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Quantitative methods emphasize objective measurements and the statistical, mathematical, or numerical analysis of data collected through polls, questionnaires, and surveys, or by manipulating pre-existing statistical data using computational techniques . Quantitative research focuses on gathering numerical data and generalizing it across groups of people or to explain a particular phenomenon.

Babbie, Earl R. The Practice of Social Research . 12th ed. Belmont, CA: Wadsworth Cengage, 2010; Muijs, Daniel. Doing Quantitative Research in Education with SPSS . 2nd edition. London: SAGE Publications, 2010.

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Characteristics of Quantitative Research

Your goal in conducting quantitative research study is to determine the relationship between one thing [an independent variable] and another [a dependent or outcome variable] within a population. Quantitative research designs are either descriptive [subjects usually measured once] or experimental [subjects measured before and after a treatment]. A descriptive study establishes only associations between variables; an experimental study establishes causality.

Quantitative research deals in numbers, logic, and an objective stance. Quantitative research focuses on numeric and unchanging data and detailed, convergent reasoning rather than divergent reasoning [i.e., the generation of a variety of ideas about a research problem in a spontaneous, free-flowing manner].

Its main characteristics are :

• The data is usually gathered using structured research instruments.
• The results are based on larger sample sizes that are representative of the population.
• The research study can usually be replicated or repeated, given its high reliability.
• Researcher has a clearly defined research question to which objective answers are sought.
• All aspects of the study are carefully designed before data is collected.
• Data are in the form of numbers and statistics, often arranged in tables, charts, figures, or other non-textual forms.
• Project can be used to generalize concepts more widely, predict future results, or investigate causal relationships.
• Researcher uses tools, such as questionnaires or computer software, to collect numerical data.

The overarching aim of a quantitative research study is to classify features, count them, and construct statistical models in an attempt to explain what is observed.

  Things to keep in mind when reporting the results of a study using quantitative methods :

• Explain the data collected and their statistical treatment as well as all relevant results in relation to the research problem you are investigating. Interpretation of results is not appropriate in this section.
• Report unanticipated events that occurred during your data collection. Explain how the actual analysis differs from the planned analysis. Explain your handling of missing data and why any missing data does not undermine the validity of your analysis.
• Explain the techniques you used to "clean" your data set.
• Choose a minimally sufficient statistical procedure ; provide a rationale for its use and a reference for it. Specify any computer programs used.
• Describe the assumptions for each procedure and the steps you took to ensure that they were not violated.
• When using inferential statistics , provide the descriptive statistics, confidence intervals, and sample sizes for each variable as well as the value of the test statistic, its direction, the degrees of freedom, and the significance level [report the actual p value].
• Avoid inferring causality , particularly in nonrandomized designs or without further experimentation.
• Use tables to provide exact values ; use figures to convey global effects. Keep figures small in size; include graphic representations of confidence intervals whenever possible.
• Always tell the reader what to look for in tables and figures .

NOTE:   When using pre-existing statistical data gathered and made available by anyone other than yourself [e.g., government agency], you still must report on the methods that were used to gather the data and describe any missing data that exists and, if there is any, provide a clear explanation why the missing data does not undermine the validity of your final analysis.

Babbie, Earl R. The Practice of Social Research . 12th ed. Belmont, CA: Wadsworth Cengage, 2010; Brians, Craig Leonard et al. Empirical Political Analysis: Quantitative and Qualitative Research Methods . 8th ed. Boston, MA: Longman, 2011; McNabb, David E. Research Methods in Public Administration and Nonprofit Management: Quantitative and Qualitative Approaches . 2nd ed. Armonk, NY: M.E. Sharpe, 2008; Quantitative Research Methods. Writing@CSU. Colorado State University; Singh, Kultar. Quantitative Social Research Methods . Los Angeles, CA: Sage, 2007.

Basic Research Design for Quantitative Studies

Before designing a quantitative research study, you must decide whether it will be descriptive or experimental because this will dictate how you gather, analyze, and interpret the results. A descriptive study is governed by the following rules: subjects are generally measured once; the intention is to only establish associations between variables; and, the study may include a sample population of hundreds or thousands of subjects to ensure that a valid estimate of a generalized relationship between variables has been obtained. An experimental design includes subjects measured before and after a particular treatment, the sample population may be very small and purposefully chosen, and it is intended to establish causality between variables. Introduction The introduction to a quantitative study is usually written in the present tense and from the third person point of view. It covers the following information:

• Identifies the research problem -- as with any academic study, you must state clearly and concisely the research problem being investigated.
• Reviews the literature -- review scholarship on the topic, synthesizing key themes and, if necessary, noting studies that have used similar methods of inquiry and analysis. Note where key gaps exist and how your study helps to fill these gaps or clarifies existing knowledge.
• Describes the theoretical framework -- provide an outline of the theory or hypothesis underpinning your study. If necessary, define unfamiliar or complex terms, concepts, or ideas and provide the appropriate background information to place the research problem in proper context [e.g., historical, cultural, economic, etc.].

Methodology The methods section of a quantitative study should describe how each objective of your study will be achieved. Be sure to provide enough detail to enable the reader can make an informed assessment of the methods being used to obtain results associated with the research problem. The methods section should be presented in the past tense.

• Study population and sampling -- where did the data come from; how robust is it; note where gaps exist or what was excluded. Note the procedures used for their selection;
• Data collection – describe the tools and methods used to collect information and identify the variables being measured; describe the methods used to obtain the data; and, note if the data was pre-existing [i.e., government data] or you gathered it yourself. If you gathered it yourself, describe what type of instrument you used and why. Note that no data set is perfect--describe any limitations in methods of gathering data.
• Data analysis -- describe the procedures for processing and analyzing the data. If appropriate, describe the specific instruments of analysis used to study each research objective, including mathematical techniques and the type of computer software used to manipulate the data.

Results The finding of your study should be written objectively and in a succinct and precise format. In quantitative studies, it is common to use graphs, tables, charts, and other non-textual elements to help the reader understand the data. Make sure that non-textual elements do not stand in isolation from the text but are being used to supplement the overall description of the results and to help clarify key points being made. Further information about how to effectively present data using charts and graphs can be found here .

• Statistical analysis -- how did you analyze the data? What were the key findings from the data? The findings should be present in a logical, sequential order. Describe but do not interpret these trends or negative results; save that for the discussion section. The results should be presented in the past tense.

Discussion Discussions should be analytic, logical, and comprehensive. The discussion should meld together your findings in relation to those identified in the literature review, and placed within the context of the theoretical framework underpinning the study. The discussion should be presented in the present tense.

• Interpretation of results -- reiterate the research problem being investigated and compare and contrast the findings with the research questions underlying the study. Did they affirm predicted outcomes or did the data refute it?
• Description of trends, comparison of groups, or relationships among variables -- describe any trends that emerged from your analysis and explain all unanticipated and statistical insignificant findings.
• Discussion of implications – what is the meaning of your results? Highlight key findings based on the overall results and note findings that you believe are important. How have the results helped fill gaps in understanding the research problem?
• Limitations -- describe any limitations or unavoidable bias in your study and, if necessary, note why these limitations did not inhibit effective interpretation of the results.

Conclusion End your study by to summarizing the topic and provide a final comment and assessment of the study.

• Summary of findings – synthesize the answers to your research questions. Do not report any statistical data here; just provide a narrative summary of the key findings and describe what was learned that you did not know before conducting the study.
• Recommendations – if appropriate to the aim of the assignment, tie key findings with policy recommendations or actions to be taken in practice.
• Future research – note the need for future research linked to your study’s limitations or to any remaining gaps in the literature that were not addressed in your study.

Black, Thomas R. Doing Quantitative Research in the Social Sciences: An Integrated Approach to Research Design, Measurement and Statistics . London: Sage, 1999; Gay,L. R. and Peter Airasain. Educational Research: Competencies for Analysis and Applications . 7th edition. Upper Saddle River, NJ: Merril Prentice Hall, 2003; Hector, Anestine. An Overview of Quantitative Research in Composition and TESOL . Department of English, Indiana University of Pennsylvania; Hopkins, Will G. “Quantitative Research Design.” Sportscience 4, 1 (2000); "A Strategy for Writing Up Research Results. The Structure, Format, Content, and Style of a Journal-Style Scientific Paper." Department of Biology. Bates College; Nenty, H. Johnson. "Writing a Quantitative Research Thesis." International Journal of Educational Science 1 (2009): 19-32; Ouyang, Ronghua (John). Basic Inquiry of Quantitative Research . Kennesaw State University.

Strengths of Using Quantitative Methods

Quantitative researchers try to recognize and isolate specific variables contained within the study framework, seek correlation, relationships and causality, and attempt to control the environment in which the data is collected to avoid the risk of variables, other than the one being studied, accounting for the relationships identified.

Among the specific strengths of using quantitative methods to study social science research problems:

• Allows for a broader study, involving a greater number of subjects, and enhancing the generalization of the results;
• Allows for greater objectivity and accuracy of results. Generally, quantitative methods are designed to provide summaries of data that support generalizations about the phenomenon under study. In order to accomplish this, quantitative research usually involves few variables and many cases, and employs prescribed procedures to ensure validity and reliability;
• Applying well established standards means that the research can be replicated, and then analyzed and compared with similar studies;
• You can summarize vast sources of information and make comparisons across categories and over time; and,
• Personal bias can be avoided by keeping a 'distance' from participating subjects and using accepted computational techniques .

Babbie, Earl R. The Practice of Social Research . 12th ed. Belmont, CA: Wadsworth Cengage, 2010; Brians, Craig Leonard et al. Empirical Political Analysis: Quantitative and Qualitative Research Methods . 8th ed. Boston, MA: Longman, 2011; McNabb, David E. Research Methods in Public Administration and Nonprofit Management: Quantitative and Qualitative Approaches . 2nd ed. Armonk, NY: M.E. Sharpe, 2008; Singh, Kultar. Quantitative Social Research Methods . Los Angeles, CA: Sage, 2007.

Limitations of Using Quantitative Methods

Quantitative methods presume to have an objective approach to studying research problems, where data is controlled and measured, to address the accumulation of facts, and to determine the causes of behavior. As a consequence, the results of quantitative research may be statistically significant but are often humanly insignificant.

Some specific limitations associated with using quantitative methods to study research problems in the social sciences include:

• Quantitative data is more efficient and able to test hypotheses, but may miss contextual detail;
• Uses a static and rigid approach and so employs an inflexible process of discovery;
• The development of standard questions by researchers can lead to "structural bias" and false representation, where the data actually reflects the view of the researcher instead of the participating subject;
• Results provide less detail on behavior, attitudes, and motivation;
• Researcher may collect a much narrower and sometimes superficial dataset;
• Results are limited as they provide numerical descriptions rather than detailed narrative and generally provide less elaborate accounts of human perception;
• The research is often carried out in an unnatural, artificial environment so that a level of control can be applied to the exercise. This level of control might not normally be in place in the real world thus yielding "laboratory results" as opposed to "real world results"; and,
• Preset answers will not necessarily reflect how people really feel about a subject and, in some cases, might just be the closest match to the preconceived hypothesis.

Research Tip

Finding Examples of How to Apply Different Types of Research Methods

SAGE publications is a major publisher of studies about how to design and conduct research in the social and behavioral sciences. Their SAGE Research Methods Online and Cases database includes contents from books, articles, encyclopedias, handbooks, and videos covering social science research design and methods including the complete Little Green Book Series of Quantitative Applications in the Social Sciences and the Little Blue Book Series of Qualitative Research techniques. The database also includes case studies outlining the research methods used in real research projects. This is an excellent source for finding definitions of key terms and descriptions of research design and practice, techniques of data gathering, analysis, and reporting, and information about theories of research [e.g., grounded theory]. The database covers both qualitative and quantitative research methods as well as mixed methods approaches to conducting research.

SAGE Research Methods Online and Cases

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• What is descriptive research?

Last updated

5 February 2023

Reviewed by

Cathy Heath

Descriptive research is a common investigatory model used by researchers in various fields, including social sciences, linguistics, and academia.

Read on to understand the characteristics of descriptive research and explore its underlying techniques, processes, and procedures.

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Descriptive research is an exploratory research method. It enables researchers to precisely and methodically describe a population, circumstance, or phenomenon.

As the name suggests, descriptive research describes the characteristics of the group, situation, or phenomenon being studied without manipulating variables or testing hypotheses . This can be reported using surveys , observational studies, and case studies. You can use both quantitative and qualitative methods to compile the data.

Besides making observations and then comparing and analyzing them, descriptive studies often develop knowledge concepts and provide solutions to critical issues. It always aims to answer how the event occurred, when it occurred, where it occurred, and what the problem or phenomenon is.

• Characteristics of descriptive research

The following are some of the characteristics of descriptive research:

Quantitativeness

Descriptive research can be quantitative as it gathers quantifiable data to statistically analyze a population sample. These numbers can show patterns, connections, and trends over time and can be discovered using surveys, polls, and experiments.

Qualitativeness

Descriptive research can also be qualitative. It gives meaning and context to the numbers supplied by quantitative descriptive research .

Researchers can use tools like interviews, focus groups, and ethnographic studies to illustrate why things are what they are and help characterize the research problem. This is because it’s more explanatory than exploratory or experimental research.

Uncontrolled variables

Descriptive research differs from experimental research in that researchers cannot manipulate the variables. They are recognized, scrutinized, and quantified instead. This is one of its most prominent features.

Cross-sectional studies

Descriptive research is a cross-sectional study because it examines several areas of the same group. It involves obtaining data on multiple variables at the personal level during a certain period. It’s helpful when trying to understand a larger community’s habits or preferences.

Carried out in a natural environment

Descriptive studies are usually carried out in the participants’ everyday environment, which allows researchers to avoid influencing responders by collecting data in a natural setting. You can use online surveys or survey questions to collect data or observe.

Basis for further research

You can further dissect descriptive research’s outcomes and use them for different types of investigation. The outcomes also serve as a foundation for subsequent investigations and can guide future studies. For example, you can use the data obtained in descriptive research to help determine future research designs.

• Descriptive research methods

There are three basic approaches for gathering data in descriptive research: observational, case study, and survey.

You can use surveys to gather data in descriptive research. This involves gathering information from many people using a questionnaire and interview .

Surveys remain the dominant research tool for descriptive research design. Researchers can conduct various investigations and collect multiple types of data (quantitative and qualitative) using surveys with diverse designs.

You can conduct surveys over the phone, online, or in person. Your survey might be a brief interview or conversation with a set of prepared questions intended to obtain quick information from the primary source.

Observation

This descriptive research method involves observing and gathering data on a population or phenomena without manipulating variables. It is employed in psychology, market research , and other social science studies to track and understand human behavior.

Observation is an essential component of descriptive research. It entails gathering data and analyzing it to see whether there is a relationship between the two variables in the study. This strategy usually allows for both qualitative and quantitative data analysis.

Case studies

A case study can outline a specific topic’s traits. The topic might be a person, group, event, or organization.

It involves using a subset of a larger group as a sample to characterize the features of that larger group.

You can generalize knowledge gained from studying a case study to benefit a broader audience.

This approach entails carefully examining a particular group, person, or event over time. You can learn something new about the study topic by using a small group to better understand the dynamics of the entire group.

• Types of descriptive research

There are several types of descriptive study. The most well-known include cross-sectional studies, census surveys, sample surveys, case reports, and comparison studies.

Case reports and case series

In the healthcare and medical fields, a case report is used to explain a patient’s circumstances when suffering from an uncommon illness or displaying certain symptoms. Case reports and case series are both collections of related cases. They have aided the advancement of medical knowledge on countless occasions.

The normative component is an addition to the descriptive survey. In the descriptive–normative survey, you compare the study’s results to the norm.

Descriptive survey

This descriptive type of research employs surveys to collect information on various topics. This data aims to determine the degree to which certain conditions may be attained.

You can extrapolate or generalize the information you obtain from sample surveys to the larger group being researched.

Correlative survey

Correlative surveys help establish if there is a positive, negative, or neutral connection between two variables.

Performing census surveys involves gathering relevant data on several aspects of a given population. These units include individuals, families, organizations, objects, characteristics, and properties.

During descriptive research, you gather different degrees of interest over time from a specific population. Cross-sectional studies provide a glimpse of a phenomenon’s prevalence and features in a population. There are no ethical challenges with them and they are quite simple and inexpensive to carry out.

Comparative studies

These surveys compare the two subjects’ conditions or characteristics. The subjects may include research variables, organizations, plans, and people.

Comparison points, assumption of similarities, and criteria of comparison are three important variables that affect how well and accurately comparative studies are conducted.

For instance, descriptive research can help determine how many CEOs hold a bachelor’s degree and what proportion of low-income households receive government help.

• Pros and cons

The primary advantage of descriptive research designs is that researchers can create a reliable and beneficial database for additional study. To conduct any inquiry, you need access to reliable information sources that can give you a firm understanding of a situation.

Quantitative studies are time- and resource-intensive, so knowing the hypotheses viable for testing is crucial. The basic overview of descriptive research provides helpful hints as to which variables are worth quantitatively examining. This is why it’s employed as a precursor to quantitative research designs.

Some experts view this research as untrustworthy and unscientific. However, there is no way to assess the findings because you don’t manipulate any variables statistically.

Cause-and-effect correlations also can’t be established through descriptive investigations. Additionally, observational study findings cannot be replicated, which prevents a review of the findings and their replication.

The absence of statistical and in-depth analysis and the rather superficial character of the investigative procedure are drawbacks of this research approach.

