descriptive research design survey

Survey descriptive research: Method, design, and examples

• November 2, 2022

What is survey descriptive research?

The observational method: monitor people while they engage with a subject, the case study method: gain an in-depth understanding of a subject, survey descriptive research: easy and cost-effective, types of descriptive research design, what is the descriptive survey research design definition by authors, 1. quantitativeness and qualitatively, 2. uncontrolled variables, 3. natural environment, 4. provides a solid basis for further research, describe a group and define its characteristics, measure data trends by conducting descriptive marketing research, understand how customers perceive a brand, descriptive survey research design: how to make the best descriptive questionnaire, create descriptive surveys with surveyplanet.

Survey descriptive research is a quantitative method that focuses on describing the characteristics of a phenomenon rather than asking why it occurs. Doing this provides a better understanding of the nature of the subject at hand and creates a good foundation for further research.

Descriptive market research is one of the most commonly used ways of examining trends and changes in the market. It is easy, low-cost, and provides valuable in-depth information on a chosen subject.

This article will examine the basic principles of the descriptive survey study and show how to make the best descriptive survey questionnaire and how to conduct effective research.

It is often said to be quantitative research that focuses more on the what, how, when, and where instead of the why. But what does that actually mean?

The answer is simple. By conducting descriptive survey research, the nature of a phenomenon is focused upon without asking about what causes it.

The main goal of survey descriptive research is to shed light on the heart of the research problem and better understand it. The technique provides in-depth knowledge of what the research problem is before investigating why it exists.

Survey descriptive research and data collection methods

Descriptive research methods can differ based on data collection. We distinguish three main data collection methods: case study, observational method, and descriptive survey method.

Of these, the descriptive survey research method is most commonly used in fields such as market research, social research, psychology, politics, etc.

Sometimes also called the observational descriptive method, this is simply monitoring people while they engage with a particular subject. The aim is to examine people’s real-life behavior by maintaining a natural environment that does not change the respondents’ behavior—because they do not know they are being observed.

It is often used in fields such as market research, psychology, or social research. For example, customers can be monitored while dining at a restaurant or browsing through the products in a shop.

When doing case studies, researchers conduct thorough examinations of individuals or groups. The case study method is not used to collect general information on a particular subject. Instead, it provides an in-depth understanding of a particular subject and can give rise to interesting conclusions and new hypotheses.

The term case study can also refer to a sample group, which is a specific group of people that are examined and, afterward, findings are generalized to a larger group of people. However, this kind of generalization is rather risky because it is not always accurate.

Additionally, case studies cannot be used to determine cause and effect because of potential bias on the researcher’s part.

The survey descriptive research method consists of creating questionnaires or polls and distributing them to respondents, who then answer the questions (usually a mix of open-ended and closed-ended).

Surveys are the easiest and most cost-efficient way to gain feedback on a particular topic. They can be conducted online or offline, the size of the sample is highly flexible, and they can be distributed through many different channels.

When doing market research , use such surveys to understand the demographic of a certain market or population, better determine the target audience, keep track of the changes in the market, and learn about customer experience and satisfaction with products and services.

Several types of survey descriptive research are classified based on the approach used:

• Descriptive surveys gather information about a certain subject.
• Descriptive-normative surveys gather information just like a descriptive survey, after which results are compared with a norm.
• Correlative surveys explore the relationship between two variables and conclude if it is positive, neutral, or negative.

A descriptive survey research design is a methodology used in social science and other fields to gather information and describe the characteristics, behaviors, or attitudes of a particular population or group of interest. While there may not be a single definition provided by specific authors, the concept is widely understood and defined similarly across the literature.

Here’s a general definition that captures the essence of a descriptive survey research design definition by authors:

A descriptive survey research design is a systematic and structured approach to collecting data from a sample of individuals or entities within a larger population, with the primary aim of providing a detailed and accurate description of the characteristics, behaviors, opinions, or attitudes that exist within the target group. This method involves the use of surveys, questionnaires, interviews, or observations to collect data, which is then analyzed and summarized to draw conclusions about the population of interest.

It’s important to note that descriptive survey research is often used when researchers want to gain insights into a population or phenomenon, but without manipulating variables or testing hypotheses, as is common in experimental research. Instead, it focuses on providing a comprehensive overview of the subject under investigation. Researchers often use various statistical and analytical techniques to summarize and interpret the collected data in descriptive survey research.

