importance of statistics in psychology essay

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Why Are Statistics in Psychology Necessary?

Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book."

Emily is a board-certified science editor who has worked with top digital publishing brands like Voices for Biodiversity, Study.com, GoodTherapy, Vox, and Verywell.

• Requirements
• Getting Help

Some psychology students are surprised (maybe even dismayed) to realize that they must take a statistics course. But almost all psychology programs have this requirement for graduation. The subject is also discussed in many other classes, highlighting the importance of statistics in psychology.

Recognizing the role of statistics in psychology is necessary when pursuing careers in this field , and may even make it easier to accept having to take this type of course. It can also be beneficial to know what type of statistics classes may be required when majoring in psychology, along with how to get help if this subject feels challenging to learn.

At a Glance

Statistics enable us to organize, describe, and understand large amounts of data. Understanding statistics in psychology can help you excel both in your classes and in this field. Many college programs have different statistics class requirements. If you struggle with learning statistics, talking with the course's instructor or joining a study group can help.

The Importance of Statistics in Psychology

Consider the sheer volume of data we encounter in a given day. How many hours did you sleep? How many students in your class ate breakfast this morning? How many people live within a one-mile radius of your home? By using statistics, we can organize and interpret all this information in a meaningful way.

In psychology, we are also confronted with enormous amounts of data. Statistics allow psychologists to:

• Organize data : When dealing with huge amounts of information, it's all too easy to become overwhelmed. Statistics enable psychologists to organize data in ways that are easier to comprehend. Visual displays such as graphs, pie charts, frequency distributions , and scatterplots provide researchers with a better overview of the information, making it easier to find patterns they might otherwise miss.
• Describe data : Think about what happens when researchers collect a great deal of information about a group of people. An example of this would be the U.S. Census. Descriptive statistics provide a way to summarize data such as the number of adults versus children or the percentage of the population that is currently employed.
• Make inferences based on data : By using what's known as inferential statistics, researchers can draw conclusions about a given sample or population. Psychologists use statistics to help determine whether their hypothesis should be accepted or rejected.

Benefits of Statistics in Psychology

In psychology research, there are often more questions than answers. How do changes in one variable impact other variables? Is there a way to demonstrate a relationship between variables ? What is the overall strength of this relationship and what does this mean? Statistics allow us to answer these kinds of questions.

Having an understanding of statistical methods can also help us excel in other classes. Whether taking a class in social psychology or human sexuality, a great deal of time is often spent learning about research. Developing a strong foundation of statistical knowledge allows us to make better sense of the research described in these psychology courses .

Plus, think about all the claims about psychology that we encounter outside of class on a daily basis. Magazines publish stories about the latest scientific findings, self-help books make proclamations about different ways to approach problems, and news reports interpret (or misinterpret) psychology research .

By understanding the research process—including the types of statistical analyses used—we become a wise consumer of psychology information and can make better judgments of the information we come across. Understanding statistics in psychology even enables us to make better decisions about our own health and well-being.

Statistics Requirements for Psychology Majors

Academic institutions have specific graduation requirements. Completing a certain number of math classes helps fulfill the school's general education requirements. For students majoring in psychology, statistics is a mathematics-based class that is also typically required.

Because every school is different, it's important to check the requirements for your specific institution. Look at both the school's general education requirements and the class requirements related to your desired psychology degree . This will tell you whether a statistics class is needed and, if so, which one or ones.

Getting Help With Statistics in Psychology

Understanding the importance of statistics in psychology can help students create a more positive mindset before even stepping into a statistics course. That said, we do recognize that this subject isn't always easy to learn.

Here's the good news: it's still possible to succeed in a stats class if you don't consider yourself "good at math." Some extra effort might be needed, but help is available.

Start with the class instructor. Ask about books, online tools, and on-campus resources that can help make your studies easier . Many colleges and universities offer a math lab where students can go to receive extra help and tutoring for any type of math course, statistics included.

Joining a study group is another option. If a statistics study group isn't available, you may even decide to create one. This can help not only you but others who may be struggling with the subject as well.

Tessler J. On the importance of learning statistics for psychology students . Association for Psychological Science.

Olsson-Collentine A, van Assen MALM, Hartgerink CHJ. The prevalence of marginally significant results in psychology over time .  Psychol Sci . 2019;30(4):576-586. doi:10.1177/0956797619830326

Gaertner S. How is the public being misled about research? Wiley.

Agnoli F, Wicherts JM, Veldkamp CL, Albiero P, Cubelli R. Questionable research practices among Italian research psychologists .  PLoS One . 2017;12(3):e0172792. doi:10.1371/journal.pone.0172792

By Kendra Cherry, MSEd Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book."

On the Importance of Learning Statistics for Psychology Students

Jessica Tessler University of California, Los Angeles

 “The quiet statisticians have changed our world; not by discovering new facts or technical developments, but by changing the ways that we reason, experiment, and form opinions” – Ian Hacking

Psychology is a very popular major for undergraduate students. Why do so many students flock to psychology programs?  While the answer to this question varies by individual, reasons typically involve interest in the topic, job prospects, the appeal of certain faculty members, or the requirements for completion of the degree (Galotti, 1999).  Often, students find interest in psychology because many concepts taught in introductory psychology classes are intuitively understood and directly applicable to their lives as human beings.  An additional draw tends to be the avoidance of taking high-level mathematics courses.  In many psychology programs, to obtain a bachelor’s degree, the only additional math course required is some form of introduction to social science statistics. However, there is quite a discrepancy between the statistics knowledge required to obtain a bachelor’s degree in psychology and what is necessary to have a career in the field of psychology.  It turns out that the importance of understanding and being able to apply and interpret statistics in psychological research cannot be understated.

The public face of psychology is often represented by the therapist and on the surface, this occupation could not seem more removed from mathematics.  However, this perception is quite misleading. From the development of new therapy techniques to evaluating the effectiveness of the techniques upon implementation, it is statistical analysis that provides the means by which conclusions can be drawn.  While a bachelor’s degree in psychology may allow for a college graduate to obtain entry level jobs in a variety of fields (e.g. human resources, education, customer service, etc.), developing a career within the field of psychology requires a graduate degree.  Further, with the exception of graduate degrees aimed at marriage and family therapy licensure, most graduate programs focus on learning to conduct, and then conducting, publishable research.

In 1990, Aiken, West, Sechrest, and Reno surveyed existing psychology programs on a number of issues to gain a sense of what was being covered in methods courses at the graduate and undergraduate levels. Generally, it was found that while analysis of variance (ANOVA) was covered at length, coverage of measurement issues and more advanced statistical methods was lacking. In revisiting this topic, Aiken, West, and Milsap (2008) found that while some improvement to the breadth of methodology courses was noted, the focus of such courses still tended to be on ANOVAs.  Although some areas of psychology still rely heavily on true experiments, research in other fields of psychology often require other types of statistics beyond the use of ANOVAs.  Rather, advanced techniques, such as structural equation modeling, multilevel modeling, and item response theory, are necessary to address contemporary research questions. Without the ability to apply more advanced statistical techniques, students lack the tools to conduct innovative and relevant research.  Psychological research may start with a “great idea,” but this idea must be followed by a solid study design, effective data collection, and appropriate data analysis, combined with the means to analyze the data and interpret any findings. The cost of ignorance lies in the failure to optimize research design, to collect the types of data to best answer research questions, and to avoid improper analysis of data, leading to inappropriate and sometimes incorrect conclusions (Aiken et al., 1990).

As such, taking an introductory statistics course will not be sufficient in providing students with the research skills that they need. Higher level data analysis courses are necessary for success as a researcher. Most psychology programs at major universities offer courses beyond introductory statistics.  Though class titles vary, a typical “advanced” statistics course will cover more complex analyses such as factorial ANOVA and multiple regression. Courses such as this provide the foundation for learning more specialized techniques that are not only more interesting, but more powerful for drawing conclusions. Many universities offer semester or quarter-length courses on structural equation modeling, where students can learn about methods like factor analysis, growth curve analysis, and multilevel modeling, which offer techniques for complex data structures and unobserved (latent) variables. Further, some courses may cover not only classical test theory, but also generalizability theory and item response theory, which constitute the future of psychological measurement. Other interesting courses may cover cluster analysis and multidimensional scaling, data mining techniques, or meta-analysis. Taking one or more of these advanced courses is extremely beneficial to undergraduates in psychology.  Not only will a student learn to apply advanced techniques in his or her research, but having such courses listed on a student’s transcript gives a considerable edge when applying to graduate programs, allowing an applicant to separate oneself from the “herd.” Further, when reading contemporary research in psychology, understanding of the methods utilized engenders an enhanced ability to evaluate the implications of substantive findings.

For those who truly love the field of psychology and wish to have a career as a psychologist, statistics courses are unavoidable, but also invaluable.  Fortunately, in some cases, those who believe they despise math may find themselves drawn in by the allure of techniques like structural equation modeling, which offer more eloquent ways of answering complex questions about systems of variables, rather than simple group differences.  Though this might not be the case for all students, taking statistics courses even if they are particularly challenging will build the necessary skills to become stronger researchers and provide better job opportunities in the future.

Aiken, L. S., West, S. G., & Millsap, R. E. (2008). Doctoral training in statistics, measurement, and methodology in psychology. American Psychologist , 62 , 32-50.

Aiken, L. S., West, S. G., Sechrest, L., & Reno, R.R. (1990). Graduate training in statistics, methodology, and measurement in psychology. American Psychologist , 45 , 721-734.

Galotti, K. M. (1999). Making a “major” real life decision:  College students choosing an academic major. Journal of Educational Psychology , 91 , 379-387.