• Descriptive research examples and applications

Several descriptive research examples are emphasized based on their types, purposes, and applications. Research questions often begin with “What is …” These studies help find solutions to practical issues in social science, physical science, and education.

Here are some examples and applications of descriptive research:

Determining consumer perception and behavior

Organizations use descriptive research designs to determine how various demographic groups react to a certain product or service.

For example, a business looking to sell to its target market should research the market’s behavior first. When researching human behavior in response to a cause or event, the researcher pays attention to the traits, actions, and responses before drawing a conclusion.

Scientific classification

Scientific descriptive research enables the classification of organisms and their traits and constituents.

Measuring data trends

A descriptive study design’s statistical capabilities allow researchers to track data trends over time. It’s frequently used to determine the study target’s current circumstances and underlying patterns.

Conduct comparison

Organizations can use a descriptive research approach to learn how various demographics react to a certain product or service. For example, you can study how the target market responds to a competitor’s product and use that information to infer their behavior.

• Bottom line

A descriptive research design is suitable for exploring certain topics and serving as a prelude to larger quantitative investigations. It provides a comprehensive understanding of the “what” of the group or thing you’re investigating.

This research type acts as the cornerstone of other research methodologies . It is distinctive because it can use quantitative and qualitative research approaches at the same time.

What is descriptive research design?

Descriptive research design aims to systematically obtain information to describe a phenomenon, situation, or population. More specifically, it helps answer the what, when, where, and how questions regarding the research problem rather than the why.

How does descriptive research compare to qualitative research?

Despite certain parallels, descriptive research concentrates on describing phenomena, while qualitative research aims to understand people better.

How do you analyze descriptive research data?

Data analysis involves using various methodologies, enabling the researcher to evaluate and provide results regarding validity and reliability.

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• Descriptive Research Design | Definition, Methods & Examples

Descriptive Research Design | Definition, Methods & Examples

Published on 5 May 2022 by Shona McCombes . Revised on 10 October 2022.

Descriptive research aims to accurately and systematically describe a population, situation or phenomenon. It can answer what , where , when , and how   questions , but not why questions.

A descriptive research design can use a wide variety of research methods  to investigate one or more variables . Unlike in experimental research , the researcher does not control or manipulate any of the variables, but only observes and measures them.

Table of contents

When to use a descriptive research design, descriptive research methods.

Descriptive research is an appropriate choice when the research aim is to identify characteristics, frequencies, trends, and categories.

It is useful when not much is known yet about the topic or problem. Before you can research why something happens, you need to understand how, when, and where it happens.

• How has the London housing market changed over the past 20 years?
• Do customers of company X prefer product Y or product Z?
• What are the main genetic, behavioural, and morphological differences between European wildcats and domestic cats?
• What are the most popular online news sources among under-18s?
• How prevalent is disease A in population B?

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Descriptive research is usually defined as a type of quantitative research , though qualitative research can also be used for descriptive purposes. The research design should be carefully developed to ensure that the results are valid and reliable .

Survey research allows you to gather large volumes of data that can be analysed for frequencies, averages, and patterns. Common uses of surveys include:

• Describing the demographics of a country or region
• Gauging public opinion on political and social topics
• Evaluating satisfaction with a company’s products or an organisation’s services

Observations

Observations allow you to gather data on behaviours and phenomena without having to rely on the honesty and accuracy of respondents. This method is often used by psychological, social, and market researchers to understand how people act in real-life situations.

Observation of physical entities and phenomena is also an important part of research in the natural sciences. Before you can develop testable hypotheses , models, or theories, it’s necessary to observe and systematically describe the subject under investigation.

Case studies

A case study can be used to describe the characteristics of a specific subject (such as a person, group, event, or organisation). Instead of gathering a large volume of data to identify patterns across time or location, case studies gather detailed data to identify the characteristics of a narrowly defined subject.

Rather than aiming to describe generalisable facts, case studies often focus on unusual or interesting cases that challenge assumptions, add complexity, or reveal something new about a research problem .

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McCombes, S. (2022, October 10). Descriptive Research Design | Definition, Methods & Examples. Scribbr. Retrieved 20 March 2024, from https://www.scribbr.co.uk/research-methods/descriptive-research-design/

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Descriptive research: what it is and how to use it.

8 min read Understanding the who, what and where of a situation or target group is an essential part of effective research and making informed business decisions.

For example you might want to understand what percentage of CEOs have a bachelor’s degree or higher. Or you might want to understand what percentage of low income families receive government support – or what kind of support they receive.

Descriptive research is what will be used in these types of studies.

In this guide we’ll look through the main issues relating to descriptive research to give you a better understanding of what it is, and how and why you can use it.

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What is descriptive research?

Descriptive research is a research method used to try and determine the characteristics of a population or particular phenomenon.

Using descriptive research you can identify patterns in the characteristics of a group to essentially establish everything you need to understand apart from why something has happened.

Market researchers use descriptive research for a range of commercial purposes to guide key decisions.

For example you could use descriptive research to understand fashion trends in a given city when planning your clothing collection for the year. Using descriptive research you can conduct in depth analysis on the demographic makeup of your target area and use the data analysis to establish buying patterns.

Conducting descriptive research wouldn’t, however, tell you why shoppers are buying a particular type of fashion item.

Descriptive research design

Descriptive research design uses a range of both qualitative research and quantitative data (although quantitative research is the primary research method) to gather information to make accurate predictions about a particular problem or hypothesis.

As a survey method, descriptive research designs will help researchers identify characteristics in their target market or particular population.

These characteristics in the population sample can be identified, observed and measured to guide decisions.

Descriptive research characteristics

While there are a number of descriptive research methods you can deploy for data collection, descriptive research does have a number of predictable characteristics.

Here are a few of the things to consider:

Measure data trends with statistical outcomes

Descriptive research is often popular for survey research because it generates answers in a statistical form, which makes it easy for researchers to carry out a simple statistical analysis to interpret what the data is saying.

Descriptive research design is ideal for further research

Because the data collection for descriptive research produces statistical outcomes, it can also be used as secondary data for another research study.

Plus, the data collected from descriptive research can be subjected to other types of data analysis .

Uncontrolled variables

A key component of the descriptive research method is that it uses random variables that are not controlled by the researchers. This is because descriptive research aims to understand the natural behavior of the research subject.

It’s carried out in a natural environment

Descriptive research is often carried out in a natural environment. This is because researchers aim to gather data in a natural setting to avoid swaying respondents.

Data can be gathered using survey questions or online surveys.

For example, if you want to understand the fashion trends we mentioned earlier, you would set up a study in which a researcher observes people in the respondent’s natural environment to understand their habits and preferences.

Descriptive research allows for cross sectional study

Because of the nature of descriptive research design and the randomness of the sample group being observed, descriptive research is ideal for cross sectional studies – essentially the demographics of the group can vary widely and your aim is to gain insights from within the group.

This can be highly beneficial when you’re looking to understand the behaviors or preferences of a wider population.

Descriptive research advantages

There are many advantages to using descriptive research, some of them include:

Cost effectiveness

Because the elements needed for descriptive research design are not specific or highly targeted (and occur within the respondent’s natural environment) this type of study is relatively cheap to carry out.

Multiple types of data can be collected

A big advantage of this research type, is that you can use it to collect both quantitative and qualitative data. This means you can use the stats gathered to easily identify underlying patterns in your respondents’ behavior.

Descriptive research disadvantages

Potential reliability issues.

When conducting descriptive research it’s important that the initial survey questions are properly formulated.

If not, it could make the answers unreliable and risk the credibility of your study.

Potential limitations

As we’ve mentioned, descriptive research design is ideal for understanding the what, who or where of a situation or phenomenon.

However, it can’t help you understand the cause or effect of the behavior. This means you’ll need to conduct further research to get a more complete picture of a situation.

Descriptive research methods

Because descriptive research methods include a range of quantitative and qualitative research, there are several research methods you can use.

Use case studies

Case studies in descriptive research involve conducting in-depth and detailed studies in which researchers get a specific person or case to answer questions.

Case studies shouldn’t be used to generate results, rather it should be used to build or establish hypothesis that you can expand into further market research .

For example you could gather detailed data about a specific business phenomenon, and then use this deeper understanding of that specific case.

Use observational methods

This type of study uses qualitative observations to understand human behavior within a particular group.

By understanding how the different demographics respond within your sample you can identify patterns and trends.

As an observational method, descriptive research will not tell you the cause of any particular behaviors, but that could be established with further research.

Use survey research

Surveys are one of the most cost effective ways to gather descriptive data.

An online survey or questionnaire can be used in descriptive studies to gather quantitative information about a particular problem.

Survey research is ideal if you’re using descriptive research as your primary research.

Descriptive research examples

Descriptive research is used for a number of commercial purposes or when organizations need to understand the behaviors or opinions of a population.

One of the biggest examples of descriptive research that is used in every democratic country, is during elections.

Using descriptive research, researchers will use surveys to understand who voters are more likely to choose out of the parties or candidates available.

Using the data provided, researchers can analyze the data to understand what the election result will be.

In a commercial setting, retailers often use descriptive research to figure out trends in shopping and buying decisions.

By gathering information on the habits of shoppers, retailers can get a better understanding of the purchases being made.

Another example that is widely used around the world, is the national census that takes place to understand the population.

The research will provide a more accurate picture of a population’s demographic makeup and help to understand changes over time in areas like population age, health and education level.

Where Qualtrics helps with descriptive research

Whatever type of research you want to carry out, there’s a survey type that will work.

Qualtrics can help you determine the appropriate method and ensure you design a study that will deliver the insights you need.

Our experts can help you with your market research needs , ensuring you get the most out of Qualtrics market research software to design, launch and analyze your data to guide better, more accurate decisions for your organization.

Related resources

Market intelligence 10 min read, marketing insights 11 min read, ethnographic research 11 min read, qualitative vs quantitative research 13 min read, qualitative research questions 11 min read, qualitative research design 12 min read, primary vs secondary research 14 min read, request demo.

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Quantitative and Qualitative Research

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What is Quantitative Research?

• What is Qualitative Research?
• Quantitative vs Qualitative
• Step 1: Accessing CINAHL
• Step 2: Create a Keyword Search
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Quantitative methodology is the dominant research framework in the social sciences. It refers to a set of strategies, techniques and assumptions used to study psychological, social and economic processes through the exploration of numeric patterns . Quantitative research gathers a range of numeric data. Some of the numeric data is intrinsically quantitative (e.g. personal income), while in other cases the numeric structure is  imposed (e.g. ‘On a scale from 1 to 10, how depressed did you feel last week?’). The collection of quantitative information allows researchers to conduct simple to extremely sophisticated statistical analyses that aggregate the data (e.g. averages, percentages), show relationships among the data (e.g. ‘Students with lower grade point averages tend to score lower on a depression scale’) or compare across aggregated data (e.g. the USA has a higher gross domestic product than Spain). Quantitative research includes methodologies such as questionnaires, structured observations or experiments and stands in contrast to qualitative research. Qualitative research involves the collection and analysis of narratives and/or open-ended observations through methodologies such as interviews, focus groups or ethnographies.

Coghlan, D., Brydon-Miller, M. (2014).  The SAGE encyclopedia of action research  (Vols. 1-2). London, : SAGE Publications Ltd doi: 10.4135/9781446294406

What is the purpose of quantitative research?

The purpose of quantitative research is to generate knowledge and create understanding about the social world. Quantitative research is used by social scientists, including communication researchers, to observe phenomena or occurrences affecting individuals. Social scientists are concerned with the study of people. Quantitative research is a way to learn about a particular group of people, known as a sample population. Using scientific inquiry, quantitative research relies on data that are observed or measured to examine questions about the sample population.

Allen, M. (2017).  The SAGE encyclopedia of communication research methods  (Vols. 1-4). Thousand Oaks, CA: SAGE Publications, Inc doi: 10.4135/9781483381411

How do I know if the study is a quantitative design?  What type of quantitative study is it?

Quantitative Research Designs: Descriptive non-experimental, Quasi-experimental or Experimental?

Studies do not always explicitly state what kind of research design is being used.  You will need to know how to decipher which design type is used.  The following video will help you determine the quantitative design type.

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What is Quantitative Research Design? Definition, Types, Methods and Best Practices

By Nick Jain

Published on: July 7, 2023

Table of Contents

What is Quantitative Research Design?

Types of quantitative research design, quantitative research design methods, quantitative research design process: 10 key steps, top 11 best practices for quantitative research design.

Quantitative research design is defined as a research method used in various disciplines, including social sciences, psychology, economics, and market research. It aims to collect and analyze numerical data to answer research questions and test hypotheses.

Quantitative research design offers several advantages, including the ability to generalize findings to larger populations, the potential for statistical analysis and hypothesis testing, and the capacity to uncover patterns and relationships among variables. However, it also has limitations, such as the potential for oversimplification of complex phenomena and the reliance on predetermined categories and measurements.

Quantitative research design key elements

Quantitative research design typically follows a systematic and structured approach. It involves the following key elements:

• Research Question: The researcher formulates a clear and specific question that can be answered through quantitative research . The question should be measurable and objective
• Variables: The researcher identifies and defines the variables relevant to the research question. Variables are attributes or characteristics that can be measured or observed. They can be independent variables (factors that are manipulated or controlled) or dependent variables (outcomes or responses that are measured).
• Hypotheses: The researcher develops one or more hypotheses based on the research question. Hypotheses are verifiable statements that make predictions about the association between variables.
• Sampling: The researcher determines the target population and selects a representative sample from that population. The sample should be large enough to provide statistically significant results and should be chosen using appropriate sampling techniques.
• Data Collection: Quantitative research design relies on the collection of numerical data. This can be done through various methods such as surveys, experiments, quantitative observations , or secondary data analysis. Standardized instruments, such as questionnaires or scales, are often used to ensure consistency and reliability.
• Data Analysis: The collected data is analyzed using statistical methods and techniques. Descriptive statistics are used to summarize and describe the data, while inferential statistics are used to draw conclusions and make generalizations about the population based on the sample data.
• Results and Conclusions: The researcher interprets the findings and draws conclusions based on the analysis. The results are typically presented in the form of tables, graphs, and statistical measures, such as means, correlations, or regression coefficients.

There are several types of quantitative research designs, each suited for different research purposes and questions. Here are some common types of quantitative research designs:

• Experimental Design

Experimental design involves the manipulation of an independent variable to observe its effect on a dependent variable while controlling for other variables. Participants are typically randomly assigned to different groups, such as a control group and one or more experimental groups, to compare the outcomes. This approach enables the establishment of cause-and-effect relationships.

• Quasi-Experimental Design

Quasi-experimental design exhibits similarities to experimental design, yet it lacks the random assignment of participants to groups. The researcher takes advantage of naturally occurring groups or pre-existing conditions to compare the effects of an independent variable on a dependent variable. While it doesn’t establish causality as strongly as experimental design, it can still provide valuable insights.

• Survey Research

Survey research involves collecting data through questionnaires or interviews administered to a sample of participants. Surveys allow researchers to gather data on a wide range of variables and can be conducted in various settings, such as online surveys or face-to-face interviews. This design is particularly useful for studying attitudes, opinions, and behaviors within a population.

• Correlational Design

The correlational design investigates the association between two or more variables without engaging in their manipulation. Researchers measure variables and determine the degree and direction of their association using statistical techniques such as correlation analysis. However, correlational research cannot establish causality, only the strength and direction of the relationship.

• Longitudinal Design

Longitudinal design involves collecting data from the same individuals or groups over an extended period. This design allows researchers to study changes and patterns over time, providing insights into the stability and development of variables. Longitudinal studies can be conducted retrospectively (looking back) or prospectively (following participants into the future).

• Cross-sectional Design

Cross-sectional design collects data from a specific population at a single point in time. Researchers examine different variables simultaneously and analyze the relationships among them. This design is often used to gather data quickly and assess the prevalence of certain characteristics or behaviors within a population.

• Ex post facto Design

Ex post facto design involves studying the effects of an independent variable that is beyond the researcher’s control. The researcher selects participants based on their exposure to the independent variable, collecting data retrospectively. This design is useful when random assignment or manipulation of variables is not feasible or ethical.

Learn more: What is Quantitative Market Research?

Quantitative research design methods refer to the specific techniques and approaches used to collect and analyze numerical data in quantitative research . Below are several commonly utilized quantitative research methods:

• Surveys: Surveys involve administering questionnaires or structured interviews to gather data from a sample of participants. Surveys can be implemented through different channels, such as conducting them in person, over the phone, via mail, or utilizing online platforms. Researchers use various question types, such as multiple-choice, Likert scales, or rating scales, to collect quantitative data on attitudes, opinions, behaviors, and demographics.
• Experiments: Experiments involve manipulating one or more independent variables and measuring their effects on dependent variables. To compare outcomes, participants are assigned randomly to various groups, including control and experimental groups. Experimental designs allow researchers to establish cause-and-effect relationships by controlling for confounding factors.
• Observational Studies: Observational studies involve systematically observing and recording behavior, events, or phenomena in natural settings. Researchers can use structured or unstructured quantitative observation methods , depending on the research objectives. Quantitative data can be collected by counting the frequency of specific behaviors or by using coding systems to categorize and analyze observed data.
• Archival Research: Archival research involves analyzing existing data collected for purposes other than the current study. Researchers may use historical documents, government records, public databases, or organizational records to extract data through quantitative research . Archival research allows for large-scale data analysis and can provide insights into long-term trends and patterns.
• Secondary Data Analysis: Similar to archival research, secondary data analysis involves using existing datasets that were collected by other researchers or organizations. Researchers analyze the data to answer new research questions or test different hypotheses. Secondary data sources can include government surveys, social surveys, or market research data.
• Content Analysis: Content analysis is a method used to analyze textual or visual data to identify patterns, themes, or relationships. Researchers code and categorize the content of documents, interviews, articles, or media sources. The coded data is then quantified and statistically analyzed to draw conclusions. Content analysis can be both qualitative and quantitative , depending on the approach used.
• Psychometric Testing: Psychometric testing involves the development and administration of tests or scales to measure psychological constructs, such as intelligence, personality traits, or attitudes. Researchers use statistical techniques to analyze the test data, such as factor analysis, reliability analysis, or item response theory.

