach. The experience of reading the textbook is less like being taught and more like a colleague sharing information. Furthermore, the style keeps the reader engaged but doesn't detract from its educational purpose. I also appreciate that the writing is appropriately concise. No explanations are so wordy as to overwhelm or lull the reader to sleep, but at the same time the information is not so vague that the reader can't understand the point at all.

The book’s main aim is to enable students to develop their own skills as researchers, so they can generate and advance common knowledge on a variety of psychological topics. The book achieves this objective by introducing its readers, step-by-step, to psychological research design, while maintaining an excellent balance between substance and attention grabbing examples that is uncommon in other research methods textbooks. Its accessible language and easy-to-follow structure and examples lend themselves to encouraging readers to move away from the mere memorization of facts, formulas and techniques towards a more critical evaluation of their own ideas and work – both inside and outside the classroom. The content of the chapters have a very good flow that help the reader to connect information in a progressive manner as they proceed through the textbook.

Each chapter goes into adequate depth in reviewing both past and current research related to the topic that it covers for an undergraduate textbook on research methods in psychology. The information within each chapter flows well from point-to-point, so that the reader comes away feeling like there is a progression in the information presented. The only limitation that I see is that I felt the author could do a little more to let the reader know how information is connected from chapter to chapter. Rather than just drawing the reader’s attention to things that were mentioned in previous chapters, it would be nice to have brief comments about how issues in one chapter relate to topics covered in previous chapters.

In my opinion the chapters are arranged in easily digestible units that are manageable in 30-40 minute reading sessions. In fact, the author designed the chapters of the textbook in a way to make it easy to chunk information, and start and stop to easily pick up where one leaves off from one reading session to another. I also found the flow of information to be appropriate, with chapters containing just the right amount of detail for use in my introductory course in research methods in psychology.

The book is organized into thirteen chapters. The order of the chapters offers a logical progression from a broad overview of information about the principles and theory behind research in psychology, to more specific issues concerning the techniques and mechanics of conducting research. Each chapter ends with a summary of key takeaways from the chapter and exercises that do more than ask for content regurgitation. I find the organization of the textbook to be effective, and matches my approach to the course very well. I would not make any changes to the overall format with the exception of moving chapter 11 on presenting research to the end of the textbook, after the chapters on statistical analysis and interpretation.

I found the quality of the appearance of the textbook to be very good. The textbook features appropriate text and section/header font sizes that allow for an adequate zooming level to read large or smalls sections of text, that will give readers flexibility to match their personal preference. There are learning objectives at the start of each chapter to help students know what to expect. Key terms are highlighted in a separate color that are easily distinguishable in the body of the page. There are very useful visuals in every chapter, including tables, figures, and graphs. Relevant supplemental information is also highlighted in well formatted text boxes that are color coded to indicate what type of information is included. My only criticism is that the photographs included in the text are of low quality, and there are so few in the textbook that I feel it would have been better to just leave them out.

I found no grammatical errors in my review of the textbook. The textbook is generally well written, and the style of writing is at a level that is appropriate for an undergraduate class.

Although the textbook contains no instances of presenting information that is cultural insensitive or offensive, it does not offer an culturally inclusive review of information pertaining to research methods in psychology. I found no inclusion of examples of research conducting with non European American samples included in the summary of studies. Likewise the authors do place much attention on the issue of cultural sensitivity when conducing research. If there is one major weakness of the textbook I would say it is in this area, but based on my experience it is not an uncommon characteristic of textbooks on research methods in psychology.

Reviewed by zehra peynircioglu, Professor, American University on 2/1/18

Comprehensiveness rating: 3 see less

Short and sweet in most areas. Covers the basic concepts, not very comprehensively but definitely adequately so for a general beginning-level research methods course. For instance, I would liked to have seen a "separate" chapter on correlational research (there is one on single subject research and one on survey research), a discussion of the importance of providing a theoretical rationale for "getting an idea" (most students are fine with finding interesting and feasible project ideas but cannot give a theoretical rationale) before or after Chapter 4 on Theory, or a chapter on neuroscientific methods, which are becoming more and more popular. Nevertheless, it touches on most traditional areas that are in other books.

I did not find any errors or biases

This is one area where there is not much danger of going obsolete any time soon. The examples might need to be updated periodically (my students tend to not like dated materials, however relevant), but that should be easy.

Very clear and accessible prose. Despite the brevity, the concepts are put forth quite clearly. I like the "not much fluff" mentality. There is also adequate explanations of jargon and technical terminology.

I could not find any inconsistencies. The style and exposition frameworks are also quite consistent.

Yes, the modularity is fine. The chapters follow a logical pattern, so there should not be too much of a need for jumping around. And even if jumping around is needed depending on teaching style, the sections are solid in terms of being able to stand alone (or as an accompaniment to lectures).

Yes, the contents is ordered logically and the high modularity helps with any reorganization that an instructor may favor. In my case, for instance, Ch. 1 is fine, but I would skip it because it's mostly a repetition of what most introductory psychology books also say. I would also discuss non-experimental methods before going into experimental design. But such changes are easy to do, and if someone followed the book's own organization, there would also be a logical flow.

As far as I could see, the text is free of significant interface issues, at least in the pdf version

I could not find any errors.

As far as I could see, the book was culturally relevant.

I loved the short and sweet learning objectives, key takeaway sections, and the exercises. They are not overwhelming and can be used in class discussions, too.

Reviewed by George Woodbury, Graduate Student, Miami University, Ohio on 6/20/17

This text covers the typical areas for an undergraduate psychology course in research design. There is no table of contents included with the downloadable version, although there is a table of contents on the website (which excludes sub-sections of chapters). The sections on statistics are not extensive enough to be useful in and of themselves, but they are useful for transitions to a follow-up statistics course. There does not seem to be a glossary of terms, which made it difficult at times for my read through and I assume later for students who decide to print the text. The text is comprehensive without being wordy or tedious.

Relatively minor errors; There does not seem to be explicit cultural or methodological bias in the text.

The content is up-to-date, and examples from the psychology literature are generally within the last 25 years. Barring extensive restructuring in the fundamentals of methodology and design in psychology, any updates will be very easy to implement.

Text will be very clear and easy to read for students fluent in English. There is little jargon/technical terminology used, and the vocabulary that is provided in the text is contemporary

There do not seem to be obvious shifts in the terminology or the framework. The text is internally consistent in that regard.

The text is well divided into chapter and subsections. Each chapter is relatively self-contained, so there are little issues with referring to past material that may have been skipped. The learning objectives at the beginning of the chapter are very useful. Blocks of text are well divided with headings.

As mentioned above, the topics of the text follow the well-established trajectory of undergraduate psychology courses. This makes it very logical and clear.

The lack of a good table of contents made it difficult to navigate the text for my read through. There were links to an outside photo-hosting website (flickr) for some of the stock photos, which contained the photos of the original creators of the photos. This may be distracting or confusing to readers. However, the hyperlinks in general helped with navigation with the PDF.

No more grammatical errors than a standard, edited textbook.

Very few examples explicitly include other races, ethnicities, or backgrounds, however the examples seem to intentionally avoid cultural bias. Overall, the writing seems to be appropriately focused on avoiding culturally insensitive or offensive content.

After having examined several textbooks on research design and methodology related to psychology, this book stands out as superior.

Reviewed by Angela Curl, Assistant Professor, Miami University (Ohio) on 6/20/17

"Research Methods in Psychology" covers most research method topics comprehensively. The author does an excellent job explaining main concepts. The chapter on causation is very detailed and well-written as well as the chapter on research ethics. However, the explanations of data analysis seem to address upper level students rather than beginners. For example, in the “Describing Statistical Relationships” chapter, the author does not give detailed enough explanations for key terms. A reader who is not versed in research terminology, in my opinion, would struggle to understand the process. While most topics are covered, there are some large gaps. For example, this textbook has very little content related to qualitative research methods (five pages).

The content appears to be accurate and unbias.

The majority of the content will not become obsolete within a short time period-- many of the information can be used for the coming years, as the information provided is, overall, general in nature. The notably exceptions are the content on APA Code of Ethics and the APA Publication Manual, which both rely heavily on outdated versions, which limits the usefulness of these sections. In addition, it would be helpful to incorporate research studies that have been published after 2011.

The majority of the text is clear, with content that is easy for undergraduate students to read and understand. The key points included in the chapters are helpful, but some chapters seem to be missing key points (i.e., the key points do not accurately represent the overall chapter).

The text seems to be internally consistent in its terminology and organization.

Each chapter is broken into subsections that can be used alone. For example, section 5.2 covers reliability and validity of measurement. This could be extremely helpful for educators to select specific content for assigned readings.

The topics are presented in a logical matter for the most part. However, the PDF version of the book does not include a table of contents, and none of the formats has a glossary or index. This can make it difficult to quickly navigate to specific topics or terms, especially when explanations do not appear where expected. For example, the definitions of independent and dependent variables is provided under the heading “Correlation Does Not Imply Causation” (p. 22).

The text is consistent but needs more visual representations throughout the book, rather than heavily in some chapters and none at all in other chapters. Similarly, the text within the chapters is not easily readable due to the large sections of text with little to no graphics or breaks.

The interface of the text is adequate. However, the formatting of the PDF is sometimes weak. For example, the textbook has a number of pages with large blank spaces and other pages are taken up with large photos or graphics. The number of pages (and cost of printing) could have been reduced, or more graphics added to maximize utility.

I found no grammatical errors.

Text appears to be culturally sensitive. I appreciated the inclusion of the content about avoiding biased language (chapter 11).

Instructors who adopt this book would likely benefit from either selecting certain chapters/modules and/or integrating multiple texts together to address the shortcomings of this text. Further, the sole focus on psychology limits the use of this textbook for introductory research methods for other disciplines (e.g., social work, sociology).

Reviewed by Pramit Nadpara, Assistant Professor, Virginia Commonwealth University on 4/11/17

The text book provides good information in certain areas, while not comprehensive information in other areas. The text provides practical information, especially the section on survey development was good. Additional information on sampling... read more

There are no errors.

Research method is a common topic and the fundamentals of it will not change over the years. Therefore, the book is relevant and will not become obsolete.

Clarity rating: 3

The text in the book is clear. Certain aspects of the text could have been presented more clearly. For example, the section on main effects and interactions are some concepts that students may have difficulty understanding. Those areas could be explained more clearly with an example.

Consistency rating: 3

Graphs in the book lacks titles and variable names. Also, the format of chapter title page needs to be consistent.

At times there were related topics spread across several chapters. This could be corrected for a better read by the audience..

The book text is very clear, and the flow from one topic to the next was adequate. However, having a outline would help the reader.

The PDF copy of the book was a easy read. There were few links that were missing though.

There were no grammatical errors.

The text is not offensive and examples in it are mostly based on historical US based experiments.

I would start of by saying that I am a supporter of the Open Textbook concept. In this day and age, there are a variety of Research Methods book/text available on the market. While this book covers research methods basics, it cannot be recommended in its current form as an acceptable alternative to the standard text. Modifications to the text as recommended by myself and other reviewers might improve the quality of this book in the future.

Reviewed by Meghan Babcock, Instructor, University of Texas at Arlington on 4/11/17

This text includes all important areas that are featured in other Research Methods textbooks and are presented in a logical order. The text includes great examples and provides the references which can be assigned as supplemental readings. In addition, the chapters end with exercises that can be completed in class or as part of a laboratory assignment. This text would be a great addition to a Research Methods course or an Introductory Statistics course for Psychology majors.

The content is accurate. I did not find any errors and the material is unbiased.

Yes - the content is up to date and would be easy to update if/when necessary.

The text is written at an appropriate level for undergraduate students and explains important terminology. The research studies that the author references are ones that undergraduate psychology majors should be familiar with. The only section that was questionable to me was that on multiple regression in section 8.3 (Complex Correlational Designs). I am unaware of other introductory Research Methods textbooks that cover this analysis, especially without describing simple regression first.

The text is consistent in terms of terminology. The framework is also consistent - the chapters begin with Learning Objectives and ends with Key Takeaways and Exercises.

The text is divisible into smaller reading sections - possibly too many. The sections are brief, and in some instances too brief (e.g., the section on qualitative research). I think that the section headers are helpful for instructors who plan on using this text in conjunction with another text in their course.

The topics were presented in a logical fashion and are similar to other published Research Methods texts. The writing is very clear and great examples are provided. I think that some of the sections are rather brief and more information and examples could be provided.

I did not see any interface issues. All of the links worked properly and the tables and figures were accurate and free of errors. I particularly liked the figures in section 5.2 on reliability of measurement.

There are three comments that I have about the interface, however. First, I was expecting the keywords in blue font to be linked to a glossary, but they were not. I would have appreciated this feature. Second, I read this text as a PDF on an iPad and this version lacking was the Table of Contents (TOC) feature. Although I was able to view the TOC in different versions, I would have appreciated it in the PDF version. Also, it would be nice if the TOC was clickable (i.e., you could click on a section and it automatically directed you to that section). Third, I think the reader of this text would benefit from a glossary at the end of each chapter and/or an index at the end of the text. The "Key Takeaways" sections at the end of each chapter were helpful, but I think that a glossary would be a nice addition as well.

I did not notice any grammatical errors of any kind. The text was easy to read and I think that undergraduate students would agree.

The text was not insensitive or offensive to any races, ethnicities, or backgrounds. I appreciated the section on avoiding biased language when writing manuscripts (e.g., using 'children with learning disabilities' instead of 'special children' or using 'African American' instead of 'minority').

I think that this text would be a nice addition to a Research Methods & Statistics course in psychology. There are some sections that I found particularly helpful: (1) 2.2 and 2.3 - the author gives detailed information about generating research questions and reviewing the literature; (2) 9.2 - this section focuses on constructing survey questionnaires; (3) 11.2 and 11.3 - the author talks about writing a research report and about presenting at conferences. These sections will be great additions to an undergraduate Research Methods course. The brief introduction to APA style was also helpful, but should be supplemented with the most recent APA style manual.

Reviewed by Shannon Layman, Lecturer, University of Texas at Arlington on 4/11/17

The sections in this textbook are overall more brief than in previous Methods texts that I have used. Sometimes this brevity is helpful in terms of getting to the point of the text and moving on. In other cases, some topics could use a bit more... read more

I did not find any incorrect information or gross language issues.

Basic statistical and/or methodological texts tend to stay current and up-to-date because the topics in this field have not changed over the decades. Any updated methodologies would be found in a more advanced methods text.

The text is very clear and the ideas are easy to follow/ presented in a logical manner. The most helpful thing about this textbook is that the author arrives at the point of the topic very quickly. Another helpful point about this textbook is the relevancy of the examples used. The examples appear to be accessible to a wide audience and do not require specialization or previous knowledge of other fields of psychology.

I feel this text is very consistent throughout. The ideas build on each other and no terms are discussed in later chapters without being established in previous chapters.

Each chapter had multiple subsections which would allow for smaller reading sections throughout the course. The amount of content in each section and chapter appeared to be less than what I have encountered in other Methods texts.

The organization of the topics in this textbook follows the same or similar organization that I see in other textbooks. As I mentioned previously, the ideas build very well throughout the text.

I did not find any issues with navigation or distortion of the figures in the text.

There were not any obvious and/or egregious grammatical errors that I encountered in this text.

This topic is not really an issue with a Methods textbook as the topics are more so conceptual as opposed to topical. That being said, I did not see an issue with any examples used.

I have no other comments than what I addressed previously.

Reviewed by Sarah Allred, Associate Professor, Rutgers University, Camden on 2/8/17

Mixed. For some topics, there is more (and more practical) information than in most textbooks. I appreciated the very practical advice to students about how to plot data (in statistics chapters). Similarly, there is practical advice about how... read more

On the other hand, there is far too little information about some subjects. For example, independent and dependent variables are introduced in passing in an early chapter and then referred to only much later in the text. In my experience, students have a surprisingly difficult time grasping this concept. Another important example is sampling; I would have preferred much more information on types of samples and sampling techniques, and the problems that arise from poor sampling. A third example is the introduction to basic experimental design. Variables, measurement, validity, and reliability are all introduced in one chapter.

I did not see an index or glossary.

I found no errors.

The fundamentals of research methods do not change much. Given the current replication crisis in psychology, it might be helpful to have something about replicability.

Mixed. The text itself is spare and clear. The style of the book is to explain a concept in very few words. There are some excellent aspects of this, but on the other hand, there are some concepts that students have a very difficult time undersatnding if they are not embedded in concrete examples. For example, the section on main effects and interactions shows bar graphs of interactions, but this is presented without variable names or axis titles, and separate from any specific experiment.

Sometimes the chapter stucture is laid out on the title page, and other times it is not. Some graphs lack titles and variable names.

The chapters can be stand alone, but sometimes I found conceptually similar pieces spread across several chapters, and conceptually different pieces in the same chapters.

The individual sentences and paragraphs are always very clear. However, I felt that more tables/outlines of major concepts would have been helpful. For example, perhaps a flow chart of different kinds of experimental designs would be useful. (See section on comprehensiveness for more about organization).

The flow from one topic to the next was adequate.

I read the pdf. Perhaps the interface is more pleasant on other devices, but I found the different formats and fonts in image/captions/main text/figure labels distracting. Many if the instances of apparently hyperlinked (blue) text to do not link to anything.

I found no grammatical errors, and prose is standard academic English.

Like most psychology textbooks available in the US, examples are focused on important experiments in US history.

I really wanted to be happy with this text. I am a supporter of the Open Textbook concept, and I wanted to find this book an acceptable alternative to the variety of Research Methods texts I’ve used. Unfortunately, I cannot recommend this book as superior in quality.

Reviewed by Joel Malin, Assistant Professor, Miami University on 8/21/16

This textbook covers all or nearly all of what I believe are important topics to provide an introduction to research methods in psychology. One minor issue is that the pdf version, which I reviewed, does not include an index or a glossary. As such, it may be difficult for readers to zero in on material that they need, and/or to get a full sense of what will be covered and in what order.

I did not notice errors.

The book provides a solid overview of key issues related to introductory research methods, many of which are nearly timeless.

The writing is clear and accessible. It was easy and pleasing to read.

Terms are clearly defined and build upon each other as the book progresses.

I believe the text is organized in such a way that it could be easily divided into smaller sections.

The order in which material is presented seems to be well thought out and sensible.

I did not notice any issues with the interface. I reviewed the pdf version and thought the images were very helpful.

The book is written in a culturally relevant manner.

Reviewed by Abbey Dvorak, Assistant Professor, University of Kansas on 8/21/16

The text includes basic, essential information needed for students in an introductory research methods course. In addition, the text includes three chapters (i.e., research ethics, theory, and APA style) that are typically absent from or inadequately covered in similar texts. However, I did have some areas of concern regarding the coverage of qualitative and mixed methods approaches, and nonparametric tests. Although the author advocates for the research question to guide the choice of approach and design, minimal attention is given to the various qualitative designs (e.g., phenomenology, narrative, participatory action, etc.) beyond grounded theory and case studies, with no mention of the different types of mixed methods designs (e.g., concurrent, explanatory, exploratory) that are prevalent today. In addition, common nonparametric tests (e.g., Wilcoxon, Mann-Whitney, etc.) and parametric tests for categorical data (e.g., chi-square, Fisher’s exact, etc.) are not mentioned.

The text overall is accurate and free of errors. I noticed in the qualitative research sub-section, the author describes qualitative research in general, but does not mention common practices associated with qualitative research, such as transcribing interviews, coding data (e.g., different approaches to coding, different types of codes), and data analysis procedures. The information that is included appears accurate.

The text appears up-to-date and includes basic research information and classic examples that rarely change, which may allow the text to be used for many years. However, the author may want to add information about mixed methods research, a growing research approach, in order for the text to stay relevant across time.

The text includes clear, accessible, straightforward language with minimal jargon. When the author introduces a new term, the term is immediately defined and described. The author also provides interesting examples to clarify and expand understanding of terms and concepts throughout the text.

The text is internally consistent and uses similar language and vocabulary throughout. The author uses real-life examples across chapters in order to provide depth and insight into the information. In addition, the vocabulary, concepts, and organization are consistent with other research methods textbooks.

The modules are short, concise, and manageable for students; the material within each module is logically focused and related to each other. I may move the modules and the sub-topics within them into a slightly different order for my class, and add the information mentioned above, but overall, this is very good.

The author presents topics and structures chapters in a logical and organized manner. The epub and online version do not include page numbers in the text, but the pdf does; this may be confusing when referencing the text or answering student questions. The book ends somewhat abruptly after the chapter on inferential statistics; the text may benefit from a concluding chapter to bring everything together, perhaps with a culminating example that walks the reader through creating the research question, choosing a research approach/design, etc., all the way to writing the research report.

I used and compared the pdf, epub, and online versions of the text. The epub and online versions include a clickable table of contents, but the pdf does not. The table format is inconsistent across the three versions; in the epub version (viewed through ibooks), the table data does not always line up correctly, making it difficult to interpret quickly. In the pdf and online versions, the table format looks different, but the data are lined up. No index made it difficult to quickly find areas of interest in the text; however, I could use the Find/Search functions in all three versions to search and find needed items.

As I read through this text, I did not detect any glaring grammatical errors. Overall, I think the text is written quite well in a style that is accessible to students.

The author uses inclusive, person-first language, and the text does not seem to be offensive or insensitive. As I read, I did notice that topics such as diversity and cultural competency are absent.

I enjoyed reading this text and am very excited to have a free research methods text for my students that I may supplement as needed. I wish there was a test question bank and/or flashcards for my students to help them study, but perhaps that could be added in the future. Overall, this is a great resource!

Reviewed by Karen Pikula, Psychology Instructor PhD, Central Lakes College on 1/7/16

The text covers all the areas and ideas of the subject of research methods in psychology for the learner that is just entering the field. The authors cover all of the content of an introductory research methods textbook and use exemplary examples that make those concepts relevent to a beginning researcher. As the authors state, the material is presented in such a manner as to encourage learners to not only be effective consumers of current research but also engage as critical thinkers in the many diverse situations one encounters in everyday life.

The content is accurate, error free, and unbiased. It explains both quantiative and qualitative methods in an unbiased manner. It is a bit slim on qualitative. It would be nice to have a bit more information on, for example, creating interview questions, coding, and qualitative data anaylisis.

The text is up to date, having just been revised. This revision was authored by Rajiv Jhangiani (Capilano University, North Vancouver) and includes the addition of a table of contents and cover page that the original text did not have, changes to Chapter 3 (Research Ethics) to include a contemporary example of an ethical breach and to reflect Canadian ethical guidelines and privacy laws, additional information regarding online data collection in Chapter 9 (Survey Research). Jhangiani has correcte of errors in the text and formulae, as well as changing spelling from US to Canadian conventions. The text is also now available in a inexpensive hard copy which students can purchase online or college bookstores can stock. This makes the text current and updates should be minimal.

The text is very easy to read and also very interesting as the authors supplement content with amazing real life examples.

The text is internally consistent in terms of terminology and framework.

This text is easily and readily divisible into smaller reading sections that can be assigned at different points within a course. I am going to use this text in conjunction with the OER OpenStax Psychology text for my Honors Psychology course. I currently use the OER Openstax Psychology textbook for my Positive Psychology course as well as my General Psychology course,

The topics in the text are presented in logical and clear fashion. The way they are presented allows the text to be used in conjuction with other textbooks as a secondary resource.

The text is free of significant interface issues. It is written in a manner that follows the natural process of doing research.

The text contained no noted grammatical errors.

The text is not culturally insensitive or offensive and actually has been revised to accomodate Canadian ethical guidelines as well as those of the APA.

I have to say that I am excited to have found this revised edition. My students will be so happy that there is also a reasonable priced hard coopy for them to purchase. They love the OpenStax Psychology text with the hard copy available from our bookstore. I do wish there were PowerPoints available for the text as well as a test bank. That is always a bonus!

Reviewed by Alyssa Gibbons, Instructor, Colorado State University on 1/7/16

This text covers everything I would consider essential for a first course in research methods, including some areas that are not consistently found in introductory texts (e.g., qualitative research, criticisms of null hypothesis significance testing). The chapters on ethics (Ch. 3) and theory (Ch. 4) are more comprehensive than most I have seen at this level, but not to the extent of information overload; rather, they anticipate and address many questions that undergraduates often have about these issues.

There is no index or table of contents provided in the PDF, and the table of contents on the website is very broad, but the material is well organized and it would not be hard for an instructor to create such a table. Chapter 2.1 is intended to be an introduction to several key terms and ideas (e.g., variable, correlation) that could serve as a sort of glossary.

I found the text to be highly accurate throughout; terms are defined precisely and correctly.

Where there are controversies or differences of opinion in the field, the author presents both sides of the argument in a respectful and unbiased manner. He explicitly discourages students from dismissing any one approach as inherently flawed, discussing not only the advantages and disadvantages of all methods (including nonexperimental ones) but also ways researchers address the disadvantages.

In several places, the textbook explicitly addresses the history and development of various methods (e.g., qualitative research, null hypothesis significance testing) and the ways in which researchers' views have changed. This allows the author to present current thinking and debate in these areas yet still expose students to older ideas they are likely to encounter as they read the research literature. I think this approach sets students up well to encounter future methodological advances; as a field, we refine our methods over time. I think the author could easily integrate new developments in future editions, or instructors could introduce such developments as supplementary material without creating confusion by contradicting the test.

The examples are generally drawn from classic psychological studies that have held up well over time; I think they will appeal to students for some time to come and not appear dated.

The only area in which I did not feel the content was entirely up to date was in the area of psychological measurement; Chapter 5.2 is based on the traditional view and not the more comprehensive modern or holistic view as presented in the 1999 AERA/APA Standards for Educational and Psychological Measurement. However, a comprehensive treatment of measurement validity is probably not necessary for most undergraduates at this stage, and they will certainly encounter the older framework in the research literature.

The textbook does an excellent job of presenting concepts in simple, accessible language without introducing error by oversimplification. The author consistently anticipates common points of confusion, clarifies terms, and even suggests ways for students to remember key distinctions. Terms are clearly and concretely defined when they are introduced. In contrast to many texts I have used, the terms that are highlighted in the text are actually the terms I would want my students to remember and study; the author refrains from using psychological jargon that is not central to the concepts he is discussing.

I noticed no major inconsistencies or gaps.

The division of sections within each chapter is useful; although I liked the overall organization of the text, there were points at which I would likely assign sections in a slightly different order and I felt I could do this easily without loss of continuity. The one place I would have liked more modularity was in the discussion of inferential statistics: t-tests, ANOVA, and Pearson's r are all covered within Chapter 13.2. On the one hand, this enables students to see the relationships and similarities among these tests, but on the other, this is a lot for students to take in at once.

I found the overall organization of the book to be quite logical, mirroring the sequence of steps a researcher would use to develop a research question, design a study, etc. As discussed above, the modularity of the book makes it easy to reorder sections to suit the structure of a particular class (for example, I might have students read the section on APA writing earlier in the semester as they begin drafting their own research proposals). I like the inclusion of ethics very early on in the text, establishing the importance of this topic for all research design choices.

One organizational feature I particularly appreciated was the consistent integration of conceptual and practical ideas; for example, in the discussion of psychological measurement, reliability and validity are discussed alongside the importance of giving clear instructions and making sure participants cannot be identified by their writing implements. This gives students an accurate and honest picture of the research process - some of the choices we make are driven by scientific ideals and some are driven by practical lessons learned. Students often have questions related to these mundane aspects of conducting research and it is helpful to have them so clearly addressed.

Although I didn't encounter any problems per se with the interface, I do think it could be made more user-friendly. For example, references to figures and tables are highlighted in blue, appearing to be hyperlinks, but they were not. Having such links, as well as a linked, easily-navigable and detailed table of contents, would also be helpful (and useful to students who use assistive technology).

I noticed no grammatical errors.

Where necessary, the author uses inclusive language and there is nothing that seems clearly offensive. The examples generally reflect American psychology research, but the focus is on the methods used and not the participants or cultural context. The text could be more intentionally or proactively inclusive, but it is not insensitive or exclusive.

I am generally hard to please when it comes to textbooks, but I found very little to quibble with in this one. It is a very well-written and accessible introduction to research methods that meets students where they are, addressing their common questions, misconceptions, and concerns. Although it's not flashy, the figures, graphics, and extra resources provided are clear, helpful, and relevant.

Reviewed by Moin Syed, Assistant Professor, University of Minnesota on 6/10/15

The text is thorough in terms of covering introductory concepts that are central to experimental and correlational/association designs. I find the general exclusion of qualitative and mixed methods designs hard to defend (despite some researchers’ distaste for the methods). While these approaches were less commonly used in the recent past, they are prevalent in the early years of psychology and are ascending once again. It strikes me as odd to just ignore two whole families of methods that are used within the practice of psychology—definitely not a sustainable approach.

I do very much appreciate the emphasis on those who will both practice and consume psychology, given the wide variety of undergraduate career paths.

One glaring omission is a Table of Contents within the PDF. It would be nice to make this a linked PDF, so that clicking on the entry in a TOC (or cross-references) would jump the reader to the relevant section.

I did not see an errors. The chapter on theory is not as clear as it could be. The section “what is theory” is not very clear, and these are difficulte concepts (difference between theory, hypothesis, etc.). A bit more time spent here could have been good. Also, the discussion of functional, mechanistic, and typological theories leaves out the fourth of Pepper’s metaphors: contextualism. I’m not sure that was intentional and accidental, but it is noticeable!

This is a research methods text focused on experimental and association designs. The basics of these designs do not change a whole lot over time, so there is little likelihood that the main content will become obsolete anytime soon. Some of the examples used are a bit dated, but then again most of them are considered “classics” in the field, which I think are important to retain (and there is at least one “new classic” included in the ethics section, namely the fraudulent research linking autism to the MMR vaccine).

The text is extremely clear and accessible. In fact, it may even be *too* simple for undergraduate use. Then again, students often struggle with methods, so simplicity is good, and the simplicity can also make the book marketable to high school courses (although I doubt many high schools have methods courses).

Yes, quite consistent throughout. Carrying through the same examples into different chapters is a major strength of the text.

I don’ anticipate any problems here.

The book flows well, with brief sections. I do wonder if maybe the sections are too brief? Perhaps too many check-ins? The “key take-aways” usually come after only a few pages. As mentioned above, the book is written at a very basic level, so this brevity is consistent with that approach. It is not a problem, per se, but those considering adopting the text should be aware of this aspect.

No problems here.

I did not detect any grammatical errors. The text flows very well.

The book is fairly typical of American research methods books in that it only focuses on the U.S. context and draws its examples from “mainstream” psychology (e.g., little inclusion of ethnic minority or cross-cultural psychology). However, the text is certainly not insensitive or offensive in any way.

Nice book, thanks for writing it!

Reviewed by Rajiv Jhangiani, Instructor, Capilano University on 10/9/13

The text is well organized and written, integrates excellent pedagogical features, and covers all of the traditional areas of the topic admirably. The final two chapters provide a good bridge between the research methods course and the follow-up course on behavioural statistics. The text integrates real psychological measures, harnesses students' existing knowledge from introductory psychology, includes well-chosen examples from real life and research, and even includes a very practical chapter on the use of APA style for writing and referencing. On the other hand, it does not include a table of contents or an index, both of which are highly desirable. The one chapter that requires significant revision is Chapter 3 (Research Ethics), which is based on the US codes of ethics (e.g., Federal policy & APA code) and does not include any mention of the Canadian Tri-Council Policy Statement.