Author Note

Jessica Tessler is a Ph.D. graduate student in Quantitative Psychology at the University of California, Los Angeles. Her research interests involve multilevel modeling, specifically studying the effects of model misspecification with cross-classified data structures. She is also a pre-doctoral fellow in the Advanced Quantitative Methods in Education Research program, which is funded by the Institute of Education Sciences (IES). In addition, she is a part-time faculty member at California State University, Fullerton, teaching a computer applications course in the psychology department.

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Statistics and Psychology Essay

Psychology has been defined differently by various authors. Dewey defines psychology as the science of facts or self phenomena (1). On the other hand, statistics is defined as the process of collecting, analyzing, interpreting and presenting data (Clark 40). The application of statistical methods in psychology enables psychologist to make informed decisions after analyzing and interpreting data. Statistical methods can be categorized as descriptive or inferential.

Descriptive methods encompass techniques that describe a set of data in an effort to condense or better understand what is there. For example, the average age group of 100 individuals might give a much better image of the group as compared to individual listing of each of the 100 ages. Moreover, inferential methods are a body of techniques which allows us to draw a conclusion about a larger group of objects by examining only a portion of those objects.

The importance of statistics in psychology can be illustrated through the following example. Consider a therapist who deals with sexual abuse. The therapist received approximately 200 patients in a month. His records show that 80 patients had anger management problems, 20 patients had promiscuous tendencies, 30 patients had signs of ADHDA, 10 presented withdrawal symptoms and 60 patients had cognitive challenges. Statistical skills will enable the therapist to analyze this data and draw logical conclusions.

Through concrete analysis of the psychological behaviors, the therapist can conduct research to shade more light on particular patterns of interest. The therapist will require statistical skills in order to conduct research efficiently.

Statistical data serve as a general aid to the managerial decision making process. One cannot make wise decisions without a proper understanding of the facts used in making those decisions.

The most successful decision makers are people that can effectively make use of the information which is available to them. Data are facts on which we perform statistical methods or techniques; i.e., facts which we collect, analyze, interpret and present (Clark 50). Data can either be qualitative or quantitative. Quantitative data are information about the world in the form of numerical data.

On the other hand, qualitative data are information about the world in the form of words. Quantitative data are necessarily structured in terms of the number system and reflect researcher imposed constructs. Qualitative data may range from structured to unstructured, and may or may not involve researcher imposed constructs. The basic difference between the two types of data lies in the process of measurement.

Thus, a study can have all quantitative data, all qualitative data or both. Which of these three apply is not a major concern. The type of data generated is what matters. The type of data intended depends on what is being investigated and practical aspects of the study. In regard to what needs to be studied, it means that substantive issues dictate methodological choices.

Collected data can be analyzed in different ways. With regard to quantitative data, the following methods can be applied (Clark 60). Frequency distributions: when the population size is small the researcher can record the sample observations, arrange them in an increasing order and see the general value and amount of variation in the individual values.

Moreover, for larger samples, frequency distribution is a more compact presentation of the data. Frequency distributions can be used to estimate sample median value and obtain continuous frequency distributions and polygons.

Mean: provides the average of the values of X in a population. Standard Deviation: a measure in the amount of variation among the values X of a population. Variance: measures the degree of difference of a set of data from the mean: Coefficient of variation: statistical way that quantifies scatter. Statisticians usually compute the coefficient of variation by dividing the standard deviation of a given set of data by their mean. Mode: The most frequent value in a population.

Statistics can be applied to a societal problem. This usually starts with the population or a process to be investigated. It is not possible to study an entire population because the study might be costly and time consuming. However, a smaller proportion of the population (sample) is usually collected from the population and used in the analysis of the entire population.

Basically, the sample is the representation of the population under study. Therefore, data collected from the sample can be used to draw inferences about the larger population. The main application of statistical methods in psychology involves the use of statistics to analyze and interpret data.

The collected data are usually put under scrutiny using various analytical methods and eventually serves two main purposes; to describe and infer. Descriptive statistics offer an explanation of what was observed in the sample. Descriptive statistics employ numerical or graphical explanations of the observed data offering a channel for comparison between the sample and population characteristics. Numerical representations of a continuous data may include mean and standard deviation.

However, for discontinuous data, frequency and percentages become essential in offering comparisons between the sample and the population. Inferential statistics provide a means through which statisticians can draw inferences about the population from the sample that was studied. Inferential statistics utilize patterns reflected in the sample to offer a channel for population-sample inferential critique thus, accounting for randomness.

Works Cited

Clark, L . The Essentials of Business Statistics. New Jersey: Research and Education Association, 1991. Print.

Dewey, John. Psychology. Montana: Kessinger Publishing, 2005. Print.

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Importance of Statistics in Psychology

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Statistics is extensively used in every discipline to draw meaningful conclusions from the data. This chapter discusses the importance of learning statistics in psychology. By going through this chapter, the readers will get a broader picture as to how different types of statistical processes can be used to address different types of research questions in psychological studies.

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Verma, J.P. (2019). Importance of Statistics in Psychology. In: Statistics and Research Methods in Psychology with Excel. Springer, Singapore. https://doi.org/10.1007/978-981-13-3429-0_1

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1.2: Why do we study statistics?

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Virtually every student of the behavioral sciences takes some form of statistics class. This is because statistics is how we communicate in science. It serves as the link between a research idea and usable conclusions. Without statistics, we would be unable to interpret the massive amounts of information contained in data. Even small datasets contain hundreds – if not thousands – of numbers, each representing a specific observation we made. Without a way to organize these numbers into a more interpretable form, we would be lost, having wasted the time and money of our participants, ourselves, and the communities we serve. Beyond its use in science, however, there is a more personal reason to study statistics. Like most people, you probably feel that it is important to “take control of your life.” But what does this mean? Partly, it means being able to properly evaluate the data and claims that bombard you every day. If you cannot distinguish good from faulty reasoning, then you are vulnerable to manipulation and to decisions that are not in your best interest. Statistics provides tools that you need in order to react intelligently to information you hear or read. In this sense, statistics is one of the most important things that you can study. To be more specific, here are some claims that we have heard on several occasions. (We are not saying that each one of these claims is true!)

• \(4\) out of \(5\) dentists recommend Dentine.
• Almost \(85\%\) of lung cancers in men and \(45\%\) in women are tobacco-related.
• Condoms are effective \(94\%\) of the time.
• People tend to be more persuasive when they look others directly in the eye and speak loudly and quickly.
• Women make \(75\) cents to every dollar a man makes when they work the same job.
• A surprising new study shows that eating egg whites can increase one's life span.
• People predict that it is very unlikely there will ever be another baseball player with a batting average over \(400\).
• There is an \(80\%\) chance that in a room full of \(30\) people that at least two people will share the same birthday.
• \(79.48\%\) of all statistics are made up on the spot.

All of these claims are statistical in character. We suspect that some of them sound familiar; if not, we bet that you have heard other claims like them. Notice how diverse the examples are. They come from psychology, health, law, sports, business, etc. Indeed, data and data interpretation show up in discourse from virtually every facet of contemporary life. Statistics are often presented in an effort to add credibility to an argument or advice. You can see this by paying attention to television advertisements. Many of the numbers thrown about in this way do not represent careful statistical analysis. They can be misleading and push you into decisions that you might find cause to regret. For these reasons, learning about statistics is a long step towards taking control of your life. (It is not, of course, the only step needed for this purpose.) The purpose of this course, beyond preparing you for a career in psychology, is to help you learn statistical essentials. It will make you into an intelligent consumer of statistical claims. You can take the first step right away. To be an intelligent consumer of statistics, your first reflex must be to question the statistics that you encounter. The British Prime Minister Benjamin Disraeli is quoted by Mark Twain as having said, “There are three kinds of lies -- lies, damned lies, and statistics.” This quote reminds us why it is so important to understand statistics. So let us invite you to reform your statistical habits from now on. No longer will you blindly accept numbers or findings. Instead, you will begin to think about the numbers, their sources, and most importantly, the procedures used to generate them. The above section puts an emphasis on defending ourselves against fraudulent claims wrapped up as statistics, but let us look at a more positive note. Just as important as detecting the deceptive use of statistics is the appreciation of the proper use of statistics. You must also learn to recognize statistical evidence that supports a stated conclusion. Statistics are all around you, sometimes used well, sometimes not. We must learn how to distinguish the two cases. In doing so, statistics will likely be the course you use most in your day to day life, even if you do not ever run a formal analysis again.

Introduction to Statistics in the Psychological Sciences

(7 reviews)

Linda R. Cote, Marymount University

Rupa Gordon, Augstana College

Chrislyn E. Randell, Metropolitan State University of Denver

Judy Schmitt, University of Missouri-St. Louis

Rudy Guerra, University of Missouri-St. Louis

Copyright Year: 2021

Last Update: 2024

Publisher: University of Missouri - St. Louis

Language: English

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Learn more about reviews.

Reviewed by Beth Mechlin, Associate Professor of Psychology & Neuroscience, Earlham College on 3/12/24

This text does an excellent job covering almost all the topics that most people would cover in an introductory statistics course in the field of Psychology. It talks about central tendency, probability, hypothesis testing, t-tests, ANOVAs,... read more

Comprehensiveness rating: 4 see less

This text does an excellent job covering almost all the topics that most people would cover in an introductory statistics course in the field of Psychology. It talks about central tendency, probability, hypothesis testing, t-tests, ANOVAs, chi-square tests, correlation, and regression. While it does a great job covering most of these topics, I do wish it went into more detail about ANOVAs.

Content Accuracy rating: 5

Everything looked correct to me. I did not notice any errors.

Relevance/Longevity rating: 5

This text is very relevant and I do not see it becoming obsolete anytime soon.