Learn more: What is Quantitative Observation?

The quantitative research design process typically involves several key steps to ensure a systematic and rigorous approach to data collection and analysis. While the specific steps may vary depending on the research context, here are the key stages commonly involved in quantitative research design:

1. Identify the Research Problem

Clearly define the research problem or objective. Determine the research question(s) and objectives that you want to address through your quantitative research study. Ensure that your research question is specific, measurable, and aligned with your research goals.

2. Review Existing Literature

Conduct a comprehensive review of existing literature and research on the topic. This helps you understand the current state of knowledge, identify gaps in the literature, and inform your research design. It also helps in selecting appropriate variables and developing hypotheses.

3. Determine Research Design

Based on your research question and objectives, determine the appropriate research design. Decide whether an experimental, quasi-experimental, correlational, or another design would best suit your research goals. Consider factors such as feasibility, ethical considerations, and resources available.

4. Define Variables and Hypotheses

Identify the variables that are pertinent to your research question. Clearly define each variable and its operational definitions (how they will be measured or observed). Develop hypotheses that state the expected relationships between variables based on existing theories or prior research.

5. Determine Sampling Strategy

Define the target population for your study and determine the sampling strategy. Decide on the sample size and the sampling method (e.g., random sampling, stratified sampling, convenience sampling). Ensure that your sample is representative of the population you want to generalize your findings to.

6. Select Data Collection Methods

Choose the appropriate data collection methods to gather data through quantitative research . This can include surveys, experiments, observations, or secondary data analysis. Develop or select validated instruments (e.g., questionnaires, scales) for data collection. Perform a pilot test on the instruments to ensure their reliability and validity.

7. Collect Data

Implement your data collection plan. Administer surveys, conduct experiments, observe participants, or extract data from existing sources. Ensure proper data management and organization to maintain accuracy and integrity. Consider ethical considerations and obtain necessary permissions or approvals.

8. Analyze Data

Perform data analysis using appropriate statistical techniques. Depending on your research design and data characteristics, apply descriptive statistics (e.g., means, frequencies) and inferential statistics (e.g., t-tests, ANOVA, regression analysis) to analyze relationships, test hypotheses, and draw conclusions. Use statistical software for efficient and accurate analysis.

9. Interpret Results

Interpret the findings of your data analysis. Examine statistical outputs, identify significant relationships or patterns, and relate them to your research question and hypotheses. Consider the limitations of your study and address any unexpected or contradictory results.

10. Communicate Findings

Prepare a research report or manuscript that summarizes your research process, findings, and conclusions. Present your results in a clear and understandable manner using appropriate visualizations (e.g., tables, graphs). Consider disseminating your findings through academic publications, conferences, or other appropriate channels.

To ensure the quality and validity of your quantitative research design, here are some best practices to consider:

1. Define Research Objectives Clearly: Initiate the process by providing a clear definition of your research objectives and formulating precise research questions. This clarity will guide your study design and data collection process.

2. Conduct a Comprehensive Literature Review: Thoroughly review existing literature and research on your topic to understand the current state of knowledge. This helps you identify research gaps, refine your research question, and avoid duplication of efforts.

3. Use Validated Measures: When selecting or developing measurement instruments, ensure that they have established validity and reliability. Use validated scales, questionnaires, or tests that have been previously tested and proven to measure the constructs of interest accurately.

4. Pilot Testing: Before implementing your data collection, conduct pilot testing to evaluate the effectiveness of your research instruments and procedures. Pilot testing helps identify any issues or shortcomings and allows for adjustments before the main data collection.

5. Ensure Sample Representativeness: Pay attention to sample selection to ensure it is representative of the target population. Use appropriate sampling techniques and consider factors such as sample size, demographics, and relevant characteristics to enhance generalizability.

6. Minimize Nonresponse Bias: Address potential nonresponse bias by employing strategies to maximize response rates, such as providing clear instructions, using follow-up reminders, and ensuring confidentiality. Analyze nonresponse patterns to assess potential bias and consider appropriate weighting techniques if needed.

7. Maintain Data Quality: Implement robust data management practices to ensure data quality and integrity. Conduct data cleaning, perform checks for outliers and missing values, and document any data transformations or manipulations. Document your data collection procedures thoroughly to facilitate replication and transparency.

8. Employ Appropriate Statistical Analysis: Choose statistical techniques that align with your research design and data characteristics. Use appropriate descriptive and inferential statistics to analyze relationships, test hypotheses, and draw valid conclusions. Ensure proper interpretation and reporting of statistical results.

9. Address Potential Confounding Factors: Identify potential confounding variables that may influence the relationship between your independent and dependent variables. Consider controlling for these factors through study design or statistical techniques to isolate the effects of the variables of interest.

10. Consider Ethical Considerations: Adhere to ethical guidelines and obtain necessary approvals or permissions before conducting your research. Protect participants’ rights, ensure informed consent, maintain confidentiality, and handle data responsibly.

11. Document and Report: Document your research design, data collection, and analysis procedures thoroughly. This helps ensure the transparency and reproducibility of your study. Prepare a comprehensive research report or manuscript that clearly presents your methodology, findings, limitations, and implications.

Learn more: What is Quantitative Research?

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Home » Descriptive Research Design – Types, Methods and Examples

Descriptive Research Design – Types, Methods and Examples

Table of Contents

Descriptive Research Design

Definition:

Descriptive research design is a type of research methodology that aims to describe or document the characteristics, behaviors, attitudes, opinions, or perceptions of a group or population being studied.

Descriptive research design does not attempt to establish cause-and-effect relationships between variables or make predictions about future outcomes. Instead, it focuses on providing a detailed and accurate representation of the data collected, which can be useful for generating hypotheses, exploring trends, and identifying patterns in the data.

Types of Descriptive Research Design

Types of Descriptive Research Design are as follows:

Cross-sectional Study

This involves collecting data at a single point in time from a sample or population to describe their characteristics or behaviors. For example, a researcher may conduct a cross-sectional study to investigate the prevalence of certain health conditions among a population, or to describe the attitudes and beliefs of a particular group.

Longitudinal Study

This involves collecting data over an extended period of time, often through repeated observations or surveys of the same group or population. Longitudinal studies can be used to track changes in attitudes, behaviors, or outcomes over time, or to investigate the effects of interventions or treatments.

This involves an in-depth examination of a single individual, group, or situation to gain a detailed understanding of its characteristics or dynamics. Case studies are often used in psychology, sociology, and business to explore complex phenomena or to generate hypotheses for further research.

Survey Research

This involves collecting data from a sample or population through standardized questionnaires or interviews. Surveys can be used to describe attitudes, opinions, behaviors, or demographic characteristics of a group, and can be conducted in person, by phone, or online.

Observational Research

This involves observing and documenting the behavior or interactions of individuals or groups in a natural or controlled setting. Observational studies can be used to describe social, cultural, or environmental phenomena, or to investigate the effects of interventions or treatments.

Correlational Research

This involves examining the relationships between two or more variables to describe their patterns or associations. Correlational studies can be used to identify potential causal relationships or to explore the strength and direction of relationships between variables.

Data Analysis Methods

Descriptive research design data analysis methods depend on the type of data collected and the research question being addressed. Here are some common methods of data analysis for descriptive research:

Descriptive Statistics

This method involves analyzing data to summarize and describe the key features of a sample or population. Descriptive statistics can include measures of central tendency (e.g., mean, median, mode) and measures of variability (e.g., range, standard deviation).

Cross-tabulation

This method involves analyzing data by creating a table that shows the frequency of two or more variables together. Cross-tabulation can help identify patterns or relationships between variables.

Content Analysis

This method involves analyzing qualitative data (e.g., text, images, audio) to identify themes, patterns, or trends. Content analysis can be used to describe the characteristics of a sample or population, or to identify factors that influence attitudes or behaviors.

Qualitative Coding

This method involves analyzing qualitative data by assigning codes to segments of data based on their meaning or content. Qualitative coding can be used to identify common themes, patterns, or categories within the data.

Visualization

This method involves creating graphs or charts to represent data visually. Visualization can help identify patterns or relationships between variables and make it easier to communicate findings to others.

Comparative Analysis

This method involves comparing data across different groups or time periods to identify similarities and differences. Comparative analysis can help describe changes in attitudes or behaviors over time or differences between subgroups within a population.

Applications of Descriptive Research Design

Descriptive research design has numerous applications in various fields. Some of the common applications of descriptive research design are:

• Market research: Descriptive research design is widely used in market research to understand consumer preferences, behavior, and attitudes. This helps companies to develop new products and services, improve marketing strategies, and increase customer satisfaction.
• Health research: Descriptive research design is used in health research to describe the prevalence and distribution of a disease or health condition in a population. This helps healthcare providers to develop prevention and treatment strategies.
• Educational research: Descriptive research design is used in educational research to describe the performance of students, schools, or educational programs. This helps educators to improve teaching methods and develop effective educational programs.
• Social science research: Descriptive research design is used in social science research to describe social phenomena such as cultural norms, values, and beliefs. This helps researchers to understand social behavior and develop effective policies.
• Public opinion research: Descriptive research design is used in public opinion research to understand the opinions and attitudes of the general public on various issues. This helps policymakers to develop effective policies that are aligned with public opinion.
• Environmental research: Descriptive research design is used in environmental research to describe the environmental conditions of a particular region or ecosystem. This helps policymakers and environmentalists to develop effective conservation and preservation strategies.

Descriptive Research Design Examples

Here are some real-time examples of descriptive research designs:

• A restaurant chain wants to understand the demographics and attitudes of its customers. They conduct a survey asking customers about their age, gender, income, frequency of visits, favorite menu items, and overall satisfaction. The survey data is analyzed using descriptive statistics and cross-tabulation to describe the characteristics of their customer base.
• A medical researcher wants to describe the prevalence and risk factors of a particular disease in a population. They conduct a cross-sectional study in which they collect data from a sample of individuals using a standardized questionnaire. The data is analyzed using descriptive statistics and cross-tabulation to identify patterns in the prevalence and risk factors of the disease.
• An education researcher wants to describe the learning outcomes of students in a particular school district. They collect test scores from a representative sample of students in the district and use descriptive statistics to calculate the mean, median, and standard deviation of the scores. They also create visualizations such as histograms and box plots to show the distribution of scores.
• A marketing team wants to understand the attitudes and behaviors of consumers towards a new product. They conduct a series of focus groups and use qualitative coding to identify common themes and patterns in the data. They also create visualizations such as word clouds to show the most frequently mentioned topics.
• An environmental scientist wants to describe the biodiversity of a particular ecosystem. They conduct an observational study in which they collect data on the species and abundance of plants and animals in the ecosystem. The data is analyzed using descriptive statistics to describe the diversity and richness of the ecosystem.

How to Conduct Descriptive Research Design

To conduct a descriptive research design, you can follow these general steps:

• Define your research question: Clearly define the research question or problem that you want to address. Your research question should be specific and focused to guide your data collection and analysis.
• Choose your research method: Select the most appropriate research method for your research question. As discussed earlier, common research methods for descriptive research include surveys, case studies, observational studies, cross-sectional studies, and longitudinal studies.
• Design your study: Plan the details of your study, including the sampling strategy, data collection methods, and data analysis plan. Determine the sample size and sampling method, decide on the data collection tools (such as questionnaires, interviews, or observations), and outline your data analysis plan.
• Collect data: Collect data from your sample or population using the data collection tools you have chosen. Ensure that you follow ethical guidelines for research and obtain informed consent from participants.
• Analyze data: Use appropriate statistical or qualitative analysis methods to analyze your data. As discussed earlier, common data analysis methods for descriptive research include descriptive statistics, cross-tabulation, content analysis, qualitative coding, visualization, and comparative analysis.
• I nterpret results: Interpret your findings in light of your research question and objectives. Identify patterns, trends, and relationships in the data, and describe the characteristics of your sample or population.
• Draw conclusions and report results: Draw conclusions based on your analysis and interpretation of the data. Report your results in a clear and concise manner, using appropriate tables, graphs, or figures to present your findings. Ensure that your report follows accepted research standards and guidelines.

When to Use Descriptive Research Design

Descriptive research design is used in situations where the researcher wants to describe a population or phenomenon in detail. It is used to gather information about the current status or condition of a group or phenomenon without making any causal inferences. Descriptive research design is useful in the following situations:

• Exploratory research: Descriptive research design is often used in exploratory research to gain an initial understanding of a phenomenon or population.
• Identifying trends: Descriptive research design can be used to identify trends or patterns in a population, such as changes in consumer behavior or attitudes over time.
• Market research: Descriptive research design is commonly used in market research to understand consumer preferences, behavior, and attitudes.
• Health research: Descriptive research design is useful in health research to describe the prevalence and distribution of a disease or health condition in a population.
• Social science research: Descriptive research design is used in social science research to describe social phenomena such as cultural norms, values, and beliefs.
• Educational research: Descriptive research design is used in educational research to describe the performance of students, schools, or educational programs.

Purpose of Descriptive Research Design

The main purpose of descriptive research design is to describe and measure the characteristics of a population or phenomenon in a systematic and objective manner. It involves collecting data that describe the current status or condition of the population or phenomenon of interest, without manipulating or altering any variables.

The purpose of descriptive research design can be summarized as follows:

• To provide an accurate description of a population or phenomenon: Descriptive research design aims to provide a comprehensive and accurate description of a population or phenomenon of interest. This can help researchers to develop a better understanding of the characteristics of the population or phenomenon.
• To identify trends and patterns: Descriptive research design can help researchers to identify trends and patterns in the data, such as changes in behavior or attitudes over time. This can be useful for making predictions and developing strategies.
• To generate hypotheses: Descriptive research design can be used to generate hypotheses or research questions that can be tested in future studies. For example, if a descriptive study finds a correlation between two variables, this could lead to the development of a hypothesis about the causal relationship between the variables.
• To establish a baseline: Descriptive research design can establish a baseline or starting point for future research. This can be useful for comparing data from different time periods or populations.

Characteristics of Descriptive Research Design

Descriptive research design has several key characteristics that distinguish it from other research designs. Some of the main characteristics of descriptive research design are:

• Objective : Descriptive research design is objective in nature, which means that it focuses on collecting factual and accurate data without any personal bias. The researcher aims to report the data objectively without any personal interpretation.
• Non-experimental: Descriptive research design is non-experimental, which means that the researcher does not manipulate any variables. The researcher simply observes and records the behavior or characteristics of the population or phenomenon of interest.
• Quantitative : Descriptive research design is quantitative in nature, which means that it involves collecting numerical data that can be analyzed using statistical techniques. This helps to provide a more precise and accurate description of the population or phenomenon.
• Cross-sectional: Descriptive research design is often cross-sectional, which means that the data is collected at a single point in time. This can be useful for understanding the current state of the population or phenomenon, but it may not provide information about changes over time.
• Large sample size: Descriptive research design typically involves a large sample size, which helps to ensure that the data is representative of the population of interest. A large sample size also helps to increase the reliability and validity of the data.
• Systematic and structured: Descriptive research design involves a systematic and structured approach to data collection, which helps to ensure that the data is accurate and reliable. This involves using standardized procedures for data collection, such as surveys, questionnaires, or observation checklists.

Advantages of Descriptive Research Design

Descriptive research design has several advantages that make it a popular choice for researchers. Some of the main advantages of descriptive research design are:

• Provides an accurate description: Descriptive research design is focused on accurately describing the characteristics of a population or phenomenon. This can help researchers to develop a better understanding of the subject of interest.
• Easy to conduct: Descriptive research design is relatively easy to conduct and requires minimal resources compared to other research designs. It can be conducted quickly and efficiently, and data can be collected through surveys, questionnaires, or observations.
• Useful for generating hypotheses: Descriptive research design can be used to generate hypotheses or research questions that can be tested in future studies. For example, if a descriptive study finds a correlation between two variables, this could lead to the development of a hypothesis about the causal relationship between the variables.
• Large sample size : Descriptive research design typically involves a large sample size, which helps to ensure that the data is representative of the population of interest. A large sample size also helps to increase the reliability and validity of the data.
• Can be used to monitor changes : Descriptive research design can be used to monitor changes over time in a population or phenomenon. This can be useful for identifying trends and patterns, and for making predictions about future behavior or attitudes.
• Can be used in a variety of fields : Descriptive research design can be used in a variety of fields, including social sciences, healthcare, business, and education.

Limitation of Descriptive Research Design

Descriptive research design also has some limitations that researchers should consider before using this design. Some of the main limitations of descriptive research design are:

• Cannot establish cause and effect: Descriptive research design cannot establish cause and effect relationships between variables. It only provides a description of the characteristics of the population or phenomenon of interest.
• Limited generalizability: The results of a descriptive study may not be generalizable to other populations or situations. This is because descriptive research design often involves a specific sample or situation, which may not be representative of the broader population.
• Potential for bias: Descriptive research design can be subject to bias, particularly if the researcher is not objective in their data collection or interpretation. This can lead to inaccurate or incomplete descriptions of the population or phenomenon of interest.
• Limited depth: Descriptive research design may provide a superficial description of the population or phenomenon of interest. It does not delve into the underlying causes or mechanisms behind the observed behavior or characteristics.
• Limited utility for theory development: Descriptive research design may not be useful for developing theories about the relationship between variables. It only provides a description of the variables themselves.
• Relies on self-report data: Descriptive research design often relies on self-report data, such as surveys or questionnaires. This type of data may be subject to biases, such as social desirability bias or recall bias.