The characteristics and advantages of a descriptive survey questionnaire

There are numerous advantages to using a descriptive survey design. First of all, it is cheap and easy to conduct. A large sample can be surveyed and extensive data gathered quickly and inexpensively.

The data collected provides both quantitative and qualitative information , which provides a holistic understanding of the topic. Moreover, it can be used in further research on this or related topics.

Here are some of the most important advantages of conducting a survey descriptive research:

The descriptive survey research design uses both quantitative and qualitative research methods. It is used primarily to conduct quantitative research and gather data that is statistically easy to analyze. However, it can also provide qualitative data that helps describe and understand the research subject.

Descriptive research explores more than one variable. However, unlike experimental research, descriptive survey research design doesn’t allow control of variables. Instead, observational methods are used during research. Even though these variables can change and have an unexpected impact on an inquiry, they will give access to honest responses.

The descriptive research is conducted in a natural environment. This way, answers gathered from responses are more honest because the nature of the research does not influence them.

The data collected through descriptive research can be used to further explore the same or related subjects. Additionally, it can help develop the next line of research and the best method to use moving forward.

Descriptive survey example: When to use a descriptive research questionnaire?

Descriptive research design can be used for many purposes. It is mainly utilized to test a hypothesis, define the characteristics of a certain phenomenon, and examine the correlations between them.

Market research is one of the main fields in which descriptive methods are used to conduct studies. Here’s what can be done using this method:

Understanding the needs of customers and their desires is the key to a business’s success. By truly understanding these, it will be possible to offer exactly what customers need and prevent them from turning to competitors.

By using a descriptive survey, different customer characteristics—such as traits, opinions, or behavior patterns—can be determined. With this data, different customer types can be defined and profiles developed that focus on their interests and the behavior they exhibit. This information can be used to develop new products and services that will be successful.

Measuring data trends is extremely important. Explore the market and get valuable insights into how consumers’ interests change over time—as well as how the competition is performing in the marketplace.

Over time, the data gathered from a descriptive questionnaire can be subjected to statistical analysis. This will deliver valuable insights.

Another important aspect to consider is brand awareness. People need to know about your brand, and they need to have a positive opinion of it. The best way to discover their perception is to conduct a brand survey , which gives deeper insight into brand awareness, perception, identity, and customer loyalty .

When conducting survey descriptive research, there are a few basic steps that are needed for a survey to be successful:

• Define the research goals.
• Decide on the research method.
• Define the sample population.
• Design the questionnaire.
• Write specific questions.
• Distribute the questionnaire.
• Analyze the data .
• Make a survey report.

First of all, define the research goals. By setting up clear objectives, every other step can be worked through. This will result in the perfect descriptive questionnaire example and collect only valuable data.

Next, decide on the research method to use—in this case, the descriptive survey method. Then, define the sample population for (that is, the target audience). After that, think about the design itself and the questions that will be asked in the survey .

If you’re not sure where to start, we’ve got you covered. As free survey software, SurveyPlanet offers pre-made themes that are clean and eye-catching, as well as pre-made questions that will save you the trouble of making new ones.

Simply scroll through our library and choose a descriptive survey questionnaire sample that best suits your needs, though our user-friendly interface can help you create bespoke questions in a process that is easy and efficient.

With a survey in hand, it will then need to be delivered to the target audience. This is easy with our survey embedding feature, which allows for the linking of surveys on a website, via emails, or by sharing on social media.

When all the responses are gathered, it’s time to analyze them. Use SurveyPlanet to easily filter data and do cross-sectional analysis. Finally, just export the results and make a survey report.

Conducting descriptive survey research is the best way to gain a deeper knowledge of a topic of interest and develop a sound basis for further research. Sign up for a free SurveyPlanet account to start improving your business today!

Photo by Scott Graham on Unsplash

• Privacy Policy

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.

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

You may also like

Explanatory Research – Types, Methods, Guide

Textual Analysis – Types, Examples and Guide

Correlational Research – Methods, Types and...

Quasi-Experimental Research Design – Types...

Observational Research – Methods and Guide

Transformative Design – Methods, Types, Guide

Have a language expert improve your writing

Run a free plagiarism check in 10 minutes, generate accurate citations for free.

• Knowledge Base

Methodology

• Descriptive Research | Definition, Types, Methods & Examples

Descriptive Research | Definition, Types, Methods & Examples

Published on May 15, 2019 by Shona McCombes . Revised on June 22, 2023.