Clarity rating: 5

The text is written in a way that is accessible and easy for college students to understand.

Consistency rating: 5

The text is very consistent. Each chapter follows a similar outline. The chapters do a great job of explaining the relevant concept, working through at least one practice problem, and then providing exercises for students to do more practice (with answers for the odd numbered questions).

Modularity rating: 5

This text is organized into chapters such that each one covers a specific statistical technique. It is easy to assign specific chapters when you are covering the relevant topic.

Organization/Structure/Flow rating: 5

The text is organized perfectly. The chapters are presented in almost exactly the same order in which I cover topics in my Research Methods and Statistics course. The organization within each chapter is consistent and easy to follow.

Interface rating: 5

This text is easy to use and everything displays perfectly for me.

Grammatical Errors rating: 5

The text is well-written. I have not noticed any grammatical errors.

Cultural Relevance rating: 5

I have not noticed anything in the text that appears culturally insensitive.

I think this is an excellent text for an introductory statistics course in the field of Psychology. I use it in my Research Methods and Statistics course, and am very happy with it. I think it does a great job explaining topics, showing calculations, and walking through examples. I do, however, supplement the text with other readings. I go into more detail about ANOVAs in my course than this text does.

Reviewed by Ruth Casper, Instructor, Rochester Community & Technical College on 11/21/23

This book covers the main concepts of statistical analysis, descriptive statistics, and inferential statistics. For the most part, these topics are covered very well for a psychological statistics course. As another reviewer mentioned, it is... read more

Comprehensiveness rating: 5 see less

This book covers the main concepts of statistical analysis, descriptive statistics, and inferential statistics. For the most part, these topics are covered very well for a psychological statistics course. As another reviewer mentioned, it is lacking detail for factorial ANOVA and repeated measures ANOVA. Other reviewers mention that there are no tables in the appendix; however, the authors submitted minor revisions of content on 6/5/2023. Perhaps this is when they added these tables ... because there are tables! There is also a glossary at the end. There are exercises at the end of each chapter with answers to odd-numbered items.

The content appears to be accurate and unbiased.

The content of this book is relevance and up-to-date. Some of the examples may be a little dated, but still known by most (e.g., James Bond).

I found this book very easy to read. There are no concerns about the clarity of the material.

There are no concerns with consistency. In the inferential statistics portion of the book, the authors introduce a four-step procedure for hypothesis-testing. This procedures is then used through the remaining chapters to illustrate each statistical procedure. I think that both instructors and students will appreciate this.

The text is easy to read, with formulas and figures set apart from the text (the authors mention that some of the figures are taken from the output of JASP).

The organization is clear and logical, following the format of most psychological statistics texts.

Navigation through the textbook is simple. There is a list of highlighted terms at the beginning of each chapter which are correctly linked to their discussion in the text.

There are no obvious grammatical errors.

No concerns here.

This book is very readable, straightforward, and easy to understand. I especially like the integration of the four-step procedure to hypothesis-testing. It does not use a particular statistical package (e.g., SPSS, R, JASP). Some instructors might consider this a drawback, but I appreciate it. As another reviewer mentioned, there isn't a lot of information about factorial designs, but this can be supplemented with other materials. I will use this textbook in my undergraduate psychology statistics course.

Reviewed by Scott Frankowski, Assistant professor, University of Texas Rio Grande Valley on 11/15/22

This textbook covers all of the material I cover in an intro psych stats class. I'd like to see a statistics software program integrated into the text - JASP or R would be great, keeping in line with being an OER. read more

This textbook covers all of the material I cover in an intro psych stats class. I'd like to see a statistics software program integrated into the text - JASP or R would be great, keeping in line with being an OER.

I haven't found any issues with accuracy in the text.

Relevance/Longevity rating: 1

In Chapter 2, there are graphs with iMacs from the 1990s. I've seen this graphic in other texts and it always throws me off because most students were not yet born when these computers were out. Also, I've found that psych stats books don't actually have examples from psychology. This book is no exception. Very few examples, if any, that I came across were from the psychological sciences.

I found no issues with clarity.

I saw no issues with consistency.

I like that the chapters are short and each cover the aspects of one type of test used.

Organization/Structure/Flow rating: 4

Organization is good and is fairly standard for the flow of a stats class. Personally, I'd prefer to start with correlation and regression, but how it is works.

Interface rating: 1

The is not indexed for a pdf reader making unusable for a course. Adding bookmarks to each chapter and chapter sub-sections would make the text much more usable.

I saw no grammar issues.

Cultural Relevance rating: 3

I didn't see anything offensive in anyway, but there was no intentionality in using psych examples or cross-cultural examples either.

Overall, if you're looking for an OER psych stats book, I would recommend Cote et al., 2021. It's very similar to this book in that it provides a good foundation and reference from which to build your course off of. The primary difference is that Cote et al. is indexed for a pdf reader which makes it much more usable. Cote et al. also provide critical value appendices for all the test types which this book does not have. Granted, I have just point students to online z, t, F, and r p value calculators, but it is nice to have analog versions as well so I can quickly take a snapshot to include in a slide.

Reviewed by Linda Cote-Reilly, Professor of Psychology, Marymount University on 12/15/20

There is a Table of Contents but no Index or Glossary. read more

There is a Table of Contents but no Index or Glossary.

The book is accurate, content is succinct, and the examples are engaging.

Statistics changes little over time, so this book can be a standard for years to come. If APA format needs to be adjusted or examples (in the text and end-of-chapter problems) need to be updated, that can be easily done.

The book is well-written (i.e., clear, concise, engaging). It is appropriate for an undergraduate taking their first statistics course.

The book uses consistent terminology and framework.

The primary way the text is organized is by chapter, with each chapter covering a different topic. Since it is a *.pdf the easiest way for the instructor to make it modular is with a *.pdf editor. This is not provided by the authors.

This textbook is organized in the typical fashion (order of presentation of material) for an introductory statistics textbook.

The format is *.pdf, so it is readable across devices and interfaces.

This book is easy to read and there were no glaring grammatical errors.

This textbook is as culturally inclusive as any statistics textbook. This is an area where the professor will want to supplement if they espouse the APA Guidelines for the Undergraduate Psychology Major 2.0. I recommend Kenneth Keith's book Culture Across the Curriculum as a starting point.

If I were going to write a statistics book, it would be very close to this. This is a readable textbook appropriate for an introductory statistics course in psychology. Examples given are succinct and easy to follow. Pros include: End-of-chapter exercises with answers to odd numbered problems, the most common measures of effect size are used (e.g., Uses Cohen’s d for z and t-tests, eta squared for ANOVA, Cramer’s V for chi-square), focus in correlations chapter is on Pearson correlation rather than Spearman (or other types of correlations) which is appropriate given that Pearson correlations are the type overwhelmingly used in psychological research. The hypotheses are written out in words (as you would in a psychological research report) and not just mathematical symbols. Cons: Ideally the symbols for mean and standard deviation would be the ones specified in APA format, but his text uses X bar instead of M for sample mean and S instead of SD for sample standard deviation. Only the derivation formula for sum of squares is provided, and not the computation formula. Chi square goodness-of-fit model offered in chapter assumes an equal frequency across cells, rather than matching proportions to those in a known population. The formula notation for chi-square is not what I’m used to seeing. There are no complete tables (partial tables are embedded within the chapters) – so you would need to link to another OER for that. That said, the tables are probably more appropriately placed in a particular chapter and not in the Appendix. I use a lot of “word problems” in statistics (summaries of real studies so that students can work on identifying DV, IV, writing hypotheses, in addition to computing the statistical tests. Overall there are about 10-12 end-of-chapter problems for each chapter and not many are word problems, so I will need to supplement. There are no instructor resources, test banks, etc. If you have taught statistics for awhile you have probably developed your own resources (i.e., Powerpoints, test questions, homework questions, word problems for in class exercises) but if you are just starting out this probably isn’t the OER for you. Despite the longer list of Cons than Pros, the content of that list is relatively minor, and I do plan to adopt this OER in the next year. Because it is an OER I can change the parts I do not like.

Reviewed by Chrislyn Randell, Professor, Metropolitan State University of Denver on 12/3/20, updated 2/26/21

This text covers all the topics I would want to cover in my statistics course, but there is not an index and/or glossary. I believe having an index is so important for students as they may not even know in what chapter to reference a term so the... read more

Comprehensiveness rating: 3 see less

This text covers all the topics I would want to cover in my statistics course, but there is not an index and/or glossary. I believe having an index is so important for students as they may not even know in what chapter to reference a term so the index would be invaluable for them in finding information.

I think the chapter on graphs (Chapter 2) covers more information than would be necessary for my students and my class. The sections covering stem and leaf displays, cumulative frequency polygons, and box plots do not seem necessary and they are not included in the material covered later in the text. If output from statistical packages was included and how that is used to test for assumptions was discussed, then stem and leaf displays and box plots may be more relevant to the rest of information in the text.

This book focuses on calculations but does not use the computational formula for sum of squares. I think this makes it more difficult for students to avoid making computational errors and it makes the calculations more difficult.

Content Accuracy rating: 4

The majority of the content in the text seems accurate. There is an error in the effect size formula for Chapter 9 - it shows the calculation for t instead of d.

Relevance/Longevity rating: 2

The content in this text is already dated as there is no integration of statistical software output, which I think should be included for descriptive statistics and hypothesis testing. Using statistical software is prevalent in the workplace and academic settings so the opportunity for students to view and interpret output is important.

Some of the graphs appear to be formatted as would be a SPSS printout so it seems like presenting them as a computer output would be reasonable.