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Muhammad Hassan

Researcher, Academic Writer, Web developer

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• What Is a Research Design | Types, Guide & Examples

What Is a Research Design | Types, Guide & Examples

Published on June 7, 2021 by Shona McCombes . Revised on November 20, 2023 by Pritha Bhandari.

A research design is a strategy for answering your   research question  using empirical data. Creating a research design means making decisions about:

• Your overall research objectives and approach
• Whether you’ll rely on primary research or secondary research
• Your sampling methods or criteria for selecting subjects
• Your data collection methods
• The procedures you’ll follow to collect data
• Your data analysis methods

A well-planned research design helps ensure that your methods match your research objectives and that you use the right kind of analysis for your data.

Table of contents

Step 1: consider your aims and approach, step 2: choose a type of research design, step 3: identify your population and sampling method, step 4: choose your data collection methods, step 5: plan your data collection procedures, step 6: decide on your data analysis strategies, other interesting articles, frequently asked questions about research design.

• Introduction

Before you can start designing your research, you should already have a clear idea of the research question you want to investigate.

There are many different ways you could go about answering this question. Your research design choices should be driven by your aims and priorities—start by thinking carefully about what you want to achieve.

The first choice you need to make is whether you’ll take a qualitative or quantitative approach.

Qualitative research designs tend to be more flexible and inductive , allowing you to adjust your approach based on what you find throughout the research process.

Quantitative research designs tend to be more fixed and deductive , with variables and hypotheses clearly defined in advance of data collection.

It’s also possible to use a mixed-methods design that integrates aspects of both approaches. By combining qualitative and quantitative insights, you can gain a more complete picture of the problem you’re studying and strengthen the credibility of your conclusions.

Practical and ethical considerations when designing research

As well as scientific considerations, you need to think practically when designing your research. If your research involves people or animals, you also need to consider research ethics .

• How much time do you have to collect data and write up the research?
• Will you be able to gain access to the data you need (e.g., by travelling to a specific location or contacting specific people)?
• Do you have the necessary research skills (e.g., statistical analysis or interview techniques)?
• Will you need ethical approval ?

At each stage of the research design process, make sure that your choices are practically feasible.

Prevent plagiarism. Run a free check.

Within both qualitative and quantitative approaches, there are several types of research design to choose from. Each type provides a framework for the overall shape of your research.

Types of quantitative research designs

Quantitative designs can be split into four main types.

• Experimental and   quasi-experimental designs allow you to test cause-and-effect relationships
• Descriptive and correlational designs allow you to measure variables and describe relationships between them.

With descriptive and correlational designs, you can get a clear picture of characteristics, trends and relationships as they exist in the real world. However, you can’t draw conclusions about cause and effect (because correlation doesn’t imply causation ).

Experiments are the strongest way to test cause-and-effect relationships without the risk of other variables influencing the results. However, their controlled conditions may not always reflect how things work in the real world. They’re often also more difficult and expensive to implement.

Types of qualitative research designs

Qualitative designs are less strictly defined. This approach is about gaining a rich, detailed understanding of a specific context or phenomenon, and you can often be more creative and flexible in designing your research.

The table below shows some common types of qualitative design. They often have similar approaches in terms of data collection, but focus on different aspects when analyzing the data.

Your research design should clearly define who or what your research will focus on, and how you’ll go about choosing your participants or subjects.

In research, a population is the entire group that you want to draw conclusions about, while a sample is the smaller group of individuals you’ll actually collect data from.

Defining the population

A population can be made up of anything you want to study—plants, animals, organizations, texts, countries, etc. In the social sciences, it most often refers to a group of people.

For example, will you focus on people from a specific demographic, region or background? Are you interested in people with a certain job or medical condition, or users of a particular product?

The more precisely you define your population, the easier it will be to gather a representative sample.

• Sampling methods

Even with a narrowly defined population, it’s rarely possible to collect data from every individual. Instead, you’ll collect data from a sample.

To select a sample, there are two main approaches: probability sampling and non-probability sampling . The sampling method you use affects how confidently you can generalize your results to the population as a whole.

Probability sampling is the most statistically valid option, but it’s often difficult to achieve unless you’re dealing with a very small and accessible population.

For practical reasons, many studies use non-probability sampling, but it’s important to be aware of the limitations and carefully consider potential biases. You should always make an effort to gather a sample that’s as representative as possible of the population.

Case selection in qualitative research

In some types of qualitative designs, sampling may not be relevant.

For example, in an ethnography or a case study , your aim is to deeply understand a specific context, not to generalize to a population. Instead of sampling, you may simply aim to collect as much data as possible about the context you are studying.

In these types of design, you still have to carefully consider your choice of case or community. You should have a clear rationale for why this particular case is suitable for answering your research question .

For example, you might choose a case study that reveals an unusual or neglected aspect of your research problem, or you might choose several very similar or very different cases in order to compare them.

Data collection methods are ways of directly measuring variables and gathering information. They allow you to gain first-hand knowledge and original insights into your research problem.

You can choose just one data collection method, or use several methods in the same study.

Survey methods

Surveys allow you to collect data about opinions, behaviors, experiences, and characteristics by asking people directly. There are two main survey methods to choose from: questionnaires and interviews .

Observation methods

Observational studies allow you to collect data unobtrusively, observing characteristics, behaviors or social interactions without relying on self-reporting.

Observations may be conducted in real time, taking notes as you observe, or you might make audiovisual recordings for later analysis. They can be qualitative or quantitative.

Other methods of data collection

There are many other ways you might collect data depending on your field and topic.

If you’re not sure which methods will work best for your research design, try reading some papers in your field to see what kinds of data collection methods they used.

Secondary data

If you don’t have the time or resources to collect data from the population you’re interested in, you can also choose to use secondary data that other researchers already collected—for example, datasets from government surveys or previous studies on your topic.

With this raw data, you can do your own analysis to answer new research questions that weren’t addressed by the original study.

Using secondary data can expand the scope of your research, as you may be able to access much larger and more varied samples than you could collect yourself.

However, it also means you don’t have any control over which variables to measure or how to measure them, so the conclusions you can draw may be limited.

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As well as deciding on your methods, you need to plan exactly how you’ll use these methods to collect data that’s consistent, accurate, and unbiased.

Planning systematic procedures is especially important in quantitative research, where you need to precisely define your variables and ensure your measurements are high in reliability and validity.

Operationalization

Some variables, like height or age, are easily measured. But often you’ll be dealing with more abstract concepts, like satisfaction, anxiety, or competence. Operationalization means turning these fuzzy ideas into measurable indicators.

If you’re using observations , which events or actions will you count?

If you’re using surveys , which questions will you ask and what range of responses will be offered?

You may also choose to use or adapt existing materials designed to measure the concept you’re interested in—for example, questionnaires or inventories whose reliability and validity has already been established.

Reliability and validity

Reliability means your results can be consistently reproduced, while validity means that you’re actually measuring the concept you’re interested in.

For valid and reliable results, your measurement materials should be thoroughly researched and carefully designed. Plan your procedures to make sure you carry out the same steps in the same way for each participant.

If you’re developing a new questionnaire or other instrument to measure a specific concept, running a pilot study allows you to check its validity and reliability in advance.

Sampling procedures

As well as choosing an appropriate sampling method , you need a concrete plan for how you’ll actually contact and recruit your selected sample.

That means making decisions about things like:

• How many participants do you need for an adequate sample size?
• What inclusion and exclusion criteria will you use to identify eligible participants?
• How will you contact your sample—by mail, online, by phone, or in person?

If you’re using a probability sampling method , it’s important that everyone who is randomly selected actually participates in the study. How will you ensure a high response rate?

If you’re using a non-probability method , how will you avoid research bias and ensure a representative sample?

Data management

It’s also important to create a data management plan for organizing and storing your data.

Will you need to transcribe interviews or perform data entry for observations? You should anonymize and safeguard any sensitive data, and make sure it’s backed up regularly.

Keeping your data well-organized will save time when it comes to analyzing it. It can also help other researchers validate and add to your findings (high replicability ).

On its own, raw data can’t answer your research question. The last step of designing your research is planning how you’ll analyze the data.

Quantitative data analysis

In quantitative research, you’ll most likely use some form of statistical analysis . With statistics, you can summarize your sample data, make estimates, and test hypotheses.

Using descriptive statistics , you can summarize your sample data in terms of:

• The distribution of the data (e.g., the frequency of each score on a test)
• The central tendency of the data (e.g., the mean to describe the average score)
• The variability of the data (e.g., the standard deviation to describe how spread out the scores are)

The specific calculations you can do depend on the level of measurement of your variables.

Using inferential statistics , you can:

• Make estimates about the population based on your sample data.
• Test hypotheses about a relationship between variables.

Regression and correlation tests look for associations between two or more variables, while comparison tests (such as t tests and ANOVAs ) look for differences in the outcomes of different groups.

Your choice of statistical test depends on various aspects of your research design, including the types of variables you’re dealing with and the distribution of your data.

Qualitative data analysis

In qualitative research, your data will usually be very dense with information and ideas. Instead of summing it up in numbers, you’ll need to comb through the data in detail, interpret its meanings, identify patterns, and extract the parts that are most relevant to your research question.

Two of the most common approaches to doing this are thematic analysis and discourse analysis .

There are many other ways of analyzing qualitative data depending on the aims of your research. To get a sense of potential approaches, try reading some qualitative research papers in your field.

If you want to know more about the research process , methodology , research bias , or statistics , make sure to check out some of our other articles with explanations and examples.

• Simple random sampling
• Stratified sampling
• Cluster sampling
• Likert scales
• Reproducibility

 Statistics

• Null hypothesis
• Statistical power
• Probability distribution
• Effect size
• Poisson distribution

Research bias

• Optimism bias
• Cognitive bias
• Implicit bias
• Hawthorne effect
• Anchoring bias
• Explicit bias

A research design is a strategy for answering your   research question . It defines your overall approach and determines how you will collect and analyze data.

A well-planned research design helps ensure that your methods match your research aims, that you collect high-quality data, and that you use the right kind of analysis to answer your questions, utilizing credible sources . This allows you to draw valid , trustworthy conclusions.

Quantitative research designs can be divided into two main categories:

• Correlational and descriptive designs are used to investigate characteristics, averages, trends, and associations between variables.
• Experimental and quasi-experimental designs are used to test causal relationships .

Qualitative research designs tend to be more flexible. Common types of qualitative design include case study , ethnography , and grounded theory designs.

The priorities of a research design can vary depending on the field, but you usually have to specify:

• Your research questions and/or hypotheses
• Your overall approach (e.g., qualitative or quantitative )
• The type of design you’re using (e.g., a survey , experiment , or case study )
• Your data collection methods (e.g., questionnaires , observations)
• Your data collection procedures (e.g., operationalization , timing and data management)
• Your data analysis methods (e.g., statistical tests  or thematic analysis )

A sample is a subset of individuals from a larger population . Sampling means selecting the group that you will actually collect data from in your research. For example, if you are researching the opinions of students in your university, you could survey a sample of 100 students.

In statistics, sampling allows you to test a hypothesis about the characteristics of a population.

Operationalization means turning abstract conceptual ideas into measurable observations.

For example, the concept of social anxiety isn’t directly observable, but it can be operationally defined in terms of self-rating scores, behavioral avoidance of crowded places, or physical anxiety symptoms in social situations.

Before collecting data , it’s important to consider how you will operationalize the variables that you want to measure.

A research project is an academic, scientific, or professional undertaking to answer a research question . Research projects can take many forms, such as qualitative or quantitative , descriptive , longitudinal , experimental , or correlational . What kind of research approach you choose will depend on your topic.

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• Descriptive Research Designs: Types, Examples & Methods

One of the components of research is getting enough information about the research problem—the what, how, when and where answers, which is why descriptive research is an important type of research. It is very useful when conducting research whose aim is to identify characteristics, frequencies, trends, correlations, and categories.

This research method takes a problem with little to no relevant information and gives it a befitting description using qualitative and quantitative research method s. Descriptive research aims to accurately describe a research problem.

In the subsequent sections, we will be explaining what descriptive research means, its types, examples, and data collection methods.

What is Descriptive Research?

Descriptive research is a type of research that describes a population, situation, or phenomenon that is being studied. It focuses on answering the how, what, when, and where questions If a research problem, rather than the why.

This is mainly because it is important to have a proper understanding of what a research problem is about before investigating why it exists in the first place. 

For example, an investor considering an investment in the ever-changing Amsterdam housing market needs to understand what the current state of the market is, how it changes (increasing or decreasing), and when it changes (time of the year) before asking for the why. This is where descriptive research comes in.

What Are The Types of Descriptive Research?

Descriptive research is classified into different types according to the kind of approach that is used in conducting descriptive research. The different types of descriptive research are highlighted below:

• Descriptive-survey

Descriptive survey research uses surveys to gather data about varying subjects. This data aims to know the extent to which different conditions can be obtained among these subjects.

For example, a researcher wants to determine the qualification of employed professionals in Maryland. He uses a survey as his research instrument , and each item on the survey related to qualifications is subjected to a Yes/No answer. 

This way, the researcher can describe the qualifications possessed by the employed demographics of this community. 

• Descriptive-normative survey

This is an extension of the descriptive survey, with the addition being the normative element. In the descriptive-normative survey, the results of the study should be compared with the norm.

For example, an organization that wishes to test the skills of its employees by a team may have them take a skills test. The skills tests are the evaluation tool in this case, and the result of this test is compared with the norm of each role.

If the score of the team is one standard deviation above the mean, it is very satisfactory, if within the mean, satisfactory, and one standard deviation below the mean is unsatisfactory.

• Descriptive-status

This is a quantitative description technique that seeks to answer questions about real-life situations. For example, a researcher researching the income of the employees in a company, and the relationship with their performance.

A survey will be carried out to gather enough data about the income of the employees, then their performance will be evaluated and compared to their income. This will help determine whether a higher income means better performance and low income means lower performance or vice versa.

• Descriptive-analysis

The descriptive-analysis method of research describes a subject by further analyzing it, which in this case involves dividing it into 2 parts. For example, the HR personnel of a company that wishes to analyze the job role of each employee of the company may divide the employees into the people that work at the Headquarters in the US and those that work from Oslo, Norway office.

A questionnaire is devised to analyze the job role of employees with similar salaries and who work in similar positions.

• Descriptive classification

This method is employed in biological sciences for the classification of plants and animals. A researcher who wishes to classify the sea animals into different species will collect samples from various search stations, then classify them accordingly.

• Descriptive-comparative

In descriptive-comparative research, the researcher considers 2 variables that are not manipulated, and establish a formal procedure to conclude that one is better than the other. For example, an examination body wants to determine the better method of conducting tests between paper-based and computer-based tests.

A random sample of potential participants of the test may be asked to use the 2 different methods, and factors like failure rates, time factors, and others will be evaluated to arrive at the best method.

• Correlative Survey

Correlative surveys are used to determine whether the relationship between 2 variables is positive, negative, or neutral. That is, if 2 variables say X and Y are directly proportional, inversely proportional or are not related to each other.

Examples of Descriptive Research

There are different examples of descriptive research, that may be highlighted from its types, uses, and applications. However, we will be restricting ourselves to only 3 distinct examples in this article.

• Comparing Student Performance:

An academic institution may wish 2 compare the performance of its junior high school students in English language and Mathematics. This may be used to classify students based on 2 major groups, with one group going ahead to study while courses, while the other study courses in the Arts & Humanities field.

Students who are more proficient in mathematics will be encouraged to go into STEM and vice versa. Institutions may also use this data to identify students’ weak points and work on ways to assist them.

• Scientific Classification

During the major scientific classification of plants, animals, and periodic table elements, the characteristics and components of each subject are evaluated and used to determine how they are classified.

For example, living things may be classified into kingdom Plantae or kingdom animal is depending on their nature. Further classification may group animals into mammals, pieces, vertebrae, invertebrae, etc. 

All these classifications are made a result of descriptive research which describes what they are.

• Human Behavior

When studying human behaviour based on a factor or event, the researcher observes the characteristics, behaviour, and reaction, then use it to conclude. A company willing to sell to its target market needs to first study the behaviour of the market.

This may be done by observing how its target reacts to a competitor’s product, then use it to determine their behaviour.

What are the Characteristics of Descriptive Research?  

The characteristics of descriptive research can be highlighted from its definition, applications, data collection methods, and examples. Some characteristics of descriptive research are:

• Quantitativeness

Descriptive research uses a quantitative research method by collecting quantifiable information to be used for statistical analysis of the population sample. This is very common when dealing with research in the physical sciences.

• Qualitativeness

It can also be carried out using the qualitative research method, to properly describe the research problem. This is because descriptive research is more explanatory than exploratory or experimental.

• Uncontrolled variables

In descriptive research, researchers cannot control the variables like they do in experimental research.

• The basis for further research

The results of descriptive research can be further analyzed and used in other research methods. It can also inform the next line of research, including the research method that should be used.

This is because it provides basic information about the research problem, which may give birth to other questions like why a particular thing is the way it is.

Why Use Descriptive Research Design?  

Descriptive research can be used to investigate the background of a research problem and get the required information needed to carry out further research. It is used in multiple ways by different organizations, and especially when getting the required information about their target audience.