The very few errors I found include the following: 1. The text should read "The fact that his F score…" instead of "The fact that his t score…" on page 364 2. Some formulae are missing the line that separates the numerator from the denominator. See pages 306, 311, 315, and 361 3. Table 12.3 on page 310 lists the variance as 288 when it is 28.8

The text is up-to-date and will not soon lose relevance. The only things I would add are a brief discussion of the contemporary case of Diederik Stapel's research fraud in the chapter on Research Ethics, as well as some research concerning the external validity of web-based studies (e.g., Gosling et al.'s 2004 article in American Psychologist).

Overall, the style of writing makes this text highly accessible. The writing flows well, is well organized, and includes excellent, detailed, and clear examples and explanations for concepts. The examples often build on concepts or theories students would have covered in their introductory psychology course. Some constructive criticism: 1. When discussing z scores on page 311 it might have been helpful to point out that the mean and SD for a set of calculated z scores are 0 and 1 respectively. Good students will come to this realization themselves, but it is not a bad thing to point it out nonetheless. 2. The introduction of the concept of multiple regression might be difficult for some students to grasp. 3. The only place where I felt short of an explanation was in the use of a research example to demonstrate the use of a line graph on page 318. In this case the explanation in question does not pertain to the line graph itself but the result of the study used, which is so fascinating that students will wish for the researchers' explanation for it.

The text is internally consistent.

The text is organized very well into chapters, modules within each chapter, and learning objectives within each module. Each module also includes useful exercises that help consolidate learning.

As mentioned earlier, the style of writing makes this text highly accessible. The writing flows well, is well organized, and includes excellent, detailed, and clear examples and explanations for concepts. The examples often build on concepts or theories students would have covered in their introductory psychology course. Only rarely did I feel that the author could have assisted the student by demonstrating the set-by-step calculation of a statistic (e.g., on page 322 for the calculation of Pearson's r)

The images, graphs, and charts are clear. The only serious issues that hamper navigation are the lack of a table of contents and an index. Many of the graphs will need to be printed in colour (or otherwise modified) for the students to follow the explanations provided in the text.

The text is written rather well and is free from grammatical errors. Of course, spellings are in the US convention.

The text is not culturally insensitive or offensive. Of course, it is not a Canadian edition and so many of the examples (all of which are easy to comprehend) come from a US context.

I have covered most of these issues in my earlier comments. The only things left to mention are that the author should have clearly distinguished between mundane and psychological realism, and that, in my opinion, the threats to internal validity could have been grouped together and might have been closer to an exhaustive list. This review originated in the BC Open Textbook Collection and is licensed under CC BY-ND.

Chapter 1: The Science of Psychology
Chapter 2: Overview of the Scientific Method
Chapter 3: Research Ethics
Chapter 4: Psychological Measurement
Chapter 5: Experimental Research
Chapter 6: Non-experimental Research
Chapter 7: Survey Research
Chapter 8: Quasi-Experimental Research
Chapter 9: Factorial Designs
Chapter 10: Single-Subject Research
Chapter 11: Presenting Your Research
Chapter 12: Descriptive Statistics
Chapter 13: Inferential Statistics

Ancillary Material

Kwantlen Polytechnic University

About the Book

This fourth edition (published in 2019) was co-authored by Rajiv S. Jhangiani (Kwantlen Polytechnic University), Carrie Cuttler (Washington State University), and Dana C. Leighton (Texas A&M University—Texarkana) and is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. Revisions throughout the current edition include changing the chapter and section numbering system to better accommodate adaptions that remove or reorder chapters; continued reversion from the Canadian edition; general grammatical edits; replacement of “he/she” to “they” and “his/her” to “their”; removal or update of dead links; embedded videos that were not embedded; moved key takeaways and exercises from the end of each chapter section to the end of each chapter; a new cover design.

About the Contributors

Dr. Carrie Cuttler received her Ph.D. in Psychology from the University of British Columbia. She has been teaching research methods and statistics for over a decade. She is currently an Assistant Professor in the Department of Psychology at Washington State University, where she primarily studies the acute and chronic effects of cannabis on cognition, mental health, and physical health. Dr. Cuttler was also an OER Research Fellow with the Center for Open Education and she conducts research on open educational resources. She has over 50 publications including the following two published books: A Student Guide for SPSS (1st and 2nd edition) and Research Methods in Psychology: Student Lab Guide. Finally, she edited another OER entitled Essentials of Abnormal Psychology. In her spare time, she likes to travel, hike, bike, run, and watch movies with her husband and son. You can find her online at @carriecuttler or carriecuttler.com.

Dr. Rajiv Jhangiani is the Associate Vice Provost, Open Education at Kwantlen Polytechnic University in British Columbia. He is an internationally known advocate for open education whose research and practice focuses on open educational resources, student-centered pedagogies, and the scholarship of teaching and learning. Rajiv is a co-founder of the Open Pedagogy Notebook, an Ambassador for the Center for Open Science, and serves on the BC Open Education Advisory Committee. He formerly served as an Open Education Advisor and Senior Open Education Research & Advocacy Fellow with BCcampus, an OER Research Fellow with the Open Education Group, a Faculty Workshop Facilitator with the Open Textbook Network, and a Faculty Fellow with the BC Open Textbook Project. A co-author of three open textbooks in Psychology, his most recent book is Open: The Philosophy and Practices that are Revolutionizing Education and Science (2017). You can find him online at @thatpsychprof or thatpsychprof.com.

Dr. Dana C. Leighton is Assistant Professor of Psychology in the College of Arts, Science, and Education at Texas A&M University—Texarkana. He earned his Ph.D. from the University of Arkansas, and has 15 years experience teaching across the psychology curriculum at community colleges, liberal arts colleges, and research universities. Dr. Leighton’s social psychology research lab studies intergroup relations, and routinely includes undergraduate students as researchers. He is also Chair of the university’s Institutional Review Board. Recently he has been researching and writing about the use of open science research practices by undergraduate researchers to increase diversity, justice, and sustainability in psychological science. He has published on his teaching methods in eBooks from the Society for the Teaching of Psychology, presented his methods at regional and national conferences, and received grants to develop new teaching methods. His teaching interests are in undergraduate research, writing skills, and online student engagement. For more about Dr. Leighton see http://www.danaleighton.net and http://danaleighton.edublogs.org

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Research Process

Choosing the Right Research Methodology: A Guide for Researchers

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Table of Contents

Choosing an optimal research methodology is crucial for the success of any research project. The methodology you select will determine the type of data you collect, how you collect it, and how you analyse it. Understanding the different types of research methods available along with their strengths and weaknesses, is thus imperative to make an informed decision.

Understanding different research methods:

There are several research methods available depending on the type of study you are conducting, i.e., whether it is laboratory-based, clinical, epidemiological, or survey based . Some common methodologies include qualitative research, quantitative research, experimental research, survey-based research, and action research. Each method can be opted for and modified, depending on the type of research hypotheses and objectives.

Qualitative vs quantitative research:

When deciding on a research methodology, one of the key factors to consider is whether your research will be qualitative or quantitative. Qualitative research is used to understand people’s experiences, concepts, thoughts, or behaviours . Quantitative research, on the contrary, deals with numbers, graphs, and charts, and is used to test or confirm hypotheses, assumptions, and theories.

Qualitative research methodology:

Qualitative research is often used to examine issues that are not well understood, and to gather additional insights on these topics. Qualitative research methods include open-ended survey questions, observations of behaviours described through words, and reviews of literature that has explored similar theories and ideas. These methods are used to understand how language is used in real-world situations, identify common themes or overarching ideas, and describe and interpret various texts. Data analysis for qualitative research typically includes discourse analysis, thematic analysis, and textual analysis.

Quantitative research methodology:

The goal of quantitative research is to test hypotheses, confirm assumptions and theories, and determine cause-and-effect relationships. Quantitative research methods include experiments, close-ended survey questions, and countable and numbered observations. Data analysis for quantitative research relies heavily on statistical methods.

Analysing qualitative vs quantitative data:

The methods used for data analysis also differ for qualitative and quantitative research. As mentioned earlier, quantitative data is generally analysed using statistical methods and does not leave much room for speculation. It is more structured and follows a predetermined plan. In quantitative research, the researcher starts with a hypothesis and uses statistical methods to test it. Contrarily, methods used for qualitative data analysis can identify patterns and themes within the data, rather than provide statistical measures of the data. It is an iterative process, where the researcher goes back and forth trying to gauge the larger implications of the data through different perspectives and revising the analysis if required.

When to use qualitative vs quantitative research:

The choice between qualitative and quantitative research will depend on the gap that the research project aims to address, and specific objectives of the study. If the goal is to establish facts about a subject or topic, quantitative research is an appropriate choice. However, if the goal is to understand people’s experiences or perspectives, qualitative research may be more suitable.

Conclusion:

In conclusion, an understanding of the different research methods available, their applicability, advantages, and disadvantages is essential for making an informed decision on the best methodology for your project. If you need any additional guidance on which research methodology to opt for, you can head over to Elsevier Author Services (EAS). EAS experts will guide you throughout the process and help you choose the perfect methodology for your research goals.

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Exploring Types of Research Methods: A Comprehensive Guide

Grasping the concept of research method is essential for anyone engaged in research or assessing the outcomes of studies. Whether you're an academic student, a dedicated researcher, or just inquisitive about the world, a thorough understanding of the diverse research methods will assist you in sifting through the extensive array of information at your disposal.

Our detailed guide will walk you through the types of research design, including qualitative and quantitative approaches, as well as descriptive, correlational, experimental, and mixed methods research. We will also touch upon the different types of research methodology, ensuring a comprehensive understanding of the various types of methods in research.

This article will also highlight the pivotal factors to consider when crafting a study and the inherent strengths and limitations of different type of research methods.Whether you're embarking on your own research project or looking to enhance your critical thinking skills Armed with the research methods definition, this guide will equip you with the essential knowledge to make well-informed decisions and formulate significant conclusions in the field of research.

Qualitative vs. Quantitative Research Methods

Qualitative and quantitative research methods represent two fundamentally different approaches to data collection and analysis. Qualitative observation delves into non-numerical data, while quantitative observation involves the scrutiny of data that is numerical and quantifiable.

Qualitative Research:

Involves gathering and interpreting non-numerical data, such as text, video, photographs, or audio recordings
Uses sources like interviews, focus groups, documents, personal accounts, cultural records, and observation
Unstructured or semi-structured format
Open-ended questions
Comprehensive perspective on individuals' experiences
Comparison of participants' feedback and input
Focus on answering the "why" behind a phenomenon, correlation, or behavior
Ethnography, for instance, seeks to gain insights into phenomena, groups, or experiences that cannot be objectively measured or quantified, offering a deep dive into the cultural fabric of a community.
This method is used to understand how an individual subjectively perceives and imparts meaning to their social reality, often revealing underlying bias that can influence the interpretation of social phenomena.
Data analysis techniques include content analysis, grounded theory, thematic analysis, or discourse analysis

Quantitative Research:

Focuses on numerical or measurable data
Uses sources such as experiments, questionnaires, surveys, and database reports
Multiple-choice format
Countable answers (e.g., "yes" or "no")
Numerical analysis
Statistical picture of a trend or connection
To define research methods, one must focus on answering the 'what' or 'how' in relation to a particular phenomenon, correlation, or behavior. This foundational approach is crucial in the realm of empirical inquiry.
Provides precise causal explanations that can be measured and communicated mathematically
The objectives of scientific inquiry often include hypothesis testing to examine causal relationships between variables, making accurate predictions, and generalizing findings to broader populations.
Aims to establish general laws of behavior and phenomenon across different settings/contexts
Used to test a theory and ultimately support or reject it
Empirical research in psychology utilizes examples of quantitative data such as standardized psychological assessments, neuroimaging data, and clinical outcome measures to inform its findings.
Data analysis techniques include descriptive and inferential statistics

When selecting research methodology types, it's important to consider various factors such as the study's primary goal, the nature of the research questions and conceptual framework, the variables involved, the context of the study, ethical issues, and whether the focus is on individuals or groups, or on comparing groups and understanding their relationships.

Research design methods play a pivotal role in determining the appropriateness of qualitative methods for studies involving individuals or groups, while quantitative methods are often chosen for studies aimed at comparing groups or deciphering the relationship between variables.

Descriptive Research

Descriptive research is a methodological approach that aims to accurately and systematically depict a population, situation, or phenomenon. It adeptly addresses 'what', 'where', 'when', and 'how' questions, although it steers clear of exploring 'why'. Employing a descriptive research design means observing and documenting variables without exerting control or manipulation, which is particularly beneficial when exploring new topics or problems to identify characteristics, frequencies, trends, and categories.

Descriptive research methods include:

Surveys: Survey research is a powerful tool that enables researchers to collect extensive data sets, which can then be meticulously analyzed to uncover frequencies, averages, and emerging patterns.
Observations: Utilizing observation allows researchers to collect data on behaviors and phenomena, ensuring the gathered information is not tainted by the honesty or accuracy of respondents.
Case studies: Case study research delves into detailed data to pinpoint the unique characteristics of a narrowly defined subject, providing in-depth insights.

Descriptive research can be conducted in different ways:

Cross-sectional : Observing a population at a single point in time.
Longitudinal : Following a population over a period of time.
Surveys or interviews : When the researcher interacts with the participant.
Observational studies or data collection using existing records : When the researcher does not interact with the participant.

Advantages of descriptive research include:

Varied data collection methods
A natural environment for respondents
Quick and cheap data collection
A holistic understanding of the research topic

Limitations of descriptive research studies:

They cannot establish cause and effect relationships.
The reliability and validity of survey responses can be compromised if respondents are not truthful or tend to provide socially desirable answers.
The choice and wording of questions on a questionnaire may influence the descriptive findings.

Correlational Research

Correlational research, a non-experimental method, delves into the dynamics between two variables, focusing on the strength and direction of their relationship without manipulating any factors, which is pivotal in understanding associations rather than causality.

Researchers may choose correlational research in the following situations

When manipulating the independent variable is impractical, impossible, or unethical
When exploring non-causal relationships between variables
When testing new measurement tools

In correlational research, the correlation coefficient is measured, which can range from -1 to +1, indicating the relationship's direction and strength. A comprehensive meta-analysis can further elucidate these types of correlations.

Positive correlation: Both variables change in the same direction
Negative correlation: Variables change in opposite directions
Zero correlation: No relationship exists between the variables

Data collection methods for correlational research include

Naturalistic observation
Archival research or secondary data

Analytical research methods, such as correlation or regression analyses, are employed to analyze correlational data, with the former yielding a coefficient that clarifies the relationship's intensity and direction, and the latter forecasting the impact of variable changes.

Experimental Research

Experimental research, a methodical scientific approach, manipulates variables to observe their effects and is indispensable for establishing cause-and-effect relationships and making informed decisions in the face of inadequate data.

Pre-experimental research design : Includes One-shot Case Study Research Design, One-group Pretest-posttest Research Design, and Static-group Comparison.
True experimental research design Statistical analysis, a cornerstone in testing hypotheses, is pivotal in research for its accuracy in proving or disproving a hypothesis. It's uniquely capable of establishing a cause-effect relationship within a group, making it an indispensable tool for researchers.
Quasi-experimental design : Similar to an experimental design but assigns participants to groups non-randomly.

Experimental research is essential for various fields, such as:

Developing new drugs and medical treatments
Understanding human behavior in psychology
Improving educational outcomes
Identifying opportunities for businesses and organizations

To conduct experimental research effectively, researchers must consider three key factors:

A Control Group and an Experimental Group
A variable that can be manipulated by the researcher
Random distribution of participants

Experimental research, whether conducted in laboratory settings with high control variables and internal validity or in field settings that boast both internal and external validity, presents a spectrum of advantages and challenges. Researchers must navigate potential threats to internal validity, including history, maturation, testing, instrumentation, mortality, and regression threats.

Mixed Methods Research

Mixed methods research, an approach that synergizes the rigor of quantitative and qualitative research methods, capitalizes on the strengths of each to provide a comprehensive analysis. This integration, which can occur during data collection, analysis, or presentation of results, is a hallmark of mixed methods research designs.

Convergent design
Explanatory sequential design
Exploratory sequential design
Embedded design

The practice of triangulation in mixed methods research enhances the integration of quantitative and qualitative data, offering multiple perspectives and a more comprehensive understanding. It also allows for a deeper explanation of statistical results, as exemplified by the EQUALITY study's exploratory sequential design for patient-centered data collection.

In mixed methods research, the intricate research design and methodology combine qualitative and quantitative data collection and analysis. This purposeful mixing of methods and data integration at strategic stages of the research process can reveal relationships between complex layers of research questions, although it demands significant resources and specialized training.

Explanatory
Exploratory
Nested (embedded) designs

Mixed methods research, characterized by its diverse research design and methods, integrates quantitative and qualitative approaches within a single study. Grounded in positivism and interpretivism, it provides a multifaceted understanding of research topics, despite the challenges of mastering both methodologies and collaborating with multidisciplinary teams.

In sum, a thorough grasp of the various research methodologies is crucial for conducting robust research and critically assessing others' findings. From qualitative to quantitative, descriptive, correlational, experimental, and mixed methods research, each approach offers distinct strengths and limitations, guiding researchers to the most suitable methods for effective data collection and analysis.

As we navigate the vast landscape of information available, understanding what are research methods empowers us to make informed decisions, draw meaningful conclusions, and contribute to the advancement of knowledge across various fields. Embracing the diversity of research methods, whether you're a student, researcher, or simply curious, will enhance your critical thinking skills and enable you to uncover valuable insights that shape our understanding of the world.

What are the seven most commonly used research methods? The seven most commonly used research methods are:

Observation / Participant Observation
Focus Groups
Experiments
Secondary Data Analysis / Archival Study
Mixed Methods (a combination of some of the above)

What does comprehensive research methodology entail?

Comprehensive research methodology involves conducting a thorough and exhaustive investigation on a specific topic, subject, or issue. This approach is characterized by the meticulous collection, analysis, and evaluation of a wide array of information, data, and sources, with the objective of achieving a deep and comprehensive understanding of the subject matter.

What are the three primary methods to investigate a specific research question?

To investigate a specific research question, you can use:

Quantitative methods for measuring something or testing a hypothesis.
Qualitative methods for exploring ideas, thoughts, and meanings.
Secondary data analysis for examining a large volume of readily-available data.

What does exploration mean in the context of research methodology?

Exploration in research methodology signifies a research approach that aims to delve into questions that have not been extensively explored before. Exploratory research, often qualitative and primary in nature, is focused on uncovering new insights and understanding. Nonetheless, it can also adopt a quantitative stance, particularly when it involves analyzing a large sample size, to further the scope of exploratory research.

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Statistical Analysis in Research: Meaning, Methods and Types

Home » Videos » Statistical Analysis in Research: Meaning, Methods and Types

The scientific method is an empirical approach to acquiring new knowledge by making skeptical observations and analyses to develop a meaningful interpretation. It is the basis of research and the primary pillar of modern science. Researchers seek to understand the relationships between factors associated with the phenomena of interest. In some cases, research works with vast chunks of data, making it difficult to observe or manipulate each data point. As a result, statistical analysis in research becomes a means of evaluating relationships and interconnections between variables with tools and analytical techniques for working with large data. Since researchers use statistical power analysis to assess the probability of finding an effect in such an investigation, the method is relatively accurate. Hence, statistical analysis in research eases analytical methods by focusing on the quantifiable aspects of phenomena.

What is Statistical Analysis in Research? A Simplified Definition

Statistical analysis uses quantitative data to investigate patterns, relationships, and patterns to understand real-life and simulated phenomena. The approach is a key analytical tool in various fields, including academia, business, government, and science in general. This statistical analysis in research definition implies that the primary focus of the scientific method is quantitative research. Notably, the investigator targets the constructs developed from general concepts as the researchers can quantify their hypotheses and present their findings in simple statistics.

When a business needs to learn how to improve its product, they collect statistical data about the production line and customer satisfaction. Qualitative data is valuable and often identifies the most common themes in the stakeholders’ responses. On the other hand, the quantitative data creates a level of importance, comparing the themes based on their criticality to the affected persons. For instance, descriptive statistics highlight tendency, frequency, variation, and position information. While the mean shows the average number of respondents who value a certain aspect, the variance indicates the accuracy of the data. In any case, statistical analysis creates simplified concepts used to understand the phenomenon under investigation. It is also a key component in academia as the primary approach to data representation, especially in research projects, term papers and dissertations.

Most Useful Statistical Analysis Methods in Research

Using statistical analysis methods in research is inevitable, especially in academic assignments, projects, and term papers. It’s always advisable to seek assistance from your professor or you can try research paper writing by CustomWritings before you start your academic project or write statistical analysis in research paper. Consulting an expert when developing a topic for your thesis or short mid-term assignment increases your chances of getting a better grade. Most importantly, it improves your understanding of research methods with insights on how to enhance the originality and quality of personalized essays. Professional writers can also help select the most suitable statistical analysis method for your thesis, influencing the choice of data and type of study.

Descriptive Statistics

Descriptive statistics is a statistical method summarizing quantitative figures to understand critical details about the sample and population. A description statistic is a figure that quantifies a specific aspect of the data. For instance, instead of analyzing the behavior of a thousand students, research can identify the most common actions among them. By doing this, the person utilizes statistical analysis in research, particularly descriptive statistics.

Measures of central tendency . Central tendency measures are the mean, mode, and media or the averages denoting specific data points. They assess the centrality of the probability distribution, hence the name. These measures describe the data in relation to the center.
Measures of frequency . These statistics document the number of times an event happens. They include frequency, count, ratios, rates, and proportions. Measures of frequency can also show how often a score occurs.
Measures of dispersion/variation . These descriptive statistics assess the intervals between the data points. The objective is to view the spread or disparity between the specific inputs. Measures of variation include the standard deviation, variance, and range. They indicate how the spread may affect other statistics, such as the mean.
Measures of position . Sometimes researchers can investigate relationships between scores. Measures of position, such as percentiles, quartiles, and ranks, demonstrate this association. They are often useful when comparing the data to normalized information.

Inferential Statistics

Inferential statistics is critical in statistical analysis in quantitative research. This approach uses statistical tests to draw conclusions about the population. Examples of inferential statistics include t-tests, F-tests, ANOVA, p-value, Mann-Whitney U test, and Wilcoxon W test. This

Common Statistical Analysis in Research Types

Although inferential and descriptive statistics can be classified as types of statistical analysis in research, they are mostly considered analytical methods. Types of research are distinguishable by the differences in the methodology employed in analyzing, assembling, classifying, manipulating, and interpreting data. The categories may also depend on the type of data used.

Predictive Analysis

Predictive research analyzes past and present data to assess trends and predict future events. An excellent example of predictive analysis is a market survey that seeks to understand customers’ spending habits to weigh the possibility of a repeat or future purchase. Such studies assess the likelihood of an action based on trends.

Prescriptive Analysis

On the other hand, a prescriptive analysis targets likely courses of action. It’s decision-making research designed to identify optimal solutions to a problem. Its primary objective is to test or assess alternative measures.

Causal Analysis

Causal research investigates the explanation behind the events. It explores the relationship between factors for causation. Thus, researchers use causal analyses to analyze root causes, possible problems, and unknown outcomes.

Mechanistic Analysis

This type of research investigates the mechanism of action. Instead of focusing only on the causes or possible outcomes, researchers may seek an understanding of the processes involved. In such cases, they use mechanistic analyses to document, observe, or learn the mechanisms involved.

Exploratory Data Analysis

Similarly, an exploratory study is extensive with a wider scope and minimal limitations. This type of research seeks insight into the topic of interest. An exploratory researcher does not try to generalize or predict relationships. Instead, they look for information about the subject before conducting an in-depth analysis.

The Importance of Statistical Analysis in Research

As a matter of fact, statistical analysis provides critical information for decision-making. Decision-makers require past trends and predictive assumptions to inform their actions. In most cases, the data is too complex or lacks meaningful inferences. Statistical tools for analyzing such details help save time and money, deriving only valuable information for assessment. An excellent statistical analysis in research example is a randomized control trial (RCT) for the Covid-19 vaccine. You can download a sample of such a document online to understand the significance such analyses have to the stakeholders. A vaccine RCT assesses the effectiveness, side effects, duration of protection, and other benefits. Hence, statistical analysis in research is a helpful tool for understanding data.

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Research Methods In Psychology

Saul Mcleod, PhD

Editor-in-Chief for Simply Psychology

BSc (Hons) Psychology, MRes, PhD, University of Manchester

Saul Mcleod, PhD., is a qualified psychology teacher with over 18 years of experience in further and higher education. He has been published in peer-reviewed journals, including the Journal of Clinical Psychology.

Learn about our Editorial Process

Olivia Guy-Evans, MSc

Associate Editor for Simply Psychology

BSc (Hons) Psychology, MSc Psychology of Education

Olivia Guy-Evans is a writer and associate editor for Simply Psychology. She has previously worked in healthcare and educational sectors.

Research methods in psychology are systematic procedures used to observe, describe, predict, and explain behavior and mental processes. They include experiments, surveys, case studies, and naturalistic observations, ensuring data collection is objective and reliable to understand and explain psychological phenomena.

Hypotheses are statements about the prediction of the results, that can be verified or disproved by some investigation.

There are four types of hypotheses :

Null Hypotheses (H0 ) – these predict that no difference will be found in the results between the conditions. Typically these are written ‘There will be no difference…’
Alternative Hypotheses (Ha or H1) – these predict that there will be a significant difference in the results between the two conditions. This is also known as the experimental hypothesis.
One-tailed (directional) hypotheses – these state the specific direction the researcher expects the results to move in, e.g. higher, lower, more, less. In a correlation study, the predicted direction of the correlation can be either positive or negative.
Two-tailed (non-directional) hypotheses – these state that a difference will be found between the conditions of the independent variable but does not state the direction of a difference or relationship. Typically these are always written ‘There will be a difference ….’

All research has an alternative hypothesis (either a one-tailed or two-tailed) and a corresponding null hypothesis.

Once the research is conducted and results are found, psychologists must accept one hypothesis and reject the other.

So, if a difference is found, the Psychologist would accept the alternative hypothesis and reject the null. The opposite applies if no difference is found.

Sampling techniques

Sampling is the process of selecting a representative group from the population under study.

A sample is the participants you select from a target population (the group you are interested in) to make generalizations about.

Representative means the extent to which a sample mirrors a researcher’s target population and reflects its characteristics.

Generalisability means the extent to which their findings can be applied to the larger population of which their sample was a part.

Volunteer sample : where participants pick themselves through newspaper adverts, noticeboards or online.
Opportunity sampling : also known as convenience sampling , uses people who are available at the time the study is carried out and willing to take part. It is based on convenience.
Random sampling : when every person in the target population has an equal chance of being selected. An example of random sampling would be picking names out of a hat.
Systematic sampling : when a system is used to select participants. Picking every Nth person from all possible participants. N = the number of people in the research population / the number of people needed for the sample.
Stratified sampling : when you identify the subgroups and select participants in proportion to their occurrences.
Snowball sampling : when researchers find a few participants, and then ask them to find participants themselves and so on.
Quota sampling : when researchers will be told to ensure the sample fits certain quotas, for example they might be told to find 90 participants, with 30 of them being unemployed.

Experiments always have an independent and dependent variable .

The independent variable is the one the experimenter manipulates (the thing that changes between the conditions the participants are placed into). It is assumed to have a direct effect on the dependent variable.
The dependent variable is the thing being measured, or the results of the experiment.

Operationalization of variables means making them measurable/quantifiable. We must use operationalization to ensure that variables are in a form that can be easily tested.

For instance, we can’t really measure ‘happiness’, but we can measure how many times a person smiles within a two-hour period.

By operationalizing variables, we make it easy for someone else to replicate our research. Remember, this is important because we can check if our findings are reliable.

Extraneous variables are all variables which are not independent variable but could affect the results of the experiment.

It can be a natural characteristic of the participant, such as intelligence levels, gender, or age for example, or it could be a situational feature of the environment such as lighting or noise.

Demand characteristics are a type of extraneous variable that occurs if the participants work out the aims of the research study, they may begin to behave in a certain way.

For example, in Milgram’s research , critics argued that participants worked out that the shocks were not real and they administered them as they thought this was what was required of them.

Extraneous variables must be controlled so that they do not affect (confound) the results.

Randomly allocating participants to their conditions or using a matched pairs experimental design can help to reduce participant variables.

Situational variables are controlled by using standardized procedures, ensuring every participant in a given condition is treated in the same way

Experimental Design

Experimental design refers to how participants are allocated to each condition of the independent variable, such as a control or experimental group.

Independent design ( between-groups design ): each participant is selected for only one group. With the independent design, the most common way of deciding which participants go into which group is by means of randomization.
Matched participants design : each participant is selected for only one group, but the participants in the two groups are matched for some relevant factor or factors (e.g. ability; sex; age).
Repeated measures design ( within groups) : each participant appears in both groups, so that there are exactly the same participants in each group.
The main problem with the repeated measures design is that there may well be order effects. Their experiences during the experiment may change the participants in various ways.
They may perform better when they appear in the second group because they have gained useful information about the experiment or about the task. On the other hand, they may perform less well on the second occasion because of tiredness or boredom.
Counterbalancing is the best way of preventing order effects from disrupting the findings of an experiment, and involves ensuring that each condition is equally likely to be used first and second by the participants.

If we wish to compare two groups with respect to a given independent variable, it is essential to make sure that the two groups do not differ in any other important way.

Experimental Methods

All experimental methods involve an iv (independent variable) and dv (dependent variable)..

Field experiments are conducted in the everyday (natural) environment of the participants. The experimenter still manipulates the IV, but in a real-life setting. It may be possible to control extraneous variables, though such control is more difficult than in a lab experiment.
Natural experiments are when a naturally occurring IV is investigated that isn’t deliberately manipulated, it exists anyway. Participants are not randomly allocated, and the natural event may only occur rarely.

Case studies are in-depth investigations of a person, group, event, or community. It uses information from a range of sources, such as from the person concerned and also from their family and friends.

Many techniques may be used such as interviews, psychological tests, observations and experiments. Case studies are generally longitudinal: in other words, they follow the individual or group over an extended period of time.

Case studies are widely used in psychology and among the best-known ones carried out were by Sigmund Freud . He conducted very detailed investigations into the private lives of his patients in an attempt to both understand and help them overcome their illnesses.

Case studies provide rich qualitative data and have high levels of ecological validity. However, it is difficult to generalize from individual cases as each one has unique characteristics.

Correlational Studies

Correlation means association; it is a measure of the extent to which two variables are related. One of the variables can be regarded as the predictor variable with the other one as the outcome variable.

Correlational studies typically involve obtaining two different measures from a group of participants, and then assessing the degree of association between the measures.

The predictor variable can be seen as occurring before the outcome variable in some sense. It is called the predictor variable, because it forms the basis for predicting the value of the outcome variable.

Relationships between variables can be displayed on a graph or as a numerical score called a correlation coefficient.

types of correlation. Scatter plot. Positive negative and no correlation

If an increase in one variable tends to be associated with an increase in the other, then this is known as a positive correlation .
If an increase in one variable tends to be associated with a decrease in the other, then this is known as a negative correlation .
A zero correlation occurs when there is no relationship between variables.

After looking at the scattergraph, if we want to be sure that a significant relationship does exist between the two variables, a statistical test of correlation can be conducted, such as Spearman’s rho.