I am torn about the use of the X-bar to represent the sample mean. For students who will be moving on to more advanced statistics the use of X-bar would be helpful, but there is a small proportion of my students who move on to more advanced statistics. The norm in social statistics is now to use the M for the sample mean and my students may be confused as they move into the research methods lab course and are presented with M instead of X-bar.

I would have liked sections in the text explaining how the results would have been presented in an APA format write-up. I think that would add context for students to see how these results are used beyond running numbers, and this also allows them better understanding of how all the parts of the analysis fit together - descriptive statistics, hypothesis testing, effect size, and confidence intervals. I believe most statistics texts include this information.

Clarity rating: 4

I think this text is written at an appropriate level for the target audience and appropriate context is introduced when covering technical terminology. I particularly liked the visual of the distribution balancing on a triangle to show symmetrical and asymmetrical distributions (Chapter 3).

I like the graphics used in chapter 5 (probability) to support the concepts presented.

Consistency rating: 4

Overall the text seems consistent in terms of terminology and framework. There are some consistency issues between the chapters. In particular, some of the formulas can be difficult to read in how they are formatted - Chapters 6, 7 and 8 the formulas that include the standard error formula look odd (the fraction in the denominator), but in the other chapters the formulas look fine. The X-bar line is too long when showing the sample mean throughout the text.

Modularity rating: 4

This book is organized into Units, which are broken down into chapters. The unit and chapter organization makes sense for coverage of the material. In my introductory statistics course we do not cover linear regression, so I cover correlation earlier in my class. Since correlation is grouped into the Unit 3 (Additional Hypothesis Tests) it makes it a little more difficult to move out of this section and integrate elsewhere, but it is not a major concern for me.

There are large blocks of text to discuss some concepts but they are broken up by headings and subheadings as would be appropriate. For example, the coverage of the steps in hypothesis testing.

The topics in the text are in a logical order, but as I stated earlier, I would move the correlation chapter in my coverage because I do not cover linear regression in my course.

The text was easy to navigate and the graphs were clear and free from distortion.

Grammatical Errors rating: 4

There were no significant problems with grammar.

The text was not culturally insensitive, but it was not inclusive of a variety of races, ethnicities, and backgrounds.

I really want to use an OER text for my introductory statistics course, but I am not sure if I can make this text work. I really like the coverage of the topics but the lack of examples using a statistical package output would require me to create a lot of materials to present that information. Even though I have created quite a bit of those materials in the past, it would be great to have that integrated in the text. I would love to review the text again if there are updates added.

Reviewed by Brian Leventhal, Assistant Professor, James Madison University on 7/10/19

The text is designed to be an introductory text for psychological statistics. As such, it begins with what statistics is, why we study statistics, and then covers basic material. It provides a nice introduction to the necessary foundational... read more

The text is designed to be an introductory text for psychological statistics. As such, it begins with what statistics is, why we study statistics, and then covers basic material. It provides a nice introduction to the necessary foundational material that will be referenced throughout the remainder of the text. The text contains a very detailed table of contents that uses clickable links for specific pages throughout the pdf. Although they are referenced through figures throughout the text, I believe it would have been beneficial to include the relevant statistical tables at the end of the book with a clickable link from the table of contents. I found it a bit odd that snippets of the tables were embedded throughout the text as figures rather than just including the full tables at the end.

In general, the content was accurate. There were a few instances where the material was oddly worded or a confusing. For example, when covering hypothesis testing, an example claims that because temperature is allowed to vary 1 degree in either direction means that the standard deviation must be 1. This is not how standard deviation is defined and can be misleading to students. Later in the text, when interpreting a correlation of -1, the authors state “as X goes up by some amount, Y goes down by the same amount, consistently”. This is an inaccurate interpretation of correlation. X and Y are more than likely on different scales, so they would not change by the same amount. This is a very important distinction as correlation quantifies the relationship of standardized scores, while slope considers the scales of the variables. It was easy to single out one or two cases because the almost the entirety of the text is accurate.

I think the content itself is up-to-date and will not need much updating. The only pieces that may need updating are those that show how to present the results. I believe it was intended to be APA style which may require updating if the APA guidelines change. I also liked the section on misleading graphics – not always included in introductory statistics books- so it was nice to see in this text. I think knowing about data visualization techniques will be a very useful skill for all students, especially in the era of big data.

The text was quite clear. The authors’ voices and senses of humor come out throughout the text making it a very enjoyable read.

In general, material is consistent. The authors do a great job of building on previous material, without the need to constantly flip between pages. There was one frustrating inconsistency. In learning statistics, it is essential that notation be kept consistent and accurate. Unfortunately, one of the most common values, sample size, was inconsistently labeled. It was the case where the same paragraph would flip between “N” and “n”. Besides sample size, there were peculiar notation choices. For example, when labeling the number of groups using subscript j, why count from 1, …, k? This seems like unconventional notation when the authors could have simply used j = 1, …, J. Other than these minor inconsistencies, the authors did a great job throughout.

The text can be divided into smaller sections as written. It would be hard to selectively chose sections to cover and not others because of the comprehensive nature of the material. However, these chapters can be selectively used if an instructor wanted to supplement their course without adapting the entire text. I am not advocating this, as I think the text would be suitable as a whole for a course, but it is possible.

I believe the authors had a logical flow to their presentation of material. They have also designed the text (as in the above comment) in a way so that pieces can be moved around to cater to the instructor.

Some of the images are a bit blurry. They were still interpretable, but it was a bit distracting. Navigation was easy – especially as I read it on my e-reader – which I think will be a big benefit to students (using tablets, e-readers, PCs, or printing the text).

There were no noticeable grammatical errors.

I don’t see any cultural bias in the text or exercises sets. Although not necessarily cultural, I like how this text is inclusive to those with color deficiencies. For example, when describing a graph with multiple colored lines, the authors also reference the position of each line on the graph. This is not only useful for those with color deficiencies but also for those who read the text on an e-reader that doesn’t have color.

I really enjoyed reading through the text and thought it was comprehensive enough for a full semester introductory psychological statistics course. If I were to adapt this text for my course, which I am strongly considering, I will have to supplement with exercises. The exercises at the end of each chapter are most likely not particularly interesting for psychology students nor do they tap into any higher thinking besides simple recall and application. They are useful practice of the basics but will not provide any indication of advanced learning. I also really enjoyed the graphics for regression that talk through the linear model. I thought these were very helpful to students. Again, I thought this text is great and am strongly considering adapting it.

Reviewed by Rupa Gordon, Assistant Professor, Augustana College on 5/16/19

We currently use Gravetter & Walleneau and this book seems to cover nearly all of the same material. The main topic that this text does not cover is factorial ANOVA, which is an important and complex topic for undergraduates. However, our... read more

We currently use Gravetter & Walleneau and this book seems to cover nearly all of the same material. The main topic that this text does not cover is factorial ANOVA, which is an important and complex topic for undergraduates. However, our current book focuses solely on calculating Factorial ANOVA and not on interpreting main effects and interactions so I have to supplement our current book significantly, so it would not change my teaching approach. It provides the definitional formula for the standard deviation which I find more useful than other texts. Good table of contents but no index or glossary. This book seems like a very good OER option, so our current plan is adopt this text for next year.

As far as I can tell, all of the content seems to be accurate, formulas are accurate, and the material is unbiased

There may need to be some updates to the examples, but the content overall is very timeless.

I primarily made this judgment by looking at one of the hardest topics for students to understand in statistics: Central Limit Theorem. This book has a very good explanation and in some ways is superior to Gravetter et al. There are excellent graphs that explain how sample size affects the variability of the normal curve. My main purpose for using a statistics textbook is so that students have access to a reference source, but also to provide practice problems. The problems are good, but not as many as I would like. Plus I think in order to get the answers to the even questions you have to contact the author of the book because instructor information is not easy to access (good if you plan on using those questions for homework).

I did not notice any issues with terminology -- the topics build easily from each other and use previous knowledge to help students follow along.

The text has distinct chapters and subheadings, and some reference to previous chapters is necessary in a statistics book. The book is not overly self-referential.

The text follows the order of most psychological statistics textbooks - it is logical and builds from each chapter. The order is exactly the order in which I cover the material (correlation and chi square at the end).

It is visually appealing, the graphs and charts are well done. The text is clear and easy to read.

I did not notice any errors.

I did not notice any issues with cultural relevance. The book has very good and universal examples that are applicable to Psychology students.

Table of Contents

• Introduction
• Chapter 1: Introduction
• Chapter 2: Describing Data Using Distributions and Graphs
• Chapter 3: Measures of Central Tendency and Spread
• Chapter 4: z Scores and the Standard Normal Distribution
• Chapter 5: Probability
• Chapter 6: Sampling Distributions
• Chapter 7: Introduction to Hypothesis Testing
• Chapter 8: Introduction to t Tests
• Chapter 9: Related Samples
• Chapter 10: Independent Samples
• Chapter 11: Analysis of Variance
• Chapter 12: Correlations
• Chapter 13: Linear Regression
• Chapter 14: Chi-Square
• Appendix A: Standard Normal Distribution Table ( z Table)
• Appendix B: t Distribution Table ( t Table)
• Appendix C: Critical Values for F ( F Table)
• Appendix D: Critical Values for Pearson’s r (Correlation Table)
• Appendix E: Chi-Square Table
• Index  

Ancillary Material

About the book.

Introduction to Statistics in the Psychological Sciences provides an accessible introduction to the fundamentals of statistics, and hypothesis testing as need for psychology students. The textbook introduces the fundamentals of statistics, an introduction to hypothesis testing, and t Tests. Related samples, independent samples, analysis of variance, correlations, linear regressions and chi-squares are all covered along with expanded appendices with z, t, F correlation, and a Chi-Square table. The text includes key terms and exercises with answers to odd-numbered exercises.