• Define subject characteristics :

It is used to determine the characteristics of the subjects, including their traits, behaviour, opinion, etc. This information may be gathered with the use of surveys, which are shared with the respondents who in this case, are the research subjects.

For example, a survey evaluating the number of hours millennials in a community spends on the internet weekly, will help a service provider make informed business decisions regarding the market potential of the community.

• Measure Data Trends

It helps to measure the changes in data over some time through statistical methods. Consider the case of individuals who want to invest in stock markets, so they evaluate the changes in prices of the available stocks to make a decision investment decision.

Brokerage companies are however the ones who carry out the descriptive research process, while individuals can view the data trends and make decisions.

Descriptive research is also used to compare how different demographics respond to certain variables. For example, an organization may study how people with different income levels react to the launch of a new Apple phone.

This kind of research may take a survey that will help determine which group of individuals are purchasing the new Apple phone. Do the low-income earners also purchase the phone, or only the high-income earners do?

Further research using another technique will explain why low-income earners are purchasing the phone even though they can barely afford it. This will help inform strategies that will lure other low-income earners and increase company sales.

• Validate existing conditions

When you are not sure about the validity of an existing condition, you can use descriptive research to ascertain the underlying patterns of the research object. This is because descriptive research methods make an in-depth analysis of each variable before making conclusions.

• Conducted Overtime

Descriptive research is conducted over some time to ascertain the changes observed at each point in time. The higher the number of times it is conducted, the more authentic the conclusion will be.

What are the Disadvantages of Descriptive Research?  

• Response and Non-response Bias

Respondents may either decide not to respond to questions or give incorrect responses if they feel the questions are too confidential. When researchers use observational methods, respondents may also decide to behave in a particular manner because they feel they are being watched.

• The researcher may decide to influence the result of the research due to personal opinion or bias towards a particular subject. For example, a stockbroker who also has a business of his own may try to lure investors into investing in his own company by manipulating results.
• A case-study or sample taken from a large population is not representative of the whole population.
• Limited scope:The scope of descriptive research is limited to the what of research, with no information on why thereby limiting the scope of the research.

What are the Data Collection Methods in Descriptive Research?  

There are 3 main data collection methods in descriptive research, namely; observational method, case study method, and survey research.

1. Observational Method

The observational method allows researchers to collect data based on their view of the behaviour and characteristics of the respondent, with the respondents themselves not directly having an input. It is often used in market research, psychology, and some other social science research to understand human behaviour.

It is also an important aspect of physical scientific research, with it being one of the most effective methods of conducting descriptive research . This process can be said to be either quantitative or qualitative.

Quantitative observation involved the objective collection of numerical data , whose results can be analyzed using numerical and statistical methods. 

Qualitative observation, on the other hand, involves the monitoring of characteristics and not the measurement of numbers. The researcher makes his observation from a distance, records it, and is used to inform conclusions.

2. Case Study Method

A case study is a sample group (an individual, a group of people, organizations, events, etc.) whose characteristics are used to describe the characteristics of a larger group in which the case study is a subgroup. The information gathered from investigating a case study may be generalized to serve the larger group.

This generalization, may, however, be risky because case studies are not sufficient to make accurate predictions about larger groups. Case studies are a poor case of generalization.

3. Survey Research

This is a very popular data collection method in research designs. In survey research, researchers create a survey or questionnaire and distribute it to respondents who give answers.

Generally, it is used to obtain quick information directly from the primary source and also conducting rigorous quantitative and qualitative research. In some cases, survey research uses a blend of both qualitative and quantitative strategies.

Survey research can be carried out both online and offline using the following methods

• Online Surveys: This is a cheap method of carrying out surveys and getting enough responses. It can be carried out using Formplus, an online survey builder. Formplus has amazing tools and features that will help increase response rates.
• Offline Surveys: This includes paper forms, mobile offline forms , and SMS-based forms.

What Are The Differences Between Descriptive and Correlational Research?  

Before going into the differences between descriptive and correlation research, we need to have a proper understanding of what correlation research is about. Therefore, we will be giving a summary of the correlation research below.

Correlational research is a type of descriptive research, which is used to measure the relationship between 2 variables, with the researcher having no control over them. It aims to find whether there is; positive correlation (both variables change in the same direction), negative correlation (the variables change in the opposite direction), or zero correlation (there is no relationship between the variables).

Correlational research may be used in 2 situations;

(i) when trying to find out if there is a relationship between two variables, and

(ii) when a causal relationship is suspected between two variables, but it is impractical or unethical to conduct experimental research that manipulates one of the variables. 

Below are some of the differences between correlational and descriptive research:

• Definitions :

Descriptive research aims is a type of research that provides an in-depth understanding of the study population, while correlational research is the type of research that measures the relationship between 2 variables. 

• Characteristics :

Descriptive research provides descriptive data explaining what the research subject is about, while correlation research explores the relationship between data and not their description.

• Predictions :

 Predictions cannot be made in descriptive research while correlation research accommodates the possibility of making predictions.

Descriptive Research vs. Causal Research

Descriptive research and causal research are both research methodologies, however, one focuses on a subject’s behaviors while the latter focuses on a relationship’s cause-and-effect. To buttress the above point, descriptive research aims to describe and document the characteristics, behaviors, or phenomena of a particular or specific population or situation. 

It focuses on providing an accurate and detailed account of an already existing state of affairs between variables. Descriptive research answers the questions of “what,” “where,” “when,” and “how” without attempting to establish any causal relationships or explain any underlying factors that might have caused the behavior.

Causal research, on the other hand, seeks to determine cause-and-effect relationships between variables. It aims to point out the factors that influence or cause a particular result or behavior. Causal research involves manipulating variables, controlling conditions or a subgroup, and observing the resulting effects. The primary objective of causal research is to establish a cause-effect relationship and provide insights into why certain phenomena happen the way they do.

Descriptive Research vs. Analytical Research

Descriptive research provides a detailed and comprehensive account of a specific situation or phenomenon. It focuses on describing and summarizing data without making inferences or attempting to explain underlying factors or the cause of the factor. 

It is primarily concerned with providing an accurate and objective representation of the subject of research. While analytical research goes beyond the description of the phenomena and seeks to analyze and interpret data to discover if there are patterns, relationships, or any underlying factors. 

It examines the data critically, applies statistical techniques or other analytical methods, and draws conclusions based on the discovery. Analytical research also aims to explore the relationships between variables and understand the underlying mechanisms or processes involved.

Descriptive Research vs. Exploratory Research

Descriptive research is a research method that focuses on providing a detailed and accurate account of a specific situation, group, or phenomenon. This type of research describes the characteristics, behaviors, or relationships within the given context without looking for an underlying cause. 

Descriptive research typically involves collecting and analyzing quantitative or qualitative data to generate descriptive statistics or narratives. Exploratory research differs from descriptive research because it aims to explore and gain firsthand insights or knowledge into a relatively unexplored or poorly understood topic. 

It focuses on generating ideas, hypotheses, or theories rather than providing definitive answers. Exploratory research is often conducted at the early stages of a research project to gather preliminary information and identify key variables or factors for further investigation. It involves open-ended interviews, observations, or small-scale surveys to gather qualitative data.

Read More – Exploratory Research: What are its Method & Examples?

Descriptive Research vs. Experimental Research

Descriptive research aims to describe and document the characteristics, behaviors, or phenomena of a particular population or situation. It focuses on providing an accurate and detailed account of the existing state of affairs. 

Descriptive research typically involves collecting data through surveys, observations, or existing records and analyzing the data to generate descriptive statistics or narratives. It does not involve manipulating variables or establishing cause-and-effect relationships.

Experimental research, on the other hand, involves manipulating variables and controlling conditions to investigate cause-and-effect relationships. It aims to establish causal relationships by introducing an intervention or treatment and observing the resulting effects. 

Experimental research typically involves randomly assigning participants to different groups, such as control and experimental groups, and measuring the outcomes. It allows researchers to control for confounding variables and draw causal conclusions.

Related – Experimental vs Non-Experimental Research: 15 Key Differences

Descriptive Research vs. Explanatory Research

Descriptive research focuses on providing a detailed and accurate account of a specific situation, group, or phenomenon. It aims to describe the characteristics, behaviors, or relationships within the given context. 

Descriptive research is primarily concerned with providing an objective representation of the subject of study without explaining underlying causes or mechanisms. Explanatory research seeks to explain the relationships between variables and uncover the underlying causes or mechanisms. 

It goes beyond description and aims to understand the reasons or factors that influence a particular outcome or behavior. Explanatory research involves analyzing data, conducting statistical analyses, and developing theories or models to explain the observed relationships.

Descriptive Research vs. Inferential Research

Descriptive research focuses on describing and summarizing data without making inferences or generalizations beyond the specific sample or population being studied. It aims to provide an accurate and objective representation of the subject of study. 

Descriptive research typically involves analyzing data to generate descriptive statistics, such as means, frequencies, or percentages, to describe the characteristics or behaviors observed.

Inferential research, however, involves making inferences or generalizations about a larger population based on a smaller sample. 

It aims to draw conclusions about the population characteristics or relationships by analyzing the sample data. Inferential research uses statistical techniques to estimate population parameters, test hypotheses, and determine the level of confidence or significance in the findings.

Related – Inferential Statistics: Definition, Types + Examples

Conclusion  

The uniqueness of descriptive research partly lies in its ability to explore both quantitative and qualitative research methods. Therefore, when conducting descriptive research, researchers have the opportunity to use a wide variety of techniques that aids the research process.

Descriptive research explores research problems in-depth, beyond the surface level thereby giving a detailed description of the research subject. That way, it can aid further research in the field, including other research methods .

It is also very useful in solving real-life problems in various fields of social science, physical science, and education.

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Research Design 101

Everything You Need To Get Started (With Examples)

By: Derek Jansen (MBA) | Reviewers: Eunice Rautenbach (DTech) & Kerryn Warren (PhD) | April 2023

Navigating the world of research can be daunting, especially if you’re a first-time researcher. One concept you’re bound to run into fairly early in your research journey is that of “ research design ”. Here, we’ll guide you through the basics using practical examples , so that you can approach your research with confidence.

Overview: Research Design 101

What is research design.

• Research design types for quantitative studies
• Video explainer : quantitative research design
• Research design types for qualitative studies
• Video explainer : qualitative research design
• How to choose a research design
• Key takeaways

Research design refers to the overall plan, structure or strategy that guides a research project , from its conception to the final data analysis. A good research design serves as the blueprint for how you, as the researcher, will collect and analyse data while ensuring consistency, reliability and validity throughout your study.

Understanding different types of research designs is essential as helps ensure that your approach is suitable  given your research aims, objectives and questions , as well as the resources you have available to you. Without a clear big-picture view of how you’ll design your research, you run the risk of potentially making misaligned choices in terms of your methodology – especially your sampling , data collection and data analysis decisions.

The problem with defining research design…

One of the reasons students struggle with a clear definition of research design is because the term is used very loosely across the internet, and even within academia.

Some sources claim that the three research design types are qualitative, quantitative and mixed methods , which isn’t quite accurate (these just refer to the type of data that you’ll collect and analyse). Other sources state that research design refers to the sum of all your design choices, suggesting it’s more like a research methodology . Others run off on other less common tangents. No wonder there’s confusion!

In this article, we’ll clear up the confusion. We’ll explain the most common research design types for both qualitative and quantitative research projects, whether that is for a full dissertation or thesis, or a smaller research paper or article.

Research Design: Quantitative Studies

Quantitative research involves collecting and analysing data in a numerical form. Broadly speaking, there are four types of quantitative research designs: descriptive , correlational , experimental , and quasi-experimental . 

Descriptive Research Design

As the name suggests, descriptive research design focuses on describing existing conditions, behaviours, or characteristics by systematically gathering information without manipulating any variables. In other words, there is no intervention on the researcher’s part – only data collection.

For example, if you’re studying smartphone addiction among adolescents in your community, you could deploy a survey to a sample of teens asking them to rate their agreement with certain statements that relate to smartphone addiction. The collected data would then provide insight regarding how widespread the issue may be – in other words, it would describe the situation.

The key defining attribute of this type of research design is that it purely describes the situation . In other words, descriptive research design does not explore potential relationships between different variables or the causes that may underlie those relationships. Therefore, descriptive research is useful for generating insight into a research problem by describing its characteristics . By doing so, it can provide valuable insights and is often used as a precursor to other research design types.

Correlational Research Design

Correlational design is a popular choice for researchers aiming to identify and measure the relationship between two or more variables without manipulating them . In other words, this type of research design is useful when you want to know whether a change in one thing tends to be accompanied by a change in another thing.

For example, if you wanted to explore the relationship between exercise frequency and overall health, you could use a correlational design to help you achieve this. In this case, you might gather data on participants’ exercise habits, as well as records of their health indicators like blood pressure, heart rate, or body mass index. Thereafter, you’d use a statistical test to assess whether there’s a relationship between the two variables (exercise frequency and health).

As you can see, correlational research design is useful when you want to explore potential relationships between variables that cannot be manipulated or controlled for ethical, practical, or logistical reasons. It is particularly helpful in terms of developing predictions , and given that it doesn’t involve the manipulation of variables, it can be implemented at a large scale more easily than experimental designs (which will look at next).

That said, it’s important to keep in mind that correlational research design has limitations – most notably that it cannot be used to establish causality . In other words, correlation does not equal causation . To establish causality, you’ll need to move into the realm of experimental design, coming up next…

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Experimental Research Design

Experimental research design is used to determine if there is a causal relationship between two or more variables . With this type of research design, you, as the researcher, manipulate one variable (the independent variable) while controlling others (dependent variables). Doing so allows you to observe the effect of the former on the latter and draw conclusions about potential causality.

For example, if you wanted to measure if/how different types of fertiliser affect plant growth, you could set up several groups of plants, with each group receiving a different type of fertiliser, as well as one with no fertiliser at all. You could then measure how much each plant group grew (on average) over time and compare the results from the different groups to see which fertiliser was most effective.

Overall, experimental research design provides researchers with a powerful way to identify and measure causal relationships (and the direction of causality) between variables. However, developing a rigorous experimental design can be challenging as it’s not always easy to control all the variables in a study. This often results in smaller sample sizes , which can reduce the statistical power and generalisability of the results.

Moreover, experimental research design requires random assignment . This means that the researcher needs to assign participants to different groups or conditions in a way that each participant has an equal chance of being assigned to any group (note that this is not the same as random sampling ). Doing so helps reduce the potential for bias and confounding variables . This need for random assignment can lead to ethics-related issues . For example, withholding a potentially beneficial medical treatment from a control group may be considered unethical in certain situations.

Quasi-Experimental Research Design

Quasi-experimental research design is used when the research aims involve identifying causal relations , but one cannot (or doesn’t want to) randomly assign participants to different groups (for practical or ethical reasons). Instead, with a quasi-experimental research design, the researcher relies on existing groups or pre-existing conditions to form groups for comparison.

For example, if you were studying the effects of a new teaching method on student achievement in a particular school district, you may be unable to randomly assign students to either group and instead have to choose classes or schools that already use different teaching methods. This way, you still achieve separate groups, without having to assign participants to specific groups yourself.

Naturally, quasi-experimental research designs have limitations when compared to experimental designs. Given that participant assignment is not random, it’s more difficult to confidently establish causality between variables, and, as a researcher, you have less control over other variables that may impact findings.

All that said, quasi-experimental designs can still be valuable in research contexts where random assignment is not possible and can often be undertaken on a much larger scale than experimental research, thus increasing the statistical power of the results. What’s important is that you, as the researcher, understand the limitations of the design and conduct your quasi-experiment as rigorously as possible, paying careful attention to any potential confounding variables .

Research Design: Qualitative Studies

There are many different research design types when it comes to qualitative studies, but here we’ll narrow our focus to explore the “Big 4”. Specifically, we’ll look at phenomenological design, grounded theory design, ethnographic design, and case study design.

Phenomenological Research Design

Phenomenological design involves exploring the meaning of lived experiences and how they are perceived by individuals. This type of research design seeks to understand people’s perspectives , emotions, and behaviours in specific situations. Here, the aim for researchers is to uncover the essence of human experience without making any assumptions or imposing preconceived ideas on their subjects.

For example, you could adopt a phenomenological design to study why cancer survivors have such varied perceptions of their lives after overcoming their disease. This could be achieved by interviewing survivors and then analysing the data using a qualitative analysis method such as thematic analysis to identify commonalities and differences.

Phenomenological research design typically involves in-depth interviews or open-ended questionnaires to collect rich, detailed data about participants’ subjective experiences. This richness is one of the key strengths of phenomenological research design but, naturally, it also has limitations. These include potential biases in data collection and interpretation and the lack of generalisability of findings to broader populations.

Grounded Theory Research Design

Grounded theory (also referred to as “GT”) aims to develop theories by continuously and iteratively analysing and comparing data collected from a relatively large number of participants in a study. It takes an inductive (bottom-up) approach, with a focus on letting the data “speak for itself”, without being influenced by preexisting theories or the researcher’s preconceptions.

As an example, let’s assume your research aims involved understanding how people cope with chronic pain from a specific medical condition, with a view to developing a theory around this. In this case, grounded theory design would allow you to explore this concept thoroughly without preconceptions about what coping mechanisms might exist. You may find that some patients prefer cognitive-behavioural therapy (CBT) while others prefer to rely on herbal remedies. Based on multiple, iterative rounds of analysis, you could then develop a theory in this regard, derived directly from the data (as opposed to other preexisting theories and models).