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, other interesting articles.

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.

Descriptive research question examples

• How has the Amsterdam housing market changed over the past 20 years?
• Do customers of company X prefer product X or product Y?
• 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?

Receive feedback on language, structure, and formatting

Professional editors proofread and edit your paper by focusing on:

• Academic style
• Vague sentences
• Style consistency

See an example

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 analyzed 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 organization’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 organization). 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 generalizable facts, case studies often focus on unusual or interesting cases that challenge assumptions, add complexity, or reveal something new about a research problem .

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

• Normal distribution
• Degrees of freedom
• Null hypothesis
• Discourse analysis
• Control groups
• Mixed methods research
• Non-probability sampling
• Quantitative research
• Ecological validity

Research bias

• Rosenthal effect
• Implicit bias
• Cognitive bias
• Selection bias
• Negativity bias
• Status quo bias

Cite this Scribbr article

If you want to cite this source, you can copy and paste the citation or click the “Cite this Scribbr article” button to automatically add the citation to our free Citation Generator.

McCombes, S. (2023, June 22). Descriptive Research | Definition, Types, Methods & Examples. Scribbr. Retrieved September 18, 2024, from https://www.scribbr.com/methodology/descriptive-research/

Is this article helpful?

Shona McCombes

Other students also liked, what is quantitative research | definition, uses & methods, correlational research | when & how to use, descriptive statistics | definitions, types, examples, "i thought ai proofreading was useless but..".

I've been using Scribbr for years now and I know it's a service that won't disappoint. It does a good job spotting mistakes”

Have a language expert improve your writing

Run a free plagiarism check in 10 minutes, automatically generate references for free.

• Knowledge Base
• Methodology
• 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?

Prevent plagiarism, run a free check.

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 .

Cite this Scribbr article

If you want to cite this source, you can copy and paste the citation or click the ‘Cite this Scribbr article’ button to automatically add the citation to our free Reference Generator.

McCombes, S. (2022, October 10). Descriptive Research Design | Definition, Methods & Examples. Scribbr. Retrieved 18 September 2024, from https://www.scribbr.co.uk/research-methods/descriptive-research-design/

Is this article helpful?

Shona McCombes

Other students also liked, a quick guide to experimental design | 5 steps & examples, correlational research | guide, design & examples, qualitative vs quantitative research | examples & methods.

Instant insights, infinite possibilities

• What is descriptive research?

Last updated

5 February 2023

Reviewed by

Cathy Heath

Short on time? Get an AI generated summary of this article instead

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.

Analyze your descriptive research

Dovetail streamlines analysis to help you uncover and share actionable insights

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.

Should you be using a customer insights hub?

Do you want to discover previous research faster?

Do you share your research findings with others?

Do you analyze research data?

Start for free today, add your research, and get to key insights faster

Editor’s picks

Last updated: 18 April 2023

Last updated: 27 February 2023

Last updated: 22 August 2024

Last updated: 5 February 2023

Last updated: 16 August 2024

Last updated: 9 March 2023

Last updated: 30 April 2024

Last updated: 12 December 2023

Last updated: 11 March 2024

Last updated: 4 July 2024

Last updated: 6 March 2024

Last updated: 5 March 2024

Last updated: 13 May 2024

Latest articles

Related topics, .css-je19u9{-webkit-align-items:flex-end;-webkit-box-align:flex-end;-ms-flex-align:flex-end;align-items:flex-end;display:-webkit-box;display:-webkit-flex;display:-ms-flexbox;display:flex;-webkit-flex-direction:row;-ms-flex-direction:row;flex-direction:row;-webkit-box-flex-wrap:wrap;-webkit-flex-wrap:wrap;-ms-flex-wrap:wrap;flex-wrap:wrap;-webkit-box-pack:center;-ms-flex-pack:center;-webkit-justify-content:center;justify-content:center;row-gap:0;text-align:center;max-width:671px;}@media (max-width: 1079px){.css-je19u9{max-width:400px;}.css-je19u9>span{white-space:pre;}}@media (max-width: 799px){.css-je19u9{max-width:400px;}.css-je19u9>span{white-space:pre;}} decide what to .css-1kiodld{max-height:56px;display:-webkit-box;display:-webkit-flex;display:-ms-flexbox;display:flex;-webkit-align-items:center;-webkit-box-align:center;-ms-flex-align:center;align-items:center;}@media (max-width: 1079px){.css-1kiodld{display:none;}} build next, decide what to build next, log in or sign up.