The test will give us a score, called a correlation coefficient . This is a value between 0 and 1, and the closer to 1 the score is, the stronger the relationship between the variables. This value can be both positive e.g. 0.63, or negative -0.63.

Types of correlation. Strong, weak, and perfect positive correlation, strong, weak, and perfect negative correlation, no correlation. Graphs or charts ...

A correlation between variables, however, does not automatically mean that the change in one variable is the cause of the change in the values of the other variable. A correlation only shows if there is a relationship between variables.

Correlation does not always prove causation, as a third variable may be involved.

Interview Methods

Interviews are commonly divided into two types: structured and unstructured.

A fixed, predetermined set of questions is put to every participant in the same order and in the same way.

Responses are recorded on a questionnaire, and the researcher presets the order and wording of questions, and sometimes the range of alternative answers.

The interviewer stays within their role and maintains social distance from the interviewee.

There are no set questions, and the participant can raise whatever topics he/she feels are relevant and ask them in their own way. Questions are posed about participants’ answers to the subject

Unstructured interviews are most useful in qualitative research to analyze attitudes and values.

Though they rarely provide a valid basis for generalization, their main advantage is that they enable the researcher to probe social actors’ subjective point of view.

Questionnaire Method

Questionnaires can be thought of as a kind of written interview. They can be carried out face to face, by telephone, or post.

The choice of questions is important because of the need to avoid bias or ambiguity in the questions, ‘leading’ the respondent or causing offense.

Open questions are designed to encourage a full, meaningful answer using the subject’s own knowledge and feelings. They provide insights into feelings, opinions, and understanding. Example: “How do you feel about that situation?”
Closed questions can be answered with a simple “yes” or “no” or specific information, limiting the depth of response. They are useful for gathering specific facts or confirming details. Example: “Do you feel anxious in crowds?”

Its other practical advantages are that it is cheaper than face-to-face interviews and can be used to contact many respondents scattered over a wide area relatively quickly.

Observations

There are different types of observation methods :

Covert observation is where the researcher doesn’t tell the participants they are being observed until after the study is complete. There could be ethical problems or deception and consent with this particular observation method.
Overt observation is where a researcher tells the participants they are being observed and what they are being observed for.
Controlled : behavior is observed under controlled laboratory conditions (e.g., Bandura’s Bobo doll study).
Natural : Here, spontaneous behavior is recorded in a natural setting.
Participant : Here, the observer has direct contact with the group of people they are observing. The researcher becomes a member of the group they are researching.
Non-participant (aka “fly on the wall): The researcher does not have direct contact with the people being observed. The observation of participants’ behavior is from a distance

Pilot Study

A pilot study is a small scale preliminary study conducted in order to evaluate the feasibility of the key s teps in a future, full-scale project.

A pilot study is an initial run-through of the procedures to be used in an investigation; it involves selecting a few people and trying out the study on them. It is possible to save time, and in some cases, money, by identifying any flaws in the procedures designed by the researcher.

A pilot study can help the researcher spot any ambiguities (i.e. unusual things) or confusion in the information given to participants or problems with the task devised.

Sometimes the task is too hard, and the researcher may get a floor effect, because none of the participants can score at all or can complete the task – all performances are low.

The opposite effect is a ceiling effect, when the task is so easy that all achieve virtually full marks or top performances and are “hitting the ceiling”.

Research Design

In cross-sectional research , a researcher compares multiple segments of the population at the same time

Sometimes, we want to see how people change over time, as in studies of human development and lifespan. Longitudinal research is a research design in which data-gathering is administered repeatedly over an extended period of time.

In cohort studies , the participants must share a common factor or characteristic such as age, demographic, or occupation. A cohort study is a type of longitudinal study in which researchers monitor and observe a chosen population over an extended period.

Triangulation means using more than one research method to improve the study’s validity.

Reliability

Reliability is a measure of consistency, if a particular measurement is repeated and the same result is obtained then it is described as being reliable.

Test-retest reliability : assessing the same person on two different occasions which shows the extent to which the test produces the same answers.
Inter-observer reliability : the extent to which there is an agreement between two or more observers.

Meta-Analysis

A meta-analysis is a systematic review that involves identifying an aim and then searching for research studies that have addressed similar aims/hypotheses.

This is done by looking through various databases, and then decisions are made about what studies are to be included/excluded.

Strengths: Increases the conclusions’ validity as they’re based on a wider range.

Weaknesses: Research designs in studies can vary, so they are not truly comparable.

Peer Review

A researcher submits an article to a journal. The choice of the journal may be determined by the journal’s audience or prestige.

The journal selects two or more appropriate experts (psychologists working in a similar field) to peer review the article without payment. The peer reviewers assess: the methods and designs used, originality of the findings, the validity of the original research findings and its content, structure and language.

Feedback from the reviewer determines whether the article is accepted. The article may be: Accepted as it is, accepted with revisions, sent back to the author to revise and re-submit or rejected without the possibility of submission.

The editor makes the final decision whether to accept or reject the research report based on the reviewers comments/ recommendations.

Peer review is important because it prevent faulty data from entering the public domain, it provides a way of checking the validity of findings and the quality of the methodology and is used to assess the research rating of university departments.

Peer reviews may be an ideal, whereas in practice there are lots of problems. For example, it slows publication down and may prevent unusual, new work being published. Some reviewers might use it as an opportunity to prevent competing researchers from publishing work.

Some people doubt whether peer review can really prevent the publication of fraudulent research.

The advent of the internet means that a lot of research and academic comment is being published without official peer reviews than before, though systems are evolving on the internet where everyone really has a chance to offer their opinions and police the quality of research.

Types of Data

Quantitative data is numerical data e.g. reaction time or number of mistakes. It represents how much or how long, how many there are of something. A tally of behavioral categories and closed questions in a questionnaire collect quantitative data.
Qualitative data is virtually any type of information that can be observed and recorded that is not numerical in nature and can be in the form of written or verbal communication. Open questions in questionnaires and accounts from observational studies collect qualitative data.
Primary data is first-hand data collected for the purpose of the investigation.
Secondary data is information that has been collected by someone other than the person who is conducting the research e.g. taken from journals, books or articles.

Validity means how well a piece of research actually measures what it sets out to, or how well it reflects the reality it claims to represent.

Validity is whether the observed effect is genuine and represents what is actually out there in the world.

Concurrent validity is the extent to which a psychological measure relates to an existing similar measure and obtains close results. For example, a new intelligence test compared to an established test.
Face validity : does the test measure what it’s supposed to measure ‘on the face of it’. This is done by ‘eyeballing’ the measuring or by passing it to an expert to check.
Ecological validit y is the extent to which findings from a research study can be generalized to other settings / real life.
Temporal validity is the extent to which findings from a research study can be generalized to other historical times.

Features of Science

Paradigm – A set of shared assumptions and agreed methods within a scientific discipline.
Paradigm shift – The result of the scientific revolution: a significant change in the dominant unifying theory within a scientific discipline.
Objectivity – When all sources of personal bias are minimised so not to distort or influence the research process.
Empirical method – Scientific approaches that are based on the gathering of evidence through direct observation and experience.
Replicability – The extent to which scientific procedures and findings can be repeated by other researchers.
Falsifiability – The principle that a theory cannot be considered scientific unless it admits the possibility of being proved untrue.

Statistical Testing

A significant result is one where there is a low probability that chance factors were responsible for any observed difference, correlation, or association in the variables tested.

If our test is significant, we can reject our null hypothesis and accept our alternative hypothesis.

If our test is not significant, we can accept our null hypothesis and reject our alternative hypothesis. A null hypothesis is a statement of no effect.

In Psychology, we use p < 0.05 (as it strikes a balance between making a type I and II error) but p < 0.01 is used in tests that could cause harm like introducing a new drug.

A type I error is when the null hypothesis is rejected when it should have been accepted (happens when a lenient significance level is used, an error of optimism).

A type II error is when the null hypothesis is accepted when it should have been rejected (happens when a stringent significance level is used, an error of pessimism).

Ethical Issues

Informed consent is when participants are able to make an informed judgment about whether to take part. It causes them to guess the aims of the study and change their behavior.
To deal with it, we can gain presumptive consent or ask them to formally indicate their agreement to participate but it may invalidate the purpose of the study and it is not guaranteed that the participants would understand.
Deception should only be used when it is approved by an ethics committee, as it involves deliberately misleading or withholding information. Participants should be fully debriefed after the study but debriefing can’t turn the clock back.
All participants should be informed at the beginning that they have the right to withdraw if they ever feel distressed or uncomfortable.
It causes bias as the ones that stayed are obedient and some may not withdraw as they may have been given incentives or feel like they’re spoiling the study. Researchers can offer the right to withdraw data after participation.
Participants should all have protection from harm . The researcher should avoid risks greater than those experienced in everyday life and they should stop the study if any harm is suspected. However, the harm may not be apparent at the time of the study.
Confidentiality concerns the communication of personal information. The researchers should not record any names but use numbers or false names though it may not be possible as it is sometimes possible to work out who the researchers were.

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Statistics and probability

Course: statistics and probability > unit 6.

Picking fairly
Using probability to make fair decisions
Techniques for generating a simple random sample
Simple random samples
Techniques for random sampling and avoiding bias
Sampling methods

Sampling methods review

Samples and surveys

What are sampling methods?

Bad ways to sample.

(Choice A) Convenience sampling A Convenience sampling
(Choice B) Voluntary response sampling B Voluntary response sampling

Good ways to sample

(Choice A) Simple random sampling A Simple random sampling
(Choice B) Stratified random sampling B Stratified random sampling
(Choice C) Cluster random sampling C Cluster random sampling
(Choice D) Systematic random sampling D Systematic random sampling

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Home » Research Methodology – Types, Examples and writing Guide

Research Methodology – Types, Examples and writing Guide

Table of Contents

Research Methodology

Definition:

Research Methodology refers to the systematic and scientific approach used to conduct research, investigate problems, and gather data and information for a specific purpose. It involves the techniques and procedures used to identify, collect , analyze , and interpret data to answer research questions or solve research problems . Moreover, They are philosophical and theoretical frameworks that guide the research process.

Structure of Research Methodology

Research methodology formats can vary depending on the specific requirements of the research project, but the following is a basic example of a structure for a research methodology section:

I. Introduction

Provide an overview of the research problem and the need for a research methodology section
Outline the main research questions and objectives

II. Research Design

Explain the research design chosen and why it is appropriate for the research question(s) and objectives
Discuss any alternative research designs considered and why they were not chosen
Describe the research setting and participants (if applicable)

III. Data Collection Methods

Describe the methods used to collect data (e.g., surveys, interviews, observations)
Explain how the data collection methods were chosen and why they are appropriate for the research question(s) and objectives
Detail any procedures or instruments used for data collection

IV. Data Analysis Methods

Describe the methods used to analyze the data (e.g., statistical analysis, content analysis )
Explain how the data analysis methods were chosen and why they are appropriate for the research question(s) and objectives
Detail any procedures or software used for data analysis

V. Ethical Considerations

Discuss any ethical issues that may arise from the research and how they were addressed
Explain how informed consent was obtained (if applicable)
Detail any measures taken to ensure confidentiality and anonymity

VI. Limitations

Identify any potential limitations of the research methodology and how they may impact the results and conclusions

VII. Conclusion

Summarize the key aspects of the research methodology section
Explain how the research methodology addresses the research question(s) and objectives

Research Methodology Types

Types of Research Methodology are as follows:

Quantitative Research Methodology

This is a research methodology that involves the collection and analysis of numerical data using statistical methods. This type of research is often used to study cause-and-effect relationships and to make predictions.

Qualitative Research Methodology

This is a research methodology that involves the collection and analysis of non-numerical data such as words, images, and observations. This type of research is often used to explore complex phenomena, to gain an in-depth understanding of a particular topic, and to generate hypotheses.

Mixed-Methods Research Methodology

This is a research methodology that combines elements of both quantitative and qualitative research. This approach can be particularly useful for studies that aim to explore complex phenomena and to provide a more comprehensive understanding of a particular topic.

Case Study Research Methodology

This is a research methodology that involves in-depth examination of a single case or a small number of cases. Case studies are often used in psychology, sociology, and anthropology to gain a detailed understanding of a particular individual or group.

Action Research Methodology

This is a research methodology that involves a collaborative process between researchers and practitioners to identify and solve real-world problems. Action research is often used in education, healthcare, and social work.

Experimental Research Methodology

This is a research methodology that involves the manipulation of one or more independent variables to observe their effects on a dependent variable. Experimental research is often used to study cause-and-effect relationships and to make predictions.

Survey Research Methodology

This is a research methodology that involves the collection of data from a sample of individuals using questionnaires or interviews. Survey research is often used to study attitudes, opinions, and behaviors.

Grounded Theory Research Methodology

This is a research methodology that involves the development of theories based on the data collected during the research process. Grounded theory is often used in sociology and anthropology to generate theories about social phenomena.

Research Methodology Example

An Example of Research Methodology could be the following:

Research Methodology for Investigating the Effectiveness of Cognitive Behavioral Therapy in Reducing Symptoms of Depression in Adults

Introduction:

The aim of this research is to investigate the effectiveness of cognitive-behavioral therapy (CBT) in reducing symptoms of depression in adults. To achieve this objective, a randomized controlled trial (RCT) will be conducted using a mixed-methods approach.

Research Design:

The study will follow a pre-test and post-test design with two groups: an experimental group receiving CBT and a control group receiving no intervention. The study will also include a qualitative component, in which semi-structured interviews will be conducted with a subset of participants to explore their experiences of receiving CBT.

Participants:

Participants will be recruited from community mental health clinics in the local area. The sample will consist of 100 adults aged 18-65 years old who meet the diagnostic criteria for major depressive disorder. Participants will be randomly assigned to either the experimental group or the control group.

Intervention :

The experimental group will receive 12 weekly sessions of CBT, each lasting 60 minutes. The intervention will be delivered by licensed mental health professionals who have been trained in CBT. The control group will receive no intervention during the study period.

Data Collection:

Quantitative data will be collected through the use of standardized measures such as the Beck Depression Inventory-II (BDI-II) and the Generalized Anxiety Disorder-7 (GAD-7). Data will be collected at baseline, immediately after the intervention, and at a 3-month follow-up. Qualitative data will be collected through semi-structured interviews with a subset of participants from the experimental group. The interviews will be conducted at the end of the intervention period, and will explore participants’ experiences of receiving CBT.

Data Analysis:

Quantitative data will be analyzed using descriptive statistics, t-tests, and mixed-model analyses of variance (ANOVA) to assess the effectiveness of the intervention. Qualitative data will be analyzed using thematic analysis to identify common themes and patterns in participants’ experiences of receiving CBT.

Ethical Considerations:

This study will comply with ethical guidelines for research involving human subjects. Participants will provide informed consent before participating in the study, and their privacy and confidentiality will be protected throughout the study. Any adverse events or reactions will be reported and managed appropriately.

Data Management:

All data collected will be kept confidential and stored securely using password-protected databases. Identifying information will be removed from qualitative data transcripts to ensure participants’ anonymity.

Limitations:

One potential limitation of this study is that it only focuses on one type of psychotherapy, CBT, and may not generalize to other types of therapy or interventions. Another limitation is that the study will only include participants from community mental health clinics, which may not be representative of the general population.

Conclusion:

This research aims to investigate the effectiveness of CBT in reducing symptoms of depression in adults. By using a randomized controlled trial and a mixed-methods approach, the study will provide valuable insights into the mechanisms underlying the relationship between CBT and depression. The results of this study will have important implications for the development of effective treatments for depression in clinical settings.

How to Write Research Methodology

Writing a research methodology involves explaining the methods and techniques you used to conduct research, collect data, and analyze results. It’s an essential section of any research paper or thesis, as it helps readers understand the validity and reliability of your findings. Here are the steps to write a research methodology:

Start by explaining your research question: Begin the methodology section by restating your research question and explaining why it’s important. This helps readers understand the purpose of your research and the rationale behind your methods.
Describe your research design: Explain the overall approach you used to conduct research. This could be a qualitative or quantitative research design, experimental or non-experimental, case study or survey, etc. Discuss the advantages and limitations of the chosen design.
Discuss your sample: Describe the participants or subjects you included in your study. Include details such as their demographics, sampling method, sample size, and any exclusion criteria used.
Describe your data collection methods : Explain how you collected data from your participants. This could include surveys, interviews, observations, questionnaires, or experiments. Include details on how you obtained informed consent, how you administered the tools, and how you minimized the risk of bias.
Explain your data analysis techniques: Describe the methods you used to analyze the data you collected. This could include statistical analysis, content analysis, thematic analysis, or discourse analysis. Explain how you dealt with missing data, outliers, and any other issues that arose during the analysis.
Discuss the validity and reliability of your research : Explain how you ensured the validity and reliability of your study. This could include measures such as triangulation, member checking, peer review, or inter-coder reliability.
Acknowledge any limitations of your research: Discuss any limitations of your study, including any potential threats to validity or generalizability. This helps readers understand the scope of your findings and how they might apply to other contexts.
Provide a summary: End the methodology section by summarizing the methods and techniques you used to conduct your research. This provides a clear overview of your research methodology and helps readers understand the process you followed to arrive at your findings.

When to Write Research Methodology

Research methodology is typically written after the research proposal has been approved and before the actual research is conducted. It should be written prior to data collection and analysis, as it provides a clear roadmap for the research project.

The research methodology is an important section of any research paper or thesis, as it describes the methods and procedures that will be used to conduct the research. It should include details about the research design, data collection methods, data analysis techniques, and any ethical considerations.

The methodology should be written in a clear and concise manner, and it should be based on established research practices and standards. It is important to provide enough detail so that the reader can understand how the research was conducted and evaluate the validity of the results.

Applications of Research Methodology

Here are some of the applications of research methodology:

To identify the research problem: Research methodology is used to identify the research problem, which is the first step in conducting any research.
To design the research: Research methodology helps in designing the research by selecting the appropriate research method, research design, and sampling technique.
To collect data: Research methodology provides a systematic approach to collect data from primary and secondary sources.
To analyze data: Research methodology helps in analyzing the collected data using various statistical and non-statistical techniques.
To test hypotheses: Research methodology provides a framework for testing hypotheses and drawing conclusions based on the analysis of data.
To generalize findings: Research methodology helps in generalizing the findings of the research to the target population.
To develop theories : Research methodology is used to develop new theories and modify existing theories based on the findings of the research.
To evaluate programs and policies : Research methodology is used to evaluate the effectiveness of programs and policies by collecting data and analyzing it.
To improve decision-making: Research methodology helps in making informed decisions by providing reliable and valid data.

Purpose of Research Methodology

Research methodology serves several important purposes, including:

To guide the research process: Research methodology provides a systematic framework for conducting research. It helps researchers to plan their research, define their research questions, and select appropriate methods and techniques for collecting and analyzing data.
To ensure research quality: Research methodology helps researchers to ensure that their research is rigorous, reliable, and valid. It provides guidelines for minimizing bias and error in data collection and analysis, and for ensuring that research findings are accurate and trustworthy.
To replicate research: Research methodology provides a clear and detailed account of the research process, making it possible for other researchers to replicate the study and verify its findings.
To advance knowledge: Research methodology enables researchers to generate new knowledge and to contribute to the body of knowledge in their field. It provides a means for testing hypotheses, exploring new ideas, and discovering new insights.
To inform decision-making: Research methodology provides evidence-based information that can inform policy and decision-making in a variety of fields, including medicine, public health, education, and business.

Advantages of Research Methodology

Research methodology has several advantages that make it a valuable tool for conducting research in various fields. Here are some of the key advantages of research methodology:

Systematic and structured approach : Research methodology provides a systematic and structured approach to conducting research, which ensures that the research is conducted in a rigorous and comprehensive manner.
Objectivity : Research methodology aims to ensure objectivity in the research process, which means that the research findings are based on evidence and not influenced by personal bias or subjective opinions.
Replicability : Research methodology ensures that research can be replicated by other researchers, which is essential for validating research findings and ensuring their accuracy.
Reliability : Research methodology aims to ensure that the research findings are reliable, which means that they are consistent and can be depended upon.
Validity : Research methodology ensures that the research findings are valid, which means that they accurately reflect the research question or hypothesis being tested.
Efficiency : Research methodology provides a structured and efficient way of conducting research, which helps to save time and resources.
Flexibility : Research methodology allows researchers to choose the most appropriate research methods and techniques based on the research question, data availability, and other relevant factors.
Scope for innovation: Research methodology provides scope for innovation and creativity in designing research studies and developing new research techniques.

Research Methodology Vs Research Methods

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Open access
Published: 06 April 2024

Statistical analyses of ordinal outcomes in randomised controlled trials: a scoping review

Chris J. Selman ORCID: orcid.org/0000-0002-1277-5538 1 , 2 ,
Katherine J. Lee 1 , 2 ,
Kristin N. Ferguson 4 ,
Clare L. Whitehead 4 , 5 ,
Brett J. Manley 4 , 6 , 7 &
Robert K. Mahar 1 , 3

Trials volume 25 , Article number: 241 ( 2024 ) Cite this article

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Randomised controlled trials (RCTs) aim to estimate the causal effect of one or more interventions relative to a control. One type of outcome that can be of interest in an RCT is an ordinal outcome, which is useful to answer clinical questions regarding complex and evolving patient states. The target parameter of interest for an ordinal outcome depends on the research question and the assumptions the analyst is willing to make. This review aimed to provide an overview of how ordinal outcomes have been used and analysed in RCTs.

The review included RCTs with an ordinal primary or secondary outcome published between 2017 and 2022 in four highly ranked medical journals (the British Medical Journal , New England Journal of Medicine , The Lancet , and the Journal of the American Medical Association ) identified through PubMed. Details regarding the study setting, design, the target parameter, and statistical methods used to analyse the ordinal outcome were extracted.

The search identified 309 studies, of which 144 were eligible for inclusion. The most used target parameter was an odds ratio, reported in 78 (54%) studies. The ordinal outcome was dichotomised for analysis in 47 ( \(33\%\) ) studies, and the most common statistical model used to analyse the ordinal outcome on the full ordinal scale was the proportional odds model (64 [ \(44\%\) ] studies). Notably, 86 (60%) studies did not explicitly check or describe the robustness of the assumptions for the statistical method(s) used.

Conclusions

The results of this review indicate that in RCTs that use an ordinal outcome, there is variation in the target parameter and the analytical approaches used, with many dichotomising the ordinal outcome. Few studies provided assurance regarding the appropriateness of the assumptions and methods used to analyse the ordinal outcome. More guidance is needed to improve the transparent reporting of the analysis of ordinal outcomes in future trials.

Peer Review reports

Randomised controlled trials (RCTs) aim to estimate the causal effect of one or more interventions relative to a control or reference intervention. Ordinal outcomes are useful in RCTs because the categories can represent multiple patient states within a single endpoint. The definition of an ordinal outcome is one that comprises monotonically ranked categories that are ordered hierarchically such that the distance between any two categories is not necessarily equal (or even meaningfully quantifiable) [ 1 ]. Ordinal outcomes should have categories that are mutually exclusive and unambiguously defined and can be used to capture improvement and deterioration relative to a baseline value where relevant [ 2 ]. If an ordinal scale is being used to capture change in patient status, then the ordinal outcome should also be symmetric to avoid favouring a better or worse health outcome [ 2 ]. Commonly used ordinal outcomes in RCTs include the modified-Rankin scale, a 7-category measure of disability following stroke or neurological insult [ 3 , 4 , 5 , 6 ], the Glasgow Outcome Scale-Extended (GOS-E), an 8-category measure of functional impairment post traumatic brain injury [ 7 ], and the World Health Organization (WHO) COVID-19 Clinical Progression Scale [ 8 ], an 11-point measure of disease severity among patients with COVID-19. The WHO Clinical Progression Scale, developed specifically for COVID-19 in 2020 [ 8 ], has been used in many RCTs evaluating COVID-19 disease severity and progression [ 9 , 10 ] and has helped to increase the familiarity of ordinal data and modelling approaches for ordinal outcomes for clinicians and statisticians alike [ 11 ].

Randomised controlled trials that use ordinal outcomes need to be designed and analysed with care. This includes the need to explicitly define the target parameter to compare the intervention groups (i.e. the target of estimation, for example, a proportional odds ratio (OR)), the analysis approach, and whether assumptions used in the analysis are valid. Although this is true for all RCTs, these issues are more complex when using an ordinal outcome compared to a binary or continuous outcome. For example, the choice of target parameter for an ordinal outcome depends on both the research question [ 12 , 13 ] and the assumptions that the analyst is willing to make about the data.

One option is to preserve the ordinal nature of the outcome, which can give rise to a number of different target parameters. Principled analysis of ordinal data often relies on less familiar statistical methods and underlying assumptions. Many statistical methods have been proposed to analyse ordinal outcomes. One approach to estimate the effect of treatment on the distribution of ordinal endpoints is to use a cumulative logistic model [ 14 , 15 ]. This model uses the distribution of the cumulative log-odds of the ordinal outcome to estimate a set of ORs [ 16 ], which, for an increase in the value of a covariate, represents the odds of being in the same or higher category at each level of the ordinal scale [ 15 ]. Modelling is vastly simplified by assuming that each covariate in the model exerts the same effect on the cumulative log odds for each binary split of the ordinal outcome, regardless of the threshold. This is known as the proportional odds (PO) assumption, with the model referred to as ordered logistic regression or the PO model (we refer to the latter term herein). The PO model has desirable properties of palindromic invariance (where the estimates of the parameters are not equivalent when the order of the categories are reversed) and invariance under collapsibility (where the estimated target parameter is changed when categories of the response are combined or removed) [ 17 ]. Studies have shown that an ordinal analysis of the outcome using a PO model increases the statistical power relative to an analysis of the dichotomised scale [ 18 , 19 ]. The target parameter from this model, the proportional or common OR, also has a relatively intuitive interpretation [ 20 , 21 ], representing a shift in the distribution of ordinal scale scores toward a better outcome in an intervention group compared to a reference group.

The PO model approach makes the assumption that the odds are proportional for each binary split of the ordinal outcome. If this assumption is violated then the proportional OR may be misleading in certain circumstances. Specifically, violation to PO can affect type I or II errors and/or may distort the magnitude of the treatment effect. For example, violation of proportional odds can increase the likelihood of making a type I error since the model may incorrectly identify evidence of a relationship between the treatment and outcome. Violation of the proportional odds assumption may also increase the likelihood of a type II error as the model may fail to identify a relationship between the treatment and the ordinal outcome because the model may fail to capture the true complexity of the relationship. In addition, a treatment may exert a harmful effect for some categories of the ordinal outcome but exert a beneficial effect for the remaining categories, which can ‘average’ out to no treatment effect when assuming a constant OR across the levels of the ordinal scale. The violation of PO may be harmful if the interest is also to estimate predicted probabilities for the categories of the ordinal scale, which will be too low or high for some outcomes when PO is assumed. Although the PO assumption will ‘average’ the treatment effect across the categories of the ordinal outcome, this may not be a problem if all of the treatment effects for each cut-point are in the same direction and the research aim is to simply show whether the treatment is effective even in the presence of non-PO. If the PO assumption is meaningfully violated and the interest is either in the treatment effect on a specific range of the outcome or to obtain predicted probabilities for each category of the scale, the PO model can be extended to a partial proportional odds (PPO) model which allows the PO assumption to be relaxed for a specific set or for all covariates in the model [ 22 ]. There are two types of PPO models: the unconstrained PPO model, in which the cumulative log-ORs across each cut-point vary freely across some or all of the cut-points [ 23 ], and the constrained PPO model, which assumes some functional relationship between the cumulative log-ORs [ 21 ]. However, such an approach may be more inefficient than using a PO model [ 24 , 25 ].

Alternative statistical methods that can be used to analyse the ordinal outcome include multinomial regression, which estimates an OR for each category of the ordinal outcome relative to the baseline category. The disadvantage of multinomial regression is that the number of ORs requiring estimation increases with the number of categories in the ordinal outcome. A larger sample size may therefore be required to ensure accurate precision of the many target parameters. Other methods are the continuation ratio model or adjacent-category logistic model, though these models lack two desirable properties: palindromic invariance and invariance under collapsibility [ 15 , 17 , 26 ].

Another option is to use alternative methods, such as the Mann-Whitney U test or Wilcoxon rank-sum test [ 27 ] (referred to as the Wilcoxon test herein). The Wilcoxon test is equivalent to the PO model with a single binary exposure variable [ 15 , 28 ]. The treatment effect from a Wilcoxon test is the concordance probability that represents the probability that a randomly chosen observation from a treatment group is greater than a randomly chosen observation from a control group [ 29 , 30 ]. This parameter closely mirrors the OR derived from the PO model. Importantly, the direction of the OR from the PO model always agrees with the direction of the concordance probability. The disadvantages of the Wilcoxon test are that the concordance probability may be unfamiliar to clinicians, and the Wilcoxon test cannot be adjusted for covariates.

Another option is to dichotomise the ordinal outcome and use an OR or risk difference as the target parameter, estimated using logistic or binomial regression. This produces an effect estimate with clear clinical interpretations that may be suitable for specific clinical settings. The disadvantage of dichotomising an ordinal outcome is that it means discarding potentially useful information within the levels of the scale. This means that the trial may require a larger sample size to maintain the same level of statistical power to detect a clinically important treatment effect [ 19 ], which may not be feasible in all RCTs depending on cost constraints or the rate of recruitment. The decision to dichotomise may also depend on when the outcome is being measured. This was highlighted in a study that showed that an ordinal analysis of the modified-Rankin scale captured differences in long-term outcomes in survivors of stroke better than an analysis that dichotomised the ordinal outcome [ 3 , 31 ].

An alternative to dichotomisation is to treat the ordinal outcome as continuous and focus on the mean difference as the target parameter. This choice to treat the outcome as continuous may be based on the number of categories, where the more categories, the more the outcome resembles a continuum if proximate categories measure similar states or if the scale reflects a latent continuous variable. This has the advantage that modelling is straightforward and familiar, but it can lead to ill-defined clinical interpretations of the treatment effect since the difference between proximate categories is unequal nor quantifiable. Such an analysis also wrongly assumes that the outcome has an unbounded range.

There has been commentary [ 32 ] and research conducted on the methodology of using ordinal outcomes in certain RCT settings that have mainly focused on the benefit of an ordinal analysis using a PO model [ 19 , 33 , 34 , 35 ], including investigations into the use of a PPO model when the PO assumption is violated [ 36 ]. However, these studies have primarily focused on a limited number of statistical methods and in mostly specific medical areas such as neurology and may not be applicable more generally. Given the growing use of ordinal outcomes in RCTs, it is crucial to gain a deeper understanding of how ordinal outcomes are utilised in practice. This understanding will help identify any issues in the use of ordinal outcomes in RCTs and facilitate discussions on improving the reporting and analysis of such outcomes. To address this, we conducted a scoping review to systematically examine the use and analysis of ordinal outcomes in the current literature. Specifically, we aimed to:

Identify which target parameters are of interest in RCTs that use an ordinal outcome and whether these are explicitly defined.

Describe how ordinal outcomes are analysed in RCTs to estimate a treatment effect.

Describe whether RCTs that use an ordinal outcome adequately report key methodological aspects specific to the analysis of the ordinal outcome.

A pre-specified protocol was developed for this scoping review [ 37 ]. Deviations from the protocol are outlined in Additional file 1 . Here, we provide an overview of the protocol and present the findings from the review which have been reported using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) checklist [ 38 ].