Grounded theory typically involves collecting data through interviews or observations and then analysing it to identify patterns and themes that emerge from the data. These emerging ideas are then validated by collecting more data until a saturation point is reached (i.e., no new information can be squeezed from the data). From that base, a theory can then be developed .

As you can see, grounded theory is ideally suited to studies where the research aims involve theory generation , especially in under-researched areas. Keep in mind though that this type of research design can be quite time-intensive , given the need for multiple rounds of data collection and analysis.

Ethnographic Research Design

Ethnographic design involves observing and studying a culture-sharing group of people in their natural setting to gain insight into their behaviours, beliefs, and values. The focus here is on observing participants in their natural environment (as opposed to a controlled environment). This typically involves the researcher spending an extended period of time with the participants in their environment, carefully observing and taking field notes .

All of this is not to say that ethnographic research design relies purely on observation. On the contrary, this design typically also involves in-depth interviews to explore participants’ views, beliefs, etc. However, unobtrusive observation is a core component of the ethnographic approach.

As an example, an ethnographer may study how different communities celebrate traditional festivals or how individuals from different generations interact with technology differently. This may involve a lengthy period of observation, combined with in-depth interviews to further explore specific areas of interest that emerge as a result of the observations that the researcher has made.

As you can probably imagine, ethnographic research design has the ability to provide rich, contextually embedded insights into the socio-cultural dynamics of human behaviour within a natural, uncontrived setting. Naturally, however, it does come with its own set of challenges, including researcher bias (since the researcher can become quite immersed in the group), participant confidentiality and, predictably, ethical complexities . All of these need to be carefully managed if you choose to adopt this type of research design.

Case Study Design

With case study research design, you, as the researcher, investigate a single individual (or a single group of individuals) to gain an in-depth understanding of their experiences, behaviours or outcomes. Unlike other research designs that are aimed at larger sample sizes, case studies offer a deep dive into the specific circumstances surrounding a person, group of people, event or phenomenon, generally within a bounded setting or context .

As an example, a case study design could be used to explore the factors influencing the success of a specific small business. This would involve diving deeply into the organisation to explore and understand what makes it tick – from marketing to HR to finance. In terms of data collection, this could include interviews with staff and management, review of policy documents and financial statements, surveying customers, etc.

While the above example is focused squarely on one organisation, it’s worth noting that case study research designs can have different variation s, including single-case, multiple-case and longitudinal designs. As you can see in the example, a single-case design involves intensely examining a single entity to understand its unique characteristics and complexities. Conversely, in a multiple-case design , multiple cases are compared and contrasted to identify patterns and commonalities. Lastly, in a longitudinal case design , a single case or multiple cases are studied over an extended period of time to understand how factors develop over time.

As you can see, a case study research design is particularly useful where a deep and contextualised understanding of a specific phenomenon or issue is desired. However, this strength is also its weakness. In other words, you can’t generalise the findings from a case study to the broader population. So, keep this in mind if you’re considering going the case study route.

How To Choose A Research Design

Having worked through all of these potential research designs, you’d be forgiven for feeling a little overwhelmed and wondering, “ But how do I decide which research design to use? ”. While we could write an entire post covering that alone, here are a few factors to consider that will help you choose a suitable research design for your study.

Data type: The first determining factor is naturally the type of data you plan to be collecting – i.e., qualitative or quantitative. This may sound obvious, but we have to be clear about this – don’t try to use a quantitative research design on qualitative data (or vice versa)!

Research aim(s) and question(s): As with all methodological decisions, your research aim and research questions will heavily influence your research design. For example, if your research aims involve developing a theory from qualitative data, grounded theory would be a strong option. Similarly, if your research aims involve identifying and measuring relationships between variables, one of the experimental designs would likely be a better option.

Time: It’s essential that you consider any time constraints you have, as this will impact the type of research design you can choose. For example, if you’ve only got a month to complete your project, a lengthy design such as ethnography wouldn’t be a good fit.

Resources: Take into account the resources realistically available to you, as these need to factor into your research design choice. For example, if you require highly specialised lab equipment to execute an experimental design, you need to be sure that you’ll have access to that before you make a decision.

Keep in mind that when it comes to research, it’s important to manage your risks and play as conservatively as possible. If your entire project relies on you achieving a huge sample, having access to niche equipment or holding interviews with very difficult-to-reach participants, you’re creating risks that could kill your project. So, be sure to think through your choices carefully and make sure that you have backup plans for any existential risks. Remember that a relatively simple methodology executed well generally will typically earn better marks than a highly-complex methodology executed poorly.

Recap: Key Takeaways

We’ve covered a lot of ground here. Let’s recap by looking at the key takeaways:

• Research design refers to the overall plan, structure or strategy that guides a research project, from its conception to the final analysis of data.
• Research designs for quantitative studies include descriptive , correlational , experimental and quasi-experimenta l designs.
• Research designs for qualitative studies include phenomenological , grounded theory , ethnographic and case study designs.
• When choosing a research design, you need to consider a variety of factors, including the type of data you’ll be working with, your research aims and questions, your time and the resources available to you.

If you need a helping hand with your research design (or any other aspect of your research), check out our private coaching services .

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This post is part of our dissertation mini-course, which covers everything you need to get started with your dissertation, thesis or research project. 

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Is there any blog article explaining more on Case study research design? Is there a Case study write-up template? Thank you.

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Please is there any template for a case study research design whose data type is a secondary data on your repository?

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Step 3 of EBP: Part 1—Evaluating Research Designs

James w. drisko.

4 School for Social Work, Smith College, Northampton, MA USA

Melissa D. Grady

5 School of Social Service, Catholic University of America, Washington, DC, USA

Step 3 of the EBP process involves evaluating the quality and client relevance of research results you have located to inform treatment planning. While some useful clinical resources include careful appraisals of research quality, clinicians must critically evaluate the content both included in these summaries and what is excluded or omitted from them. For individual research studies, clinicians must first identify and evaluate the research designs and methods reported. The terminology used to describe research designs in EBM/EBP may not always be consistent with that used in most social work research courses. This chapter provides a review of the key research designs used in EBM and EBP in order to orient clinicians to core terminology found in EBP summaries and reports.

Once you have located some research that can help answer your practice question, Step 3 in the evidence-based medicine (EBM) and evidence-based practice (EBP) decision-making model is to appraise the quality of this research. An initial inspection of materials should help differentiate those that are generally relevant for your purposes from those that are not. Relevance may be initially determined by examining the research question that each study addresses. Studies should have clear and relevant research questions, fitting your practice needs. Once these “apparently relevant” studies are identified, the appraisal shifts to issues of research methodology. Even studies that appear quite relevant initially may later on prove to have important limitations as the details of their methods are explored.

Evaluating the quality of research reports can be a complex process. It involves several components. We will begin by reviewing research designs used in EBP. While many of these designs should be familiar to social workers, they may be described using different terminologies in EBM and EBP research reports (Drisko, 2011 ). Chapter 10.1007/978-3-030-15224-6_7 will review several other methodological steps in appraising research (sampling, defining the treatment or other intervention, test and measures, and statistics). These provide the basis for examining meta-analysis and systematic reviews, two widely used methods for aggregating research results in EBM and EBP, examined in Chap. 10.1007/978-3-030-15224-6_8.

Research design is the first methodological issue a clinical social worker must identify in appraising the quality of a research study. A research design is the orienting plan that shapes and organizes a research project. Different research designs are used for research projects with distinct goals and purposes. Sometimes this is a researcher-determined choice, and other times practical and ethical issues force the use of specific research designs. In EBM/EBP, research designs are one key part of appraising study quality.

While all clinical social workers are introduced to research methods as part of their required course work, most do not make much use of this knowledge after graduation. Doing EBP, however, will require that clinical social workers and other mental health professionals make greater use of their knowledge about evaluating research for practice.

Research designs are so important to EBM/EBP that this chapter will focus on them exclusively. Other very important—and very closely related—aspects of research methods will be examined in the following chapter (sampling, measures, definitions of treatments, and analysis). Our goal is to provide a useful refresher and reference for clinical social workers. For readers who have a basic grasp of research designs and methods, this chapter can serve as a brief review and resource. Still, some terminology, drawn from medicine, will no doubt be unfamiliar. For others who need only an update, this chapter offers it. Many excellent follow-up resources are identified in each section of the chapter.

Research Designs

This review of research designs has three main purposes. First, it will introduce the variety of terminology used in EBP research, which is often drawn from medical research. This terminology sometimes differs from the terminology used in most social work research texts that draw on social sciences research terminology. Second, the strengths and limitations of each research design are examined and compared. Third, the research designs are rank ordered from “strongest” to “weakest” following the EBM/EBP research hierarchy. This allows readers to quickly understand why some research designs are favored in the EBM/EBP literature.

Thyer ( 2011 ) states, quite accurately, that the EBP practice decision-making process does not include any hierarchy of research designs. This is indeed correct. The EBP practice decision-making process states that clinicians should use the “best available evidence.” It does not state that only the results of research with certain types of research designs are to be valued. That is, it is entirely appropriate to use the results of case study research or even “practice wisdom” when no better evidence is available. Yet many organizations and institutions make quite explicit that there is a de facto hierarchy of evidence within EBP. This hierarchy is even clearly stated in the early writing of Dr. Archie Cochrane ( 1972 ), who promoted the use of experimental research knowledge to inform contemporary practice decision-making. Littell ( 2011 ) notes that the Cochrane Collaboration publishes “empty reviews” that report no research results deemed to be of sufficient design quality to guide practice decision-making. This practice contradicts the idea of identifying the best available evidence. In effect, the best available evidence is reduced to evidence generated by experimental research designs. This practice creates confusion about what constitutes the best available evidence for clinicians, policy planners, and researchers.

Some EBP/EBM authors do not report all the best available evidence, but instead report only the experimental evidence that they deem worthy of guiding practice. They make this choice because only well-designed experiments allow attribution of causal relationships to say that an intervention caused observed changes with minimal error. Still, this practice represents some academic and economic politics within EBP research summaries. As discussed in Chap. 10.1007/978-3-030-15224-6_2, there are good arguments for and against this position, but it is not entirely consistent with the stated EBM/EBP practice decision-making model. Clinical social workers should be aware that this difference in viewpoints about the importance of research design quality is not always clearly stated in the EBP literature. Critical, and well-informed, thinking by the clinician is always necessary.

Research designs differ markedly. They have different purposes, strengths, and limitations. Some seek to explore and clarify new disorders or concerns and to illustrate innovative practices. Others seek to describe the characteristics of client populations. Some track changes in clients over time. Still others seek to determine if a specific intervention caused a specific change. While we agree that the EBP practice decision-making process states that clinicians should use “the best available evidence” and not solely evidence derived from experimental results, we will present research designs in a widely used hierarchy drawn from the Oxford University’s Centre for Evidence-Based Medicine ( 2009 , 2016 ). This hierarchy does very clearly give greater weight to experimental, randomized controlled trial [RCT] research results. It should be seen as representing a specific point of view, applied for specific purposes. At the same time, such research designs do provide a strong basis for arguing that a treatment caused any changes found, so long as the measures are appropriate, valid, and reliable and the sample tested is of adequate size and variety. Due to the strong interval validity offered by experimental research designs, results based on RCTs design are often privileged in EBM/EBP reports. We will begin this listing with the experimental research designs that allow causal attribution. We will then progress from experiments to quasi-experiments, then move to observational or descriptive research, and end with case studies. The organization of this section follows the format of the research evidence hierarchy created by Oxford University’s Centre for Evidence-Based Medicine ( 2009 , 2100; 2016 , 2018).

Types of Clinical Studies

Part 1: experimental studies or rcts.

EBP researchers view properly conceptualized and executed experimental studies. These are also called randomized controlled trials or RCTs. RCTs provide internally valid empirical evidence of treatment effectiveness. They are prospective in nature as they start at the beginning of treatment and follow changes over time (Anastas, 1999 ). Random assignment of participants symmetrically distributes potential confounding variables and sources of error to each group. Probability samples further provide a suitable foundation for most statistical analytic procedures.

The key benefit of an experimental research design is that they minimize threats to internal validity (Campbell & Stanley, 1963 ). This means the conclusions of well-done experiments allow researchers to say an intervention caused the observed changes. This is why experiments are highly regarded in the EBM/EBP model. The main limitations of experiments are their high cost in money, participation, effort, and time. They may be ethically inappropriate for some studies where random assignment is inappropriate. A final disadvantage is that volunteers willing to participate may not reflect clinical populations well. This may lead to bias in external validity or how well results from controlled experiments can be generalized to less controlled practice settings (Oxford Centre for Evidence-Based Medicine, 2019 ).

In the European medical literature, experiments and quasi-experiments may alternately be called analytic studies . This is to distinguish them from descriptive studies that, as the name implies, simply describe clinical populations. Analytic studies are those that quantify the relationship between identified variables. Such analytic studies fit well with the PICO or PICOT treatment decision-making model (Oxford Centre for Evidence-Based Medicine, 2019 ).

The Randomized Controlled Trial (RCT) or Classic Experiment

It is a quantitative, prospective, group-based study based on primary data from the clinical environment (Solomon, Cavanaugh, & Draine, 2009 ). Researchers randomly assign individuals who have the same disorder or problem at the start to one of two (or more) groups. Later, the outcomes for each group are compared at the completion of treatment. Since researchers create the two groups by random assignment to generate two very similar groups, the RCT is sometimes called a parallel group design . Usually one group is treated and the other is used as an untreated control group. Researchers sometimes use placebo interventions with the control group. However, researchers may alternately design experiments comparing two or more different treatments where one has been previously demonstrated to produce significantly better results than does an untreated control group. Pre- to post-comparisons demonstrate the changes for each group. Comparison of post-scores across the treated groups allows for demonstration of any greater improvement due to the treatment. Follow-up comparisons may also be undertaken, but this is not a requirement of an experiment.

The experiment or RCT can be summarized graphically as:

where R stands for random assignment of participants, O 1 stands from the pretest assessment (most often with a standardized measure), X represents the intervention given to just one group, and O 2 stands for the posttest, done after treatment, but using the same measure. There may also be additional follow-up posttests to document how results vary over time. These would be represented as O 3, O 4, etc. There may be two or more groups under comparison in an RCT. Further, more than one measure of outcome may be used in the same experiment.

In medical studies, particularly of medications or devices, it is possible to blind participants, clinicians, and even researchers to their experimental group assignments. The goal is to reduce differences in expectancies that might lead to different outcomes. In effect, either conscious or unconscious bias is limited to strengthen the internal validity of the study results. A double blind RCT design keeps even group assignments unknown to participants and to the treating clinicians. Single blind experiments keep only the participants unaware of group assignments. Blinding is more possible where placebo pills or devices can be used to hide the nature of the intervention. Blinding is much more difficult in mental health and social service research where interactions between clients and providers over time are common.

While blinding is common in EBM studies of medications and devices, it is rare in mental health research. There is, however, research that shows that clinical practitioners and researchers may act consciously or unconsciously to favor treatment theories and models that they support (Dana & Loewenstein, 2003 ). This phenomenon is known as attribution bias , in which people invested in a particular theory or treatment model view it more positively than do others. Attribution bias may work consciously or unconsciously to influence study implementation and results. In turn, it is stronger research evidence if clinicians and researchers who do outcome studies are not the originators or promoters of the treatment under study.

The American Psychological Association standards for empirically supported treatments (ESTs) require that persons other than the originators of a treatment do some of the outcome studies used to designate an EST. That is, at least one study not done by the originator of a treatment is required for the EST label. How clinician and researcher biases are assessed in the EBM/EBP model is less clear. However, most Cochrane and Campbell Collaboration systematic reviews do assess and evaluate the potential for bias when the originators of treatments are the only sources of outcome research on their treatments (Higgins & Green, 2018 ; Littell, Corcoran, & Pillai, 2008 ). In addition, all Cochrane and Campbell Collaboration systematic reviews must include a statement of potential conflicts of interest by each of the authors.

It is important to keep in mind that experiments may have serious limitations despite their use of a “strong” research design. Sample size is one such issue. Many clinical studies compare small groups (roughly under 20 people in a group). Studies using small samples may lack the statistical power to identify any differences across the groups correctly and fully. That is, for group differences to be identified, a specific sample size is required. The use of an experimental research design alone does not mean that the results will always be valid and meaningful. (We will examine issue beyond research design that impacts research quality later in the next two chapters.) Still, done carefully, the experimental research design or RCT has many merits in allowing cause-effect attribution.

The CONSORT Statement ( 2010 ) established standards for the reporting of RCTs. CONSORT is an acronym for “CONsolidated Standards of Reporting Trials.” The people who make up the CONSORT group are an international organization of physicians, researchers, methodologists, and publishers. To aid in the reporting of RCTs, CONSORT provides a free 37-item checklist for reporting or assessing the quality of RCTs online at http://www.consort-statement.org/ . The CONSORT Statement is available in many different languages. The CONSORT group also provides a free template for a flow chart of the RCT process and statement. These tools can be very helpful to the consumer of experimental research since they serve as guides for assessing the quality of RCTs. A CONSORT flow chart (also called a Quorum chart) is often found in published reports of recent RCTs.