Get started for free

Root out friction in every digital experience, super-charge conversion rates, and optimize digital self-service

Uncover insights from any interaction, deliver AI-powered agent coaching, and reduce cost to serve

Increase revenue and loyalty with real-time insights and recommendations delivered to teams on the ground

Know how your people feel and empower managers to improve employee engagement, productivity, and retention

Take action in the moments that matter most along the employee journey and drive bottom line growth

Whatever they’re saying, wherever they’re saying it, know exactly what’s going on with your people

Get faster, richer insights with qual and quant tools that make powerful market research available to everyone

Run concept tests, pricing studies, prototyping + more with fast, powerful studies designed by UX research experts

Track your brand performance 24/7 and act quickly to respond to opportunities and challenges in your market

Explore the platform powering Experience Management

• Free Account
• Product Demos
• For Digital
• For Customer Care
• For Human Resources
• For Researchers
• Financial Services
• All Industries

Popular Use Cases

• Customer Experience
• Employee Experience
• Net Promoter Score
• Voice of Customer
• Customer Success Hub
• Product Documentation
• Training & Certification
• XM Institute
• Popular Resources
• Customer Stories
• Artificial Intelligence

Market Research

• Partnerships
• Marketplace

The annual gathering of the experience leaders at the world’s iconic brands building breakthrough business results, live in Salt Lake City.

• English/AU & NZ
• Español/Europa
• Español/América Latina
• Português Brasileiro
• REQUEST DEMO
• Experience Management
• Descriptive Research

Try Qualtrics for free

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.

Free eBook: 2024 global market research trends report

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

Mixed methods research 17 min read, 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, request demo.

Ready to learn more about Qualtrics?

Copyright © SurveySparrow Inc. 2024 Privacy Policy Terms of Service SurveySparrow Inc.

Descriptive Research 101: Definition, Methods and Examples

Parvathi Vijayamohan

Last Updated: 16 July 2024

10 min read

Table Of Contents

• Descriptive Research 101: The Definitive Guide

What is Descriptive Research?

• Key Characteristics
• Observation
• Case Studies
• Types of Descriptive Research
• Question Examples
• Real-World Examples

Tips to Excel at Descriptive Research

• More Interesting Reads

Imagine you are a detective called to a crime scene. Your job is to study the scene and report whatever you find: whether that’s the half-smoked cigarette on the table or the large “RACHE” written in blood on the wall. That, in a nutshell, is  descriptive research .

Researchers often need to do descriptive research on a problem before they attempt to solve it. So in this guide, we’ll take you through:

• What is descriptive research + its characteristics
• Descriptive research methods
• Types of descriptive research
• Descriptive research examples
• Tips to excel at the descriptive method

Click to jump to the section that interests you.

Let’s begin by going through what descriptive studies can and cannot do.

Definition: As its name says, descriptive research  describes  the characteristics of the problem, phenomenon, situation, or group under study.

So the goal of all descriptive studies is to  explore  the background, details, and existing patterns in the problem to fully understand it. In other words, preliminary research.

However, descriptive research can be both  preliminary and conclusive . You can use the data from a descriptive study to make reports and get insights for further planning.

What descriptive research isn’t: Descriptive research finds the  what/when/where  of a problem, not the  why/how .

Because of this, we can’t use the descriptive method to explore cause-and-effect relationships where one variable (like a person’s job role) affects another variable (like their monthly income).

Key Characteristics of Descriptive Research

• Answers the “what,” “when,” and “where”  of a research problem. For this reason, it is popularly used in  market research ,  awareness surveys , and  opinion polls .
• Sets the stage  for a research problem. As an early part of the research process, descriptive studies help you dive deeper into the topic.
• Opens the door  for further research. You can use descriptive data as the basis for more profound research, analysis and studies.
• Qualitative and quantitative research . It is possible to get a balanced mix of numerical responses and open-ended answers from the descriptive method.
• No control or interference with the variables . The researcher simply observes and reports on them. However, specific research software has filters that allow her to zoom in on one variable.
• Done in natural settings . You can get the best results from descriptive research by talking to people, surveying them, or observing them in a suitable environment. For example, suppose you are a website beta testing an app feature. In that case, descriptive research invites users to try the feature, tracking their behavior and then asking their opinions .
• Can be applied to many research methods and areas. Examples include healthcare, SaaS, psychology, political studies, education, and pop culture.