Eligibility criteria

Studies were included in the review if they were published in one of four highly ranked medical journals ( British Medical Journal (BMJ), New England Journal of Medical (NEJM), Journal of the American Medical Association (JAMA), or The Lancet) between 1 January 2017 and 31 July 2022 and reported the results of at least one RCT (e.g. if reporting results from multiple trials) with either a primary or secondary outcome that was measured on an ordinal scale. These journals were chosen because they are leading medical journals that publish original and peer-reviewed research with primarily clinical aims and have been used in other reviews of trial methodology [ 39 , 40 ]. RCTs were defined using the Cochrane definition of an RCT, which is a study that prospectively assigns individuals to one of two (or more) interventions using some random or quasi-random method of allocation [ 41 ].

Studies were excluded from this review if they were written in a language other than English, since we did not have sufficient resources to translate studies written in another language. We also excluded studies which were purely methodological, where the abstract or full-text was not available, which reported data from non-human subjects, and those that provided a commentary, review opinion, or were description only. Manuscripts that reported only a trial protocol or statistical analysis plan were also excluded, since one of the main objectives of this review was to determine which statistical methods are being used to analyse trial data. Studies that used ordinal outcomes that were measured on a numerical rating or visual analogue scale were also excluded. Although these scales are often considered ordinal, they imply equidistance between contiguous categories, and can conceivably be analysed as continuous data.

Information sources

Studies were identified and included in the review by searching the online bibliographic database, PubMed, executed on 3 August, 2022.

Search strategy

The search strategy for this review was developed by CJS in consultation with KJL and RKM. The search strategy employed terms that have been developed to identify RCTs [ 41 ] and terms that have been used to describe an ordinal outcome in published manuscripts for RCTs. The complete search strategy that was used in this review is described in Table 1 .

Selection of sources of evidence

There was no pre-specified sample size for this review. All eligible studies that were identified via the search strategy were included in the review.

Piloting of the eligibility criteria was conducted by CJS and RKM who independently assessed the titles and abstracts of 20 studies to ensure consistency between reviewers. CJS then performed the search on the PubMed database. All titles and abstracts identified were extracted into Covidence, a web-based tool for managing systematic reviews [ 42 ]. A two-phase screening process was employed, where all abstracts and titles were screened by CJS in the first phase. Those studies that were not excluded were then moved to the second phase of the screening process, where the full text was evaluated against the eligibility criteria by CJS. A random sample of 40 studies were also assessed for eligibility by a second reviewer (one of KJL, RKM, BJM, or CLW). All studies that were deemed eligible were included in the data extraction.

Data extraction

A data extraction questionnaire was developed in Covidence [ 42 ] and piloted by CJS and RKM using a sample of 10 studies, which was further refined. The final version of the questionnaire is shown in Additional file 2 , and a full list of the data extraction items is provided in Table 2 . Data was extracted from both the main manuscript and any supplementary material, including statistical analysis plans. CJS extracted data from all eligible studies in the review. Double data extraction was performed by KJL and RKM on a random sample of 20 studies. Any uncertainties in the screening and data extraction process were discussed and resolved by consensus among all reviewers. Simplifications and assumptions that were made for eligibility and data extraction are outlined in Additional file 1 .

Synthesis of results

The data extracted from Covidence were cleaned and analysed using Stata [ 43 ]. Descriptive statistics were used to summarise the data. Frequencies and percentages and medians and interquartile ranges (IQRs) were reported for categorical and continuous variables respectively. Qualitative data were synthesised in a narrative format.

Results of the search

The initial search identified 309 studies, of which 46 were excluded for not being an RCT. There were 263 studies that underwent full text review. Of these, 119 were excluded: 110 because they did not have an ordinal outcome, and nine because they were not an RCT. In total, 144 studies were eligible for data extraction [ 44 , 45 , 46 , 47 , 48 , 49 , 50 , 51 , 52 , 53 , 54 , 55 , 56 , 57 , 58 , 59 , 60 , 61 , 62 , 63 , 64 , 65 , 66 , 67 , 68 , 69 , 70 , 71 , 72 , 73 , 74 , 75 , 76 , 77 , 78 , 79 , 80 , 81 , 82 , 83 , 84 , 85 , 86 , 87 , 88 , 89 , 90 , 91 , 92 , 93 , 94 , 95 , 96 , 97 , 98 , 99 , 100 , 101 , 102 , 103 , 104 , 105 , 106 , 107 , 108 , 109 , 110 , 111 , 112 , 113 , 114 , 115 , 116 , 117 , 118 , 119 , 120 , 121 , 122 , 123 , 124 , 125 , 126 , 127 , 128 , 129 , 130 , 131 , 132 , 133 , 134 , 135 , 136 , 137 , 138 , 139 , 140 , 141 , 142 , 143 , 144 , 145 , 146 , 147 , 148 , 149 , 150 , 151 , 152 , 153 , 154 , 155 , 156 , 157 , 158 , 159 , 160 , 161 , 162 , 163 , 164 , 165 , 166 , 167 , 168 , 169 , 170 , 171 , 172 , 173 , 174 , 175 , 176 , 177 , 178 , 179 , 180 , 181 , 182 , 183 , 184 , 185 , 186 , 187 ]. A flow diagram of the study selection is shown in Fig. 1 . The questionnaire that was used to extract the data from each study is provided in Additional file 2 .

Flow diagram of the study

Study characteristics

A summary of the study characteristics is presented in Table 3 . The highest proportion of studies were published in the NEJM (61 studies, \(42\%\) ), followed by JAMA (40, 28%) and The Lancet (34, 24%), with only nine studies published in the BMJ ( \(6\%\) ). The number of studies that used an ordinal outcome were higher in 2020 and 2021 ( \(30, 21\%\) in each year) compared to earlier years ( \(21, 15\%\) in 2019, \(24, 17\%\) in 2018 and \(23, 16\%\) in 2017). Nearly all studies were conducted in a clinical setting ( \(141, 98\%\) ). The most common medical condition being studied was stroke ( \(39, 28\%\) ), followed by COVID-19 ( \(22, 16\%\) ) and atopic dermatitis ( \(6, 4\%\) ). The most common medical field was neurology ( \(54, 38\%\) ) followed by infectious diseases ( \(22, 16\%\) , all of which were COVID-19 studies), dermatology ( \(13, 9\%\) ), and psychiatry ( \(12, 9\%\) ). Studies were mostly funded by public sources ( \(104, 72\%\) ). The median number of participants in the primary analysis of the ordinal outcome was 380 (interquartile range (IQR): 202–803).

Of the 144 included studies, 58 (40%) used some form of adaptive design, with 47 ( \(33\%\) ) having explicitly defined early stopping rules for efficacy or futility, 18 ( \(13\%\) ) used sample size re-estimation, three ( \(2\%\) ) used response adaptive randomisation, three ( \(2\%\) ) used covariate adaptive randomisation, three ( \(2\%\) ) were platform trials, and three ( \(2\%\) ) used adaptive enrichment that focused on specific subgroups of patients.

Ordinal outcomes and target parameters

A summary of the properties of the ordinal outcomes used in the studies is shown in Table 4 . An ordinal scale was used as a primary outcome in 59 ( \(41\%\) ) of studies. Most studies used an ordinal scale to describe an outcome at a single point in time ( \(128, 89\%\) ), with 16 studies using an ordinal outcome to capture changes over time ( \(11\%\) ). One study used a Likert scale where the categories were ambiguously defined in the manuscript. Another study used an ordinal outcome to measure change over time, but it was asymmetric and biased towards a favourable outcome. The median number of categories in the ordinal outcome was 7 (IQR: 6–7) and ranged from 3 to 23 categories.

There were 32 studies that determined the sample size in advance based on the ordinal outcome, of which 26 out of 32 studies ( \(81\%\) ) used an analytical approach and 6 out of 32 studies ( \(19\%\) ) used simulation to estimate the sample size. Among those studies that used an analytical approach, five studies reported to have used the Whitehead method and three studies reported to have used a t -test. Among the remaining studies that used an analytical approach, it was unclear which specific method was used to compute the sample size.

The ordinal outcome was dichotomised for analysis in 47 ( \(33\%\) ) studies. Some justifications for the dichotomisation of the ordinal outcome included that it represented a clinically meaningful effect and/or that it was common in the analysis of the outcome in similar studies (reported in 24 studies), that the dichotomised outcome represented an agreeable endpoint based on feedback between clinicians and/or patients and families (two studies), or that the assumptions of the statistical model for the categorical outcome were violated (reported in three studies).

There were a variety of target parameters used for the ordinal outcomes. In 130 studies, the target parameter could be determined; however, 59 of these studies ( \(45\%\) ) did not clearly or explicitly define the target parameter of interest. Of those where the target parameter could be determined based on the information provided in the manuscript (e.g. since it was not reported), an OR was the most common target parameter ( \(78, 54\%\) ), followed by a risk difference ( \(31, 22\%\) ). A difference in mean or median was the target parameter in 11 (8%) and 8 (6%) studies respectively. There were 14 ( \(10\%\) ) studies that did not estimate a target parameter. This was either because the study was descriptive in nature, the analysis used a non-parametric procedure, or the target parameter could not be determined (or some combination thereof).

Statistical methods and assumptions

There was a variety of descriptive measures used to summarise the distribution of the ordinal outcome by intervention groups (Table 5 ). The most common descriptive statistics were frequencies and/or percentages in each category of the ordinal outcome ( \(116, 81\%\) ), followed by the median score across all categories ( \(33, 23\%\) ) and IQRs ( \(31, 22\%\) ). The mean and standard deviation across the categories of the ordinal outcome were only summarised in 16 (11%) and 10 (7%) studies respectively.

Many different statistical methods were used to analyse the ordinal outcome (Table 5 ). The PO model was the most common statistical method used to analyse the ordinal outcome (64, \(44\%\) ) that was used to estimate a proportional OR in 62 studies. In studies that used a PO model for the analysis, the interpretation of the target parameter varied between studies (see Additional file 3 ). The most frequent definition used was that the proportional OR represented an ordinal shift in the distribution of ordinal scale scores toward a better outcome in the intervention relative to the control group ( \(12, 19\%\) ). When the outcome was dichotomised, logistic regression was used in 16 studies ( \(11\%\) of all studies) that usually estimated an OR or a risk difference using g-computation. Seven studies estimated a risk difference or risk ratio using binomial regression. Studies also calculated and reported a risk difference with corresponding \(95\%\) confidence intervals estimated using methods such as the Wald method or bootstrapping ( \(31, 22\%\) ). There were 19 (13%) studies that used a non-parametric method to analyse the ordinal outcome (either dichotomised or not), including the Cochran-Mantel-Haenszel test ( \(15, 10\%\) ) to estimate an odds ratio, the Wilcoxon test ( \(14, 10\%\) ), of which no study reported a concordance probability as the target parameter, or the Fisher’s exact or Chi-Square test (12, \(8\%\) ). Other statistical methods that were used were the Hodges-Lehmann estimator, used to estimate a median difference ( \(3, 2\%\) ) and the Van Elteren test ( \(2, 1\%\) ), an extension of the Wilcoxon test for comparing treatments in a stratified experiment. Linear regression was used in 16 ( \(11\%\) ) studies that tended to estimate a mean or risk difference (despite the model having an unbounded support).

The majority of studies ( \(86, 60\%\) ) did not explicitly check the validity of the assumptions for the statistical method(s) used. For example, no study that analysed the ordinal outcome using linear regression commented on the appropriateness of assigning specific numbers of the outcome categories. Among the 64 studies that used a PO model, 20 (31%) did not report whether the assumption of PO was satisfied. Overall, there were 46 studies that reported checking key modelling assumptions; however, the method that was used to check these assumptions were not reported in 6 ( \(13\%)\) of these studies. The most common way to verify model assumptions was to use statistical methods ( \(31, 67\%\) ), followed by graphical methods ( \(2, 4\%\) ).

Among the 44 studies that assessed the validity of the PO assumption for a PO model, 13 studies ( \(30\%\) ) used a likelihood ratio test, 10 studies ( \(23\%\) ) used the Brant test, and 10 studies ( \(23\%\) ) also used the Score test. Six ( \(14\%\) ) studies assessed the robustness of the PO assumption by fitting a logistic regression model to every level of the ordinal outcome across the scale, in which the OR for each dichotomous break was presented. Two studies assessed the PO assumption using graphical methods, which plotted either the inverse cumulative log odds or the empirical cumulative log odds. It was unclear which method was used to assess the PO assumption in ten studies that reported to have checked the assumption.

There were 12 studies ( \(8\%\) ) that reported using a different statistical method than originally planned. Ten of these studies had originally planned to use a PO model, but the PO assumption was determined to have been violated and an alternative method was chosen. One study removed the covariate that was reported to have violated the PO assumption and still used a PO model to analyse the outcome. Two studies used an unconstrained PPO model that reported an adjusted OR for each binary split of the ordinal outcome. Three studies used a Wilcoxon test, with one study stratifying by a baseline covariate that violated the PO assumption. Another study dichotomised the ordinal outcome for the analysis. One study used a Van Elteren test that estimated a median difference (which inappropriately assumes that there is an equal distance between proximate categories), another used a Poisson model with robust standard errors, and one study retained the analysis despite the violation in PO. Notably, a PPO model was not reported to have been used in studies that reported that a covariate other than the treatment violated the PO assumption. Seven studies also did not report which covariate(s) violated the PO assumption.

Frequentist inference was the most common framework for conducting the analysis (133, 92%), with Bayesian methods being used in eight (6%) studies (where two studies used both), of which all eight studies used an adaptive design. Of those using Bayesian methods, seven studies used a Bayesian PO model for analysis. Of these studies, four used a Dirichlet prior distribution to model the baseline probabilities, and three used a normally distributed prior on the proportional log-OR scale. Two of these studies reported to use the median proportional OR with corresponding \(95\%\) credible interval, while one study reported the mean proportional OR. Three studies reported that the models were fitted with the use of a Markov-chain Monte Carlo algorithm with either 10, 000 (one study) or 100, 000 (two studies) samples from the joint posterior distribution. No study reported how the goodness-of-fit of the model was assessed.

For the 38 studies that collected repeated measurements on the ordinal outcome, 18 adjusted for the baseline measurement ( \(47\%\) ), 14 used mixed effects models ( \(37\%\) ), and four used generalised estimated equations ( \(11\%\) ) to capture the correlation among the repeated measures for an individual.

A range of statistical packages were used for the analysis of the ordinal outcome, with SAS ( \(81, 56\%\) ) and R ( \(35, 24\%\) ) being most common. Twelve ( \(8\%\) ) studies did not report the software used.

This review has provided an overview of how ordinal outcomes are used and analysed in contemporary RCTs. We describe the insight this review has provided on the study design, statistical analyses and reporting of trials using ordinal outcomes.

Target parameter

The target parameter of interest is an important consideration when planning any trial and should be aligned with the research question [ 12 , 13 ]. The most common target parameter in this review was an OR, either for a dichotomised version of the ordinal outcome or in an analysis that used the ordinal scale. When an ordinal analysis was used, it was common that the target parameter was a proportional OR, although there was variation in the interpretation of this parameter between studies. We found that it was most common to interpret the proportional OR as an average shift in the distribution of the ordinal scale scores toward a better outcome in the intervention, relative to the comparator(s) [ 19 , 35 , 188 , 189 ]. In the studies that dichotomised the ordinal outcome, many lacked justification for doing so and, in one case, dichotomisation was used only due to the violation of PO, despite the fact that this changed the target parameter.

Some studies in our review treated the ordinal outcome as if it were continuous, and used a difference in means or medians as the target parameter. These quantities do not represent a clinically meaningful effect when the outcome is ordinal, since proximate categories in the scale are not necessarily separated by a quantifiable or equal distance, which can affect the translation of the trial results into practice. If a study is to use a mean difference then the researchers should justify the appropriateness of assigning specific numbers used to the ordinal outcome categories.

The target parameter and statistical method used to estimate it could not be determined in some studies. Notably, the definition of the target parameter was not explicitly defined in almost half of the studies, despite the current recommendations on the importance of clearly defining the estimand of interest, one component of which is the target parameter [ 12 , 13 ]. Furthermore, there is a lack of clarity in defining the target parameter when a PO model was used, despite the interpretation being analogous to the OR for a binary outcome, but applying to an interval of the ordinal scale instead of a single value. Consistency in the definition of a target parameter in RCTs can allow easy interpretation for clinicians and applied researchers. Explicit definition of the target parameter of interest is essential for readers to understand the interpretation of a clinically meaningful treatment effect, and also reflects the present push within clinical research with regards to estimands [ 12 , 13 ].

Statistical methods

It is important to summarise the distribution of the outcome by intervention group in any RCT. When the outcome is ordinal, frequencies and percentages in each category can provide a useful summary of this distribution. Most studies in this review reported frequencies and percentages in each category, although some studies that dichotomised the outcome only reported these summaries for the dichotomised scale. Some studies reported means and standard deviations across the categories which, as mentioned previously, may not have a valid interpretation.

Although there are a range of statistical methods that can be used to analyse an ordinal outcome, we found that the PO model was the most commonly used. This is likely because the PO model is relatively well-known among statisticians and is quite straightforward to fit in most statistical packages, and it possesses the desirable properties of palindromic invariance and invariance under collapsibility. However, when using this approach to estimate a specific treatment effect across all levels of the outcome, it is important to assess and report whether the PO assumption has been met when the aim is to estimate the treatment effect across the different categories or to estimate predicted probabilities in each category. The validity of the PO assumption is less important when the objective is to understand whether one treatment is ‘better’ on average compared to a comparator. In this review, it was common for studies that used a PO model to define the target parameter that related to a treatment benefiting patients with regard to every level of the outcome scale. However, only 44 out of 64 studies reported to have checked the PO assumption, which highlights the deficiency in this practice. Statistical methods were commonly used to assess the PO assumption, although it may be preferable to avoid hypothesis testing when assessing the PO assumption, particularly with small sample sizes, as these statistical tests can have poor statistical power [ 22 , 190 ]. Also, researchers should keep in mind that when the PO assumption is tested, the type I error of the analysis may change and that p -values and confidence intervals based on the updated model ignore the model-fitting uncertainty [ 191 ].

When the PO assumption was violated, a PPO model was rarely used, and instead baseline covariates were removed from the model to address the departure to PO. The fact that the PPO model is underused could be due to a lack of knowledge that such models exist and can be used to address violations in PO. Such a model could have been particularly useful in these studies that had only covariates other than the treatment variable that violated the PO assumption, as the PPO model could have been used to estimate a single proportional OR for the treatment effect. Of note, however, is that an unconstrained PPO model does not necessarily require ordinality as the categories can be arranged and the model fit would be hardly affected [ 192 ], and that estimated probabilities can be negative [ 193 ].

There are other methods that can be used to assess the validity of the PO assumption, such as plotting the differences in predicted log-odds between different categories of the ordinal outcome that should be parallel [ 16 ]. Another option is to fit a logistic regression model to every level of the ordinal outcome across the scale and compare the estimated ORs and corresponding confidence interval for each binary split of the ordinal outcome or simulating predictive distributions. However, estimating separate ORs in this way can be inefficient, particularly when the ordinal outcome has a high number of categories. Arguably, more important than assessing the validity of the PO assumption is to assess the impact of making compared to not making the assumption. If the treatment effect goes in the same direction across each category of the ordinal scale and the objective is to simply understand whether one treatment is better overall, then departures from PO may not be important. If, however, the interest is in estimating a treatment effect for every level of the ordinal outcome and/or the treatment has a detrimental effect for one end of the ordinal scale but a beneficial effect for the remaining categories, there should be careful consideration as to the validity to the type I and II error and the treatment effect if the PO model is used.

Finally, a handful of studies also used the Wilcoxon, Chi-Square, or Fisher’s exact test (the latter being too conservative [ 194 ] and potentially providing misleading results), where commonly only a p -value, not a target parameter, was reported when these methods were used. The lack of a target parameter for the treatment effect can make it difficult for clinicians to translate the results to practice.

Strengths and limitations

The strengths of this study are that we present a review of a large number of RCTs that used ordinal outcomes published in four highly ranked medical journals to highlight the current state of practice for analysing ordinal outcomes. The screening and data extraction process was conducted systematically, and pilot tests and double data extraction ensured the consistency and reliability of the extracted data. The PRISMA-ScR checklist was used to ensure that reporting has been conducted to the highest standard.

This review does, however, have limitations. The restriction to the PubMed database and four highly ranked medical journals may affect the generalisability of this review. We made this decision given the scoping nature of the review, to ensure reproducibility and to ensure that the total number of studies included in the review was manageable. We also aimed to include studies that are likely to reflect best practice of how research using ordinal outcomes is being conducted and reported upon at present. Given the selected journals represent highly ranked medical journals, these findings are likely to reflect the best-case scenario given these journals' reputation for rigour. In addition, our search strategy may have missed certain phrases or variants (particularly related to an ordinal outcome); however, we attempted to mitigate this through our piloting phase. Finally, we also did not review the protocol papers of the trials that may have included additional information related to the statistical methodology. This includes methods that were planned to be used to assess the PO assumption, and any alternative methods that were to be used instead.

Implications of this research

This review has implications for researchers designing RCTs that use an ordinal outcome. Although the majority of studies included in this review were in the fields of neurology and infectious diseases, the results of this review would apply to RCTs in all medical fields that use an ordinal outcome. We have shown that there is substantial variation in the analysis and reporting of ordinal outcomes in practice. Our results suggest that researchers should carefully consider the target parameter of interest and explicitly report what the target parameter represents; this is particularly important for an ordinal outcome which can be unfamiliar to readers. Defining the target parameter upfront will help to ensure that appropriate analytical methods are used to analyse the ordinal outcome and make transparent the assumptions the researchers are willing to make.

Our review also highlights the need for careful assessment and reporting of the validity of the model assumptions made during the analysis of an ordinal outcome. Doing so will ensure that robust statistical methods that align with the research question and categorical nature of the ordinal outcome are used to estimate a valid, clinically relevant target parameter that can be translated to practice.

Availability of data and materials

The datasets and code generated and/or analysed during the current study are available on GitHub [ 195 ].

Abbreviations

Randomised controlled trial

Proportional odds

Partial proportional odds

Statistical analysis plan

Velleman PF, Wilkinson L. Nominal, ordinal, interval, and ratio typologies are misleading. Am Stat. 1993;47(1):65–72.

Article Google Scholar

MacKenzie CR, Charlson ME. Standards for the use of ordinal scales in clinical trials. Br Med J (Clin Res Ed). 1986;292(6512):40–3.

Article CAS PubMed Google Scholar

Banks JL, Marotta CA. Outcomes validity and reliability of the modified Rankin scale: implications for stroke clinical trials: a literature review and synthesis. Stroke. 2007;38(3):1091–6.

Article PubMed Google Scholar

de la Ossa NP, Abilleira S, Jovin TG, García-Tornel Á, Jimenez X, Urra X, et al. Effect of direct transportation to thrombectomy-capable center vs local stroke center on neurological outcomes in patients with suspected large-vessel occlusion stroke in nonurban areas: the RACECAT randomized clinical Trial. JAMA. 2022;327(18):1782–94.

Hubert GJ, Hubert ND, Maegerlein C, Kraus F, Wiestler H, Müller-Barna P, et al. Association between use of a flying intervention team vs patient interhospital transfer and time to endovascular thrombectomy among patients with acute ischemic stroke in nonurban Germany. JAMA. 2022;327(18):1795–805.

Article PubMed PubMed Central Google Scholar

Bösel J, Niesen WD, Salih F, Morris NA, Ragland JT, Gough B, et al. Effect of early vs standard approach to tracheostomy on functional outcome at 6 months among patients with severe stroke receiving mechanical ventilation: the SETPOINT2 Randomized Clinical Trial. JAMA. 2022;327(19):1899–909.

Wilson L, Boase K, Nelson LD, Temkin NR, Giacino JT, Markowitz AJ, et al. A manual for the glasgow outcome scale-extended interview. J Neurotrauma. 2021;38(17):2435–46.

Marshall JC, Murthy S, Diaz J, Adhikari N, Angus DC, Arabi YM, et al. A minimal common outcome measure set for COVID-19 clinical research. Lancet Infect Dis. 2020;20(8):e192–7.

Article CAS Google Scholar

Lovre D, Bateman K, Sherman M, Fonseca VA, Lefante J, Mauvais-Jarvis F. Acute estradiol and progesterone therapy in hospitalised adults to reduce COVID-19 severity: a randomised control trial. BMJ Open. 2021;11(11):e053684.

Song AT, Rocha V, Mendrone-Júnior A, Calado RT, De Santis GC, Benites BD, et al. Treatment of severe COVID-19 patients with either low-or high-volume of convalescent plasma versus standard of care: a multicenter Bayesian randomized open-label clinical trial (COOP-COVID-19-MCTI). Lancet Reg Health-Am. 2022;10:100216.

PubMed PubMed Central Google Scholar

Mathioudakis AG, Fally M, Hashad R, Kouta A, Hadi AS, Knight SB, et al. Outcomes evaluated in controlled clinical trials on the management of COVID-19: a methodological systematic review. Life. 2020;10(12):350.

Akacha M, Bretz F, Ohlssen D, Rosenkranz G, Schmidli H. Estimands and their role in clinical trials. Stat Biopharm Res. 2017;9(3):268–71.

Mallinckrodt C, Molenberghs G, Lipkovich I, Ratitch B. Estimands, estimators and sensitivity analysis in clinical trials. CRC Press; 2019.

Walker SH, Duncan DB. Estimation of the probability of an event as a function of several independent variables. Biometrika. 1967;54(1–2):167–79.

McCullagh P. Regression models for ordinal data. J R Stat Soc Ser B Methodol. 1980;42(2):109–27.

Google Scholar

Harrell FE, et al. Regression modeling strategies: with applications to linear models, logistic and ordinal regression, and survival analysis, vol 3. Springer; 2015.

Ananth CV, Kleinbaum DG. Regression models for ordinal responses: a review of methods and applications. Int J Epidemiol. 1997;26(6):1323–33.

Armstrong BG, Sloan M. Ordinal regression models for epidemiologic data. Am J Epidemiol. 1989;129(1):191–204.

Roozenbeek B, Lingsma HF, Perel P, Edwards P, Roberts I, Murray GD, et al. The added value of ordinal analysis in clinical trials: an example in traumatic brain injury. Crit Care. 2011;15(3):1–7.

Breheny P. Proportional odds models. 2015. MyWeb. https://myweb.uiowa.edu/pbreheny/uk/teaching/760-s13/notes/4-23.pdf .

Abreu MNS, Siqueira AL, Cardoso CS, Caiaffa WT. Ordinal logistic regression models: application in quality of life studies. Cad Saúde Pública. 2008;24:s581–91.

Peterson B, Harrell FE Jr. Partial proportional odds models for ordinal response variables. J R Stat Soc: Ser C: Appl Stat. 1990;39(2):205–17.

Fullerton AS. A conceptual framework for ordered logistic regression models. Sociol Methods Res. 2009;38(2):306–47.

Senn S, Julious S. Measurement in clinical trials: a neglected issue for statisticians? Stat Med. 2009;28(26):3189–209.

Maas AI, Steyerberg EW, Marmarou A, McHugh GS, Lingsma HF, Butcher I, et al. IMPACT recommendations for improving the design and analysis of clinical trials in moderate to severe traumatic brain injury. Neurotherapeutics. 2010;7:127–34.

McFadden D, et al. Conditional logit analysis of qualitative choice behavior. Front Econ. 1973;105–142.

Wilcoxon F. Individual comparisons by ranking methods. Springer; 1992.

Liu Q, Shepherd BE, Li C, Harrell FE Jr. Modeling continuous response variables using ordinal regression. Stat Med. 2017;36(27):4316–35.

Fay MP, Brittain EH, Shih JH, Follmann DA, Gabriel EE. Causal estimands and confidence intervals associated with Wilcoxon-Mann-Whitney tests in randomized experiments. Stat Med. 2018;37(20):2923–37.

De Neve J, Thas O, Gerds TA. Semiparametric linear transformation models: effect measures, estimators, and applications. Stat Med. 2019;38(8):1484–501.

Ganesh A, Luengo-Fernandez R, Wharton RM, Rothwell PM. Ordinal vs dichotomous analyses of modified Rankin Scale, 5-year outcome, and cost of stroke. Neurology. 2018;91(21):e1951–60.

French B, Shotwell MS. Regression models for ordinal outcomes. JAMA. 2022;328(8):772–3.

Bath PM, Geeganage C, Gray LJ, Collier T, Pocock S. Use of ordinal outcomes in vascular prevention trials: comparison with binary outcomes in published trials. Stroke. 2008;39(10):2817–23.

Scott SC, Goldberg MS, Mayo NE. Statistical assessment of ordinal outcomes in comparative studies. J Clin Epidemiol. 1997;50(1):45–55.

McHugh GS, Butcher I, Steyerberg EW, Marmarou A, Lu J, Lingsma HF, et al. A simulation study evaluating approaches to the analysis of ordinal outcome data in randomized controlled trials in traumatic brain injury: results from the IMPACT Project. Clin Trials. 2010;7(1):44–57.

DeSantis SM, Lazaridis C, Palesch Y, Ramakrishnan V. Regression analysis of ordinal stroke clinical trial outcomes: an application to the NINDS t-PA trial. Int J Stroke. 2014;9(2):226–31.

Selman CJ, Lee KJ, Whitehead CL, Manley BJ, Mahar RK. Statistical analyses of ordinal outcomes in randomised controlled trials: protocol for a scoping review. Trials. 2023;24(1):1–7.

Tricco AC, Lillie E, Zarin W, O’Brien KK, Colquhoun H, Levac D, et al. PRISMA extension for scoping reviews (PRISMA-ScR): checklist and explanation. Ann Intern Med. 2018;169(7):467–73.

Bell ML, Fiero M, Horton NJ, Hsu CH. Handling missing data in RCTs; a review of the top medical journals. BMC Med Res Methodol. 2014;14(1):1–8.

Berwanger O, Ribeiro RA, Finkelsztejn A, Watanabe M, Suzumura EA, Duncan BB, et al. The quality of reporting of trial abstracts is suboptimal: survey of major general medical journals. J Clin Epidemiol. 2009;62(4):387–92.

Higgins JP, Thomas J, Chandler J, Cumpston M, Li T, Page MJ, et al.. Cochrane handbook for systematic reviews of interventions. John Wiley & Sons; 2019.

Veritas Health Innovation. Covidence systematic review software. Melbourne; 2022.

StataCorp L. Stata statistical software: Release 17 (2021). College Station: StataCorp LP; 2021.

Hanley DF, Lane K, McBee N, Ziai W, Tuhrim S, Lees KR, et al. Thrombolytic removal of intraventricular haemorrhage in treatment of severe stroke: results of the randomised, multicentre, multiregion, placebo-controlled CLEAR III trial. Lancet. 2017;389(10069):603–11. https://doi.org/10.1016/S0140-6736(16)32410-2 .

Article CAS PubMed PubMed Central Google Scholar

Nangia J, Wang T, Osborne C, Niravath P, Otte K, Papish S, et al. Effect of a scalp cooling device on alopecia in women undergoing chemotherapy for breast cancer: the SCALP randomized clinical trial. JAMA. 2017;317(6):596–605. https://doi.org/10.1001/jama.2016.20939 . United States.