Psychology students often find statistics courses to be different from their other psychology classes. There are some distinct differences, especially involving study strategies for class success. The first difference is learning a new vocabulary—it is similar to learning a new language. Knowing the meaning of certain words will help as you are reading the material and working through the problems. Secondly, practice is critical for success; reading over the material is not enough. Statistics is a subject learned by doing, so make sure you work through any homework questions, chapter questions, and practice problems available. Statistical knowledge gives you a set of skills employable in graduate school and the workplace. Data science is a burgeoning field, and there is practical significance in learning this material. The statistics presented in this book are some of the most common ones used in research articles, and we hope by the end of this OER you’ll feel comfortable reading (and not skipping!) the results section of an article. This work is broken into 14 chapters, covering the fundamentals of statistics, and hypothesis testing.

About the Contributors

Linda R. Cote , Marymount University

Rupa Gordon , Augstana College

Chrislyn E. Randell , Metropolitan State University of Denver

Judy Schmitt , University of Missouri-St. Louis

Rudy Guerra , University of Missouri-St. Louis

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Why are Statistics Useful in Psychology?

Why Psychology Majors Study Statistics

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MA121: Introduction to Statistics

What are Statistics?

Read this brief introduction to the field of statistics and some relevant examples of how statistics can lend credibility to making arguments. Complete the practice questions in these sections.

Importance of Statistics

Learning objectives.

• Give examples of statistics encountered in everyday life
• Give examples of how statistics can lend credibility to an argument

Like most people, you probably feel that it is important to "take control of your life". But what does this mean? Partly, it means being able to properly evaluate the data and claims that bombard you every day. If you cannot distinguish good from faulty reasoning, then you are vulnerable to manipulation and to decisions that are not in your best interest. Statistics provides tools that you need in order to react intelligently to information you hear or read. In this sense, statistics is one of the most important things that you can study.

To be more specific, here are some claims that we have heard on several occasions. (We are not saying that each one of these claims is true!)

• 4 out of 5 dentists recommend Dentine.
• Almost 85% of lung cancers in men and 45% in women are tobacco-related.
• Condoms are effective 94% of the time.
• Native Americans are significantly more likely to be hit crossing the street than are people of other ethnicities.
• People tend to be more persuasive when they look others directly in the eye and speak loudly and quickly.
• Women make 75 cents to every dollar a man makes when they work the same job.
• A surprising new study shows that eating egg whites can increase one's life span.
• People predict that it is very unlikely there will ever be another baseball player with a batting average over 400.
• There is an 80% chance that in a room full of 30 people that at least two people will share the same birthday.
• 79.48% of all statistics are made up on the spot.

All of these claims are statistical in character. We suspect that some of them sound familiar; if not, we bet that you have heard other claims like them. Notice how diverse the examples are. They come from psychology, health, law, sports, business, etc. Indeed, data and data interpretation show up in discourse from virtually every facet of contemporary life.

Statistics are often presented in an effort to add credibility to an argument or advice. You can see this by paying attention to television advertisements. Many of the numbers thrown about in this way do not represent careful statistical analysis. They can be misleading and push you into decisions that you might find cause to regret. For these reasons, learning about statistics is a long step towards taking control of your life. (It is not, of course, the only step needed for this purpose). The present textbook is designed to help you learn statistical essentials. It will make you into an intelligent consumer of statistical claims.

You can take the first step right away. To be an intelligent consumer of statistics, your first reflex must be to question the statistics that you encounter. The British Prime Minister Benjamin Disraeli is quoted by Mark Twain as having said, "There are three kinds of lies - lies, damned lies, and statistics". This quote reminds us why it is so important to understand statistics. So let us invite you to reform your statistical habits from now on. No longer will you blindly accept numbers or findings. Instead, you will begin to think about the numbers, their sources, and most importantly, the procedures used to generate them.

We have put the emphasis on defending ourselves against fraudulent claims wrapped up as statistics. We close this section on a more positive note. Just as important as detecting the deceptive use of statistics is the appreciation of the proper use of statistics. You must also learn to recognize statistical evidence that supports a stated conclusion. Statistics are all around you, sometimes used well, sometimes not. We must learn how to distinguish the two cases.

Now let us get to work!

Statistics Made Easy

The Importance of Statistics in Research (With Examples)

The field of statistics is concerned with collecting, analyzing, interpreting, and presenting data.

In the field of research, statistics is important for the following reasons:

Reason 1 : Statistics allows researchers to design studies such that the findings from the studies can be extrapolated to a larger population.

Reason 2 : Statistics allows researchers to perform hypothesis tests to determine if some claim about a new drug, new procedure, new manufacturing method, etc. is true.

Reason 3 : Statistics allows researchers to create confidence intervals to capture uncertainty around population estimates.

In the rest of this article, we elaborate on each of these reasons.

Reason 1: Statistics Allows Researchers to Design Studies

Researchers are often interested in answering questions about populations like:

• What is the average weight of a certain species of bird?
• What is the average height of a certain species of plant?
• What percentage of citizens in a certain city support a certain law?

One way to answer these questions is to go around and collect data on every single individual in the population of interest.

However, this is typically too costly and time-consuming which is why researchers instead take a  sample  of the population and use the data from the sample to draw conclusions about the population as a whole.

There are many different methods researchers can potentially use to obtain individuals to be in a sample. These are known as  sampling methods .

There are two classes of sampling methods:

• Probability sampling methods : Every member in a population has an equal probability of being selected to be in the sample.
• Non-probability sampling methods : Not every member in a population has an equal probability of being selected to be in the sample.

By using probability sampling methods, researchers can maximize the chances that they obtain a sample that is representative of the overall population.

This allows researchers to extrapolate the findings from the sample to the overall population.

Read more about the two classes of sampling methods here .

Reason 2: Statistics Allows Researchers to Perform Hypothesis Tests

Another way that statistics is used in research is in the form of hypothesis tests .

These are tests that researchers can use to determine if there is a statistical significance between different medical procedures or treatments.

For example, suppose a scientist believes that a new drug is able to reduce blood pressure in obese patients. To test this, he measures the blood pressure of 30 patients before and after using the new drug for one month.

He then performs a paired samples t- test using the following hypotheses:

• H 0 : μ after = μ before (the mean blood pressure is the same before and after using the drug)
• H A : μ after < μ before (the mean blood pressure is less after using the drug)

If the p-value of the test is less than some significance level (e.g. α = .05), then he can reject the null hypothesis and conclude that the new drug leads to reduced blood pressure.

Note : This is just one example of a hypothesis test that is used in research. Other common tests include a one sample t-test , two sample t-test , one-way ANOVA , and two-way ANOVA .

Reason 3: Statistics Allows Researchers to Create Confidence Intervals

Another way that statistics is used in research is in the form of confidence intervals .

A confidence interval is a range of values that is likely to contain a population parameter with a certain level of confidence.

For example, suppose researchers are interested in estimating the mean weight of a certain species of turtle.

Instead of going around and weighing every single turtle in the population, researchers may instead take a simple random sample of turtles with the following information:

• Sample size  n = 25
• Sample mean weight  x  = 300
• Sample standard deviation  s = 18.5

Using the confidence interval for a mean formula , researchers may then construct the following 95% confidence interval:

95% Confidence Interval:  300 +/-  1.96*(18.5/√ 25 ) =  [292.75, 307.25]

The researchers would then claim that they’re 95% confident that the true mean weight for this population of turtles is between 292.75 pounds and 307.25 pounds.

Additional Resources

The following articles explain the importance of statistics in other fields:

The Importance of Statistics in Healthcare The Importance of Statistics in Nursing The Importance of Statistics in Business The Importance of Statistics in Economics The Importance of Statistics in Education

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Hey there. My name is Zach Bobbitt. I have a Master of Science degree in Applied Statistics and I’ve worked on machine learning algorithms for professional businesses in both healthcare and retail. I’m passionate about statistics, machine learning, and data visualization and I created Statology to be a resource for both students and teachers alike.  My goal with this site is to help you learn statistics through using simple terms, plenty of real-world examples, and helpful illustrations.

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The Use of Research Methods in Psychological Research: A Systematised Review

Salomé elizabeth scholtz.

1 Community Psychosocial Research (COMPRES), School of Psychosocial Health, North-West University, Potchefstroom, South Africa

Werner de Klerk

Leon t. de beer.

2 WorkWell Research Institute, North-West University, Potchefstroom, South Africa

Research methods play an imperative role in research quality as well as educating young researchers, however, the application thereof is unclear which can be detrimental to the field of psychology. Therefore, this systematised review aimed to determine what research methods are being used, how these methods are being used and for what topics in the field. Our review of 999 articles from five journals over a period of 5 years indicated that psychology research is conducted in 10 topics via predominantly quantitative research methods. Of these 10 topics, social psychology was the most popular. The remainder of the conducted methodology is described. It was also found that articles lacked rigour and transparency in the used methodology which has implications for replicability. In conclusion this article, provides an overview of all reported methodologies used in a sample of psychology journals. It highlights the popularity and application of methods and designs throughout the article sample as well as an unexpected lack of rigour with regard to most aspects of methodology. Possible sample bias should be considered when interpreting the results of this study. It is recommended that future research should utilise the results of this study to determine the possible impact on the field of psychology as a science and to further investigation into the use of research methods. Results should prompt the following future research into: a lack or rigour and its implication on replication, the use of certain methods above others, publication bias and choice of sampling method.