The Randomized Crossover Clinical Trial

It is a prospective, group-based, quantitative, experimental study based on primary data from the clinical environment. Individuals with the same disorder, most often of a chronic or long-term type, are randomly assigned to one of two groups, and treatment is begun for both groups. After a designated period of treatment (sufficient to show positive results), groups are assessed and a “washout” phase is begun in which all treatments are withheld. After the washout period is completed, the treatments for the groups are then switched so that each group receives both treatments. After the second treatment is completed, a second assessment is undertaken. Comparison of outcomes for each treatment at both end points allows for determination of treatment effectiveness on the same groups of patients/clients for both treatments. This strengthens the internal validity of the study. A comparison of active treatment outcomes for all patients is possible. However, if the washout period is not sufficient, there may be carry-over effects from the initial treatment that in turn undermines the validity of the second comparison. Used with medications, there are often lab tests that allow determination of effective washout periods. Secondary effects, such as learning or behavior changes that occur during the initial treatment, may not wash out. Similarly, it may not be possible to wash out learned or internalized cognitions, skills, attitudes, or behaviors. This is a limitation of crossover research designs in mental health and social services.

The merit of crossover designs is that each participant serves as his or her own control which reduces variance due to individual differences among participants. This may also allow use of smaller sample sizes while generating a large enough sample to demonstrate differences, known as statistical power. All participants receive both treatments, which benefits them. Random assignment provides a solid foundation for statistical tests. Disadvantage of crossover studies includes that all participants receive a placebo or less effective treatment at some point which may not benefit them immediately. Further, washout periods can be lengthy and curtail active treatment for the washout period. Finally, crossover designs cannot be used where the effects of treatment are permanent, such as in educational programs or surgeries.

Crossover trials may also be undertaken with single cases (rather than groups of participants). These are called single-case crossover trials. The basic plan of the single-case crossover trial mimics that used for groups but is used with just a single case. The crossover trial may be represented graphically as:

where A 1 stands for the initial assessment, B 1 represents the first intervention given, the second A 2 represents the next assessment which is made at the end of the first intervention after washout, and B 2 stands for second type of intervention or the crossover. Finally, A 3 represents the assessment of the second intervention done when it is completed. Note that a washout period is not specifically included in this design but may be if the researchers chose to do so. Comparison of treatment outcomes for each intervention with the initial baseline assessment allows determination of the intervention effects. More than one measure may be used in the same crossover study.

Since random assignment is not possible with single cases, the results of single-case crossover studies are often viewed as “weaker” than are group study results. However, each individual, each case, serves as its own control. Since the same person is studied, there is usually little reason to assume confounding variables arise due to physiologic changes, personal history, or social circumstances.

It is possible to aggregate the results of single-case designs. This is done by closely matching participants and replicating the single-case study over a number of different participants and settings. This model is known as replication logic , in which similar outcomes over many cases build confidence in the results (Anastas, 1999 ). It is in contrast to sampling logic used in group experimental designs in which potentially confounding variables are assumed to be equally distributed across the study groups through random assignment of participants. In replication logic, repetition over many cases is assumed to include and address potentially confounding variables. If treatment outcomes are positive over many cases, treatment effectiveness may be inferred. In EBM, single-case studies are not designated as providing strong research evidence, but consistent findings from more than ten single-case study outcomes are rated as strong evidence in the American Psychological Association’s designation of empirically supported treatments (ESTs).

The Randomized Controlled Laboratory Study

It is a prospective, group, quantitative, experimental study based on laboratory rather than direct clinical data. These are called analog studies since the lab situation is a good, but not necessarily perfect, replication of the clinical situation. Laboratory studies are widely used in “basic” research since all other variables of influences except the one under study can be controlled or identified. This allows testing of single variables but is unlike the inherent variation found in real-world clinical settings. Randomized controlled laboratory studies are often conducted on animals where genetics can be controlled or held constant. Ethical issues, of course, limit laboratory tests on humans. Applying the results of laboratory studies in clinical practice has some limitations, as single, “pure” forms of disorders or problems are infrequent and contextual factors can impact of treatment delivery and outcome.

Effectiveness vs. Efficacy Studies: Experiments Done in Different Settings

In mental health research, a distinction is drawn between clinical research done in the real-world clinical settings and that done much more selectively for research purposes. Experimental studies done in everyday clinical practice setting are called effectiveness studies. Such studies have some potentially serious limitations in that they often include comorbid disorders and may not be able to ensure that treatments are provided fully and consistently. This reduces their interval validity for research purposes. On the other hand, using real-world settings enhances their external validity, meaning that the results are more likely to fit with actual practice with everyday clients and settings. In contrast, more carefully controlled studies that ensure experimental study of just a single disorder are known as efficacy studies. Efficacy studies carefully document that a fully applied treatment for a single, carefully screened disorder is effective (or are not effective).

One well-known example of a clinical efficacy study is the NIMH Cross-site Study of Depression (Elkin, Shea, Watkins, et al., 1989 ). This study rigorously compared medication and two forms of psychotherapy for depression. Strict exclusion criteria targeted only people with depression and no other comorbid disorders. Medication “washouts” were required of all participants. Such efficacy studies emphasize internal validity; they focus on showing that the treatment alone caused any change. The limitations of applying efficacy studies results are that real-world practice settings may not be able to take the time and effort needed to identify only clients with a single disorder. Such efforts might make treatment unavailable to people with comorbid disorders, which may not be practical or ethical in many clinical settings. Further, the careful monitoring of treatment fidelity required in efficacy studies may not be possible to provide in many clinical settings (often for reasons of funding and time).

Efficacy studies are somewhat like laboratory research, but the similarity is not quite exact since they are done in clinical settings, just with extra steps. Efficacy studies add an extra measure of rigor to clinical research. They do show with great precision that a treatment works for a specific disorder. However, results of efficacy studies may be very difficult to apply fully in everyday clinical practice (given its ethical, funding, and practical limitations).

Part 2: Quasi-experimental and Cohort Studies—Comparisons Without Random Participant Assignment

Random assignment of participants to treated versus control groups is a way to strengthen internal validity and to limit bias in research results. Random assignment ideally generates (two or more) equivalent groups for the comparison of treatment effects versus an untreated control group. Quasi-experimental research designs lack random assignment but do seek to limit other threats to the interval validity of study results. They are often used where random assignment is unethical or is not feasible for practical reasons.

The Quasi-experimental Study or Cohort Study

In studies of clinical practice in mental health, it is sometimes unethical or impractical to randomly assign participants to treated or control groups. For example, policy-makers may only fund a new type of therapy or a new prevention program for a single community or with payment by only certain types of insurance. In such situations, researchers use existing groups or available groups to examine the impact of interventions. The groups, settings, or communities to be compared are chosen to be as similar as possible in their key characteristics. The goal is to approximate the equivalent groups created by random assignment. Where pre- and post-comparisons are done on such similar groups, such a research design is called a quasi-experiment. The key difference from a true experiment is the lack of random assignment of participants to the treated or control groups.

The quasi-experiment can be summarized graphically as:

Once again, O 1 stands from the pretest assessment (most often with a standardized measure), X represents the intervention given to just one group, and O 2 stands for the posttest, done after treatment, but using the same measure. More than two groups may be included in a quasi-experimental study. There may also be additional follow-up posttests to document how results vary over time. More than one measure may be used in the same quasi-experiment. Note carefully that the key difference from a true experiment is the lack of random assignment of participants.

The lack of random assignment in a quasi-experiment introduces some threats to the internal validity of the study. That is, it may introduce unknown differences across the groups that ultimately affect study outcomes. The purpose of random assignment is to distribute unknown variables or influences to each groups as equally as possible. Without random assignment, the studied groups may have important differences that are not equally distributed across the groups. Say, for example, that positive social supports interact with a treatment to enhance its outcome. Without random assignment, the treated group might be biased in that it includes more people with strong social supports than does the control group. The interaction of the treatment with the impact of social supports might make the results appear better than they might have been if random assignment was used. Thus in some EBM/EBP hierarchies of research evidence, quasi-experimental study results are rated as “weaker” than are results of true experiments or RCTs. That said, they are still useful sources of knowledge and are often the best available research evidence for some treatments and service programs. To reduce potential assignment bias, quasi-experimental studies use “matching” in which as many characteristics of participants in each group are matched as closely as possible. Of course, matching is only possible where the variables are fully known at the start of the study.

Advantages of quasi-experimental or cohort studies include their ethical appropriateness in that participants are not assigned to groups and can make their own personal treatment choices on an informed basis. Cohort studies are usually less expensive in cost than are true experiments, though they may both be financially costly. Disadvantages of cohort studies are potentially confounding variables may be operative but unknown. Further, comparison groups can be difficult to identify. For rare disorders, large samples are required which can be difficult to obtain and may take a long time to complete.

The “All or None” Study

The Centre for Evidence-Based Medicine at Oxford University ( 2009 , B13) includes in its rating of evidence the “All or None” research design. This is a research design in which, in very difficult circumstances, clinicians give an intervention to a group of people at high risk of serious harm or death. If essentially all the people who received the intervention improve or survive, while those who do not receive it continue to suffer or die, the inference is that the intervention caused the improvement. This is actually an observational research design, but given the nature of the groups compared, all or none results are viewed as strong evidence that the treatment caused the change. However, given their very important effects, such research results are highly valued so long as all or a large fraction of people who receive the intervention improve. Such designs fit crisis medical issues much better than most mental health issues, so all or none design is extremely rare in the mental health literature. They do have a valuable role in informing practice in some situations.

Part 3: Non-interventive Research Designs and Their Purposes

Not all practice research is intended to show an intervention causes a change. While EBM/EBP hierarchies of research evidence rank most highly, those research designs that do show an intervention cause a change, even these studies stand on a foundation built from the results of other types of research. In the EBM/EBP hierarchy, clinicians are reminded that exploratory and descriptive research may not be the best evidence on which to make practice decisions. At the same time, exploratory and descriptive research designs are essential in setting the stage for rigorous and relevant experimental research. These types of studies may also be the “best available evidence” for EBP if experiments are lacking or are of poor quality. Critical thinking is crucial to determining just what constitutes “the best available evidence” in any clinical situation.

The Observational Study

It is a prospective, longitudinal, usually quantitative, tracking study of groups or of individuals with a single disorder or problem (Kazdin, 2010 ). Researchers follow participants over time to assess the course (progression) of symptoms. Participants may be either untreated or treated with a specified treatment. People are not randomly assigned to treated or control groups. Because participants may differ on unknown or unidentified variables, observational studies have potential for bias due to the impact of these other variables. That is, certain variables such as genetic influences or nutrition or positive social support may lead to different outcomes for participants receiving the same treatment (or even no treatment). Some scholars view observational studies as a form of descriptive clinical research that is very helpful in preparing the way for more rigorous experimental studies.

The Longitudinal Study

It is a prospective, quantitative and/or qualitative, observational study ideally based on primary data, tracking a group in which members have had, or will have, exposure or involvement with specific variables. For example, researchers might track the development of behavioral problems among people following a specific natural disaster or the development of children living in communities with high levels of street violence. In medicine, researchers might track people exposed to the SARs virus. Longitudinal studies help identify the probability of occurrence of a given condition or need within a population over a set time period. While such variables are often stressors, cohort studies may also be used to track responses to positive events, such as inoculation programs or depression screen programs.

Graphically a longitudinal study can be represented as:

Here the X stands for exposure to a risk factor and O stands for each assessment. The exposure or event X may either mark the start of the study or may occur while assessments are ongoing. Participants are not randomly assigned which may introduce biases. Note, too, that there is no control or comparison group though studies of other people without the target exposure can serve as rough comparison groups.

In contrast to experimental studies with random assignment, participants in longitudinal studies may be selected with unknown strengths or challenges that, over time, affect the study results. Thus, confounding variables can influence longitudinal study results. Over time, loss of participants may also bias study results. For instance, if the more stressed participants dropout of a study, their loss may make the study results appear more positive than they would be if all participants continued to the study’s conclusion. Because longitudinal studies are prospective in design, rather than retrospective, they are often viewed as stronger than are case-control studies. Longitudinal studies do not demonstrate cause and effect relationships but can provide strong correlational evidence.

Case-Control Study

It is a retrospective, usually quantitative, observational study often based on secondary data (or data already collected, often for different initial purposes). Looking back in time, case-control studies compare the proportion of cases with a potential risk or resiliency factor against the proportion of controls that do not have the same factor. For example, people who have very poor treatment outcomes for their anxiety disorder may be compared with a closely matched group of people who had very positive outcomes. A careful look at their demographic characteristics, medical histories, and mental health histories might identify risk factors that distinguish most people in the two groups. Rare differences in risk or resiliency factors are often identified by such studies. Case-control studies are relatively inexpensive but are subject to multiple sources of bias if used to attribute “cause” to the risk or resiliency factors they identify.

Cross-Sectional Study or Incidence Study

These are descriptive, usually quantitative , studies of the relationship between disorders or problems and other factors at a single point in time. Incidence designs are used descriptively in epidemiology. They can be useful for learning baseline information on the incidence of disorders in specific areas. Cross-sectional studies are very valuable in a descriptive manner to policy planning, but do not demonstrate cause and effect relationships. They are not highly valued in the EBM/EBP research design hierarchy. An example of a cross-sectional study would be to look at the rate of poverty in a community during 1 month of the year. It is simply a snapshot picture of how many individuals would be classified as living in poverty during that month of the study. Comparing the number of persons in poverty with the total population of the community gives the incidence rate or prevalence rate for poverty.

The Case Series

It is a descriptive, observational study of a series of cases, typically describing the manifestations, clinical course, and prognosis of a condition. Both qualitative and quantitative data are commonly included. Case series can be used as exploratory research to identify the features and progression of a new or poorly understood disorder. They can be very useful in identifying culture-bound or context-specific aspects of mental health problems. Case series are inherently descriptive in nature, but they are most often based on small and nonrandom samples. The results of case series may not generalize to all potential patients/clients.

Despite its limitations, many scholars point out that the case series is the most frequently found research design in the clinical literature. It may be the type of study most like real-world practice and is a type of study practitioners can undertake easily. In some EBM/EBP research design hierarchies, the case series are among the least valued form of clinical evidence, as they do not demonstrate that an intervention caused a specific outcome. They nonetheless offer a valuable method for making innovative information about new disorders or problems and new treatment methods available at an exploratory and descriptive level.

One example of this type of research design is the Nurses’ Health Study (Colditz, Manson, & Hankinson, 1997 ). This is a study of female nurses who worked at Brigham and Women’s Hospital in Boston and who completed a detailed questionnaire every second years on their lifestyle, hormones, exercise, and more. Researchers did not intervene with these women in any way but have used the information compiled by the study over several decades to identify trends in women’s health. These results can then be generalized to other women or used to provide information on health trends that could be explored further through more intervention-based research (Colditz et al., 1997 ).

The Case Study (or Case Report)

It is a research design using descriptive but “anecdotal” evidence drawn from a single case. The data may be qualitative and/or quantitative. Case studies may be the best research design for the identification of new clinical disorders or problems. They can be very useful forms of exploratory clinical research. They usually include the description of a single case, highlighting the manifestations of the disorder, its clinical course, and outcomes of intervention (if any). Because case studies draw on the experiences of a single case, and often a single clinician, they are often labeled “anecdotal.” This differentiates evidence collected on multiple cases from that based on just a single case. Further, case study reports often lack the systematic pre- post-assessment found in single-case research designs. The main (and often major) limitation of the case study is that the characteristics of the single case may, or may not, be similar to other cases in different people and circumstances. Another key limitation is that reporting of symptoms, interventions, course of the problem, and outcomes may be piecemeal. This may be because the disorder is unfamiliar or unique in some way (making it worth publishing about), but since there are few widely accepted standards for case studies, authors provide very different kinds and quality of information to readers.

Case studies offer a valuable method for generating innovative information about new disorders or problems, even new treatment methods, available on an exploratory or formative basis. These ideas may become the starting point for future experimental studies.

We note again that case studies may be “best available evidence” found in an EBP search. If research based on other designs is not available, case study research may be used to guide practice decision-making.

Expert Opinion or Practice Wisdom

The EBM/EBP research design hierarchy reminds clinicians that expert opinion may not (necessarily) have a strong evidence base. This is not to say that the experiences of supervisors, consultants, and talented colleagues have no valuable role in practice. It is simply to point out that they are not always systematic and may not work well for all clients in all situations. As research evidence, unwritten expert opinion lacks planned and systematic testing and control for potential biases. This is why it is the least valued form of evidence in most EBM/EBP evidence hierarchies. Such studies may still be quite useful and informative to clinicians in specific circumstances. They serve to point to new ways of thinking and intervening that may be valuable to specific clinical situations and settings.

Resources on Research Design in EBP

Many textbooks offer good introductions to research design issues and offer more illustrations than we do in this chapter. Note, however, that the terminology used in EBM/EBP studies and summaries may not be the same as is used in core social work textbooks. Resources addressing issues in research design are found in Table 6.1 .

More resources on research design

This chapter has reviewed the range of research designs used in clinical research. The different types of research designs have different purposes and different strengths. These purposes range from exploratory, discovery-oriented purposes for the least structured designs like case studies to allowing attribution of cause and effect relationships for highly structured experimental designs. This chapter has also explored the research design terminology used in EBM/EBP. Some of this terminology draws heavily on medical research and may be unfamiliar to persons trained in social work or social science research. Still, most key research design concepts can be identified despite differences in terminology. The EBM/EBP research design hierarchy places great emphasis on research designs that can document that a specific treatment caused the changes found after treatment. This is an important step in determining the effectiveness or efficacy of a treatment. Many documents portray experiments, or RCTs, as the best form of evidence upon which to base practice decisions. Critical consumers of research should pay close attention to the kind of research designs used in the studies they examine for practice application.