Ruzicka T, Hanifin JM, Furue M, Pulka G, Mlynarczyk I, Wollenberg A, et al. Anti-interleukin-31 receptor A antibody for atopic dermatitis. N Engl J Med. 2017;376(9):826–35. https://doi.org/10.1056/NEJMoa1606490 . United States.

Németh G, Laszlovszky I, Czobor P, Szalai E, Szatmári B, Harsányi J, et al. Cariprazine versus risperidone monotherapy for treatment of predominant negative symptoms in patients with schizophrenia: a randomised, double-blind, controlled trial. Lancet. 2017;389(10074):1103–13. https://doi.org/10.1016/S0140-6736(17)30060-0 . England.

Mathieson S, Maher CG, McLachlan AJ, Latimer J, Koes BW, Hancock MJ, et al. Trial of pregabalin for acute and chronic sciatica. N Engl J Med. 2017;376(12):1111–20. https://doi.org/10.1056/NEJMoa1614292 . United States.

Baud O, Trousson C, Biran V, Leroy E, Mohamed D, Alberti C. Association between early low-dose hydrocortisone therapy in extremely preterm neonates and neurodevelopmental outcomes at 2 years of age. JAMA. 2017;317(13):1329–37. https://doi.org/10.1001/jama.2017.2692 . United States.

van den Berg LA, Dijkgraaf MG, Berkhemer OA, Fransen PS, Beumer D, Lingsma HF, et al. Two-year outcome after endovascular treatment for acute ischemic stroke. N Engl J Med. 2017;376(14):1341–9. https://doi.org/10.1056/NEJMoa1612136 . United States.

Kaufman J, Fitzpatrick P, Tosif S, Hopper SM, Donath SM, Bryant PA, et al. Faster clean catch urine collection (Quick-Wee method) from infants: randomised controlled trial. BMJ. 2017;357:j1341. https://doi.org/10.1136/bmj.j1341 .

Costa Leme A, Hajjar LA, Volpe MS, Fukushima JT, De Santis Santiago RR, Osawa EA, et al. Effect of intensive vs moderate alveolar recruitment strategies added to lung-protective ventilation on postoperative pulmonary complications: a randomized clinical trial. JAMA. 2017;317(14):1422–32. https://doi.org/10.1001/jama.2017.2297 . United States.

Breitenstein C, Grewe T, Flöel A, Ziegler W, Springer L, Martus P, et al. Intensive speech and language therapy in patients with chronic aphasia after stroke: a randomised, open-label, blinded-endpoint, controlled trial in a health-care setting. Lancet. 2017;389(10078):1528–38. https://doi.org/10.1016/S0140-6736(17)30067-3 . England.

Wechsler ME, Akuthota P, Jayne D, Khoury P, Klion A, Langford CA, et al. Mepolizumab or placebo for eosinophilic granulomatosis with polyangiitis. N Engl J Med. 2017;376(20):1921–32. https://doi.org/10.1056/NEJMoa1702079 .

Devinsky O, Cross JH, Laux L, Marsh E, Miller I, Nabbout R, et al. Trial of cannabidiol for drug-resistant seizures in the Dravet syndrome. N Engl J Med. 2017;376(21):2011–20. https://doi.org/10.1056/NEJMoa1611618 . United States.

Anderson CS, Arima H, Lavados P, Billot L, Hackett ML, Olavarría VV, et al. Cluster-randomized, crossover trial of head positioning in acute stroke. N Engl J Med. 2017;376(25):2437–47. https://doi.org/10.1056/NEJMoa1615715 . United States.

Juch JNS, Maas ET, Ostelo RWJG, Groeneweg JG, Kallewaard JW, Koes BW, et al. Effect of radiofrequency denervation on pain intensity among patients with chronic low back pain: the Mint randomized clinical trials. JAMA. 2017;318(1):68–81. https://doi.org/10.1001/jama.2017.7918 .

Mohamed S, Johnson GR, Chen P, Hicks PB, Davis LL, Yoon J, et al. Effect of antidepressant switching vs augmentation on remission among patients with major depressive disorder unresponsive to antidepressant treatment: the VAST-D randomized clinical trial. JAMA. 2017;318(2):132–45. https://doi.org/10.1001/jama.2017.8036 .

Kanes S, Colquhoun H, Gunduz-Bruce H, Raines S, Arnold R, Schacterle A, et al. Brexanolone (SAGE-547 injection) in post-partum depression: a randomised controlled trial. Lancet. 2017;390(10093):480–9. https://doi.org/10.1016/S0140-6736(17)31264-3 . England.

Lapergue B, Blanc R, Gory B, Labreuche J, Duhamel A, Marnat G, et al. Effect of endovascular contact aspiration vs stent retriever on revascularization in patients with acute ischemic stroke and large vessel occlusion: the ASTER randomized clinical trial. JAMA. 2017;318(5):443–52. https://doi.org/10.1001/jama.2017.9644 .

Lindley RI, Anderson CS, Billot L, Forster A, Hackett ML, Harvey LA, et al. Family-led rehabilitation after stroke in India (ATTEND): a randomised controlled trial. Lancet. 2017;390(10094):588–99. https://doi.org/10.1016/S0140-6736(17)31447-2 . England.

Berlowitz DR, Foy CG, Kazis LE, Bolin LP, Conroy MB, Fitzpatrick P, et al. Effect of intensive blood-pressure treatment on patient-reported outcomes. N Engl J Med. 2017;377(8):733–44. https://doi.org/10.1056/NEJMoa1611179 .

Hui D, Frisbee-Hume S, Wilson A, Dibaj SS, Nguyen T, De La Cruz M, et al. Effect of lorazepam with haloperidol vs haloperidol alone on agitated delirium in patients with advanced cancer receiving palliative care: a randomized clinical trial. JAMA. 2017;318(11):1047–56. https://doi.org/10.1001/jama.2017.11468 .

Roffe C, Nevatte T, Sim J, Bishop J, Ives N, Ferdinand P, et al. Effect of routine low-dose oxygen supplementation on death and disability in adults with acute stroke: the stroke oxygen study randomized clinical trial. JAMA. 2017;318(12):1125–35. https://doi.org/10.1001/jama.2017.11463 .

Dwivedi R, Ramanujam B, Chandra PS, Sapra S, Gulati S, Kalaivani M, et al. Surgery for drug-resistant epilepsy in children. N Engl J Med. 2017;377(17):1639–47. https://doi.org/10.1056/NEJMoa1615335 . United States.

Nogueira RG, Jadhav AP, Haussen DC, Bonafe A, Budzik RF, Bhuva P, et al. Thrombectomy 6 to 24 hours after stroke with a mismatch between deficit and infarct. N Engl J Med. 2018;378(1):11–21. https://doi.org/10.1056/NEJMoa1706442 . United States.

Zheng MX, Hua XY, Feng JT, Li T, Lu YC, Shen YD, et al. Trial of Contralateral seventh cervical nerve transfer for spastic arm paralysis. N Engl J Med. 2018;378(1):22–34. https://doi.org/10.1056/NEJMoa1615208 . United States.

Atri A, Frölich L, Ballard C, Tariot PN, Molinuevo JL, Boneva N, et al. Effect of idalopirdine as adjunct to cholinesterase inhibitors on change in cognition in patients with Alzheimer disease: three randomized clinical trials. JAMA. 2018;319(2):130–42. https://doi.org/10.1001/jama.2017.20373 .

Bassler D, Shinwell ES, Hallman M, Jarreau PH, Plavka R, Carnielli V, et al. Long-term effects of inhaled budesonide for bronchopulmonary dysplasia. N Engl J Med. 2018;378(2):148–57. https://doi.org/10.1056/NEJMoa1708831 . United States.

Raskind MA, Peskind ER, Chow B, Harris C, Davis-Karim A, Holmes HA, et al. Trial of prazosin for post-traumatic stress disorder in military veterans. N Engl J Med. 2018;378(6):507–17. https://doi.org/10.1056/NEJMoa1507598 . United States.

Albers GW, Marks MP, Kemp S, Christensen S, Tsai JP, Ortega-Gutierrez S, et al. Thrombectomy for stroke at 6 to 16 hours with selection by perfusion imaging. N Engl J Med. 2018;378(8):708–18. https://doi.org/10.1056/NEJMoa1713973 .

Bath PM, Woodhouse LJ, Appleton JP, Beridze M, Christensen H, Dineen RA, et al. Antiplatelet therapy with aspirin, clopidogrel, and dipyridamole versus clopidogrel alone or aspirin and dipyridamole in patients with acute cerebral ischaemia (TARDIS): a randomised, open-label, phase 3 superiority trial. Lancet. 2018;391(10123):850–9. https://doi.org/10.1016/S0140-6736(17)32849-0 .

Krebs EE, Gravely A, Nugent S, Jensen AC, DeRonne B, Goldsmith ES, et al. Effect of opioid vs nonopioid medications on pain-related function in patients with chronic back pain or hip or knee osteoarthritis pain: the SPACE randomized clinical trial. JAMA. 2018;319(9):872–82. https://doi.org/10.1001/jama.2018.0899 .

Campbell BCV, Mitchell PJ, Churilov L, Yassi N, Kleinig TJ, Dowling RJ, et al. Tenecteplase versus alteplase before thrombectomy for ischemic stroke. N Engl J Med. 2018;378(17):1573–82. https://doi.org/10.1056/NEJMoa1716405 . United States.

Mellor R, Bennell K, Grimaldi A, Nicolson P, Kasza J, Hodges P, et al. Education plus exercise versus corticosteroid injection use versus a wait and see approach on global outcome and pain from gluteal tendinopathy: prospective, single blinded, randomised clinical trial. BMJ. 2018;361. https://doi.org/10.1136/bmj.k1662 .

Sprigg N, Flaherty K, Appleton JP, Al-Shahi Salman R, Bereczki D, Beridze M, et al. Tranexamic acid for hyperacute primary IntraCerebral Haemorrhage (TICH-2): an international randomised, placebo-controlled, phase 3 superiority trial. Lancet. 2018;391(10135):2107–15. https://doi.org/10.1016/S0140-6736(18)31033-X .

Jolly K, Sidhu MS, Hewitt CA, Coventry PA, Daley A, Jordan R, et al. Self management of patients with mild COPD in primary care: randomised controlled trial. BMJ. 2018;361. https://doi.org/10.1136/bmj.k2241 .

Brock PR, Maibach R, Childs M, Rajput K, Roebuck D, Sullivan MJ, et al. Sodium thiosulfate for protection from cisplatin-induced hearing loss. N Engl J Med. 2018;378(25):2376–85. https://doi.org/10.1056/NEJMoa1801109 .

Khatri P, Kleindorfer DO, Devlin T, Sawyer RN Jr, Starr M, Mejilla J, et al. Effect of alteplase vs aspirin on functional outcome for patients with acute ischemic stroke and minor nondisabling neurologic deficits: the PRISMS randomized clinical trial. JAMA. 2018;320(2):156–66. https://doi.org/10.1001/jama.2018.8496 .

Wang Y, Li Z, Zhao X, Wang C, Wang X, Wang D, et al. Effect of a multifaceted quality improvement intervention on hospital personnel adherence to performance measures in patients with acute ischemic stroke in china: a randomized clinical trial. JAMA. 2018;320(3):245–54. https://doi.org/10.1001/jama.2018.8802 . United States.

Fossat G, Baudin F, Courtes L, Bobet S, Dupont A, Bretagnol A, et al. Effect of in-bed leg cycling and electrical stimulation of the quadriceps on global muscle strength in critically ill adults: a randomized clinical trial. JAMA. 2018;320(4):368–78. https://doi.org/10.1001/jama.2018.9592 .

Thomalla G, Simonsen CZ, Boutitie F, Andersen G, Berthezene Y, Cheng B, et al. MRI-guided thrombolysis for stroke with unknown time of onset. N Engl J Med. 2018;379(7):611–22. https://doi.org/10.1056/NEJMoa1804355 . United States.

Perkins GD, Ji C, Deakin CD, Quinn T, Nolan JP, Scomparin C, et al. A randomized trial of epinephrine in out-of-hospital cardiac arrest. N Engl J Med. 2018;379(8):711–21. https://doi.org/10.1056/NEJMoa1806842 . United States.

Wang HE, Schmicker RH, Daya MR, Stephens SW, Idris AH, Carlson JN, et al. Effect of a strategy of initial laryngeal tube insertion vs endotracheal intubation on 72-hour survival in adults with out-of-hospital cardiac arrest: a randomized clinical trial. JAMA. 2018;320(8):769–78. https://doi.org/10.1001/jama.2018.7044 .

Benger JR, Kirby K, Black S, Brett SJ, Clout M, Lazaroo MJ, et al. Effect of a strategy of a supraglottic airway device vs tracheal intubation during out-of-hospital cardiac arrest on functional outcome: the AIRWAYS-2 randomized clinical trial. JAMA. 2018;320(8):779–91. https://doi.org/10.1001/jama.2018.11597 .

Meltzer-Brody S, Colquhoun H, Riesenberg R, Epperson CN, Deligiannidis KM, Rubinow DR, et al. Brexanolone injection in post-partum depression: two multicentre, double-blind, randomised, placebo-controlled, phase 3 trials. Lancet. 2018;392(10152):1058–70. https://doi.org/10.1016/S0140-6736(18)31551-4 . England.

Cooper DJ, Nichol AD, Bailey M, Bernard S, Cameron PA, Pili-Floury S, et al. Effect of early sustained prophylactic hypothermia on neurologic outcomes among patients with severe traumatic brain injury: the POLAR randomized clinical trial. JAMA. 2018;320(21):2211–20. https://doi.org/10.1001/jama.2018.17075 .

Bonell C, Allen E, Warren E, McGowan J, Bevilacqua L, Jamal F, et al. Effects of the Learning Together intervention on bullying and aggression in English secondary schools (INCLUSIVE): a cluster randomised controlled trial. Lancet. 2018;392(10163):2452–64. https://doi.org/10.1016/S0140-6736(18)31782-3 .

Stunnenberg BC, Raaphorst J, Groenewoud HM, Statland JM, Griggs RC, Woertman W, et al. Effect of mexiletine on muscle stiffness in patients with nondystrophic myotonia evaluated using aggregated N-of-1 trials. JAMA. 2018;320(22):2344–53. https://doi.org/10.1001/jama.2018.18020 .

Burt RK, Balabanov R, Burman J, Sharrack B, Snowden JA, Oliveira MC, et al. Effect of nonmyeloablative hematopoietic stem cell transplantation vs continued disease-modifying therapy on disease progression in patients with relapsing-remitting multiple sclerosis: a randomized clinical trial. JAMA. 2019;321(2):165–74. https://doi.org/10.1001/jama.2018.18743 .

Dennis M, Mead G, Forbes J, Graham C, Hackett M, Hankey GJ, et al. Effects of fluoxetine on functional outcomes after acute stroke (FOCUS): a pragmatic, double-blind, randomised, controlled trial. Lancet. 2019;393(10168):265–74. https://doi.org/10.1016/S0140-6736(18)32823-X .

Anderson CS, Huang Y, Lindley RI, Chen X, Arima H, Chen G, et al. Intensive blood pressure reduction with intravenous thrombolysis therapy for acute ischaemic stroke (ENCHANTED): an international, randomised, open-label, blinded-endpoint, phase 3 trial. Lancet. 2019;393(10174):877–88. https://doi.org/10.1016/S0140-6736(19)30038-8 . England.

Basner M, Asch DA, Shea JA, Bellini LM, Carlin M, Ecker AJ, et al. Sleep and alertness in a duty-hour flexibility trial in internal medicine. N Engl J Med. 2019;380(10):915–23. https://doi.org/10.1056/NEJMoa1810641 .

Bath PM, Scutt P, Anderson CS, Appleton JP, Berge E, Cala L, et al. Prehospital transdermal glyceryl trinitrate in patients with ultra-acute presumed stroke (RIGHT-2): an ambulance-based, randomised, sham-controlled, blinded, phase 3 trial. Lancet. 2019;393(10175):1009–20. https://doi.org/10.1016/S0140-6736(19)30194-1 .

Hanley DF, Thompson RE, Rosenblum M, Yenokyan G, Lane K, McBee N, et al. Efficacy and safety of minimally invasive surgery with thrombolysis in intracerebral haemorrhage evacuation (MISTIE III): a randomised, controlled, open-label, blinded endpoint phase 3 trial. Lancet. 2019;393(10175):1021–32. https://doi.org/10.1016/S0140-6736(19)30195-3 .

Turk AS 3rd, Siddiqui A, Fifi JT, De Leacy RA, Fiorella DJ, Gu E, et al. Aspiration thrombectomy versus stent retriever thrombectomy as first-line approach for large vessel occlusion (COMPASS): a multicentre, randomised, open label, blinded outcome, non-inferiority trial. Lancet. 2019;393(10175):998–1008. https://doi.org/10.1016/S0140-6736(19)30297-1 . England.

Ma H, Campbell BCV, Parsons MW, Churilov L, Levi CR, Hsu C, et al. Thrombolysis guided by perfusion imaging up to 9 hours after onset of stroke. N Engl J Med. 2019;380(19):1795–803. https://doi.org/10.1056/NEJMoa1813046 . United States.

Fischer K, Al-Sawaf O, Bahlo J, Fink AM, Tandon M, Dixon M, et al. Venetoclax and obinutuzumab in patients with CLL and coexisting conditions. N Engl J Med. 2019;380(23):2225–36. https://doi.org/10.1056/NEJMoa1815281 . United States.

Shehabi Y, Howe BD, Bellomo R, Arabi YM, Bailey M, Bass FE, et al. Early sedation with dexmedetomidine in critically ill patients. N Engl J Med. 2019;380(26):2506–17. https://doi.org/10.1056/NEJMoa1904710 . United States.

Johnston KC, Bruno A, Pauls Q, Hall CE, Barrett KM, Barsan W, et al. Intensive vs standard treatment of hyperglycemia and functional outcome in patients with acute ischemic stroke: the SHINE randomized clinical trial. JAMA. 2019;322(4):326–35. https://doi.org/10.1001/jama.2019.9346 .

Widmark A, Gunnlaugsson A, Beckman L, Thellenberg-Karlsson C, Hoyer M, Lagerlund M, et al. Ultra-hypofractionated versus conventionally fractionated radiotherapy for prostate cancer: 5-year outcomes of the HYPO-RT-PC randomised, non-inferiority, phase 3 trial. Lancet. 2019;394(10196):385–95. https://doi.org/10.1016/S0140-6736(19)31131-6 . England.

Pittock SJ, Berthele A, Fujihara K, Kim HJ, Levy M, Palace J, et al. Eculizumab in aquaporin-4-positive neuromyelitis optica spectrum disorder. N Engl J Med. 2019;381(7):614–25. https://doi.org/10.1056/NEJMoa1900866 . United States.

Gunduz-Bruce H, Silber C, Kaul I, Rothschild AJ, Riesenberg R, Sankoh AJ, et al. Trial of SAGE-217 in patients with major depressive disorder. N Engl J Med. 2019;381(10):903–11. https://doi.org/10.1056/NEJMoa1815981 . United States.

Nave AH, Rackoll T, Grittner U, Bläsing H, Gorsler A, Nabavi DG, et al. Physical Fitness Training in Patients with Subacute Stroke (PHYS-STROKE): multicentre, randomised controlled, endpoint blinded trial. BMJ. 2019;366:l5101. https://doi.org/10.1136/bmj.l5101 .

Sands BE, Peyrin-Biroulet L, Loftus EV Jr, Danese S, Colombel JF, Törüner M, et al. Vedolizumab versus adalimumab for moderate-to-severe ulcerative colitis. N Engl J Med. 2019;381(13):1215–26. https://doi.org/10.1056/NEJMoa1905725 . United States.

Cree BAC, Bennett JL, Kim HJ, Weinshenker BG, Pittock SJ, Wingerchuk DM, et al. Inebilizumab for the treatment of neuromyelitis optica spectrum disorder (N-MOmentum): a double-blind, randomised placebo-controlled phase 2/3 trial. Lancet. 2019;394(10206):1352–63. https://doi.org/10.1016/S0140-6736(19)31817-3 . England.

Cooper K, Breeman S, Scott NW, Scotland G, Clark J, Hawe J, et al. Laparoscopic supracervical hysterectomy versus endometrial ablation for women with heavy menstrual bleeding (HEALTH): a parallel-group, open-label, randomised controlled trial. Lancet. 2019;394(10207):1425–36. https://doi.org/10.1016/S0140-6736(19)31790-8 .

Reddihough DS, Marraffa C, Mouti A, O’Sullivan M, Lee KJ, Orsini F, et al. Effect of fluoxetine on obsessive-compulsive behaviors in children and adolescents with autism spectrum disorders: a randomized clinical trial. JAMA. 2019;322(16):1561–9. https://doi.org/10.1001/jama.2019.14685 .

John LK, Loewenstein G, Marder A, Callaham ML. Effect of revealing authors’ conflicts of interests in peer review: randomized controlled trial. BMJ. 2019;367. https://doi.org/10.1136/bmj.l5896 .

Yamamura T, Kleiter I, Fujihara K, Palace J, Greenberg B, Zakrzewska-Pniewska B, et al. Trial of satralizumab in neuromyelitis optica spectrum disorder. N Engl J Med. 2019;381(22):2114–24. https://doi.org/10.1056/NEJMoa1901747 . United States.

Hoskin PJ, Hopkins K, Misra V, Holt T, McMenemin R, Dubois D, et al. Effect of single-fraction vs multifraction radiotherapy on ambulatory status among patients with spinal canal compression from metastatic cancer: the SCORAD randomized clinical trial. JAMA. 2019;322(21):2084–94. https://doi.org/10.1001/jama.2019.17913 .

Lascarrou JB, Merdji H, Le Gouge A, Colin G, Grillet G, Girardie P, et al. Targeted temperature management for cardiac arrest with nonshockable rhythm. N Engl J Med. 2019;381(24):2327–37. https://doi.org/10.1056/NEJMoa1906661 . United States.

Ständer S, Yosipovitch G, Legat FJ, Lacour JP, Paul C, Narbutt J, et al. Trial of nemolizumab in moderate-to-severe prurigo nodularis. N Engl J Med. 2020;382(8):706–16. https://doi.org/10.1056/NEJMoa1908316 . United States.

Hill MD, Goyal M, Menon BK, Nogueira RG, McTaggart RA, Demchuk AM, et al. Efficacy and safety of nerinetide for the treatment of acute ischaemic stroke (ESCAPE-NA1): a multicentre, double-blind, randomised controlled trial. Lancet. 2020;395(10227):878–87. https://doi.org/10.1016/S0140-6736(20)30258-0 . England.

Olsen HT, Nedergaard HK, Strøm T, Oxlund J, Wian KA, Ytrebø LM, et al. Nonsedation or light sedation in critically ill, mechanically ventilated patients. N Engl J Med. 2020;382(12):1103–11. https://doi.org/10.1056/NEJMoa1906759 . United States.

Campbell BCV, Mitchell PJ, Churilov L, Yassi N, Kleinig TJ, Dowling RJ, et al. Effect of intravenous tenecteplase dose on cerebral reperfusion before thrombectomy in patients with large vessel occlusion ischemic stroke: the EXTEND-IA TNK Part 2 randomized clinical trial. JAMA. 2020;323(13):1257–65. https://doi.org/10.1001/jama.2020.1511 .

Deyle GD, Allen CS, Allison SC, Gill NW, Hando BR, Petersen EJ, et al. Physical therapy versus glucocorticoid injection for osteoarthritis of the knee. N Engl J Med. 2020;382(15):1420–29. https://doi.org/10.1056/NEJMoa1905877 . United States.

Koblan KS, Kent J, Hopkins SC, Krystal JH, Cheng H, Goldman R, et al. A non-D2-receptor-binding drug for the treatment of schizophrenia. N Engl J Med. 2020;382(16):1497–506. https://doi.org/10.1056/NEJMoa1911772 . United States.

Cao B, Wang Y, Wen D, Liu W, Wang J, Fan G, et al. A trial of lopinavir-ritonavir in adults hospitalized with severe COVID-19. N Engl J Med. 2020;382(19):1787–99. https://doi.org/10.1056/NEJMoa2001282 .

Wang Y, Zhang D, Du G, Du R, Zhao J, Jin Y, et al. Remdesivir in adults with severe COVID-19: a randomised, double-blind, placebo-controlled, multicentre trial. Lancet. 2020;395(10236):1569–78. https://doi.org/10.1016/S0140-6736(20)31022-9 .

Yang P, Zhang Y, Zhang L, Treurniet KM, Chen W, Peng Y, et al. Endovascular thrombectomy with or without intravenous alteplase in acute stroke. N Engl J Med. 2020;382(21):1981–93. https://doi.org/10.1056/NEJMoa2001123 . United States.

Martins SO, Mont’Alverne F, Rebello LC, Abud DG, Silva GS, Lima FO, et al. Thrombectomy for stroke in the public health care system of Brazil. N Engl J Med. 2020;382(24):2316–26. https://doi.org/10.1056/NEJMoa2000120 . United States.

Kabashima K, Matsumura T, Komazaki H, Kawashima M. Trial of nemolizumab and topical agents for atopic dermatitis with pruritus. N Engl J Med. 2020;383(2):141–50. https://doi.org/10.1056/NEJMoa1917006 . United States.

Johnston SC, Amarenco P, Denison H, Evans SR, Himmelmann A, James S, et al. Ticagrelor and aspirin or aspirin alone in acute ischemic stroke or TIA. N Engl J Med. 2020;383(3):207–17. https://doi.org/10.1056/NEJMoa1916870 . United States.

Lebwohl MG, Papp KA, Stein Gold L, Gooderham MJ, Kircik LH, Draelos ZD, et al. Trial of roflumilast cream for chronic plaque psoriasis. N Engl J Med. 2020;383(3):229–39. https://doi.org/10.1056/NEJMoa2000073 . United States.

Simpson EL, Sinclair R, Forman S, Wollenberg A, Aschoff R, Cork M, et al. Efficacy and safety of abrocitinib in adults and adolescents with moderate-to-severe atopic dermatitis (JADE MONO-1): a multicentre, double-blind, randomised, placebo-controlled, phase 3 trial. Lancet. 2020;396(10246):255–66. https://doi.org/10.1016/S0140-6736(20)30732-7 . England.

Rowell SE, Meier EN, McKnight B, Kannas D, May S, Sheehan K, et al. Effect of out-of-hospital tranexamic acid vs placebo on 6-month functional neurologic outcomes in patients with moderate or severe traumatic brain injury. JAMA. 2020;324(10):961–74. https://doi.org/10.1001/jama.2020.8958 .

van der Vlist AC, van Oosterom RF, van Veldhoven PLJ, Bierma-Zeinstra SMA, Waarsing JH, Verhaar JAN, et al. Effectiveness of a high volume injection as treatment for chronic Achilles tendinopathy: randomised controlled trial. BMJ. 2020;370. https://doi.org/10.1136/bmj.m3027 .

Spinner CD, Gottlieb RL, Criner GJ, Arribas López JR, Cattelan AM, Soriano Viladomiu A, et al. Effect of remdesivir vs standard care on clinical status at 11 days in patients with moderate COVID-19: a randomized clinical trial. JAMA. 2020;324(11):1048–57. https://doi.org/10.1001/jama.2020.16349 .

Horne AW, Vincent K, Hewitt CA, Middleton LJ, Koscielniak M, Szubert W, et al. Gabapentin for chronic pelvic pain in women (GaPP2): a multicentre, randomised, double-blind, placebo-controlled trial. Lancet. 2020;396(10255):909–17. https://doi.org/10.1016/S0140-6736(20)31693-7 .

Furtado RHM, Berwanger O, Fonseca HA, Corrêa TD, Ferraz LR, Lapa MG, et al. Azithromycin in addition to standard of care versus standard of care alone in the treatment of patients admitted to the hospital with severe COVID-19 in Brazil (COALITION II): a randomised clinical trial. Lancet. 2020;396(10256):959–67. https://doi.org/10.1016/S0140-6736(20)31862-6 .

Tomazini BM, Maia IS, Cavalcanti AB, Berwanger O, Rosa RG, Veiga VC, et al. Effect of dexamethasone on days alive and ventilator-free in patients with moderate or severe acute respiratory distress syndrome and COVID-19: the CoDEX randomized clinical trial. JAMA. 2020;324(13):1307–16. https://doi.org/10.1001/jama.2020.17021 .

Beigel JH, Tomashek KM, Dodd LE, Mehta AK, Zingman BS, Kalil AC, et al. Remdesivir for the treatment of COVID-19 - final report. N Engl J Med. 2020;383(19):1813–26. https://doi.org/10.1056/NEJMoa2007764 .

Goldman JD, Lye DCB, Hui DS, Marks KM, Bruno R, Montejano R, et al. Remdesivir for 5 or 10 days in patients with severe COVID-19. N Engl J Med. 2020;383(19):1827–37. https://doi.org/10.1056/NEJMoa2015301 .

Cavalcanti AB, Zampieri FG, Rosa RG, Azevedo LCP, Veiga VC, Avezum A, et al. Hydroxychloroquine with or without azithromycin in mild-to-moderate COVID-19. N Engl J Med. 2020;383(21):2041–52. https://doi.org/10.1056/NEJMoa2019014 .

Self WH, Semler MW, Leither LM, Casey JD, Angus DC, Brower RG, et al. Effect of hydroxychloroquine on clinical status at 14 days in hospitalized patients with COVID-19: a randomized clinical trial. JAMA. 2020;324(21):2165–76. https://doi.org/10.1001/jama.2020.22240 .

Martínez-Fernández R, Máñez-Miró JU, Rodríguez-Rojas R, Del Álamo M, Shah BB, Hernández-Fernández F, et al. Randomized trial of focused ultrasound subthalamotomy for Parkinson’s disease. N Engl J Med. 2020;383(26):2501–13. https://doi.org/10.1056/NEJMoa2016311 . United States.

Hutchinson PJ, Edlmann E, Bulters D, Zolnourian A, Holton P, Suttner N, et al. Trial of dexamethasone for chronic subdural hematoma. N Engl J Med. 2020;383(27):2616–27. https://doi.org/10.1056/NEJMoa2020473 . United States.

Klein AL, Imazio M, Cremer P, Brucato A, Abbate A, Fang F, et al. Phase 3 trial of interleukin-1 trap rilonacept in recurrent pericarditis. N Engl J Med. 2021;384(1):31–41. https://doi.org/10.1056/NEJMoa2027892 . United States.

Post R, Germans MR, Tjerkstra MA, Vergouwen MDI, Jellema K, Koot RW, et al. Ultra-early tranexamic acid after subarachnoid haemorrhage (ULTRA): a randomised controlled trial. Lancet. 2021;397(10269):112–8. https://doi.org/10.1016/S0140-6736(20)32518-6 . England.

Suzuki K, Matsumaru Y, Takeuchi M, Morimoto M, Kanazawa R, Takayama Y, et al. Effect of mechanical thrombectomy without vs with intravenous thrombolysis on functional outcome among patients with acute ischemic stroke: the SKIP randomized clinical trial. JAMA. 2021;325(3):244–53. https://doi.org/10.1001/jama.2020.23522 .

Zi W, Qiu Z, Li F, Sang H, Wu D, Luo W, et al. Effect of endovascular treatment alone vs intravenous alteplase plus endovascular treatment on functional independence in patients with acute ischemic stroke: the DEVT randomized clinical trial. JAMA. 2021;325(3):234–43. https://doi.org/10.1001/jama.2020.23523 .