Introduction

Psychology is an ever-growing and popular field (Gough and Lyons, 2016 ; Clay, 2017 ). Due to this growth and the need for science-based research to base health decisions on (Perestelo-Pérez, 2013 ), the use of research methods in the broad field of psychology is an essential point of investigation (Stangor, 2011 ; Aanstoos, 2014 ). Research methods are therefore viewed as important tools used by researchers to collect data (Nieuwenhuis, 2016 ) and include the following: quantitative, qualitative, mixed method and multi method (Maree, 2016 ). Additionally, researchers also employ various types of literature reviews to address research questions (Grant and Booth, 2009 ). According to literature, what research method is used and why a certain research method is used is complex as it depends on various factors that may include paradigm (O'Neil and Koekemoer, 2016 ), research question (Grix, 2002 ), or the skill and exposure of the researcher (Nind et al., 2015 ). How these research methods are employed is also difficult to discern as research methods are often depicted as having fixed boundaries that are continuously crossed in research (Johnson et al., 2001 ; Sandelowski, 2011 ). Examples of this crossing include adding quantitative aspects to qualitative studies (Sandelowski et al., 2009 ), or stating that a study used a mixed-method design without the study having any characteristics of this design (Truscott et al., 2010 ).

The inappropriate use of research methods affects how students and researchers improve and utilise their research skills (Scott Jones and Goldring, 2015 ), how theories are developed (Ngulube, 2013 ), and the credibility of research results (Levitt et al., 2017 ). This, in turn, can be detrimental to the field (Nind et al., 2015 ), journal publication (Ketchen et al., 2008 ; Ezeh et al., 2010 ), and attempts to address public social issues through psychological research (Dweck, 2017 ). This is especially important given the now well-known replication crisis the field is facing (Earp and Trafimow, 2015 ; Hengartner, 2018 ).

Due to this lack of clarity on method use and the potential impact of inept use of research methods, the aim of this study was to explore the use of research methods in the field of psychology through a review of journal publications. Chaichanasakul et al. ( 2011 ) identify reviewing articles as the opportunity to examine the development, growth and progress of a research area and overall quality of a journal. Studies such as Lee et al. ( 1999 ) as well as Bluhm et al. ( 2011 ) review of qualitative methods has attempted to synthesis the use of research methods and indicated the growth of qualitative research in American and European journals. Research has also focused on the use of research methods in specific sub-disciplines of psychology, for example, in the field of Industrial and Organisational psychology Coetzee and Van Zyl ( 2014 ) found that South African publications tend to consist of cross-sectional quantitative research methods with underrepresented longitudinal studies. Qualitative studies were found to make up 21% of the articles published from 1995 to 2015 in a similar study by O'Neil and Koekemoer ( 2016 ). Other methods in health psychology, such as Mixed methods research have also been reportedly growing in popularity (O'Cathain, 2009 ).

A broad overview of the use of research methods in the field of psychology as a whole is however, not available in the literature. Therefore, our research focused on answering what research methods are being used, how these methods are being used and for what topics in practice (i.e., journal publications) in order to provide a general perspective of method used in psychology publication. We synthesised the collected data into the following format: research topic [areas of scientific discourse in a field or the current needs of a population (Bittermann and Fischer, 2018 )], method [data-gathering tools (Nieuwenhuis, 2016 )], sampling [elements chosen from a population to partake in research (Ritchie et al., 2009 )], data collection [techniques and research strategy (Maree, 2016 )], and data analysis [discovering information by examining bodies of data (Ktepi, 2016 )]. A systematised review of recent articles (2013 to 2017) collected from five different journals in the field of psychological research was conducted.

Grant and Booth ( 2009 ) describe systematised reviews as the review of choice for post-graduate studies, which is employed using some elements of a systematic review and seldom more than one or two databases to catalogue studies after a comprehensive literature search. The aspects used in this systematised review that are similar to that of a systematic review were a full search within the chosen database and data produced in tabular form (Grant and Booth, 2009 ).

Sample sizes and timelines vary in systematised reviews (see Lowe and Moore, 2014 ; Pericall and Taylor, 2014 ; Barr-Walker, 2017 ). With no clear parameters identified in the literature (see Grant and Booth, 2009 ), the sample size of this study was determined by the purpose of the sample (Strydom, 2011 ), and time and cost constraints (Maree and Pietersen, 2016 ). Thus, a non-probability purposive sample (Ritchie et al., 2009 ) of the top five psychology journals from 2013 to 2017 was included in this research study. Per Lee ( 2015 ) American Psychological Association (APA) recommends the use of the most up-to-date sources for data collection with consideration of the context of the research study. As this research study focused on the most recent trends in research methods used in the broad field of psychology, the identified time frame was deemed appropriate.

Psychology journals were only included if they formed part of the top five English journals in the miscellaneous psychology domain of the Scimago Journal and Country Rank (Scimago Journal & Country Rank, 2017 ). The Scimago Journal and Country Rank provides a yearly updated list of publicly accessible journal and country-specific indicators derived from the Scopus® database (Scopus, 2017b ) by means of the Scimago Journal Rank (SJR) indicator developed by Scimago from the algorithm Google PageRank™ (Scimago Journal & Country Rank, 2017 ). Scopus is the largest global database of abstracts and citations from peer-reviewed journals (Scopus, 2017a ). Reasons for the development of the Scimago Journal and Country Rank list was to allow researchers to assess scientific domains, compare country rankings, and compare and analyse journals (Scimago Journal & Country Rank, 2017 ), which supported the aim of this research study. Additionally, the goals of the journals had to focus on topics in psychology in general with no preference to specific research methods and have full-text access to articles.

The following list of top five journals in 2018 fell within the abovementioned inclusion criteria (1) Australian Journal of Psychology, (2) British Journal of Psychology, (3) Europe's Journal of Psychology, (4) International Journal of Psychology and lastly the (5) Journal of Psychology Applied and Interdisciplinary.

Journals were excluded from this systematised review if no full-text versions of their articles were available, if journals explicitly stated a publication preference for certain research methods, or if the journal only published articles in a specific discipline of psychological research (for example, industrial psychology, clinical psychology etc.).

The researchers followed a procedure (see Figure 1 ) adapted from that of Ferreira et al. ( 2016 ) for systematised reviews. Data collection and categorisation commenced on 4 December 2017 and continued until 30 June 2019. All the data was systematically collected and coded manually (Grant and Booth, 2009 ) with an independent person acting as co-coder. Codes of interest included the research topic, method used, the design used, sampling method, and methodology (the method used for data collection and data analysis). These codes were derived from the wording in each article. Themes were created based on the derived codes and checked by the co-coder. Lastly, these themes were catalogued into a table as per the systematised review design.

Systematised review procedure.

According to Johnston et al. ( 2019 ), “literature screening, selection, and data extraction/analyses” (p. 7) are specifically tailored to the aim of a review. Therefore, the steps followed in a systematic review must be reported in a comprehensive and transparent manner. The chosen systematised design adhered to the rigour expected from systematic reviews with regard to full search and data produced in tabular form (Grant and Booth, 2009 ). The rigorous application of the systematic review is, therefore discussed in relation to these two elements.

Firstly, to ensure a comprehensive search, this research study promoted review transparency by following a clear protocol outlined according to each review stage before collecting data (Johnston et al., 2019 ). This protocol was similar to that of Ferreira et al. ( 2016 ) and approved by three research committees/stakeholders and the researchers (Johnston et al., 2019 ). The eligibility criteria for article inclusion was based on the research question and clearly stated, and the process of inclusion was recorded on an electronic spreadsheet to create an evidence trail (Bandara et al., 2015 ; Johnston et al., 2019 ). Microsoft Excel spreadsheets are a popular tool for review studies and can increase the rigour of the review process (Bandara et al., 2015 ). Screening for appropriate articles for inclusion forms an integral part of a systematic review process (Johnston et al., 2019 ). This step was applied to two aspects of this research study: the choice of eligible journals and articles to be included. Suitable journals were selected by the first author and reviewed by the second and third authors. Initially, all articles from the chosen journals were included. Then, by process of elimination, those irrelevant to the research aim, i.e., interview articles or discussions etc., were excluded.

To ensure rigourous data extraction, data was first extracted by one reviewer, and an independent person verified the results for completeness and accuracy (Johnston et al., 2019 ). The research question served as a guide for efficient, organised data extraction (Johnston et al., 2019 ). Data was categorised according to the codes of interest, along with article identifiers for audit trails such as authors, title and aims of articles. The categorised data was based on the aim of the review (Johnston et al., 2019 ) and synthesised in tabular form under methods used, how these methods were used, and for what topics in the field of psychology.

The initial search produced a total of 1,145 articles from the 5 journals identified. Inclusion and exclusion criteria resulted in a final sample of 999 articles ( Figure 2 ). Articles were co-coded into 84 codes, from which 10 themes were derived ( Table 1 ).

Journal article frequency.

Codes used to form themes (research topics).

These 10 themes represent the topic section of our research question ( Figure 3 ). All these topics except, for the final one, psychological practice , were found to concur with the research areas in psychology as identified by Weiten ( 2010 ). These research areas were chosen to represent the derived codes as they provided broad definitions that allowed for clear, concise categorisation of the vast amount of data. Article codes were categorised under particular themes/topics if they adhered to the research area definitions created by Weiten ( 2010 ). It is important to note that these areas of research do not refer to specific disciplines in psychology, such as industrial psychology; but to broader fields that may encompass sub-interests of these disciplines.

Topic frequency (international sample).

In the case of developmental psychology , researchers conduct research into human development from childhood to old age. Social psychology includes research on behaviour governed by social drivers. Researchers in the field of educational psychology study how people learn and the best way to teach them. Health psychology aims to determine the effect of psychological factors on physiological health. Physiological psychology , on the other hand, looks at the influence of physiological aspects on behaviour. Experimental psychology is not the only theme that uses experimental research and focuses on the traditional core topics of psychology (for example, sensation). Cognitive psychology studies the higher mental processes. Psychometrics is concerned with measuring capacity or behaviour. Personality research aims to assess and describe consistency in human behaviour (Weiten, 2010 ). The final theme of psychological practice refers to the experiences, techniques, and interventions employed by practitioners, researchers, and academia in the field of psychology.