Descriptive Research Methods: The Top Three You Need to Know!

In short, survey research is a brief interview or conversation with a set of prepared questions about a topic. So you create a questionnaire, share it, and analyze the data you collect for further action.

Read more : The difference between surveys vs questionnaires

• Surveys can be hyper-local, regional, or global, depending on your objectives.
• Share surveys in-person, offline, via SMS, email, or QR codes – so many options!
• Easy to automate if you want to conduct many surveys over a period.

FYI: If you’re looking for the perfect tool to conduct descriptive research, SurveySparrow’s got you covered. Our AI-powered text and sentiment analysis help you instantly capture detailed insights for your studies.

With 1,000+ customizable (and free) survey templates , 20+ question types, and 1500+ integrations , SurveySparrow makes research super-easy.

Want to try out our platform? Click on the template below to start using it.👇

Product Market Research Survey Template

Preview Template

2. Observation

The observational method is a type of descriptive research in which you, the researcher, observe ongoing behavior.

Now, there are several (non-creepy) ways you can observe someone. In fact, observational research has three main approaches:

• Covert observation: In true spy fashion, the researcher mixes in with the group undetected or observes from a distance.
• Overt observation : The researcher identifies himself as a researcher – “The name’s Bond. J. Bond.” – and explains the purpose of the study.
• Participatory observation : The researcher participates in what he is observing to understand his topic better.
• Observation is one of the most accurate ways to get data on a subject’s behavior in a natural setting.
• You don’t need to rely on people’s willingness to share information.
• Observation is a universal method that can be applied to any area of research.

3. Case Studies

In the case study method, you do a detailed study of a specific group, person, or event over a period.

This brings us to a frequently asked question: “What’s the difference between case studies and longitudinal studies?”

A case study will go  very in-depth into the subject with one-on-one interviews, observations, and archival research. They are also qualitative, though sometimes they will use numbers and stats.

An example of longitudinal research would be a study of the health of night shift employees vs. general shift employees over a decade. An example of a case study would involve in-depth interviews with Casey, an assistant director of nursing who’s handled the night shift at the hospital for ten years now.

• Due to the focus on a few people, case studies can give you a tremendous amount of information.
• Because of the time and effort involved, a case study engages both researchers and participants.
• Case studies are helpful for ethically investigating unusual, complex, or challenging subjects. An example would be a study of the habits of long-term cocaine users.

7 Types of Descriptive Research

Descriptive Research Question Examples

• How have teen social media habits changed in 10 years?
• What causes high employee turnover in tech?
• How do urban and rural diets differ in India?
• What are consumer preferences for electric vs. gasoline cars in Germany?
• How common is smartphone addiction among UK college students?
• What drives customer satisfaction in banking?
• How have adolescent mental health issues changed in 15 years?
• What leisure activities are popular among retirees in Japan?
• How do commute times vary in US metro areas?
• What makes e-commerce websites successful?

Descriptive Research: Real-World Examples To Build Your Next Study

1. case study: airbnb’s growth strategy.

In an excellent case study, Tam Al Saad, Principal Consultant, Strategy + Growth at Webprofits, deep dives into how Airbnb attracted and retained 150 million users .

“What Airbnb offers isn’t a cheap place to sleep when you’re on holiday; it’s the opportunity to experience your destination as a local would. It’s the chance to meet the locals, experience the markets, and find non-touristy places.

Sure, you can visit the Louvre, see Buckingham Palace, and climb the Empire State Building, but you can do it as if it were your hometown while staying in a place that has character and feels like a home.” – Tam al Saad, Principal Consultant, Strategy + Growth at Webprofits

2. Observation – Better Tech Experiences for the Elderly

We often think that our elders are so hopeless with technology. But we’re not getting any younger either, and tech is changing at a hair trigger! This article by Annemieke Hendricks shares a wonderful example where researchers compare the levels of technological familiarity between age groups and how that influences usage.

“It is generally assumed that older adults have difficulty using modern electronic devices, such as mobile telephones or computers. Because this age group is growing in most countries, changing products and processes to adapt to their needs is increasingly more important. “ – Annemieke Hendricks, Marketing Communication Specialist, Noldus

3. Surveys – Decoding Sleep with SurveySparrow

SRI International (formerly Stanford Research Institute) – an independent, non-profit research center – wanted to investigate the impact of stress on an adolescent’s sleep. To get those insights, two actions were essential: tracking sleep patterns through wearable devices and sending surveys at a pre-set time – the pre-sleep period.