Veiga VC, Prats JAGG, Farias DLC, Rosa RG, Dourado LK, Zampieri FG, et al. Effect of tocilizumab on clinical outcomes at 15 days in patients with severe or critical coronavirus disease 2019: randomised controlled trial. BMJ. 2021;372. https://doi.org/10.1136/bmj.n84 .

Gordon KB, Foley P, Krueger JG, Pinter A, Reich K, Vender R, et al. Bimekizumab efficacy and safety in moderate to severe plaque psoriasis (BE READY): a multicentre, double-blind, placebo-controlled, randomised withdrawal phase 3 trial. Lancet. 2021;397(10273):475–86. https://doi.org/10.1016/S0140-6736(21)00126-4 . England.

Reich K, Papp KA, Blauvelt A, Langley RG, Armstrong A, Warren RB, et al. Bimekizumab versus ustekinumab for the treatment of moderate to severe plaque psoriasis (BE VIVID): efficacy and safety from a 52-week, multicentre, double-blind, active comparator and placebo controlled phase 3 trial. Lancet. 2021;397(10273):487–98. https://doi.org/10.1016/S0140-6736(21)00125-2 . England.

Blauvelt A, Kempers S, Lain E, Schlesinger T, Tyring S, Forman S, et al. Phase 3 trials of tirbanibulin ointment for actinic keratosis. N Engl J Med. 2021;384(6):512–20. https://doi.org/10.1056/NEJMoa2024040 . United States.

Simonovich VA, Burgos Pratx LD, Scibona P, Beruto MV, Vallone MG, Vázquez C, et al. A randomized trial of convalescent plasma in COVID-19 severe pneumonia. N Engl J Med. 2021;384(7):619–29. https://doi.org/10.1056/NEJMoa2031304 .

Brannan SK, Sawchak S, Miller AC, Lieberman JA, Paul SM, Breier A. Muscarinic cholinergic receptor agonist and peripheral antagonist for schizophrenia. N Engl J Med. 2021;384(8):717–26. https://doi.org/10.1056/NEJMoa2017015 .

Lundgren JD, Grund B, Barkauskas CE, Holland TL, Gottlieb RL, Sandkovsky U, et al. A neutralizing monoclonal antibody for hospitalized patients with COVID-19. N Engl J Med. 2021;384(10):905–14. https://doi.org/10.1056/NEJMoa2033130 .

Bieber T, Simpson EL, Silverberg JI, Thaçi D, Paul C, Pink AE, et al. Abrocitinib versus placebo or dupilumab for atopic dermatitis. N Engl J Med. 2021;384(12):1101–12. https://doi.org/10.1056/NEJMoa2019380 . United States.

Gordon AC, Mouncey PR, Al-Beidh F, Rowan KM, Nichol AD, Arabi YM, et al. Interleukin-6 receptor antagonists in critically ill patients with COVID-19. N Engl J Med. 2021;384(16):1491–502. https://doi.org/10.1056/NEJMoa2100433 .

Rosas IO, Bräu N, Waters M, Go RC, Hunter BD, Bhagani S, et al. Tocilizumab in hospitalized patients with severe COVID-19 pneumonia. N Engl J Med. 2021;384(16):1503–16. https://doi.org/10.1056/NEJMoa2028700 .

Aspvall K, Andersson E, Melin K, Norlin L, Eriksson V, Vigerland S, et al. Effect of an Internet-delivered stepped-care program vs in-person cognitive behavioral therapy on obsessive-compulsive disorder symptoms in children and adolescents: a randomized clinical trial. JAMA. 2021;325(18):1863–73. https://doi.org/10.1001/jama.2021.3839 .

Langezaal LCM, van der Hoeven EJRJ, Mont’Alverne FJA, de Carvalho JJF, Lima FO, Dippel DWJ, et al. Endovascular therapy for stroke due to basilar-artery occlusion. N Engl J Med. 2021;384(20):1910–20. https://doi.org/10.1056/NEJMoa2030297 . United States.

Roquilly A, Moyer JD, Huet O, Lasocki S, Cohen B, Dahyot-Fizelier C, et al. Effect of continuous infusion of hypertonic saline vs standard care on 6-month neurological outcomes in patients with traumatic brain injury: the COBI randomized clinical trial. JAMA. 2021;325(20):2056–66. https://doi.org/10.1001/jama.2021.5561 .

Guttman-Yassky E, Teixeira HD, Simpson EL, Papp KA, Pangan AL, Blauvelt A, et al. Once-daily upadacitinib versus placebo in adolescents and adults with moderate-to-severe atopic dermatitis (Measure Up 1 and Measure Up 2): results from two replicate double-blind, randomised controlled phase 3 trials. Lancet. 2021;397(10290):2151–68. https://doi.org/10.1016/S0140-6736(21)00588-2 . England.

Reich K, Teixeira HD, de Bruin-Weller M, Bieber T, Soong W, Kabashima K, et al. Safety and efficacy of upadacitinib in combination with topical corticosteroids in adolescents and adults with moderate-to-severe atopic dermatitis (AD Up): results from a randomised, double-blind, placebo-controlled, phase 3 trial. Lancet. 2021;397(10290):2169–81. https://doi.org/10.1016/S0140-6736(21)00589-4 . England.

Dankiewicz J, Cronberg T, Lilja G, Jakobsen JC, Levin H, Ullén S, et al. Hypothermia versus normothermia after out-of-hospital cardiac arrest. N Engl J Med. 2021;384(24):2283–94. https://doi.org/10.1056/NEJMoa2100591 . United States.

Tariot PN, Cummings JL, Soto-Martin ME, Ballard C, Erten-Lyons D, Sultzer DL, et al. Trial of pimavanserin in dementia-related psychosis. N Engl J Med. 2021;385(4):309–19. https://doi.org/10.1056/NEJMoa2034634 . United States.

Guimarães PO, Quirk D, Furtado RH, Maia LN, Saraiva JF, Antunes MO, et al. Tofacitinib in patients hospitalized with COVID-19 pneumonia. N Engl J Med. 2021;385(5):406–15. https://doi.org/10.1056/NEJMoa2101643 .

Lawler PR, Goligher EC, Berger JS, Neal MD, McVerry BJ, Nicolau JC, et al. Therapeutic anticoagulation with heparin in noncritically ill patients with COVID-19. N Engl J Med. 2021;385(9):790–802. https://doi.org/10.1056/NEJMoa2105911 .

Goligher EC, Bradbury CA, McVerry BJ, Lawler PR, Berger JS, Gong MN, et al. Therapeutic Anticoagulation with heparin in critically ill patients with COVID-19. N Engl J Med. 2021;385(9):777–89. https://doi.org/10.1056/NEJMoa2103417 .

Schwarzschild MA, Ascherio A, Casaceli C, Curhan GC, Fitzgerald R, Kamp C, et al. Effect of urate-elevating inosine on early Parkinson disease progression: the SURE-PD3 randomized clinical trial. JAMA. 2021;326(10):926–39. https://doi.org/10.1001/jama.2021.10207 .

Halliday A, Bulbulia R, Bonati LH, Chester J, Cradduck-Bamford A, Peto R, et al. Second asymptomatic carotid surgery trial (ACST-2): a randomised comparison of carotid artery stenting versus carotid endarterectomy. Lancet. 2021;398(10305):1065–73. https://doi.org/10.1016/S0140-6736(21)01910-3 .

Paget LDA, Reurink G, de Vos RJ, Weir A, Moen MH, Bierma-Zeinstra SMA, et al. Effect of platelet-rich plasma injections vs placebo on ankle symptoms and function in patients with ankle osteoarthritis: a randomized clinical trial. JAMA. 2021;326(16):1595–605. https://doi.org/10.1001/jama.2021.16602 .

Estcourt LJ, Turgeon AF, McQuilten ZK, McVerry BJ, Al-Beidh F, Annane D, et al. Effect of convalescent plasma on organ support-free days in critically ill patients with COVID-19: a randomized clinical trial. JAMA. 2021;326(17):1690–702. https://doi.org/10.1001/jama.2021.18178 .

LeCouffe NE, Kappelhof M, Treurniet KM, Rinkel LA, Bruggeman AE, Berkhemer OA, et al. A randomized trial of intravenous alteplase before endovascular treatment for stroke. N Engl J Med. 2021;385(20):1833–44. https://doi.org/10.1056/NEJMoa2107727 . United States.

Korley FK, Durkalski-Mauldin V, Yeatts SD, Schulman K, Davenport RD, Dumont LJ, et al. Early convalescent plasma for high-risk outpatients with COVID-19. N Engl J Med. 2021;385(21):1951–60. https://doi.org/10.1056/NEJMoa2103784 .

Bennell KL, Paterson KL, Metcalf BR, Duong V, Eyles J, Kasza J, et al. Effect of intra-articular platelet-rich plasma vs placebo injection on pain and medial tibial cartilage volume in patients with knee osteoarthritis: the RESTORE randomized clinical trial. JAMA. 2021;326(20):2021–30. https://doi.org/10.1001/jama.2021.19415 .

Ospina-Tascón GA, Calderón-Tapia LE, García AF, Zarama V, Gómez-Álvarez F, Álvarez-Saa T, et al. Effect of high-flow oxygen therapy vs conventional oxygen therapy on invasive mechanical ventilation and clinical recovery in patients with severe COVID-19: a randomized clinical trial. JAMA. 2021;326(21):2161–71. https://doi.org/10.1001/jama.2021.20714 .

Lebwohl MG, Stein Gold L, Strober B, Papp KA, Armstrong AW, Bagel J, et al. Phase 3 Trials of tapinarof cream for plaque psoriasis. N Engl J Med. 2021;385(24):2219–29. https://doi.org/10.1056/NEJMoa2103629 . United States.

Berger JS, Kornblith LZ, Gong MN, Reynolds HR, Cushman M, Cheng Y, et al. Effect of P2Y12 Inhibitors on survival free of organ support among non-critically ill hospitalized patients with COVID-19: a randomized clinical trial. JAMA. 2022;327(3):227–36. https://doi.org/10.1001/jama.2021.23605 .

Polizzotto MN, Nordwall J, Babiker AG, Phillips A, Vock DM, Eriobu N, et al. Hyperimmune immunoglobulin for hospitalised patients with COVID-19 (ITAC): a double-blind, placebo-controlled, phase 3, randomised trial. Lancet. 2022;399(10324):530–40. https://doi.org/10.1016/S0140-6736(22)00101-5 .

Gadjradj PS, Rubinstein SM, Peul WC, Depauw PR, Vleggeert-Lankamp CL, Seiger A, et al. Full endoscopic versus open discectomy for sciatica: randomised controlled non-inferiority trial. BMJ. 2022;376. https://doi.org/10.1136/bmj-2021-065846 .

Preskorn SH, Zeller S, Citrome L, Finman J, Goldberg JF, Fava M, et al. Effect of sublingual dexmedetomidine vs placebo on acute agitation associated with bipolar disorder: a randomized clinical trial. JAMA. 2022;327(8):727–36. https://doi.org/10.1001/jama.2022.0799 .

Ruijter BJ, Keijzer HM, Tjepkema-Cloostermans MC, Blans MJ, Beishuizen A, Tromp SC, et al. Treating rhythmic and periodic EEG patterns in comatose survivors of cardiac arrest. N Engl J Med. 2022;386(8):724–34. https://doi.org/10.1056/NEJMoa2115998 . United States.

Renú A, Millán M, San Román L, Blasco J, Martí-Fàbregas J, Terceño M, et al. Effect of intra-arterial alteplase vs placebo following successful thrombectomy on functional outcomes in patients with large vessel occlusion acute ischemic stroke: the CHOICE randomized clinical trial. JAMA. 2022;327(9):826–35. https://doi.org/10.1001/jama.2022.1645 .

van der Steen W, van de Graaf RA, Chalos V, Lingsma HF, van Doormaal PJ, Coutinho JM, et al. Safety and efficacy of aspirin, unfractionated heparin, both, or neither during endovascular stroke treatment (MR CLEAN-MED): an open-label, multicentre, randomised controlled trial. Lancet. 2022;399(10329):1059–69. https://doi.org/10.1016/S0140-6736(22)00014-9 . England.

Paskins Z, Bromley K, Lewis M, Hughes G, Hughes E, Hennings S, et al. Clinical effectiveness of one ultrasound guided intra-articular corticosteroid and local anaesthetic injection in addition to advice and education for hip osteoarthritis (HIT trial): single blind, parallel group, three arm, randomised controlled trial. BMJ. 2022;377:e068446. https://doi.org/10.1136/bmj-2021-068446 .

Yoshimura S, Sakai N, Yamagami H, Uchida K, Beppu M, Toyoda K, et al. Endovascular therapy for acute stroke with a large ischemic region. N Engl J Med. 2022;386(14):1303–13. https://doi.org/10.1056/NEJMoa2118191 . United States.

Pérez de la Ossa N, Abilleira S, Jovin TG, García-Tornel Á, Jimenez X, Urra X, et al. Effect of direct transportation to thrombectomy-capable center vs local stroke center on neurological outcomes in patients with suspected large-vessel occlusion stroke in nonurban areas: the RACECAT randomized clinical trial. JAMA. 2022;327(18):1782–94. https://doi.org/10.1001/jama.2022.4404 .

Bösel J, Niesen WD, Salih F, Morris NA, Ragland JT, Gough B, et al. Effect of early vs standard approach to tracheostomy on functional outcome at 6 months among patients with severe stroke receiving mechanical ventilation: the SETPOINT2 randomized clinical trial. JAMA. 2022;327(19):1899–909. https://doi.org/10.1001/jama.2022.4798 .

Perry DC, Achten J, Knight R, Appelbe D, Dutton SJ, Dritsaki M, et al. Immobilisation of torus fractures of the wrist in children (FORCE): a randomised controlled equivalence trial in the UK. Lancet. 2022;400(10345):39–47. https://doi.org/10.1016/S0140-6736(22)01015-7 . England.

Fischer U, Kaesmacher J, Strbian D, Eker O, Cognard C, Plattner PS, et al. Thrombectomy alone versus intravenous alteplase plus thrombectomy in patients with stroke: an open-label, blinded-outcome, randomised non-inferiority trial. Lancet. 2022;400(10346):104–15. https://doi.org/10.1016/S0140-6736(22)00537-2 . England.

Mitchell PJ, Yan B, Churilov L, Dowling RJ, Bush SJ, Bivard A, et al. Endovascular thrombectomy versus standard bridging thrombolytic with endovascular thrombectomy within 4 \(\cdot\) 5 h of stroke onset: an open-label, blinded-endpoint, randomised non-inferiority trial. Lancet. 2022;400(10346):116–25. https://doi.org/10.1016/S0140-6736(22)00564-5 . England.

Wu YW, Comstock BA, Gonzalez FF, Mayock DE, Goodman AM, Maitre NL, et al. Trial of erythropoietin for hypoxic-ischemic encephalopathy in newborns. N Engl J Med. 2022;387(2):148–59. https://doi.org/10.1056/NEJMoa2119660 . United States.

Menon BK, Buck BH, Singh N, Deschaintre Y, Almekhlafi MA, Coutts SB, et al. Intravenous tenecteplase compared with alteplase for acute ischaemic stroke in Canada (AcT): a pragmatic, multicentre, open-label, registry-linked, randomised, controlled, non-inferiority trial. Lancet. 2022;400(10347):161–9. https://doi.org/10.1016/S0140-6736(22)01054-6 . England.

Valenta Z, Pitha J, Poledne R. Proportional odds logistic regression—effective means of dealing with limited uncertainty in dichotomizing clinical outcomes. Stat Med. 2006;25(24):4227–34.

Saver JL. Novel end point analytic techniques and interpreting shifts across the entire range of outcome scales in acute stroke trials. Stroke. 2007;38(11):3055–62.

Fullerton AS, Xu J. The proportional odds with partial proportionality constraints model for ordinal response variables. Soc Sci Res. 2012;41(1):182–98.

Faraway JJ. On the cost of data analysis. J Comput Graph Stat. 1992;1(3):213–29.

Clogg CC, Shihadeh ES. Statistical models for ordinal variables. AGE Publications; 1994.

McCullagh P. Generalized linear models. Routledge; 2019.

Crans GG, Shuster JJ. How conservative is Fisher’s exact test? A quantitative evaluation of the two-sample comparative binomial trial. Stat Med. 2008;27(18):3598–611.

Selman CJ. Statistic analyses of ordinal outcomes in RCTs: scoping review data. GitHub. 2023. https://github.com/chrisselman/ordscopingreview . Accessed 31 May 2023.

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Acknowledgements

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This work forms part of Chris Selman’s PhD, which is supported by the Research Training Program Scholarship, administered by the Australian Commonwealth Government and The University of Melbourne, Australia. Chris Selman’s PhD was also supported by a Centre of Research Excellence grant from the National Health and Medical Research Council of Australia ID 1171422, to the Australian Trials Methodology (AusTriM) Research Network. Research at the Murdoch Children’s Research Institute is supported by the Victorian Government’s Operational Infrastructure Support Program. This work was supported by the Australian National Health and Medical Research Council (NHMRC) Centre for Research Excellence grants to the Victorian Centre for Biostatistics (ID1035261) and the Australian Trials Methodology Research Network (ID1171422), including through seed funding awarded to Robert Mahar. Katherine Lee is funded by an NHMRC Career Development Fellowship (ID1127984). Brett Manley is funded by the NHMRC Investigator Grant (Leadership 1). The funding bodies played no role in the study conception, design, data collection, data analysis, data interpretation, or writing of the report.

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CJS, RKM, KJL, CLW, and BJM conceived the study and CJS wrote the first draft of the manuscript. All authors contributed to the design of the study, revision of the manuscript, and take responsibility for its content.

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Supplementary information

Additional file 1..

Deviations from the protocol. This presents a summary of the deviations from the protocol, with reasons. We also provide an explanation of any simplifications and assumptions that were made for eligibility criteria and data extraction.

Additional file 2.

Data extraction questionnaire. This is a copy of the data extraction questionnaire that will be used for this review in PDF format.

Additional file 3.

Interpretation of the proportional odds ratio in proportional odds models. This presents a summary of the ways that the proportional odds ratio was interpreted across the studies.

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Selman, C.J., Lee, K.J., Ferguson, K.N. et al. Statistical analyses of ordinal outcomes in randomised controlled trials: a scoping review. Trials 25 , 241 (2024). https://doi.org/10.1186/s13063-024-08072-2

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http://orcid.org/0000-0003-0157-5319 Ahtisham Younas 1 , 2 ,
http://orcid.org/0000-0002-7839-8130 Parveen Ali 3 , 4
1 Memorial University of Newfoundland , St John's , Newfoundland , Canada
2 Swat College of Nursing , Pakistan
3 School of Nursing and Midwifery , University of Sheffield , Sheffield , South Yorkshire , UK
4 Sheffield University Interpersonal Violence Research Group , Sheffield University , Sheffield , UK
Correspondence to Ahtisham Younas, Memorial University of Newfoundland, St John's, NL A1C 5C4, Canada; ay6133{at}mun.ca

https://doi.org/10.1136/ebnurs-2021-103417

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Introduction

Literature reviews offer a critical synthesis of empirical and theoretical literature to assess the strength of evidence, develop guidelines for practice and policymaking, and identify areas for future research. 1 It is often essential and usually the first task in any research endeavour, particularly in masters or doctoral level education. For effective data extraction and rigorous synthesis in reviews, the use of literature summary tables is of utmost importance. A literature summary table provides a synopsis of an included article. It succinctly presents its purpose, methods, findings and other relevant information pertinent to the review. The aim of developing these literature summary tables is to provide the reader with the information at one glance. Since there are multiple types of reviews (eg, systematic, integrative, scoping, critical and mixed methods) with distinct purposes and techniques, 2 there could be various approaches for developing literature summary tables making it a complex task specialty for the novice researchers or reviewers. Here, we offer five tips for authors of the review articles, relevant to all types of reviews, for creating useful and relevant literature summary tables. We also provide examples from our published reviews to illustrate how useful literature summary tables can be developed and what sort of information should be provided.

Tip 1: provide detailed information about frameworks and methods

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Tabular literature summaries from a scoping review. Source: Rasheed et al . 3

The provision of information about conceptual and theoretical frameworks and methods is useful for several reasons. First, in quantitative (reviews synthesising the results of quantitative studies) and mixed reviews (reviews synthesising the results of both qualitative and quantitative studies to address a mixed review question), it allows the readers to assess the congruence of the core findings and methods with the adapted framework and tested assumptions. In qualitative reviews (reviews synthesising results of qualitative studies), this information is beneficial for readers to recognise the underlying philosophical and paradigmatic stance of the authors of the included articles. For example, imagine the authors of an article, included in a review, used phenomenological inquiry for their research. In that case, the review authors and the readers of the review need to know what kind of (transcendental or hermeneutic) philosophical stance guided the inquiry. Review authors should, therefore, include the philosophical stance in their literature summary for the particular article. Second, information about frameworks and methods enables review authors and readers to judge the quality of the research, which allows for discerning the strengths and limitations of the article. For example, if authors of an included article intended to develop a new scale and test its psychometric properties. To achieve this aim, they used a convenience sample of 150 participants and performed exploratory (EFA) and confirmatory factor analysis (CFA) on the same sample. Such an approach would indicate a flawed methodology because EFA and CFA should not be conducted on the same sample. The review authors must include this information in their summary table. Omitting this information from a summary could lead to the inclusion of a flawed article in the review, thereby jeopardising the review’s rigour.

Tip 2: include strengths and limitations for each article

Critical appraisal of individual articles included in a review is crucial for increasing the rigour of the review. Despite using various templates for critical appraisal, authors often do not provide detailed information about each reviewed article’s strengths and limitations. Merely noting the quality score based on standardised critical appraisal templates is not adequate because the readers should be able to identify the reasons for assigning a weak or moderate rating. Many recent critical appraisal checklists (eg, Mixed Methods Appraisal Tool) discourage review authors from assigning a quality score and recommend noting the main strengths and limitations of included studies. It is also vital that methodological and conceptual limitations and strengths of the articles included in the review are provided because not all review articles include empirical research papers. Rather some review synthesises the theoretical aspects of articles. Providing information about conceptual limitations is also important for readers to judge the quality of foundations of the research. For example, if you included a mixed-methods study in the review, reporting the methodological and conceptual limitations about ‘integration’ is critical for evaluating the study’s strength. Suppose the authors only collected qualitative and quantitative data and did not state the intent and timing of integration. In that case, the strength of the study is weak. Integration only occurred at the levels of data collection. However, integration may not have occurred at the analysis, interpretation and reporting levels.

Tip 3: write conceptual contribution of each reviewed article

While reading and evaluating review papers, we have observed that many review authors only provide core results of the article included in a review and do not explain the conceptual contribution offered by the included article. We refer to conceptual contribution as a description of how the article’s key results contribute towards the development of potential codes, themes or subthemes, or emerging patterns that are reported as the review findings. For example, the authors of a review article noted that one of the research articles included in their review demonstrated the usefulness of case studies and reflective logs as strategies for fostering compassion in nursing students. The conceptual contribution of this research article could be that experiential learning is one way to teach compassion to nursing students, as supported by case studies and reflective logs. This conceptual contribution of the article should be mentioned in the literature summary table. Delineating each reviewed article’s conceptual contribution is particularly beneficial in qualitative reviews, mixed-methods reviews, and critical reviews that often focus on developing models and describing or explaining various phenomena. Figure 2 offers an example of a literature summary table. 4

Tabular literature summaries from a critical review. Source: Younas and Maddigan. 4

Tip 4: compose potential themes from each article during summary writing

While developing literature summary tables, many authors use themes or subthemes reported in the given articles as the key results of their own review. Such an approach prevents the review authors from understanding the article’s conceptual contribution, developing rigorous synthesis and drawing reasonable interpretations of results from an individual article. Ultimately, it affects the generation of novel review findings. For example, one of the articles about women’s healthcare-seeking behaviours in developing countries reported a theme ‘social-cultural determinants of health as precursors of delays’. Instead of using this theme as one of the review findings, the reviewers should read and interpret beyond the given description in an article, compare and contrast themes, findings from one article with findings and themes from another article to find similarities and differences and to understand and explain bigger picture for their readers. Therefore, while developing literature summary tables, think twice before using the predeveloped themes. Including your themes in the summary tables (see figure 1 ) demonstrates to the readers that a robust method of data extraction and synthesis has been followed.

Tip 5: create your personalised template for literature summaries

Often templates are available for data extraction and development of literature summary tables. The available templates may be in the form of a table, chart or a structured framework that extracts some essential information about every article. The commonly used information may include authors, purpose, methods, key results and quality scores. While extracting all relevant information is important, such templates should be tailored to meet the needs of the individuals’ review. For example, for a review about the effectiveness of healthcare interventions, a literature summary table must include information about the intervention, its type, content timing, duration, setting, effectiveness, negative consequences, and receivers and implementers’ experiences of its usage. Similarly, literature summary tables for articles included in a meta-synthesis must include information about the participants’ characteristics, research context and conceptual contribution of each reviewed article so as to help the reader make an informed decision about the usefulness or lack of usefulness of the individual article in the review and the whole review.

In conclusion, narrative or systematic reviews are almost always conducted as a part of any educational project (thesis or dissertation) or academic or clinical research. Literature reviews are the foundation of research on a given topic. Robust and high-quality reviews play an instrumental role in guiding research, practice and policymaking. However, the quality of reviews is also contingent on rigorous data extraction and synthesis, which require developing literature summaries. We have outlined five tips that could enhance the quality of the data extraction and synthesis process by developing useful literature summaries.

Aromataris E ,
Rasheed SP ,

Twitter @Ahtisham04, @parveenazamali

Funding The authors have not declared a specific grant for this research from any funding agency in the public, commercial or not-for-profit sectors.

Competing interests None declared.

Patient consent for publication Not required.

Provenance and peer review Not commissioned; externally peer reviewed.

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What types of studies are there.

Created: June 15, 2016 ; Last Update: September 8, 2016 ; Next update: 2020.

There are various types of scientific studies such as experiments and comparative analyses, observational studies, surveys, or interviews. The choice of study type will mainly depend on the research question being asked.

When making decisions, patients and doctors need reliable answers to a number of questions. Depending on the medical condition and patient's personal situation, the following questions may be asked:

What is the cause of the condition?
What is the natural course of the disease if left untreated?
What will change because of the treatment?
How many other people have the same condition?
How do other people cope with it?

Each of these questions can best be answered by a different type of study.

In order to get reliable results, a study has to be carefully planned right from the start. One thing that is especially important to consider is which type of study is best suited to the research question. A study protocol should be written and complete documentation of the study's process should also be done. This is vital in order for other scientists to be able to reproduce and check the results afterwards.

The main types of studies are randomized controlled trials (RCTs), cohort studies, case-control studies and qualitative studies.

Randomized controlled trials

If you want to know how effective a treatment or diagnostic test is, randomized trials provide the most reliable answers. Because the effect of the treatment is often compared with "no treatment" (or a different treatment), they can also show what happens if you opt to not have the treatment or diagnostic test.

When planning this type of study, a research question is stipulated first. This involves deciding what exactly should be tested and in what group of people. In order to be able to reliably assess how effective the treatment is, the following things also need to be determined before the study is started:

How long the study should last
How many participants are needed
How the effect of the treatment should be measured

For instance, a medication used to treat menopause symptoms needs to be tested on a different group of people than a flu medicine. And a study on treatment for a stuffy nose may be much shorter than a study on a drug taken to prevent strokes.

“Randomized” means divided into groups by chance. In RCTs participants are randomly assigned to one of two or more groups. Then one group receives the new drug A, for example, while the other group receives the conventional drug B or a placebo (dummy drug). Things like the appearance and taste of the drug and the placebo should be as similar as possible. Ideally, the assignment to the various groups is done "double blinded," meaning that neither the participants nor their doctors know who is in which group.

The assignment to groups has to be random in order to make sure that only the effects of the medications are compared, and no other factors influence the results. If doctors decided themselves which patients should receive which treatment, they might – for instance – give the more promising drug to patients who have better chances of recovery. This would distort the results. Random allocation ensures that differences between the results of the two groups at the end of the study are actually due to the treatment and not something else.

Randomized controlled trials provide the best results when trying to find out if there is a cause-and-effect relationship. RCTs can answer questions such as these:

Is the new drug A better than the standard treatment for medical condition X?
Does regular physical activity speed up recovery after a slipped disk when compared to passive waiting?
Cohort studies

A cohort is a group of people who are observed frequently over a period of many years – for instance, to determine how often a certain disease occurs. In a cohort study, two (or more) groups that are exposed to different things are compared with each other: For example, one group might smoke while the other doesn't. Or one group may be exposed to a hazardous substance at work, while the comparison group isn't. The researchers then observe how the health of the people in both groups develops over the course of several years, whether they become ill, and how many of them pass away. Cohort studies often include people who are healthy at the start of the study. Cohort studies can have a prospective (forward-looking) design or a retrospective (backward-looking) design. In a prospective study, the result that the researchers are interested in (such as a specific illness) has not yet occurred by the time the study starts. But the outcomes that they want to measure and other possible influential factors can be precisely defined beforehand. In a retrospective study, the result (the illness) has already occurred before the study starts, and the researchers look at the patient's history to find risk factors.

Cohort studies are especially useful if you want to find out how common a medical condition is and which factors increase the risk of developing it. They can answer questions such as:

How does high blood pressure affect heart health?
Does smoking increase your risk of lung cancer?

For example, one famous long-term cohort study observed a group of 40,000 British doctors, many of whom smoked. It tracked how many doctors died over the years, and what they died of. The study showed that smoking caused a lot of deaths, and that people who smoked more were more likely to get ill and die.

Case-control studies

Case-control studies compare people who have a certain medical condition with people who do not have the medical condition, but who are otherwise as similar as possible, for example in terms of their sex and age. Then the two groups are interviewed, or their medical files are analyzed, to find anything that might be risk factors for the disease. So case-control studies are generally retrospective.

Case-control studies are one way to gain knowledge about rare diseases. They are also not as expensive or time-consuming as RCTs or cohort studies. But it is often difficult to tell which people are the most similar to each other and should therefore be compared with each other. Because the researchers usually ask about past events, they are dependent on the participants’ memories. But the people they interview might no longer remember whether they were, for instance, exposed to certain risk factors in the past.

Still, case-control studies can help to investigate the causes of a specific disease, and answer questions like these:

Do HPV infections increase the risk of cervical cancer?
Is the risk of sudden infant death syndrome (“cot death”) increased by parents smoking at home?

Cohort studies and case-control studies are types of "observational studies."

Cross-sectional studies

Many people will be familiar with this kind of study. The classic type of cross-sectional study is the survey: A representative group of people – usually a random sample – are interviewed or examined in order to find out their opinions or facts. Because this data is collected only once, cross-sectional studies are relatively quick and inexpensive. They can provide information on things like the prevalence of a particular disease (how common it is). But they can't tell us anything about the cause of a disease or what the best treatment might be.

Cross-sectional studies can answer questions such as these:

How tall are German men and women at age 20?
How many people have cancer screening?
Qualitative studies

This type of study helps us understand, for instance, what it is like for people to live with a certain disease. Unlike other kinds of research, qualitative research does not rely on numbers and data. Instead, it is based on information collected by talking to people who have a particular medical condition and people close to them. Written documents and observations are used too. The information that is obtained is then analyzed and interpreted using a number of methods.

Qualitative studies can answer questions such as these:

How do women experience a Cesarean section?
What aspects of treatment are especially important to men who have prostate cancer?
How reliable are the different types of studies?