Articles under these themes were further subdivided into methodologies: method, sampling, design, data collection, and data analysis. The categorisation was based on information stated in the articles and not inferred by the researchers. Data were compiled into two sets of results presented in this article. The first set addresses the aim of this study from the perspective of the topics identified. The second set of results represents a broad overview of the results from the perspective of the methodology employed. The second set of results are discussed in this article, while the first set is presented in table format. The discussion thus provides a broad overview of methods use in psychology (across all themes), while the table format provides readers with in-depth insight into methods used in the individual themes identified. We believe that presenting the data from both perspectives allow readers a broad understanding of the results. Due a large amount of information that made up our results, we followed Cichocka and Jost ( 2014 ) in simplifying our results. Please note that the numbers indicated in the table in terms of methodology differ from the total number of articles. Some articles employed more than one method/sampling technique/design/data collection method/data analysis in their studies.

What follows is the results for what methods are used, how these methods are used, and which topics in psychology they are applied to . Percentages are reported to the second decimal in order to highlight small differences in the occurrence of methodology.

Firstly, with regard to the research methods used, our results show that researchers are more likely to use quantitative research methods (90.22%) compared to all other research methods. Qualitative research was the second most common research method but only made up about 4.79% of the general method usage. Reviews occurred almost as much as qualitative studies (3.91%), as the third most popular method. Mixed-methods research studies (0.98%) occurred across most themes, whereas multi-method research was indicated in only one study and amounted to 0.10% of the methods identified. The specific use of each method in the topics identified is shown in Table 2 and Figure 4 .

Research methods in psychology.

Research method frequency in topics.

Secondly, in the case of how these research methods are employed , our study indicated the following.

Sampling −78.34% of the studies in the collected articles did not specify a sampling method. From the remainder of the studies, 13 types of sampling methods were identified. These sampling methods included broad categorisation of a sample as, for example, a probability or non-probability sample. General samples of convenience were the methods most likely to be applied (10.34%), followed by random sampling (3.51%), snowball sampling (2.73%), and purposive (1.37%) and cluster sampling (1.27%). The remainder of the sampling methods occurred to a more limited extent (0–1.0%). See Table 3 and Figure 5 for sampling methods employed in each topic.

Sampling use in the field of psychology.

Sampling method frequency in topics.

Designs were categorised based on the articles' statement thereof. Therefore, it is important to note that, in the case of quantitative studies, non-experimental designs (25.55%) were often indicated due to a lack of experiments and any other indication of design, which, according to Laher ( 2016 ), is a reasonable categorisation. Non-experimental designs should thus be compared with experimental designs only in the description of data, as it could include the use of correlational/cross-sectional designs, which were not overtly stated by the authors. For the remainder of the research methods, “not stated” (7.12%) was assigned to articles without design types indicated.

From the 36 identified designs the most popular designs were cross-sectional (23.17%) and experimental (25.64%), which concurred with the high number of quantitative studies. Longitudinal studies (3.80%), the third most popular design, was used in both quantitative and qualitative studies. Qualitative designs consisted of ethnography (0.38%), interpretative phenomenological designs/phenomenology (0.28%), as well as narrative designs (0.28%). Studies that employed the review method were mostly categorised as “not stated,” with the most often stated review designs being systematic reviews (0.57%). The few mixed method studies employed exploratory, explanatory (0.09%), and concurrent designs (0.19%), with some studies referring to separate designs for the qualitative and quantitative methods. The one study that identified itself as a multi-method study used a longitudinal design. Please see how these designs were employed in each specific topic in Table 4 , Figure 6 .

Design use in the field of psychology.

Design frequency in topics.

Data collection and analysis —data collection included 30 methods, with the data collection method most often employed being questionnaires (57.84%). The experimental task (16.56%) was the second most preferred collection method, which included established or unique tasks designed by the researchers. Cognitive ability tests (6.84%) were also regularly used along with various forms of interviewing (7.66%). Table 5 and Figure 7 represent data collection use in the various topics. Data analysis consisted of 3,857 occurrences of data analysis categorised into ±188 various data analysis techniques shown in Table 6 and Figures 1 – 7 . Descriptive statistics were the most commonly used (23.49%) along with correlational analysis (17.19%). When using a qualitative method, researchers generally employed thematic analysis (0.52%) or different forms of analysis that led to coding and the creation of themes. Review studies presented few data analysis methods, with most studies categorising their results. Mixed method and multi-method studies followed the analysis methods identified for the qualitative and quantitative studies included.

Data collection in the field of psychology.

Data collection frequency in topics.

Data analysis in the field of psychology.

Results of the topics researched in psychology can be seen in the tables, as previously stated in this article. It is noteworthy that, of the 10 topics, social psychology accounted for 43.54% of the studies, with cognitive psychology the second most popular research topic at 16.92%. The remainder of the topics only occurred in 4.0–7.0% of the articles considered. A list of the included 999 articles is available under the section “View Articles” on the following website: https://methodgarden.xtrapolate.io/ . This website was created by Scholtz et al. ( 2019 ) to visually present a research framework based on this Article's results.

This systematised review categorised full-length articles from five international journals across the span of 5 years to provide insight into the use of research methods in the field of psychology. Results indicated what methods are used how these methods are being used and for what topics (why) in the included sample of articles. The results should be seen as providing insight into method use and by no means a comprehensive representation of the aforementioned aim due to the limited sample. To our knowledge, this is the first research study to address this topic in this manner. Our discussion attempts to promote a productive way forward in terms of the key results for method use in psychology, especially in the field of academia (Holloway, 2008 ).

With regard to the methods used, our data stayed true to literature, finding only common research methods (Grant and Booth, 2009 ; Maree, 2016 ) that varied in the degree to which they were employed. Quantitative research was found to be the most popular method, as indicated by literature (Breen and Darlaston-Jones, 2010 ; Counsell and Harlow, 2017 ) and previous studies in specific areas of psychology (see Coetzee and Van Zyl, 2014 ). Its long history as the first research method (Leech et al., 2007 ) in the field of psychology as well as researchers' current application of mathematical approaches in their studies (Toomela, 2010 ) might contribute to its popularity today. Whatever the case may be, our results show that, despite the growth in qualitative research (Demuth, 2015 ; Smith and McGannon, 2018 ), quantitative research remains the first choice for article publication in these journals. Despite the included journals indicating openness to articles that apply any research methods. This finding may be due to qualitative research still being seen as a new method (Burman and Whelan, 2011 ) or reviewers' standards being higher for qualitative studies (Bluhm et al., 2011 ). Future research is encouraged into the possible biasness in publication of research methods, additionally further investigation with a different sample into the proclaimed growth of qualitative research may also provide different results.

Review studies were found to surpass that of multi-method and mixed method studies. To this effect Grant and Booth ( 2009 ), state that the increased awareness, journal contribution calls as well as its efficiency in procuring research funds all promote the popularity of reviews. The low frequency of mixed method studies contradicts the view in literature that it's the third most utilised research method (Tashakkori and Teddlie's, 2003 ). Its' low occurrence in this sample could be due to opposing views on mixing methods (Gunasekare, 2015 ) or that authors prefer publishing in mixed method journals, when using this method, or its relative novelty (Ivankova et al., 2016 ). Despite its low occurrence, the application of the mixed methods design in articles was methodologically clear in all cases which were not the case for the remainder of research methods.

Additionally, a substantial number of studies used a combination of methodologies that are not mixed or multi-method studies. Perceived fixed boundaries are according to literature often set aside, as confirmed by this result, in order to investigate the aim of a study, which could create a new and helpful way of understanding the world (Gunasekare, 2015 ). According to Toomela ( 2010 ), this is not unheard of and could be considered a form of “structural systemic science,” as in the case of qualitative methodology (observation) applied in quantitative studies (experimental design) for example. Based on this result, further research into this phenomenon as well as its implications for research methods such as multi and mixed methods is recommended.

Discerning how these research methods were applied, presented some difficulty. In the case of sampling, most studies—regardless of method—did mention some form of inclusion and exclusion criteria, but no definite sampling method. This result, along with the fact that samples often consisted of students from the researchers' own academic institutions, can contribute to literature and debates among academics (Peterson and Merunka, 2014 ; Laher, 2016 ). Samples of convenience and students as participants especially raise questions about the generalisability and applicability of results (Peterson and Merunka, 2014 ). This is because attention to sampling is important as inappropriate sampling can debilitate the legitimacy of interpretations (Onwuegbuzie and Collins, 2017 ). Future investigation into the possible implications of this reported popular use of convenience samples for the field of psychology as well as the reason for this use could provide interesting insight, and is encouraged by this study.

Additionally, and this is indicated in Table 6 , articles seldom report the research designs used, which highlights the pressing aspect of the lack of rigour in the included sample. Rigour with regards to the applied empirical method is imperative in promoting psychology as a science (American Psychological Association, 2020 ). Omitting parts of the research process in publication when it could have been used to inform others' research skills should be questioned, and the influence on the process of replicating results should be considered. Publications are often rejected due to a lack of rigour in the applied method and designs (Fonseca, 2013 ; Laher, 2016 ), calling for increased clarity and knowledge of method application. Replication is a critical part of any field of scientific research and requires the “complete articulation” of the study methods used (Drotar, 2010 , p. 804). The lack of thorough description could be explained by the requirements of certain journals to only report on certain aspects of a research process, especially with regard to the applied design (Laher, 20). However, naming aspects such as sampling and designs, is a requirement according to the APA's Journal Article Reporting Standards (JARS-Quant) (Appelbaum et al., 2018 ). With very little information on how a study was conducted, authors lose a valuable opportunity to enhance research validity, enrich the knowledge of others, and contribute to the growth of psychology and methodology as a whole. In the case of this research study, it also restricted our results to only reported samples and designs, which indicated a preference for certain designs, such as cross-sectional designs for quantitative studies.