“With SurveySparrow’s recurring surveys feature, SRI was able to share engaging surveys with their participants exactly at the time they wanted and at the frequency they preferred.”

Read more about this project : How SRI International decoded sleep patterns with SurveySparrow

1: Answer the six Ws –

• Who should we consider?
• What information do we need?
• When should we collect the information?
• Where should we collect the information?
• Why are we obtaining the information?
• Way to collect the information

#2: Introduce and explain your methodological approach

#3: Describe your methods of data collection and/or selection.

#4: Describe your methods of analysis.

#5: Explain the reasoning behind your choices.

#6: Collect data.

#7: Analyze the data. Use software to speed up the process and reduce overthinking and human error.

#8: Report your conclusions and how you drew the results.

Wrapping Up

Whether it’s social media habits, consumer preferences, or mental health trends, descriptive research provides a clear snapshot into what people actually think.

If you want to know more about feedback methodology, or research, check out some of our other articles below.

👉 Desk Research 101: Definition, Methods, and Examples

👉 Exploratory Research: Your Guide to Unraveling Insights

👉 Design Research: Types, Methods, and Importance

Content marketer at SurveySparrow.

Parvathi is a sociologist turned marketer. After 6 years as a copywriter, she pivoted to B2B, diving into growth marketing for SaaS. Now she uses content and conversion optimization to fuel growth - focusing on CX, reputation management and feedback methodology for businesses.

You Might Also Like

21 Must-Have SaaS Platforms with Exclusive Black Friday Deals

JotForm vs Cognito Forms: Choose the Right Form Builder For You

Alternative

6 Best AskNicely Alternatives You Need to Try in 2024

A Guide to Effective Lead Generation Strategies

Turn every feedback into a growth opportunity.

14-day free trial • Cancel Anytime • No Credit Card Required • Need a Demo?

• Skip to main content
• Skip to primary sidebar
• Skip to footer
• QuestionPro

• Solutions Industries Gaming Automotive Sports and events Education Government Travel & Hospitality Financial Services Healthcare Cannabis Technology Use Case AskWhy Communities Audience Contactless surveys Mobile LivePolls Member Experience GDPR Positive People Science 360 Feedback Surveys
• Resources Blog eBooks Survey Templates Case Studies Training Help center

Home Market Research

Descriptive Research: Definition, Characteristics, Methods + Examples

Suppose an apparel brand wants to understand the fashion purchasing trends among New York’s buyers, then it must conduct a demographic survey of the specific region, gather population data, and then conduct descriptive research on this demographic segment.

The study will then uncover details on “what is the purchasing pattern of New York buyers,” but will not cover any investigative information about “ why ” the patterns exist. Because for the apparel brand trying to break into this market, understanding the nature of their market is the study’s main goal. Let’s talk about it.

What is descriptive research?

Descriptive research is a research method describing the characteristics of the population or phenomenon studied. This descriptive methodology focuses more on the “what” of the research subject than the “why” of the research subject.

The method primarily focuses on describing the nature of a demographic segment without focusing on “why” a particular phenomenon occurs. In other words, it “describes” the research subject without covering “why” it happens.

Characteristics of descriptive research

The term descriptive research then refers to research questions, the design of the study, and data analysis conducted on that topic. We call it an observational research method because none of the research study variables are influenced in any capacity.

Some distinctive characteristics of descriptive research are:

• Quantitative research: It is a quantitative research method that attempts to collect quantifiable information for statistical analysis of the population sample. It is a popular market research tool that allows us to collect and describe the demographic segment’s nature.
• Uncontrolled variables: In it, none of the variables are influenced in any way. This uses observational methods to conduct the research. Hence, the nature of the variables or their behavior is not in the hands of the researcher.
• Cross-sectional studies: It is generally a cross-sectional study where different sections belonging to the same group are studied.
• The basis for further research: Researchers further research the data collected and analyzed from descriptive research using different research techniques. The data can also help point towards the types of research methods used for the subsequent research.