Each type of study has its advantages and disadvantages. It is always important to find out the following: Did the researchers select a study type that will actually allow them to find the answers they are looking for? You can’t use a survey to find out what is causing a particular disease, for instance.

It is really only possible to draw reliable conclusions about cause and effect by using randomized controlled trials. Other types of studies usually only allow us to establish correlations (relationships where it isn’t clear whether one thing is causing the other). For instance, data from a cohort study may show that people who eat more red meat develop bowel cancer more often than people who don't. This might suggest that eating red meat can increase your risk of getting bowel cancer. But people who eat a lot of red meat might also smoke more, drink more alcohol, or tend to be overweight. The influence of these and other possible risk factors can only be determined by comparing two equal-sized groups made up of randomly assigned participants.

That is why randomized controlled trials are usually the only suitable way to find out how effective a treatment is. Systematic reviews, which summarize multiple RCTs, are even better. In order to be good-quality, though, all studies and systematic reviews need to be designed properly and eliminate as many potential sources of error as possible.

German Network for Evidence-based Medicine. Glossar: Qualitative Forschung. Berlin: DNEbM; 2011.
Greenhalgh T. Einführung in die Evidence-based Medicine: kritische Beurteilung klinischer Studien als Basis einer rationalen Medizin. Bern: Huber; 2003.
Institute for Quality and Efficiency in Health Care (IQWiG, Germany). General methods . Version 5.0. Cologne: IQWiG; 2017.
Klug SJ, Bender R, Blettner M, Lange S. Wichtige epidemiologische Studientypen. Dtsch Med Wochenschr 2007; 132:e45-e47. [ PubMed : 17530597 ]
Schäfer T. Kritische Bewertung von Studien zur Ätiologie. In: Kunz R, Ollenschläger G, Raspe H, Jonitz G, Donner-Banzhoff N (eds.). Lehrbuch evidenzbasierte Medizin in Klinik und Praxis. Cologne: Deutscher Ärzte-Verlag; 2007.

IQWiG health information is written with the aim of helping people understand the advantages and disadvantages of the main treatment options and health care services.

Because IQWiG is a German institute, some of the information provided here is specific to the German health care system. The suitability of any of the described options in an individual case can be determined by talking to a doctor. We do not offer individual consultations.

Our information is based on the results of good-quality studies. It is written by a team of health care professionals, scientists and editors, and reviewed by external experts. You can find a detailed description of how our health information is produced and updated in our methods.

Cite this Page InformedHealth.org [Internet]. Cologne, Germany: Institute for Quality and Efficiency in Health Care (IQWiG); 2006-. What types of studies are there? 2016 Jun 15 [Updated 2016 Sep 8].

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Methodology

Sampling Methods | Types, Techniques & Examples

Sampling Methods | Types, Techniques & Examples

Published on September 19, 2019 by Shona McCombes . Revised on June 22, 2023.

When you conduct research about a group of people, it’s rarely possible to collect data from every person in that group. Instead, you select a sample . The sample is the group of individuals who will actually participate in the research.

To draw valid conclusions from your results, you have to carefully decide how you will select a sample that is representative of the group as a whole. This is called a sampling method . There are two primary types of sampling methods that you can use in your research:

Probability sampling involves random selection, allowing you to make strong statistical inferences about the whole group.
Non-probability sampling involves non-random selection based on convenience or other criteria, allowing you to easily collect data.

You should clearly explain how you selected your sample in the methodology section of your paper or thesis, as well as how you approached minimizing research bias in your work.

Population vs. sample, probability sampling methods, non-probability sampling methods, other interesting articles, frequently asked questions about sampling.

First, you need to understand the difference between a population and a sample , and identify the target population of your research.

The population is the entire group that you want to draw conclusions about.
The sample is the specific group of individuals that you will collect data from.

The population can be defined in terms of geographical location, age, income, or many other characteristics.

It is important to carefully define your target population according to the purpose and practicalities of your project.

If the population is very large, demographically mixed, and geographically dispersed, it might be difficult to gain access to a representative sample. A lack of a representative sample affects the validity of your results, and can lead to several research biases , particularly sampling bias .

Sampling frame

The sampling frame is the actual list of individuals that the sample will be drawn from. Ideally, it should include the entire target population (and nobody who is not part of that population).

Sample size

The number of individuals you should include in your sample depends on various factors, including the size and variability of the population and your research design. There are different sample size calculators and formulas depending on what you want to achieve with statistical analysis .

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Probability sampling means that every member of the population has a chance of being selected. It is mainly used in quantitative research . If you want to produce results that are representative of the whole population, probability sampling techniques are the most valid choice.

There are four main types of probability sample.

1. Simple random sampling

In a simple random sample, every member of the population has an equal chance of being selected. Your sampling frame should include the whole population.

To conduct this type of sampling, you can use tools like random number generators or other techniques that are based entirely on chance.

2. Systematic sampling

Systematic sampling is similar to simple random sampling, but it is usually slightly easier to conduct. Every member of the population is listed with a number, but instead of randomly generating numbers, individuals are chosen at regular intervals.

If you use this technique, it is important to make sure that there is no hidden pattern in the list that might skew the sample. For example, if the HR database groups employees by team, and team members are listed in order of seniority, there is a risk that your interval might skip over people in junior roles, resulting in a sample that is skewed towards senior employees.

3. Stratified sampling

Stratified sampling involves dividing the population into subpopulations that may differ in important ways. It allows you draw more precise conclusions by ensuring that every subgroup is properly represented in the sample.

To use this sampling method, you divide the population into subgroups (called strata) based on the relevant characteristic (e.g., gender identity, age range, income bracket, job role).

Based on the overall proportions of the population, you calculate how many people should be sampled from each subgroup. Then you use random or systematic sampling to select a sample from each subgroup.

4. Cluster sampling

Cluster sampling also involves dividing the population into subgroups, but each subgroup should have similar characteristics to the whole sample. Instead of sampling individuals from each subgroup, you randomly select entire subgroups.

If it is practically possible, you might include every individual from each sampled cluster. If the clusters themselves are large, you can also sample individuals from within each cluster using one of the techniques above. This is called multistage sampling .

This method is good for dealing with large and dispersed populations, but there is more risk of error in the sample, as there could be substantial differences between clusters. It’s difficult to guarantee that the sampled clusters are really representative of the whole population.

In a non-probability sample, individuals are selected based on non-random criteria, and not every individual has a chance of being included.

This type of sample is easier and cheaper to access, but it has a higher risk of sampling bias . That means the inferences you can make about the population are weaker than with probability samples, and your conclusions may be more limited. If you use a non-probability sample, you should still aim to make it as representative of the population as possible.

Non-probability sampling techniques are often used in exploratory and qualitative research . In these types of research, the aim is not to test a hypothesis about a broad population, but to develop an initial understanding of a small or under-researched population.

1. Convenience sampling

A convenience sample simply includes the individuals who happen to be most accessible to the researcher.

This is an easy and inexpensive way to gather initial data, but there is no way to tell if the sample is representative of the population, so it can’t produce generalizable results. Convenience samples are at risk for both sampling bias and selection bias .

2. Voluntary response sampling

Similar to a convenience sample, a voluntary response sample is mainly based on ease of access. Instead of the researcher choosing participants and directly contacting them, people volunteer themselves (e.g. by responding to a public online survey).

Voluntary response samples are always at least somewhat biased , as some people will inherently be more likely to volunteer than others, leading to self-selection bias .

3. Purposive sampling

This type of sampling, also known as judgement sampling, involves the researcher using their expertise to select a sample that is most useful to the purposes of the research.

It is often used in qualitative research , where the researcher wants to gain detailed knowledge about a specific phenomenon rather than make statistical inferences, or where the population is very small and specific. An effective purposive sample must have clear criteria and rationale for inclusion. Always make sure to describe your inclusion and exclusion criteria and beware of observer bias affecting your arguments.

4. Snowball sampling

If the population is hard to access, snowball sampling can be used to recruit participants via other participants. The number of people you have access to “snowballs” as you get in contact with more people. The downside here is also representativeness, as you have no way of knowing how representative your sample is due to the reliance on participants recruiting others. This can lead to sampling bias .

5. Quota sampling

Quota sampling relies on the non-random selection of a predetermined number or proportion of units. This is called a quota.

You first divide the population into mutually exclusive subgroups (called strata) and then recruit sample units until you reach your quota. These units share specific characteristics, determined by you prior to forming your strata. The aim of quota sampling is to control what or who makes up your sample.

If you want to know more about statistics , methodology , or research bias , make sure to check out some of our other articles with explanations and examples.

Student’s t -distribution
Normal distribution
Null and Alternative Hypotheses
Chi square tests
Confidence interval
Quartiles & Quantiles
Cluster sampling
Stratified sampling
Data cleansing
Reproducibility vs Replicability
Peer review
Prospective cohort study

Research bias

Implicit bias
Cognitive bias
Placebo effect
Hawthorne effect
Hindsight bias
Affect heuristic
Social desirability bias

A sample is a subset of individuals from a larger population . Sampling means selecting the group that you will actually collect data from in your research. For example, if you are researching the opinions of students in your university, you could survey a sample of 100 students.

In statistics, sampling allows you to test a hypothesis about the characteristics of a population.

Samples are used to make inferences about populations . Samples are easier to collect data from because they are practical, cost-effective, convenient, and manageable.

Probability sampling means that every member of the target population has a known chance of being included in the sample.

Probability sampling methods include simple random sampling , systematic sampling , stratified sampling , and cluster sampling .

In non-probability sampling , the sample is selected based on non-random criteria, and not every member of the population has a chance of being included.

Common non-probability sampling methods include convenience sampling , voluntary response sampling, purposive sampling , snowball sampling, and quota sampling .

In multistage sampling , or multistage cluster sampling, you draw a sample from a population using smaller and smaller groups at each stage.

This method is often used to collect data from a large, geographically spread group of people in national surveys, for example. You take advantage of hierarchical groupings (e.g., from state to city to neighborhood) to create a sample that’s less expensive and time-consuming to collect data from.

Sampling bias occurs when some members of a population are systematically more likely to be selected in a sample than others.

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Systematic Review
Open access
Published: 12 September 2023

Treatment options for digital nerve injury: a systematic review and meta-analysis

Yi Zhang 1 , 2 ,
Nianzong Hou 2 , 4 ,
Jian Zhang 1 ,
Bing Xie 2 ,
Jiahui Liang 1 ,
Xiaohu Chang 1 ,
Kai Wang 3 &
Xin Tang 1

Journal of Orthopaedic Surgery and Research volume 18 , Article number: 675 ( 2023 ) Cite this article

1596 Accesses

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Metrics details

Surgical treatment of finger nerve injury is common for hand trauma. However, there are various surgical options with different functional outcomes. The aims of this study are to compare the outcomes of various finger nerve surgeries and to identify factors associated with the postsurgical outcomes via a systematic review and meta-analysis.

The literature related to digital nerve repairs were retrieved comprehensively by searching the online databases of PubMed from January 1, 1965, to August 31, 2021. Data extraction, assessment of bias risk and the quality evaluation were then performed. Meta-analysis was performed using the postoperative static 2-point discrimination (S2PD) value, moving 2-point discrimination (M2PD) value, and Semmes–Weinstein monofilament testing (SWMF) good rate, modified Highet classification of nerve recovery good rate. Statistical analysis was performed using the R (V.3.6.3) software. The random effects model was used for the analysis. A systematic review was also performed on the other influencing factors especially the type of injury and postoperative complications of digital nerve repair.

Sixty-six studies with 2446 cases were included in this study. The polyglycolic acid conduit group has the best S2PD value (6.71 mm), while the neurorrhaphy group has the best M2PD value (4.91 mm). End-to-side coaptation has the highest modified Highet’s scoring (98%), and autologous nerve graft has the highest SWMF (91%). Age, the size of the gap, and the type of injury were factors that may affect recovery. The type of injury has an impact on the postoperative outcome of neurorrhaphy. Complications reported in the studies were mainly neuroma, cold sensitivity, paresthesia, postoperative infection, and pain.

Type of study/level of evidence Therapeutic IV.

Finger nerve laceration is one of the most common injuries in hand trauma, and its incidence rate is high in the peripheral nerve injuries of the upper limbs [ 1 ]. Most hand injuries with nerve damage require surgical treatment [ 2 ]. Potential common complications from either surgical or non-surgical treatments include numbness, paresthesia, neuroma, and cold intolerance [ 3 ].

Finger nerve repair currently has two main surgical approaches. End-to-end tension-free neurorrhaphy has traditionally been the preferred repair method in lesions with a gap smaller than 5 mm [ 2 ]. When the nerve ends cannot be approximated without tension, nerve reconstruction becomes the most commonly used method. [ 4 ] Various materials are available for reconstruction, such as autograft, nerve autograft, nerve allograft, and artificial conduit. End-to-side anastomosis is also commonly used to reconstruct large nerve defects. The repair materials of autograft mainly include veins and muscle-in-vein [ 5 ]. The autologous nerve graft is the historical gold standard for nerve reconstruction [ 2 ]. However, the autologous nerve graft damages the patient’s own tissue, which can increase operative time for harvesting donor nerve and increase potential donor site morbidity [ 6 ]. With the improvement of technology and repair materials, nerve duct repair technology and allogeneic nerve repair technology are now available. These two techniques avoid donor site complications caused by autologous nerve transplantation [ 5 ]. Synthetic nerve conduits have polyglycolic acid (PGA) tubes and collagen tubes. However, potential complications of allogeneic transplantation include the transmission of infectious diseases [ 5 ]. For large-segment defects or proximal nerve damage, some scholars have tried the technique of end-to-side nerve anastomosis. This method can bridge the damaged nerve to the healthy nerve [ 7 ].

In addition to the surgical method that may affect the functional outcomes, other predictors of sensory recovery have been evaluated in several studies, such as mechanism of injury gender, age, involved digit, level of injury, time from injury till repair, and gap length. The main one is the type of injury, which can affect the severity of the nerve damage, the gap between the nerve defects, and the recovery after surgery. According to Kusuhara et al. [ 8 ], avulsion injuries had significantly lower levels of meaningful recovery when compared with those of clean-cut and crush types of injury. However, Schmauss et al.’s study [ 9 ] suggested that it did not observe significant differences in sharp versus crush injuries.

Few systematic reviews and meta-analyses have been conducted to compare surgical approaches and factors associated with sensory outcomes of digital nerve repair. [ 2 , 3 , 5 , 10 , 11 , 12 , 13 ] In 2013 Paprottka et al.’s research, some of the included studies were low quality, and they did not compare allogeneic nerve repairs [ 5 ]. Herman et al. and Mauch et al.’s research in 2019 [ 8 ] included fewer studies and performed limited subgroups analyzed due to small sample size [ 2 , 10 ]. Thus, we aimed to perform a comprehensive meta-analysis and systematic review of finger nerve repair to include high-quality studies with large sample sizes and conduct detailed subgroup analysis to compare different surgical approaches. We also aimed to identify factors associated with the functional outcomes of finger nerve repair.

We performed and reported this review based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines.

Search strategy and inclusion/exclusion criteria

We performed systematic literature search in PubMed. The search terms “digital nerve,” “operation,” “surgery,” “nerve injury,” “nerve repair,” were combined using Boolean operators. Both “free-text term” and “MeSH term” searches were completed. We did not impose any restrictions on the language. The publication date was set from January 1, 1965, to August 31, 2021, because the clinical implementation of the surgical microscope started around 1965. The previous surgeries without microscopes were not included in the study [ 14 ]. Additionally, we reviewed the reference lists of the included papers and previously published reviews to ensure relevant studies had been considered. We merged all search results and discarded duplicate citations [ 2 , 3 , 5 , 10 , 11 , 12 , 13 ].

Two authors screened the articles independently based on the titles and abstracts, and each author independently retrieved and examined the full texts of the relevant papers for inclusion/exclusion based on predefined stratified criteria. Finally, we included all prospective and retrospective studies on surgical treatment of finger nerve injuries, including observational cohort studies, randomized controlled trials, and case reports with detailed data. We included patients of all ages with finger nerve injuries. The data published on the included studies were analyzed for the outcomes. We included results with at least 6-month follow-up. Exclusion criteria were peripheral nerve lesions not localized to the digital nerves in the hand, duplicated data, without appropriate data analysis methods, inconsistent data, reviews, unpublished literature, conference papers, studies without adequate information. The PRISMA flowchart is shown in Fig. 1 .

Flowchart of studies identified, included, and excluded

Data extraction and outcome measures

The primary author extracted data onto a predefined electronic data extraction form, and then, the other author checked all the data. Any disagreements were resolved through discussion, if necessary, with the involvement of a third reviewer. We extract the following data from each included literature, the characteristics of the literature (author, nationality, research type, hospital, date), population characteristics (age, gender, sample size, number of lost follow-up, number of injured nerves, smoking, type of injury), damage and repair status (nerve gap, repair time, type of surgery, follow-up time), complications (postoperative neuroma, cold stimulation, paresthesia, postoperative infection, pain).

The outcome measurements we used included: static 2-point discrimination (S2PD), moving 2-point dis crimination (M2PD), Semmes–Weinstein monofilament testing (SWMF), and modified Highet classification of nerve recovery [ 3 ]. Weber first described S2PD in 1835 which was the most widely used outcome measure. Normal values of S2PD in an uninjured fingertip range from 2 to 6 mm. M2PD was described by Dellon, and we used it as the second outcome indicator to evaluate the recovery of the finger nerves after surgery. S2PD and M2PD use actual measurement distance to evaluate the degree of nerve recovery. They are both continuous variables. The shorter the measurement distance, the better the response.

We used a modified classification system derived from Imai et al. to group SWMF outcomes. The SWMF scores ≤ 2.83 mean “normal” for sensation, scores from 2.83 to 4.31 mean “diminished light touch,” scores from 4.31 to 4.56 mean “diminished protective sensation,” scores from 4.56 to 6.10 mean “loss of protective sensation,” and scores > 6.10 mean “anesthetic” [ 15 ]. We counted the number of people with a score less than 4.31 (full sensation and diminished light touch) to calculate the excellent rate for the degree of recovery.

Medical Research Council scoring system from 1954, modified by MacKinnon and Dellon often referred to as modified Highet, grouped a range of values into subjective headings [ 3 ]. This scoring system was often used to evaluate the recovery after nerve repair. The specific evaluation criteria are shown in Table 1 . We extracted the sensory recovery as good and excellent nerve numbers in the table to evaluate the effect of the treatment.

In the S2PD and Highet data sets, there were many accounting articles, large amounts of data, and more detailed data. Therefore, we divided artificial catheters into two subgroups: collagen tubes and polyglycolic acid catheters. We divided venous catheters and muscle-in-vein grafts into groups in the autograft method. Direct suture and end-to-side anastomosis were split into two subgroups of neurorrhaphy for analysis. For these two data groups, we divided them into artificial conduit: polyglycolic acid, artificial conduit: collagen, nerve allograft, autograft repair: muscle-in-vein graft, autograft repair: vein graft, autologous nerve graft, end-to-end coaptation, end-to-side coaptation, total 8 repair types.

There were fewer articles in the M2PD and SWMF data sets, so the data we extracted were limited. When summarizing and analyzing the data, we did not conduct a detailed subgroup analysis but merged them into five repair Types for analysis. They were: artificial conduit (collagen tubes/polyglycolic acid catheters), nerve allograft, autograft repair (muscle-in-vein graft/vein graft), autologous nerve graft, and neurorrhaphy (end-to-end coaptation/end-to-side coaptation).

In addition, to evaluate the outcomes of the surgical repair methods, we also summarized and analyzed other factors associated with the result. These factors mainly included age, never gap, injury type, repair time, and smoking. Of course, the most important of these factors is the type of injury, which affects the degree of nerve damage, the choice of the surgical method, and postoperative recovery. We analyzed 25 articles [ 1 , 7 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 ] with specific injury descriptions through further screening of the included literature. We divided the injury types into sharp injury and crush injury. Sharp injuries include cutting injuries, acute or semi-sharp injuries, and stab injuries. Crush injuries include serious crush injuries, mangled injuries, and lacerated injuries. We analyzed patients with two types of injury in four types of surgery, and the analysis indexes were S2PD and modified Highet score excellent rate.

Complications reported in the studies were mainly neuroma, cold sensitivity, paresthesia, postoperative infection, and pain. We also conducted a summary analysis.

Statistical analysis, risk of bias, and study quality assessment

Our meta-analysis was performed by R (V.3.6.3) and package of meta. Heterogeneity variance parameter I 2 test was used to assess the heterogeneity of the model. However, in order to reduce the difference between the parameters and avoid error of the results caused by heterogeneity, the random effects model was used to merge the statistics. For postoperative S2PD and M2PD of various surgical methods, we use a combined statistical analysis of mean and standard deviation. For the SWMF excellent rate and modified Highet score excellent rate, we adopted a combined statistical analysis of the rates. The results of the merger were displayed in a forest diagram, and the statistics were compared in the form of a table. We used funnel chart and egger test for publication bias. In the analysis by surgical method and injury type, the continuous variables of S2PD were compared by T test, and the excellent and good rates were compared using the chi-square test.

We used standardized critical appraisal instruments from the JBI Meta-Analysis of Statistics Assessment and Review Instrument (JBI-MAStARI) (Appendix II) to evaluate all included literature. Because all the included studies were case series or cohort studies, we used JBI Critical Appraisal Checklist for Descriptive/Case Series to evaluate the quality of the literature. This evaluation checklist includes 9 quality items, and the judging options include yes, no, unclear, and not applicable. Studies that blinded the evaluators and had “yes” scores of 80% were considered high quality; those with “yes” scores of 60–80% were rated as medium, and the quality of studies with a score of less than 60% was considered low. Any disagreements that arose between the reviewers were resolved through discussion.

Study selection

We searched the PubMed database using keywords and got 403 different publications. At the same time, we examined the reference lists of the included papers and previous reviews to add 45 records. Sixty-six articles were included in the final data analysis [ 1 , 7 , 8 , 9 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 , 51 , 52 , 53 , 54 , 55 , 56 , 57 , 58 , 59 , 60 , 61 , 62 , 63 , 64 , 65 , 66 , 67 , 68 , 69 , 70 , 71 , 72 , 73 , 74 , 75 , 76 , 86 ] (Fig. 1 ).

Study characteristics

The 66 articles included a total of 2446 cases. Fifty studies [ 1 , 7 , 16 , 19 , 21 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 41 , 42 , 45 , 46 , 47 , 48 , 49 , 50 , 51 , 52 , 59 , 60 , 61 , 62 , 63 , 64 , 65 , 66 , 67 , 68 , 69 , 70 , 71 , 72 , 73 , 74 , 75 , 76 , 86 ] were retrospective case series, and 16 [ 8 , 9 , 17 , 18 , 20 , 22 , 23 , 24 , 40 , 43 , 44 , 53 , 54 , 55 , 56 , 57 ] were prospective. Of these studies, 16 control studies were available [ 20 , 21 , 28 , 29 , 38 , 40 , 41 , 42 , 53 , 54 , 55 , 56 , 57 , 58 , 59 , 60 ]. There were 3 papers that we only extracted part of the data because they included other nerve injuries in addition to the finger nerves [ 7 , 32 , 61 ]. The age range of patients included in these studies was 1–81 years old. The time from injury to surgical repair ranged between 0 and 37 months, and follow-up time ranged between 6 and 202 months. The detailed characteristics of eligible studies are shown in Table 2 .

Quality assessment and publication bias

All 66 articles were evaluated for the quality assessment using the JBI-MAStARI evaluation tool, and the research evaluation levels were high or medium. The specific evaluation results are shown in Tables 2 , 3 and 4 . The P values derived from Egger’s test indicated their inexistence of the publication bias in most meta-analyses. The results of the Egger test are summarized in Tables 5 , 6 , 7 , 8 and 9 .

Synthesis of results

All the data extracted from the literature are shown in Table 2 . The S2PD, Highet score, M2PD, and SWMF sensory results are summarized in Tables 5 , 6 , 7 and 8 .

A total of 51 articles reported the S2PD data [ 8 , 9 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 27 , 28 , 29 , 30 , 31 , 35 , 36 , 37 , 38 , 39 , 40 , 42 , 44 , 45 , 46 , 47 , 48 , 49 , 50 , 51 , 52 , 53 , 54 , 55 , 56 , 57 , 58 , 59 , 60 , 61 , 62 , 63 , 64 , 65 , 66 , 67 , 68 , 69 , 70 , 71 , 76 , 86 ]. After a summary analysis, the polyglycolic acid conduit group was 6.71 mm (95% CI 4.46; 8.96), which was the smallest discrimination distance, the end-to-end coaptation group was 8.80 mm (95% CI 7.63; 9.97), and the postoperative discrimination distance was the largest. The values of the other groups were distributed between them, but they have yet to reach excellent (2–6 mm), just at the good level (7–15 mm) (Table 5 , Figs. 2 , 3 ).

Static 2-point discrimination results for each repair technique

Forest plot of static 2-point discrimination results for each repair technique. a Forest plot of S2PD—Artificial conduit: polyglycolic acid; b Forest plot of S2PD—Artificial conduit: collagen; c Forest plot of S2PD—nerve allografts; d Forest plot of S2PD—autograft repair: muscle-in-vein graft; e Forest plot of S2PD—autograft repair: vein graft; f Forest plot of S2PD—autologous nerve graft; g Forest plot of S2PD—end-to-end coaptation; and h Forest plot of S2PD—end-to-side coaptation

The excellent rate of modified Highet’s scoring includes 61 articles [ 1 , 7 , 8 , 9 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 41 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 , 51 , 52 , 53 , 54 , 55 , 56 , 58 , 59 , 60 , 61 , 62 , 64 , 65 , 66 , 67 , 68 , 69 , 71 , 72 , 73 , 74 , 75 , 76 , 86 ]. The end-to-side coaptation group was 98% (95% CI 0.85, 1.00), and the postoperative felt the excellent rate was the highest. The polyglycolic acid conduit group was 74% (95% CI 0.53; 0.91), and the excellent rate was the lowest (Table 6 , Figs. 4 , 5 ).

Modified Highet classification good rate for each repair technique

Forest plot of modified Highet classification good rate for each repair technique. a Forest plot of modified Highet classification good rate—Artificial conduit: polyglycolic acid; b Forest plot of modified Highet classification good rate—Artificial conduit: collagen; c Forest plot of modified Highet classification good rate—nerve allograft; d Forest plot of modified Highet classification good rate—autograft repair: muscle-in-vein graft; e Forest plot of modified Highet classification good rate—autograft repair: vein graft; f Forest plot of modified Highet classification good rate—autologous nerve graft; g Forest plot of modified Highet classification good rate—end-to-end coaptation; and h Forest plot of modified Highet classification good rate—end-to-side coaptation

The M2PD group included 19 articles [ 17 , 20 , 23 , 24 , 27 , 28 , 36 , 37 , 39 , 40 , 41 , 45 , 47 , 50 , 54 , 57 , 60 , 68 , 69 ]. The neurorrhaphy group was 4.91 mm (95% CI 3.72, 6.09), and the discrimination distance was the smallest; the autograft repair group was 7.06 mm (95% CI 5.58, 8.54), and the postoperative discrimination distance was the largest. The five data sets have yet to reach excellent (2–3 mm) but at a good level (4–7 mm) (Table 7 , Figs. 6 , 7 ).

Moving 2-point discrimination results for each repair technique

Forest plot of moving 2-point discrimination results for each repair technique. a Forest plot of M2PD—artificial conduit; b Forest plot of M2PD—nerve allograft; c Forest plot of M2PD—autograft repair; d Forest plot of M2PD—autologous nerve graft; and e Forest plot of M2PD—neurorrhaphy

There were 29 documents included in the SWMF data set [ 9 , 16 , 18 , 19 , 20 , 22 , 23 , 25 , 27 , 28 , 29 , 30 , 36 , 45 , 46 , 47 , 49 , 52 , 53 , 64 , 65 , 66 , 67 , 68 , 69 , 70 , 71 , 73 , 76 , 86 ]. The highest excellent and good rate was 91% (95% CI 0.80, 0.99) in the autologous nerve graft group. The lowest was 61% (95% CI 0.40, 0.80) in the autograft repair group (Table 8 , Figs. 8 , 9 ).

Semmes–Weinstein monofilament testing good rate for each repair technique

Forest plot of Semmes–Weinstein monofilament testing good rate for each repair technique. a Forest plot of Semmes–Weinstein monofilament testing good rate—artificial conduit; b Forest plot of Semmes–Weinstein monofilament testing good rate—nerve allografts; c Forest plot of Semmes–Weinstein monofilament testing good rate—autograft repair; d Forest plot of Semmes–Weinstein monofilament testing good rate—autologous nerve graft; and e Forest plot of Semmes–Weinstein monofilament testing good rate—neurorrhaphy

Finally, we conducted a summary analysis of all the data in the 4 outcome indicators. S2PD was 8.18 mm (95% CI 7.66, 8.70), M2PD was 5.90 mm (95% CI 5.34, 6.46), Highet score excellent and good rate was 80% (95% CI 0.74, 0.86), and SWMF excellent and good rate was 81% (95% CI 0.72, 0.88) (Table 9 , Figs. 10 , 11 , 12 , 13 ).

Forest plot of static 2-point discrimination results

Forest plot of moving 2-point discrimination results

Forest plot of modified Highet classification good rate

Forest plot of Semmes–Weinstein monofilament testing good rate

We extracted data from 25 articles for statistical analysis in subgroups by injury type. In terms of S2PD values, there was no significant difference in sharp and blunt injuries among the four surgical methods ( P > 0.05). In terms of the excellent and good rate, the recovery effect of sharp injury was better than that of blunt injury only in the surgical method of neurorrhaphy ( P = 0.00004472), and there was no statistical difference in the other methods (Tables 12 , 13 ).

We performed statistics on the analysis of other influencing factors in the included literature and completed a summary analysis of complications. In the study of influencing factors, in terms of age factor, 13 articles considered it to have an impact [ 1 , 21 , 32 , 33 , 34 , 36 , 55 , 57 , 60 , 67 , 72 , 73 , 74 ], and nine assumed it to have no effect [ 9 , 20 , 43 , 45 , 63 , 65 , 66 , 71 , 75 ]. In terms of nerve injury interval, 11 papers were deemed to be influential [ 9 , 21 , 26 , 40 , 43 , 44 , 51 , 52 , 71 , 72 , 74 ], and five pieces that have no influence [ 20 , 32 , 60 , 65 , 67 ]; four articles were considered to be compelling, [ 8 , 27 , 52 , 60 ], and ten articles were considered to be unaffected by the repair time factor [ 9 , 32 , 35 , 43 , 63 , 65 , 66 , 71 , 73 , 75 ]; in terms of smoking factors, three papers were supposed to be affected [ 33 , 40 , 73 ], and four pieces were not affected [ 9 , 43 , 45 , 63 ] (Table 10 ).