Data collection and analysis were for the most part clearly stated. A key result was the versatile use of questionnaires. Researchers would apply a questionnaire in various ways, for example in questionnaire interviews, online surveys, and written questionnaires across most research methods. This may highlight a trend for future research.

With regard to the topics these methods were employed for, our research study found a new field named “psychological practice.” This result may show the growing consciousness of researchers as part of the research process (Denzin and Lincoln, 2003 ), psychological practice, and knowledge generation. The most popular of these topics was social psychology, which is generously covered in journals and by learning societies, as testaments of the institutional support and richness social psychology has in the field of psychology (Chryssochoou, 2015 ). The APA's perspective on 2018 trends in psychology also identifies an increased amount of psychology focus on how social determinants are influencing people's health (Deangelis, 2017 ).

This study was not without limitations and the following should be taken into account. Firstly, this study used a sample of five specific journals to address the aim of the research study, despite general journal aims (as stated on journal websites), this inclusion signified a bias towards the research methods published in these specific journals only and limited generalisability. A broader sample of journals over a different period of time, or a single journal over a longer period of time might provide different results. A second limitation is the use of Excel spreadsheets and an electronic system to log articles, which was a manual process and therefore left room for error (Bandara et al., 2015 ). To address this potential issue, co-coding was performed to reduce error. Lastly, this article categorised data based on the information presented in the article sample; there was no interpretation of what methodology could have been applied or whether the methods stated adhered to the criteria for the methods used. Thus, a large number of articles that did not clearly indicate a research method or design could influence the results of this review. However, this in itself was also a noteworthy result. Future research could review research methods of a broader sample of journals with an interpretive review tool that increases rigour. Additionally, the authors also encourage the future use of systematised review designs as a way to promote a concise procedure in applying this design.

Our research study presented the use of research methods for published articles in the field of psychology as well as recommendations for future research based on these results. Insight into the complex questions identified in literature, regarding what methods are used how these methods are being used and for what topics (why) was gained. This sample preferred quantitative methods, used convenience sampling and presented a lack of rigorous accounts for the remaining methodologies. All methodologies that were clearly indicated in the sample were tabulated to allow researchers insight into the general use of methods and not only the most frequently used methods. The lack of rigorous account of research methods in articles was represented in-depth for each step in the research process and can be of vital importance to address the current replication crisis within the field of psychology. Recommendations for future research aimed to motivate research into the practical implications of the results for psychology, for example, publication bias and the use of convenience samples.

Ethics Statement

This study was cleared by the North-West University Health Research Ethics Committee: NWU-00115-17-S1.

Author Contributions

All authors listed have made a substantial, direct and intellectual contribution to the work, and approved it for publication.

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

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Top 3 Importance of Statistics in Psychology ( Real Life)

If you want to know what is the importance of statistics in psychology, then you are at the right place. Many psychology students are surprised and sometimes disappointed to learn that statistics courses are required for graduation in their major. Yes, statistics courses are required in almost all psychology programs. 

Not only will you need to take at least one or two statistics courses, but you will also most likely encounter statistics in many of your other classes, particularly those involving experimental design or research methods. In other words, you must not only be able to pass a statistics class, but you must also understand statistics.

However, if you are looking for a statistics assignment helper, don’t worry you can get the best statistics assignment help from our experts. So, what are you waiting for get help now!

So, let’s first know what statistics in psychology is, and then we further move to the importance of statistics in psychology. 

What is Statistics in Psychology?

Table of Contents

Statistics in Psychology – Applying formulas, theorems, numbers, and laws to psychology is known as psychological statistics. Statistical methods for psychology include statistical theory development and application, as well as methods for modeling psychological data.

What Are The Types Of Statistics In Psychology?

Here in this section, we will tell you four main types, which are as follows:

1. Descriptive Statistics

Descriptive Statistics is used to describe and summarize data in psychology. This includes measures of central tendency (mean, median, mode) and variability (range, variance, standard deviation).

2. Inferential Statistics

Inferential statistics is used to draw conclusions and make predictions about a larger population based on a smaller sample of data. 

This includes

• Hypothesis testing
• Confidence intervals
• Effect sizes

3. Correlational Analysis

Correlational analysis examines the relationship between two or more variables, such as the strength and direction of the relationship between depression and anxiety.

4. Experimental Design

Experimental design is used to investigate cause-and-effect relationships, such as whether a new therapy is effective for treating depression compared to a placebo.

What is the Importance of Statistics in Psychology: Top 3

First, think about the importance of statistics in real life. Statistics enables us to make sense of and analyze vast amounts of data. Consider how much data you encounter in a single day. How many hours did you sleep last night? How many of your classmates ate breakfast this morning? How many people live within a mile of your house? We can organize and interpret all of this information in a meaningful way by using statistics.

On the other hand, in psychology, we also organize a great amount of data. How can changes in one variable affect another variable? Or can we somehow measure that relationship? And many more. As a result, statistics allow us to answer these types of data. 

Below are the top 3 importance of statistics in psychology in real life:

1. Organize Data

If you are dealing with an enormous amount of data, then it is quite obvious to become overwhelmed. Statistics enable psychologists to present data in more understandable ways. 

On the other hand, visual displays such as graphs, pie charts, and scatterplots help researchers gain a better understanding of data and spot patterns that they might miss. As a result, organizing data comes under the importance of statistics in psychology.

2. Describe Data

Think about what happens when researchers assemble a large amount of data about a group of people. The Census Bureau of the USA is a great example of it. We can accurately describe the information gathered using statistics in an understandable manner. 

On the other hand, statistics are used to summarise what already exists in a given population, such as the number of men and women, children, and people who are currently employed.

3. Make Inferences Based on Data

With the help of inference statistics, researchers can gather the information about a given sample or population. On the other hand, psychologists use the data that they have collected to test a hypothesis. Or researchers can use statistical analysis to determine the possibility that a hypothesis will be accepted or rejected.

Well, the question that arises here is how statistics help a researcher, let’s find out the answer.

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How Statistics Help a Researcher in the Field of Psychology

Statistics is required for conducting research in psychology and social sciences. It benefits the researcher in the following ways:

• The most important aspect of a study is selecting a sample representing the study’s population; statistics are used to determine the sample size.
• What kinds of information are required for the study?
• What methods will be used to analyze the data?
• How to conclude data analysis

Statistics allows a researcher to design, describe, analyze, and draw conclusions from his research in this way.

What Is The Application Of Psychology In Statistics?

Psychology and statistics are interrelated fields, with psychology providing insights into human behavior and statistics providing the tools to analyze and interpret data. Here are seven applications of psychology in statistics:

1. Survey Design

Psychological research can help design surveys that measure specific attitudes, behaviors, and traits to collect meaningful data.

2. Psychometric Testing

Psychometrics is the scientific study of measuring psychological traits, which often involves the use of statistical methods to analyze data. If you want to  attain a doctorate in psychology , this is a process with which you’ll become intimately familiar, as this type of testing could be used to underpin the thesis of your entire degree.

3. Experimentation

Psychology experiments often require statistical analysis to determine the significance of results.

4. Regression Analysis

This statistical method helps identify relationships between variables and can be used to predict outcomes based on specific predictors.

5. Meta Analysis

This method is used to combine and analyze results from multiple studies to identify common patterns or trends. So, if you want to analyze the result of multiple studies, then you know what to use.

7. Factor Analysis

This statistical method is used to identify underlying factors that explain the relationship between multiple variables.

8. Data Visualisation

Psychologists can use statistics to create visual representations of data, such as graphs and charts, to understand patterns and relationships in the data better.

So, here are seven applications of psychology in statistics that you can use to do statistical analysis. 

Some Limitations of the Use of Statistics

If there is a good thing about something, then there is a bad thing also. Some of the limitations of the uses of statistics are as follows:

1. As you already know that statistics is a branch of mathematics, but it still has its limitations. The main drawback of statistics is that it provides an estimate of the data, not accurate results. 

2. Furthermore, it is concerned with social sciences, which are concerned with humans. We cannot predict human behavior with 100% accuracy, but it provides the most accurate findings.

3. Statistics only work with quantitative data , which is made up of numbers. To make statistics work with qualitative data, we must first convert the data to numbers.

4. Statistics only works with a group of people which means that statistics does not work with an individual. 

5. Lastly, sometimes, the result of statistics may lead the reader to some confusing information. This means that statistics is hard for those who do not know how to read information.

Conclusion 

Statistics is the branch of mathematics dealing with the organization, analysis, and interpretation of a collection of numbers. Statistics are essential in psychology for conducting research.

On the other hand, in this post, we discuss some of the importance of statistics in psychology and some of the limitations of statistics. I hope you get your answer by reading this post. 

Q1. What type of statistics is used in psychology?

There are two types of statistics that are used in statistics which are as follows: Descriptive statistics and Inferential statistics. Descriptive statistics describe and summarise data, whereas inferential statistics allows researchers to conclude the data described by descriptive statistics.

Q2. What is the role of statistics in psychology?

Statistics play multiple roles in psychology. It can predict what is most likely to happen, what is most likely to occur, and what is typical or normal for a specific group. On the other hand, it can also help a psychologist make sense of the large amount of data gathered through research. These are some of the roles of statistics in psychology. 

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