Key reviews of outcome research on a specific topic, such as those from the Cochrane Collaboration and Campbell Collaboration, use research design as a key selection criterion for defining high-quality research results. That is, where little or no experimental or RCT research is available, the research summary may indicate there is inadequate research knowledge to point to effective treatments. “Empty” summaries pointing to no high-quality research evidence on some disorders are found in the Cochrane Review database. This reflects their high standards and careful review. It also fails to state just what constitutes the best available evidence. Empty reviews do not aid clinicians and clients in practice decision-making. They simply indicate that clinicians should undertake an article-by-article review of research evidence on their clinical topic. Clinicians must bear in mind that the EBP practice decision-making process promotes the use of “the best available evidence.” If such evidence is not based on experimental research, it should still be used, but used with caution. It is entirely appropriate in the EBP framework to look for descriptive or case study research when there is no experimental evidence available on a specific disorder or concern.

Even when experimental or RCT research designs set the framework for establishing cause and effect relationships, a number of related methodological choices also are important to making valid knowledge claims. These include the quality of sampling, the inclusion of diverse participants in the sample, the quality of the outcome measures used, the definitions of the treatments, and the careful use of the correct statistical tests. Adequate sample size and representativeness are important to generalizing study results to other similar people and settings. Appropriately conceptualized, valid, reliable, and sensitive outcome measures document any changes. How treatments are defined and delivered will have a major impact on the merit and worth of study results. Statistics serve as a decision-making tool to determine if the results are unlikely to have happened by chance alone. All these methods work in tandem to yield valid and rigorous results. These issues will be explored in the next two chapters on Step 3 of the EBP process, further appraising some additional methodological issues in practice research.

• Anastas JW. Research design for social work and the human services. 2. New York: Columbia University Press; 1999. [ Google Scholar ]
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• Littell, J. (2011, January 15). Evidence-based practice versus practice-based research . Paper presented at the Society for Social Work and Research, Tampa, FL.
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• Quantitative descriptive research: definition,types,methodology,methods,characteristics and examples

On this Page

• Introduction to Quantitative descriptive research

Types of survey research

Quantitative descriptive research methodology.

• Quantitative Descriptive Research Methodology-Diagrammatic Approach
• Logical Steps; Quantitative Descriptive Research Methodology

Quantitative descriptive research methods

Characteristics of quantitative descriptive research, advantages of quantitative descriptive research, disadvantages of quantitative descriptive research, quantitative descriptive research: definition, types,methodology,methods,characteristics,examples and advantages.

How do we define the term quantitative descriptive research? Is it the same as quantitative correlational research or quantitative causal-comparative? Look at this…  

1.1 Definition

Quantitative Descriptive Research is a Non-Experimental type of research whereby the variables are measured using numerical terms although the variables under interrogation are not manipulated by the researcher. This type of research is commonly known as Descriptive Research as stated in our article on descriptive research which classifies types of research on the basis of “ purpose of research ”. In our current discussion, we are adding the word “quantitative” so as to emphasize that the variables are measured using numerical terms.  Quantitative descriptive research uses two methodologies/designs, namely; observational research and survey research methodologies.

NOTE 1: Observational Research

Observational research is quantitative descriptive research where by data collection is done through observation. The design requires that the researcher collect data by devising a method where by although the process of data collection is through observation, the information is translated in to numeric form such as frequency, percentage, tally numbers etc. So, the key point here is that the researcher has to quantify character which have been observed. For example, if the researcher wants to measure corruption in a country. Then, he/she can design a method of using tally numbers such that anytime there is a case of corruption, then this is recorded in a tally sheet. Further, for the researcher to know how a country is corrupt, he/she will then compare the tally numbers in a certain period with the set benchmark tally score which has already been predetermined or given.

NOTE 2: Survey Research

Survey research is another type of quantitative descriptive research which advocates use of surveys or questionnaires to collect data pertaining the behavior of subject matter. It entails distributing of surveys or questionnaires to a big sample or the whole population if it is manageable so as to collect data. Since the purpose of this survey research is to describe physical characteristics of a population, it is much in order that the sample be selected using a probability sampling technique to ensure more accurate representation of the sample to the population.

Survey research can be classified in to three aspects based on the manner in which the researcher is formulating the research problem. These categories are; descriptive, cross-sectional, and longitudinal.

1.Descriptive Survey Research

It is a research design which is set to collect data for the simple purpose of describing the character of the subject matter. It is a one point in time approach of collecting data. For instance, if the researcher wants to study on the level of concentration of members in the church, he/she will randomly distribute the survey instruments to the selected sample and have them fill them and return as supposedly.

2.Cross-sectional Survey Research

It is a research design/study which involves investigation of features of different samples or populations where by the are taken at one point in time. For instance, the researcher may wish to carry out a measurement on cross-sectional study about the study habits of grade six pupils in two, three or four different schools in a certain location. All the pupils making up the sample would be surveyed at the same point in time. Where the cross-sectional survey is conducted for the whole population, it is referred to as a census.

3.Longitudinal Survey Research

It is a research design/study which involves the investigation of the characteristics of respondents where by the measurements are taken at different point in time. For instance, the researcher may wish to carry out longitudinal study on the study habits of grade six pupils in certain school in a particular location. All the pupils making up the sample would be surveyed at different point in time. In other words, the same group of participants is studied over an extended period of time, which naturally involves the administration of several surveys at particular time intervals such as after one year, after another one year and after another one year and so on and so on.

Longitudinal survey research is further categorized into;

T rend study is a longitudinal survey study that scrutinizes changes within a specifically identified population over time with an aim of learning the trend thereof for decision or planning purposes.

Cohort study is longitudinal research which investigates characteristics of a certain group which is a sub-group of another group which was being studied in the previous time. The sub-group is commonly referred to as “cohort”. The kind of longitudinal research happens when the initial group identified was being investigated over a certain characteristic and then further, another sub-group is picked from the previous main group to be investigated over another characteristic. The sub-group should be having the characteristics of the main group for it is a member. For example, a longitudinal study may focus on studying the sleeping habits of children who are under five years in certain families. If the researcher establishes that the children selected have that particular sleeping habit, another group out of this group (i.e., sub-group) is selected to find out if they know how to communicate fluently in their mother tongue. The sub-group being tested of their fluence in mother tongue is a cohort. Hence the study is cohort research.

Panel Research is a study which investigates a characteristic in the same group same sample over a long period. In a panel study, the researcher examines the exact same respondents over a specified time frame. For example, the researcher would select and survey a group of children in year 2018 survey the same children in 2020, and once again repeat the same interrogation for the same children in 2022. So, longitudinal study of panel type deals with the same sample, population or group over a long period set.

2.1 Definition

Quantitative Descriptive Research Methodology is the rational process or step by step blueprint on how to solve a research problem that entails a variable which can be measured numerically in terms of 0, 0.5, 1, 2, 3.3, 4…. nth digit etc. Quantitative Descriptive Research methodology involves selecting a logical process on the topic to be studied. That is the study or research problem, how specific objectives of the study will be recognized/or framed. Identification of research gaps to be filled, the methods used in documentation of the study population and sample size determination, type of data to be collected and how it will be collected and analyzed, data presentation and clarifications thereof and the broadcasting of the investigation outcome.

Quantitative Descriptive Research methodology is the intellectual aspect behind the methods we use in the context of our research study. This provides a groundwork as to why one is using a particular method or procedure at a specific phase in the research progression and not others so that research outcome is accomplished either by the researcher or another scholar.

2.2 Quantitative Descriptive Questions Research Methodology tries to Answer

Quantitative research aims at answering one aspect of a question. That is;

What kind of questions only!

The following matrix portrays the link between quantitative descriptive type of research and the type of research methodology adopted and then an explanation of the logical approach associated with this category and then in the last column, the research method(s) used in formulating the research problem. Remember these methods are specifically for quantitative descriptive research which is a sub-set of Quantitative research.

2.3 Quantitative Descriptive Research Methodology-Diagrammatic Approach

The following diagram represents a summary of logical roadmap to be adhered to in descriptive research methodology where Quantitative or Numerical methods are used to measure/gauge the study variables. This case is more biased on survey descriptive research.

2.4 Logical Steps; Quantitative Descriptive Research Methodology

The following logical steps describe the Quantitative Descriptive Research methodology. From step one to nine, it represents a logical way of how systematically the subject matter need to be dealt with. Remember that in this approach, the researcher is only curious of establishing how things work.

2.4.1 Step 1; Topic Identification

This is the first step in Quantitative Descriptive research where by the researcher has to come up with the area of study based on the area of interest. Under step one, the researcher will embrace thematic topic by posing him/herself descriptive affiliated research questions such as “what is the purchases cost level of product M in the regional market?”  OR

 “What is the purchasing pattern of a certain magazine amongst the married people in Kingstone in Jamaica?”

2.4.2 Step 2: Literature Review

In this step, the researcher, interrogates past studies relevant to the area of interest or topic of study. The aim being to highlight the conceptual, methodological and contextual research gaps to help in development of the appropriate survey, interview inquiries/questions and framing of data collection procedures as well.

2.4.3 Step 3: Identification & Selection of Research Participants

Since the main aim of Quantitative descriptive research is to generalize the end results on the population, there is need at this stage to identify the target population . Out of this population, a sample is drawn using a probabilistic sampling technique so as to give each individual equal opportunity to participate in the study.

2.4.4 Step 4: Identification of Appropriate Data Collection Tool

This step involves identification of the most appropriate data collection tool by the researcher. Quantitative descriptive research has a wide spectrum of such tools such as direct administration of a survey, a mail survey, a telephone survey, interviews, e-mail surveys, and web-based surveys. The most suitable approach depends on circumstances prevailing which may favor one method as compared to another.

The researcher chooses the most suitable tool to collect data. Based on the nature or the circumstances the participants are in, the researcher can rely on either face-to-face survey, E-mail survey, a telephone call or survey, or interviews, to mention but a few.

2.4.5 Step 5: Undertake Ethical Precautions

After pre-determining the participants in the study and selecting the approach to use when collecting data, the next step is to disseminate survey to the sample selected, this is achieved by seeking permission from the right authority. So, the researcher prepares a cover or introductory letter to go together with the surveys. Basically, the content of the letter entails the message of assurance of privacy and confidentiality protection strategies for the respondents and also the benefits thereof.

2.4.6 Step 6: Test of Data Collection Tool

After choosing the right data collection tool, the next step is to assess the appropriateness of the tool. This can be achieved through many ways one of them being the pilot test. Pilot testing is a key process in data collection mission for the researcher has to ensure that the data collection tool is effective in capturing the information that is required for data analysis. The purpose of pilot testing of the data collection instrument/tool is to find out if the level of understandability of the respondents are as per the expectations. This approach avoids cases where by the questions in the tool are vague and not clear. It can be frustrating if the participants captured to play a role in the research assignment do not understand the questions or they may do wrong interpretation.

Therefore, a pilot test should be undertaken before actual data collection is carried out. This exercise entails randomly selecting a smaller sample from the main sample and go forth to collect data from respondents using the same data collection tool to test the waters. This process gives the researcher a hint on how effective the tool is and whether there is need to revise the tool before actual data collection. Classical authors such as Kothari (2009) and Sekaran (2006) recommend a 1% sample size for a pilot study and Mugenda and Mugenda (2009) too, states that the size of a sample for the purpose of piloting should be between 1% and 10% of the sample size. It is advisable to exclude the portion of sample used for piloting from main data collection exercise.

2.4.7 Step 7: Data Collection

As usual, in this step, the actual data is collected using the appropriate data collection tool. In this case, a survey tool is suitable if the data collected is quantitative descriptive in nature.

2.4.8 Step 8: Data Analysis

Data analysis for survey cases apply statistical/numerical procedures where hard statistics such as frequency distribution, descriptive statistics such as sample mean, sample standard deviation, sample variance, correlation coefficients and group comparisons are relevant.

2.4.9 Step 9: Research Findings

Research findings stage is the last step and the end to the means in research exercise. The step involves provision of solutions to the research question(s) the investigator had from the beginning. On getting the answers to the research questions, then the researcher can make inferences about the population.in other words, he/she can generalize.

Does quantitative descriptive research methods for formulating a research problem the same as quantitative descriptive research method for data analysis ? The answer is NO. Look at the definitional differences as per our explanation below

3.1 Definition

Research methods are all the techniques that are utilized in all the stages of research processes. They are tools used to ensure the end results of research task are accomplished. These techniques vary from one stage of research process to another. These methods are further classified in to two categories, namely;

a) Pre-Data analysis methods

b) Data Analysis related methods

Quantitative descriptive research uses survey, systematic observation and secondary research methods for the purposes of formulating the research problem which are some of the methods which fall under pre-data analysis category.  However, in this discussion of Quantitative descriptive research, we will focus on main methods of data collection which are also pre-data analysis in nature. That is; observation and survey methods.

Observation data collection method

As discussed earlier, this method involves collecting of data by doing observation of the respondent’s character in the natural/physical settings. The data should be recorded in numeric terms or mode.

Survey Data Collection Method

This method of data collection involves use of questionnaires and other different types of surveys to capture the respondent’s characteristics. Those types of surveys are, namely; direct administration of surveys, mail surveys, telephone surveys, interviews, e-mail surveys, and web-based surveys. 

Direct Administration Survey

Direct administration survey is a method of data collection applicable when the whole population is reachable. All the members of the targeted population are available and it is possible to collect data at 100% assurance level.  The researcher disseminates the survey instrument on his own without a research assistant. This approach translates in to a very high survey return rate which assures the researcher valid results.  This approach works well when the researcher is in proximity to the location where the respondents are.

Mail surveys data collection method

As the name suggests, mail survey data collection method is an approach of distributing surveys to the potential respondents by using mails. It involves administering the survey instrument to the population or the sample identified whereby a hard copy is issued and   the researcher expects the survey to be filled and returned as soon as stated in the instructions given. Mail survey method enables the researcher to cover a wider coverage of the respondents although it is a little bit expensive. 

Telephone surveys data collection method

As the name suggests, the method uses telephone gargets to communicate with the respondents. This has proven to be costly. The method entails making calls either using land line telephones or cell phones to communicate. This survey method requires both parties to be having a phone handset and the surveyor has to recite all the questions to avoid making mistakes when interrogating the respondent.  

Interview survey data collection method

Interview data collection method involves collection of data face to face whereby the researcher has to aval him/herself to the physical locale of the respondent.  Interviews are costly for the researcher has to travel to and from and again if research assistants are used, then they have to be trained on the interview protocols to avoid ineffective responses from the participants.

E-Mail survey data collection method

As the name suggests, the method uses E-Mail accounts to communicate with the respondents. This has proven to be cheap. The method entails sending surveys to individual respondents using their e-mail accounts. This survey method requires both parties to be having an e-mail account that is active. The prepared survey is attached to the email platform and sent to the respondent who is expected to respond within a specific period.

Web-based survey data collection method

As the name suggests, the method uses website platform to communicate with the respondents. This is also cheap. The method entails sending surveys to individual respondents using their e-mail accounts via the sender’s website. This survey method requires the respondent to have a way of accessing information in the website. The prepared survey is attached to the website platform and sent to the respondent who is expected to respond within a specific period.

• Measurement of variables which represent the characteristics of the subject matter is in numeric form.
• Data collection is either through observation or surveys.
• The data is collected from the subject matter which is in its natural or physical phenomenon.
• There is no manipulation of the variables for this type of research is non-experimental in nature.
• Data is collected either at one data point or several data points.
• It is descriptive in nature-this research only aims at describing or giving a narration of the character of the subject matter and does not show any relationship or causality.
• Foundational-this research is the basis of other highly ranked research for it lays the basis of the nature of the variables that exists. Hence giving a hint of whether they correlate or cause other variables to change. This then becomes the basis for further research.
• Generalizability-research findings are always generalized. That is, the research findings gotten from the sample is used to generalize about the characteristics of the whole population.  
• Objectivity-the researcher just observes the characteristics of the subject matter or unit of observation with his/her hands off from any manipulation. Hence no researcher biased influence.
• Time saving- this type of research is time saving especially when the method of observation is used.
• Increased response rate-the data collection that involves use of direct administration of survey instruments tend to have almost 100% survey response. Even when questionnaires are issued in this manner, the questionnaire response rate is very high.
• Wide coverage of data collection-the survey method of data collection especially when website and e-mail mode of distribution is used reaches far and wide places. This assures the researcher of sample size which is a true representation of the population under study.
• Cost effective-when collecting data using web-based survey and e-mail approach, fewer financial resources are utilized. Hence the research design is cheap.
• Efficient population representation-the sample size used in survey is always large enough to represent the whole population. This assures the researcher of valid data for conclusive research findings.
• Generalization- this type of research allows for generalization of research findings from the sample and therefore it is not a must to use the whole population which may be costly and time consuming.
• Customization-under quantitative descriptive research, there are various/wide optional methods of data collection such as one data point collection approach, many data point data collection, panel research design, cohort and so on and so on. These approaches make it possible to meet the needs of many stakeholders.
• Lack applicability in some circumstances-methods of reaching the respondents such as use of e-mails require both parties, that is the researcher and the respondents to have a cell phone or a computer. This is not the case especially in the rural areas for the developing economies.
• Outdated data-where data collection involves many data points in time, the old data collected may turn to be irrelevant in decision making. For example, if the researcher was collecting data on consumer behavior of customers loyal to product EXE of X company limited for the next ten (10) years. You see, by the time ten years will be over, the product version or customer taste will have changed to other more appealing products. So, the initial data pertaining product EXE will be useless.
• Does not show causality effects between or amongst variables. As the name suggests, quantitative descriptive research does not show cause-effect relationships between variables.
• Bias personal opinion-responses gotten from the survey study have high chances of being wrong. This is because the respondents may answer questions in a manner to please the researcher or the research assistant. This makes the data unreliable.