The results of the pooled analysis of complications are shown that there were 12 articles of the literature reporting neuroma [ 21 , 29 , 32 , 38 , 44 , 47 , 56 , 57 , 62 , 63 , 64 , 68 ], and 14 cases can be counted (artificial conduit: 2 articles, 3 cases; autograft repair: 7 articles, 7 cases; and nerve sutures: 3 articles, 4 cases); 13 publications reporting cold stimulation [ 27 , 29 , 30 , 32 , 37 , 38 , 49 , 58 , 63 , 67 , 68 , 69 , 70 ], and 50 cases were counted (autograft repair: 10 articles, 47 cases; nerve sutures: 3 articles, 3 cases); 17 papers reporting paresthesia [ 1 , 9 , 21 , 27 , 29 , 30 , 32 , 33 , 38 , 44 , 49 , 62 , 63 , 65 , 67 , 71 , 76 ], and 15 cases were counted (artificial conduit: 3 articles, 1 case; autograft repair:11 articles,14 cases; and nerve sutures: 3 articles); 6 articles reporting postoperative infections [ 20 , 21 , 40 , 45 , 53 , 69 ], and 10 cases were counted (artificial conduit: 3 articles, 5 cases; nerve allograft: 2 articles, 4 cases; autograft repair: 1 articles, 1 case); 13 articles reported pain [ 20 , 21 , 23 , 29 , 37 , 38 , 39 , 49 , 50 , 53 , 58 , 67 , 70 ], and 23 cases were counted (artificial conduit: 2 articles, 1 cases; nerve allograft: 3 articles, 9 cases; autograft repair: 6 articles, 12 cases; and nerve sutures: 2 articles, 1 cases) (Table 10 ).

We analyzed the maximum extent of neurological defects treated by various surgical methods in the literature. The direct suture is the minimum tension-free suture required to repair the defect within 0.5 cm. The largest defect was repaired by autogenous nerve graft, ranging from 0.5 to 9.0 cm. The end-to-side anastomosis technique had no limitation on the length of the defect and was a method of nerve transplantation or bridging (Table 11 ).

It has been reported that among all peripheral nerve injuries, the digital nerves were the most common peripheral nerves injured [ 77 ]. In the published literature, there were many ways to repair digital nerve injury. However, the clinical practice of digital nerve repair has been lack of consensus. Thus, we analyzed the published literature on finger nerve injury .

Using the S2PD and modified Highet’s scoring systems, tension-free end-to-end coaptation was the most common method for nerve repair. We found that compared with the other nerve defect repair methods, it seemed that there was no obvious advantage. Autologous nerve transplantation also showed no absolute advantage. As a new material to repair nerve defects, allogeneic nerves have been widely used. Compared with the autologous nerves, it has no obvious advantages. However, it can avoid other postoperative complications caused by nerve extraction and has the same effect as autologous nerve in nerve regeneration. There were some differences between PGA tubes and collagen tubes. In 2003, Laroas et al. published their results on 28 PGA-conduit repairs that with sensory re-education, the success rate could be increased to 100% [ 78 ]. In 2007, Waitayawinyu et al. study found better results with collagen conduits than with PGA conduits [ 79 ]. Our statistical results showed that there was no significant difference between the two catheters. Vein graft and muscle-in-vein graft as autografts also needed to be obtained from the donor site, but they were not as damaging to the donor site as autologous nerves. The two surgical methods had equivalent results, and there was no absolute advantage when compared with other methods. For large-segment defects or proximal nerve damage, the end-to-side anastomosis technique was an effective method. Its excellent rate was the highest among the 8 methods. Experimental end-to-side nerve suture was first introduced by Kennedy [ 80 ], but somehow it was not widely used clinically then. Viterbo et al., the creators of the modern approach of end-to-side neurorrhaphy without harming the donor’s nerve, something that broke paradigm, against all acknowledges, conducted their research by rats, in which they had the peroneal nerve sectioned, the distal ending sutured to the lateral face of the tibial nerve after removing a small epineural window, demonstrating that the anastomosed nerve endings had electrophysiological functions and successfully proving that the end-to-side nerve anastomosis technique was feasible [ 81 , 82 , 83 ]. Mennen first reported the use of this technique in humans in 1996 with good results [ 84 ]. In the 2003 literature, Mennen reported 56 cases of end-to-side anastomosis, including 5 cases of digital nerve repair, with a good level of neurological functional recovery [ 7 ]. Since then, four other scholars have reported related studies, but the number of cases they reported was very small. Recently, new techniques and materials have been used as variants for end-to-side coaptation; however, Geuna S et al. proposed that the bioactive materials as conduits or gene therapy, the role of Schwann cells, and attracting factors derived from the severed trunk should be on the way with further studies [ 85 ]. As a new surgical method of nerve repair, there are few studies on the repair of digital nerve. A total of 5 articles [ 7 , 37 , 64 , 70 , 86 ] and 49 cases were included in our study, and some data could not be extracted. Thus, there may be publication bias.

The data on the excellent rate of SWMF and M2PD of the autograft (muscle-in-vein graft/vein graft) were the worst. These 2 techniques have disadvantages for longer distances such as the collapse of the vein or dispersion of the regenerating axons out of the muscle [ 47 ]. We found that none of these methods had significantly different results. Our results were similar as shown in the meta-analysis performed by [ 11 , 12 , 13 ].

Through a summary analysis of all the data in the 4 outcome measures, we found that most patients had a good recovery after nerve injury repair. According to the modified Highet classification of nerve recovery, both S2PD and M2PD achieved S3 + or better. The Highet score and SWMF excellent and good rate were all above 80% (Table 1 ). We found that surgical repair was significantly better than no repair. Our results are consistent with the study performed by Chow et al., which had the same conclusion. [ 56 ] In Chow’s literature, 2-year follow-up outcomes were compared between digital nerve repair and no repair. 90% of the 76 patients with nerve repair achieved S3 + or better at 2 years, compared with only 6% of the 36 patients with unrepaired digital nerves. On the other hand, the meta-analysis of Dunlop et al. found that there were little difference between repair and non-repair. The differences in conclusions may be due to different studies included in the analysis [ 3 ].

The surgical approach significantly impacts nerve injury and is a critical factor in surgical intervention. The mechanism of injury is another important factor that may affect the degree of damage, the length of nerve defect, the choice of the surgical method, and the outcome of postoperative recovery. Many scholars have researched this factor in the literature included in our study. Kusuhara et al.’s nine studies [ 8 , 18 , 21 , 33 , 43 , 52 , 60 , 72 , 74 ] suggested that the type of injury had an impact on postoperative neurological recovery. Schmauss et al.’s nine studies [ 1 , 9 , 34 , 45 , 57 , 63 , 66 , 73 , 75 ] reported that the type of injury did not affect nerve recovery. We also did a statistical analysis of the data for this factor; through further screening of the included literature, we analyzed 25 kinds of literature with specific injury descriptions. Regarding S2PD value, sharp injury recovered better than blunt injury after four types of surgery, but there was no apparent absolute advantage. In terms of the excellent and reasonable rate, sharp injury has apparent benefits in the recovery of blunt injury after neurorrhaphy, and there is no significant difference between the other three surgical methods. This should be related to the fact that blunt injury can lead to large nerve damage, so only conduit or nerve transplantation can be selected for treatment. After the damaged nerve segment is removed, the nerve stumps become healthy. At this time, there is no significant difference in the effect of the two injury mechanisms on the nerve. However, if the damaged nerve segment is not resected but directly anastomosed, the blunt injury of the nerve is unhealthy and will affect the postoperative recovery. Sharp injury has less damage to the nerve, and the recovery effect after neurorrhaphy is good, while the blunt injury is poor. Therefore, when dealing with blunt nerve injury, the damaged nerve segment should be removed, and the appropriate surgical method should be selected according to the length of the nerve defect.

There are other factors that may affect the postoperative recovery of neuroremediation. In the 5 studies included, it has been shown that age was a factor that affected nerve recovery, especially in children, whose recovery after nerve repair was better than that of adults and the elderly [ 1 , 33 , 34 , 36 , 74 ]. Repair time, smoking, and follow-up time may have little effect on the recovery after nerve repair. In 2015, a study by Fakin et al. found that the experience of the surgeon was also one of the predicting factors of the outcomes. The repair of the finger artery accompanying the finger nerve had little effect on the postoperative recovery, which was also concluded by Hohendorff et al. [ 63 , 87 ] In 1985, Sullivan et al. and Murakami et al. found that the number of finger nerve repairs had no difference in the effect of restoration [ 35 , 88 ]. In a 2016 study done by Bulut et al., it was found that the recovery after finger nerve injury repair was independent of gender and which finger [ 73 ]. In 1981, Young et al. compared simple epineurium repair versus perineurium repair, and there was no significant difference in the recovery [ 55 ]. In a 2016 study by Sladana et al., it was deemed necessary to use splints after nerve repair [ 72 ]. Thomas et al. found that the result of using a microscope was significantly better than using a magnifying glass [ 89 ].

Our analysis of the postoperative complications in the included literature found that neuroma, cold stimulation, paresthesia, and pain were the most reported after autograft surgeries. This may be due to the damage to the donor site and poor recovery of the recipient site after transplantation. For complications, the application of allogeneic nerves and nerve conduits was better than autograft.

Our analysis has shown that the length of the nerve defect would affect the postoperative recovery, as well as limit the choice of surgical methods. Of course, we must also consider other factors, such as complications, economic conditions, local hospital technology, repair materials, etc. When there were multiple options to choose from for the optimal repair gap, we had to consider clinical factors associated with recovery when making the decision. There were no significant differences in the outcomes of various surgical methods, and the surgeon should choose a reasonable treatment plan based on the clinical scenario.

There were several limitations of our study. First, the quality of our study is limited by the quality of the included studies, which were mostly case series (level 4 evidence). Second, the strength of our conclusions was limited by the heterogeneous and incomplete outcome data reported across the included studies, and publication bias for the individual studies analyzed. In addition, when analyzing the excellent rate of Highet score, not every study reported outcomes in the same manner. We were forced to use S2PD and M2PD classification systems to group the results into categories that were comparable across sensory outcomes.

Conclusions

Our study demonstrated that the results of surgical treatment of digital nerve injury are generally satisfactory; however, no nerve repair method has absolute advantages. When choosing a surgical method to repair finger nerve injury, we must comprehensively consider various factors, especially the type of injury, the gap size of the nerve defect, the injury to the patient’s donor site, postoperative complications, the patient’s economic conditions, and the medical level of the local hospital. Whenever tension-free nerve coaptation was possible, end-to-end nerve coaptation was still the method of choice. In the case of nerve defects, the advantages of nerve conduits and allogeneic nerves were relatively high. When the proximal nerve was damaged and could not be connected, the end-to-side anastomosis technique could be selected for bridging to repair. Simultaneously, age, the size of the gap, and the type of injury were also factors that may affect recovery. Certainly, in consideration of the limitations of the study, such as the low qualities, the high heterogeneous, incomplete outcome data reported, and publication bias for the individual studies, conclusions in our study should be interpreted with caution. Therefore, more high-quality randomized controlled studies were definitely needed in order to give a conclusive statement.

Availability of data and materials

This study included articles which are available via PubMed. All information analyzed in this study was collected in a data set, and this is available from the corresponding author on reasonable request.

Abbreviations

Static 2-point discrimination

Moving 2-point discrimination

Semmes–Weinstein monofilament testing

Polyglycolic acid tubes

Preferred Reporting Items for Systematic Reviews and Meta-Analyses

Confidence intervals

JBI Meta-Analysis of Statistics Assessment and Review Instrument

Australia’s Joanna Briggs Institute

Efstathopoulos D, Gerostathopoulos N, Misitzis D, Bouchlis G, Anagnostou S, Daoutis NK. Clinical assessment of primary digital nerve repair. Acta Orthop Scand Suppl. 1995;264:45–7.

Article CAS PubMed Google Scholar

Herman ZJ, Ilyas AM. Sensory outcomes in digital nerve repair techniques: an updated meta-analysis and systematic review. Hand. 2019;15(2):157–64.

Article PubMed PubMed Central Google Scholar

Dunlop RLE, Wormald JCR, Jain A. Outcome of surgical repair of adult digital nerve injury: a systematic review. BMJ Open. 2019;9(3):e025443.

Griffin JW, Hogan MV, Chhabra AB, Deal DN. Peripheral nerve repair and reconstruction. J Bone Jt Surg Am. 2013;95(23):2144–51.

Article Google Scholar

Paprottka FJ, Wolf P, Harder Y, Kern Y, Paprottka PM, Machens HG, Lohmeyer JA. Sensory recovery outcome after digital nerve repair in relation to different reconstructive techniques: meta-analysis and systematic review. Plast Surg Int. 2013;2013: 704589.

PubMed PubMed Central Google Scholar

Staniforth P, Fisher TR. The effects of sural nerve excision in autogenous nerve grafting. Hand. 1978;10(2):187–90.

Mennen U. End-to-side nerve suture in clinical practice. Hand Surg Int J Devoted Hand Upper Limb Surg Relat Res J Asia-Pac Fed Soc Surg Hand. 2003;8(1):33–42.

Google Scholar

Kusuhara H, Hirase Y, Isogai N, Sueyoshi Y. A clinical multi-center registry study on digital nerve repair using a biodegradable nerve conduit of PGA with external and internal collagen scaffolding. Microsurgery. 2019;39(5):395–9.

Article PubMed Google Scholar

Schmauss D, Finck T, Liodaki E, Stang F, Megerle K, Machens HG, Lohmeyer JA. Is nerve regeneration after reconstruction with collagen nerve conduits terminated after 12 months? The long-term follow-up of two prospective clinical studies. J Reconstr Microsurg. 2014;30(8):561–8.

Mauch JT, Bae A, Shubinets V, Lin IC. A systematic review of sensory outcomes of digital nerve gap reconstruction with autograft, allograft, and conduit. Ann Plast Surg. 2019;82:S247–55.

Rinkel WD, Huisstede BM, van der Avoort DJ, Coert JH, Hovius SE. What is evidence based in the reconstruction of digital nerves? A systematic review. J Plast Reconstr Aesthet Surg. 2013;66(2):151–64.

Kim JS, Bonsu N-Y, Leland HA, Carey JN, Patel KM, Seruya M. A systematic review of prognostic factors for sensory recovery after digital nerve reconstruction. Ann Plast Surg. 2018;80:S311–6.

Mermans JF, Franssen BB, Serroyen J, Van der Hulst RR. Digital nerve injuries: a review of predictors of sensory recovery after microsurgical digital nerve repair. Hand (N Y). 2012;7(3):233–41.

Smith JW. Microsurgery of peripheral nerves. Plast Reconstr Surg. 1964;33:317–29.

Imai H, Tajima T, Natsuma Y. Interpretation of cutaneous pressure threshold (Semmes–Weinstein monofilament measurement) following median nerve repair and sensory reeducation in the adult. Microsurgery. 1989;10(2):142–4.

Bushnell BD, McWilliams AD, Whitener GB, Messer TM. Early clinical experience with collagen nerve tubes in digital nerve repair. J Hand Surg. 2008;33(7):1081–7.

Taras JS, Jacoby SM, Lincoski CJ. Reconstruction of digital nerves with collagen conduits. J Hand Surg Am. 2011;36(9):1441–6.

Arnaout A, Fontaine C, Chantelot C. Sensory recovery after primary repair of palmar digital nerves using a Revolnerv ((R)) collagen conduit: a prospective series of 27 cases. Chir Main. 2014;33(4):279–85.

Thomsen L, Bellemere P, Loubersac T, Gaisne E, Poirier P, Chaise F. Treatment by collagen conduit of painful post-traumatic neuromas of the sensitive digital nerve: a retrospective study of 10 cases. Chir Main. 2010;29(4):255–62.

Means KR, Rinker BD, Higgins JP, Payne SH, Merrell GA, Wilgis EFS. A multicenter, prospective, randomized, pilot study of outcomes for digital nerve repair in the hand using hollow conduit compared with processed allograft nerve. Hand. 2016;11(2):144–51.

Rbia N, Bulstra LF, Saffari TM, Hovius SER, Shin AY. Collagen nerve conduits and processed nerve allografts for the reconstruction of digital nerve gaps: a single-institution case series and review of the literature. World Neurosurg. 2019;127:e1176–84.

Guo Y, Chen G, Tian G, Tapia C. Sensory recovery following decellularized nerve allograft transplantation for digital nerve repair. J Plast Surg Hand Surg. 2013;47:1–3.

Taras JS, Amin N, Patel N, McCabe LA. Allograft reconstruction for digital nerve loss. J Hand Surg Am. 2013;38(10):1965–71.

Karabekmez FE, Duymaz A, Moran SL. Early clinical outcomes with the use of decellularized nerve allograft for repair of sensory defects within the hand. Hand (N Y). 2009;4(3):245–9.

Tos P, Battiston B, Ciclamini D, Geuna S, Artiaco S. Primary repair of crush nerve injuries by means of biological tubulization with muscle-vein-combined grafts. Microsurgery. 2012;32(5):358–63.

Risitano G, Cavallaro G, Merrino T, Coppolino S, Ruggeri F. Clinical results and thoughts on sensory nerve repair by autologous vein graft in emergency hand reconstruction. Chir Main. 2002;21(3):194–7.

Alligand-Perrin P, Rabarin F, Jeudy J, Cesari B, Saint-Cast Y, Fouque PA, Raimbeau G. Vein conduit associated with microsurgical suture for complete collateral digital nerve severance. Orthop Traumatol Surg Res. 2011;97(4 Suppl):S16-20.

Laveaux C, Pauchot J, Obert L, Choserot V, Tropet Y. Retrospective monocentric comparative evaluation by sifting of vein grafts versus nerve grafts in palmar digital nerves defects. Report of 32 cases. Ann Chir Plast Esthet. 2010;55(1):19–34.

Chen C, Tang P, Zhang X. Reconstruction of proper digital nerve defects in the thumb using a pedicle nerve graft. Plast Reconstr Surg. 2012;130(5):1089–97.

Pilanci O, Ozel A, Basaran K, Celikdelen A, Berkoz O, Saydam FA, Kuvat SV. Is there a profit to use the lateral antebrachial cutaneous nerve as a graft source in digital nerve reconstruction? Microsurgery. 2014;34(5):367–71.

Inoue S, Ogino T, Tsutida H. Digital nerve grafting using the terminal branch of posterior interosseous nerve: a report of three cases. Hand Surg Int J Devot Hand Upper Limb Surg Relat Res J Asia-Pac Fed Soc Surg Hand. 2002;7(2):305–7.

Meek MF, Coert JH, Robinson PH. Poor results after nerve grafting in the upper extremity: Quo vadis? Microsurgery. 2005;25(5):396–402.

Al-Ghazal SK, McKiernan M, Khan K, McCann J. Results of clinical assessment after primary digital nerve repair. J Hand Surg (Edinburgh, Scotland). 1994;19(2):255–7.

Article CAS Google Scholar

Altissimi M, Mancini GB, Azzarà A. Results of primary repair of digital nerves. J Hand Surg (Edinburgh, Scotland). 1991;16(5):546–7.

Sullivan DJ. Results of digital neurorrhaphy in adults. J Hand Surg (Edinburgh, Scotland). 1985;10(1):41–4.

Segalman KA, Cook PA, Wang BH, Theisen L. Digital neurorrhaphy after the age of 60 years. J Reconstr Microsurg. 2001;17(2):85–8.

Voche P, Ouattara D. End-to-side neurorrhaphy for defects of palmar sensory digital nerves. Br J Plast Surg. 2005;58(2):239–44.

Pereira JH, Bowden RE, Gattuso JM, Norris RW. Comparison of results of repair of digital nerves by denatured muscle grafts and end-to-end sutures. J Hand Surg (Edinburgh, Scotland). 1991;16(5):519–23.

Mackinnon SE, Dellon AL. Clinical nerve reconstruction with a bioabsorbable polyglycolic acid tube. Plast Reconstr Surg. 1990;85(3):419–24.

Rinker B, Liau JY. A prospective randomized study comparing woven polyglycolic acid and autogenous vein conduits for reconstruction of digital nerve gaps. J Hand Surg. 2011;36(5):775–81.

Battiston B, Geuna S, Ferrero M, Tos P. Nerve repair by means of tubulization: literature review and personal clinical experience comparing biological and synthetic conduits for sensory nerve repair. Microsurgery. 2005;25(4):258–67.

Neubrech F, Heider S, Otte M, Hirche C, Kneser U, Kremer T. Nerve tubes for the repair of traumatic sensory nerve lesions of the hand: review and planning study for a randomised controlled multicentre trial. Handchir Mikrochir Plast Chir. 2016;48(3):148–54.

CAS PubMed Google Scholar

Lohmeyer JA, Kern Y, Schmauss D, Paprottka F, Stang F, Siemers F, Mailaender P, Machens HG. Prospective clinical study on digital nerve repair with collagen nerve conduits and review of literature. J Reconstr Microsurg. 2014;30(4):227–34.

PubMed Google Scholar

Lohmeyer J, Zimmermann S, Sommer B, Machens HG, Lange T, Mailander P. Bridging peripheral nerve defects by means of nerve conduits. Chirurg. 2007;78(2):142–7.

Buncke G, Safa B, Thayer W, Greenberg J, Ingari J, Rinker B. Outcomes of short-gap sensory nerve injuries reconstructed with processed nerve allografts from a multicenter registry study. J Reconstr Microsurg. 2015;31(05):384–90.

Rinker B, Zoldos J, Weber RV, Ko J, Thayer W, Greenberg J, Leversedge FJ, Safa B, Buncke G: Use of Processed Nerve Allografts to Repair Nerve Injuries Greater Than 25 mm in the Hand. Ann Plast Surg 2017, 78(6S Suppl 5):S292-s295.

Marcoccio I, Vigasio A. Muscle-in-vein nerve guide for secondary reconstruction in digital nerve lesions. J Hand Surg Am. 2010;35(9):1418–26.

Norris RW, Glasby MA, Gattuso JM, Bowden RE. Peripheral nerve repair in humans using muscle autografts. A new technique. J Bone Jt Surg Br. 1988;70(4):530–3.

Laveaux C, Pauchot J, Obert L, Choserot V, Tropet Y. Emergency management of traumatic collateral palmar digital nerve defect inferior to 30 mm by venous grafting. Report on 12 clinical cases. Chir Main. 2011;30(1):16–9.

Lee Y-H, Shieh S-J. Secondary nerve reconstruction using vein conduit grafts for neglected digital nerve injuries. Microsurgery. 2008;28(6):436–40.

Tang JB, Gu YQ, Song YS. Repair of digital nerve defect with autogenous vein graft during flexor tendon surgery in zone 2. J Hand Surg (Edinburgh, Scotland). 1993;18(4):449–53.

Walton RL, Brown RE, Matory WE Jr, Borah GL, Dolph JL. Autogenous vein graft repair of digital nerve defects in the finger: a retrospective clinical study. Plast Reconstr Surg. 1989;84(6):944–9 ( discussion 950–942 ).

He B, Zhu Q, Chai Y, Ding X, Tang J, Gu L, Xiang J, Yang Y, Zhu J, Liu X. Safety and efficacy evaluation of a human acellular nerve graft as a digital nerve scaffold: a prospective, multicentre controlled clinical trial. J Tissue Eng Regen Med. 2015;9(3):286–95.

Chiu DT, Strauch B. A prospective clinical evaluation of autogenous vein grafts used as a nerve conduit for distal sensory nerve defects of 3 cm or less. Plast Reconstr Surg. 1990;86(5):928–34.

Young L, Wray RC, Weeks PM. A randomized prospective comparison of fascicular and epineural digital nerve repairs. Plast Reconstr Surg. 1981;68(1):89–93.

Chow SP, Ng C. Can a divided digital nerve on one side of the finger be left unrepaired? J Hand Surg (Edinburgh, Scotland). 1993;18(5):629–30.

Calcagnotto GN, Braga Silva J. The treatment of digital nerve defects by the technique of vein conduit with nerve segment. A randomized prospective study. Chir Main. 2006;25(3–4):126–30.

Chen C, Tang P, Zhang X. Finger sensory reconstruction with transfer of the proper digital nerve dorsal branch. J Hand Surg. 2013;38(1):82–9.

Oruç M, Ozer K, Çolak Ö, Kankaya Y, Koçer U. Does crossover innervation really affect the clinical outcome? A comparison of outcome between unilateral and bilateral digital nerve repair. Neural Regen Res. 2016;11(9):1499–505.

Wang WZ, Crain GM, Baylis W, Tsai TM. Outcome of digital nerve injuries in adults. J Hand Surg Am. 1996;21(1):138–43.

Young VL, Wray RC, Weeks PM. The results of nerve grafting in the wrist and hand. Ann Plast Surg. 1980;5(3):212–5.

Stang F, Stollwerck P, Prommersberger KJ, van Schoonhoven J. Posterior interosseus nerve vs. medial cutaneous nerve of the forearm: differences in digital nerve reconstruction. Arch Orthop Trauma Surg. 2013;133(6):875–80.

Fakin RM, Calcagni M, Klein HJ, Giovanoli P. Long-term clinical outcome after epineural coaptation of digital nerves. J Hand Surg (Eur Vol). 2015;41(2):148–54.

Artiaco S, Tos P, Conforti LG, Geuna S, Battiston B. Termino-lateral nerve suture in lesions of the digital nerves: clinical experience and literature review. J Hand Surg Eur. 2010;35(2):109–14.

McFarlane RM, Mayer JR. Digital nerve grafts with the lateral antebrachial cutaneous nerve. J Hand Surg. 1976;1(3):169–73.

Poppen NK, McCarroll HR, Doyle JR, Niebauer JJ. Recovery of sensibility after suture of digital nerves. J Hand Surg. 1979;4(3):212–26.

Chevrollier J, Pedeutour B, Dap F, Dautel G. Evaluation of emergency nerve grafting for proper palmar digital nerve defects: a retrospective single centre study. Orthop Traumatol Surg Res. 2014;100(6):605–10.

Rose EH, Kowalski TA, Norris MS. The reversed venous arterialized nerve graft in digital nerve reconstruction across scarred beds. Plast Reconstr Surg. 1989;83(4):593–604.

Kim J, Lee YH, Kim MB, Lee SH, Baek GH. Innervated reverse digital artery island flap through bilateral neurorrhaphy using direct small branches of the proper digital nerve. Plast Reconstr Surg. 2015;135(6):1643–50.

Landwehrs GM, Brüser P. Clinical results of terminolateral neurorrhaphy in digital nerves. Handchir Mikrochir Plast Chir. 2008;40(5):318–21.

Unal MB, Gokkus K, Sirin E, Cansü E. Lateral antebrachial cutaneous nerve as a donor source for digital nerve grafting: a concept revisited. Open Orthop J. 2017;11(1):1041–8.

Andelkovic SZ, Lesic AR, Bumbasirevic MZ, Rasulic LG. The outcomes of 150 consecutive patients with digital nerve injuries treated in a single center. Turk Neurosurg. 2017;27(2):289–93.

Bulut T, Akgun U, Citlak A, Aslan C, Sener U, Sener M. Prognostic factors in sensory recovery after digital nerve repair. Acta Orthop Traumatol Turc. 2016;50(2):157–61.

Vahvanen V, Gripenberg L, Nuutinen P. Peripheral nerve injuries of the hand in children. A follow-up study of 38 patients. Scand J Plast Reconstr Surg. 1981;15(1):49–51.

Acar E, Turkmen F, Korucu IH, Karaduman M, Karalezli N. Outcomes of primary surgical repair of zone 2 dDigital nerve injury. Acta Orthop Belg. 2018;84(1):84–93.

Nunley JA, Ugino MR, Goldner RD, Regan N, Urbaniak JR. Use of the anterior branch of the medial antebrachial cutaneous nerve as a graft for the repair of defects of the digital nerve. J Bone Jt Surg Am. 1989;71(4):563–7.

Asplund M, Nilsson M, Jacobsson A, von Holst H. Incidence of traumatic peripheral nerve injuries and amputations in Sweden between 1998 and 2006. Neuroepidemiology. 2009;32(3):217–28.

Laroas G, Battiston B, Sard A, Ferrero M, Dellon AL. Digital nerve reconstruction with the bioabsorbable neurotube. Rivista Italiana di Chirurgia Plastica. 2003;35:125–8.

Waitayawinyu T, Parisi DM, Miller B, Luria S, Morton HJ, Chin SH, Trumble TE. A comparison of polyglycolic acid versus type 1 collagen bioabsorbable nerve conduits in a rat model: an alternative to autografting. J Hand Surg Am. 2007;32(10):1521–9.

Kennedy R. On the restoration of co-ordinated movements after nerve section. Proc R Soc Edinb. 1901;22:636–40.

Viterbo F, Trindade JC, Hoshino K, Mazzoni Neto A. Latero-terminal neurorrhaphy without removal of the epineural sheath. Experimental study in rats. Rev Paul Med. 1992;110(6):267–75.

Viterbo F, Trindade JC, Hoshino K, Mazzoni Neto A. End-to-side neurorrhaphy with removal of the epineurial sheath: an experimental study in rats. Plast Reconstr Surg. 1994;94(7):1038–47.

Viterbo F, Trindade JC, Hoshino K, Mazzoni A. Two end-to-side neurorrhaphies and nerve graft with removal of the epineural sheath: experimental study in rats. Br J Plast Surg. 1994;47(2):75–80.

Mennen U. End-to-side nerve suture in the human patient. Hand Surg. 1998;3(1):7–15.

Geuna S, Papalia I, Ronchi G, d’Alcontres FS, Natsis K, Papadopulos NA, Colonna MR. The reasons for end-to-side coaptation: How does lateral axon sprouting work? Neural Regen Res. 2017;12(4):529–33.

Li Q, Liu Z, Lu J, Shao W, Feng X. Transferring the ulnaris proper digital nerve of index finger and its dorsal branch to repair the thumb nerve avulsion. Zhongguo Xiu Fu Chong Jian Wai Ke Za Zhi. 2017;31(8):992–5.

Hohendorff B, Staub L, Fritsche E, Wartburg U. Sensible Nervenfunktion nach unilateraler digitaler Gefäß-Nerven-Verletzung: Nervennaht mit und ohne Arterienanastomose. Handchir Mikrochir Plast Chir. 2009;41(05):306–11.

Murakami T, Ikuta Y, Tsuge K. Relationship between the number of digital nerves sutured and sensory recovery in replanted fingers. J Reconstr Microsurg. 1985;1(4):283–6.

Thomas PR, Saunders RJ, Means KR. Comparison of digital nerve sensory recovery after repair using loupe or operating microscope magnification. J Hand Surg Eur. 2015;40(6):608–13.

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Department of Orthopedics, First Affiliated Hospital of Dalian Medical University, Dalian, 116011, Liaoning, China

Yi Zhang, Jian Zhang, Jiahui Liang, Xiaohu Chang & Xin Tang

Department of Hand and Foot Surgery, Zibo Central Hospital, No. 54 Gongqingtuan West Road, Zibo, Shandong, China

Yi Zhang, Nianzong Hou & Bing Xie

Department of Critical Care Medicine, Zibo Central Hospital, No. 54 Gongqingtuan West Road, Zibo, Shandong, China

Center of Gallbladder Disease, Shanghai East Hospital, Institute of Gallstone Disease, School of Medicine, Tongji University, Shanghai, China

Nianzong Hou

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XT and YZ contributed to conception and design of the study, literature search, data extraction, methodological quality assessment, writing, and final approval; and XT and KW were involved in literature search, data extraction, methodological quality assessment, analysis, interpretation of data, and final approval; NH and JZ contributed to revision and final approval, and BX, JL, and XC were involved in supervision and final approval. All authors read and approved the final manuscript.

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Zhang, Y., Hou, N., Zhang, J. et al. Treatment options for digital nerve injury: a systematic review and meta-analysis. J Orthop Surg Res 18 , 675 (2023). https://doi.org/10.1186/s13018-023-04076-x

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