Applications of descriptive research with examples

A descriptive research method can be used in multiple ways and for various reasons. Before getting into any survey , though, the survey goals and survey design are crucial. Despite following these steps, there is no way to know if one will meet the research outcome. How to use descriptive research? To understand the end objective of research goals, below are some ways organizations currently use descriptive research today:

• Define respondent characteristics: The aim of using close-ended questions is to draw concrete conclusions about the respondents. This could be the need to derive patterns, traits, and behaviors of the respondents. It could also be to understand from a respondent their attitude, or opinion about the phenomenon. For example, understand millennials and the hours per week they spend browsing the internet. All this information helps the organization researching to make informed business decisions.
• Measure data trends: Researchers measure data trends over time with a descriptive research design’s statistical capabilities. Consider if an apparel company researches different demographics like age groups from 24-35 and 36-45 on a new range launch of autumn wear. If one of those groups doesn’t take too well to the new launch, it provides insight into what clothes are like and what is not. The brand drops the clothes and apparel that customers don’t like.
• Conduct comparisons: Organizations also use a descriptive research design to understand how different groups respond to a specific product or service. For example, an apparel brand creates a survey asking general questions that measure the brand’s image. The same study also asks demographic questions like age, income, gender, geographical location, geographic segmentation , etc. This consumer research helps the organization understand what aspects of the brand appeal to the population and what aspects do not. It also helps make product or marketing fixes or even create a new product line to cater to high-growth potential groups.
• Validate existing conditions: Researchers widely use descriptive research to help ascertain the research object’s prevailing conditions and underlying patterns. Due to the non-invasive research method and the use of quantitative observation and some aspects of qualitative observation , researchers observe each variable and conduct an in-depth analysis . Researchers also use it to validate any existing conditions that may be prevalent in a population.
• Conduct research at different times: The analysis can be conducted at different periods to ascertain any similarities or differences. This also allows any number of variables to be evaluated. For verification, studies on prevailing conditions can also be repeated to draw trends.

Advantages of descriptive research

Some of the significant advantages of descriptive research are:

• Data collection: A researcher can conduct descriptive research using specific methods like observational method, case study method, and survey method. Between these three, all primary data collection methods are covered, which provides a lot of information. This can be used for future research or even for developing a hypothesis for your research object.
• Varied: Since the data collected is qualitative and quantitative, it gives a holistic understanding of a research topic. The information is varied, diverse, and thorough.
• Natural environment: Descriptive research allows for the research to be conducted in the respondent’s natural environment, which ensures that high-quality and honest data is collected.
• Quick to perform and cheap: As the sample size is generally large in descriptive research, the data collection is quick to conduct and is inexpensive.

Descriptive research methods

There are three distinctive methods to conduct descriptive research. They are:

Observational method

The observational method is the most effective method to conduct this research, and researchers make use of both quantitative and qualitative observations.

A quantitative observation is the objective collection of data primarily focused on numbers and values. It suggests “associated with, of or depicted in terms of a quantity.” Results of quantitative observation are derived using statistical and numerical analysis methods. It implies observation of any entity associated with a numeric value such as age, shape, weight, volume, scale, etc. For example, the researcher can track if current customers will refer the brand using a simple Net Promoter Score question .

Qualitative observation doesn’t involve measurements or numbers but instead just monitoring characteristics. In this case, the researcher observes the respondents from a distance. Since the respondents are in a comfortable environment, the characteristics observed are natural and effective. In a descriptive research design, the researcher can choose to be either a complete observer, an observer as a participant, a participant as an observer, or a full participant. For example, in a supermarket, a researcher can from afar monitor and track the customers’ selection and purchasing trends. This offers a more in-depth insight into the purchasing experience of the customer.

Case study method

Case studies involve in-depth research and study of individuals or groups. Case studies lead to a hypothesis and widen a further scope of studying a phenomenon. However, case studies should not be used to determine cause and effect as they can’t make accurate predictions because there could be a bias on the researcher’s part. The other reason why case studies are not a reliable way of conducting descriptive research is that there could be an atypical respondent in the survey. Describing them leads to weak generalizations and moving away from external validity.

Survey research

In survey research, respondents answer through surveys or questionnaires or polls . They are a popular market research tool to collect feedback from respondents. A study to gather useful data should have the right survey questions. It should be a balanced mix of open-ended questions and close ended-questions . The survey method can be conducted online or offline, making it the go-to option for descriptive research where the sample size is enormous.

Examples of descriptive research

Some examples of descriptive research are:

• A specialty food group launching a new range of barbecue rubs would like to understand what flavors of rubs are favored by different people. To understand the preferred flavor palette, they conduct this type of research study using various methods like observational methods in supermarkets. By also surveying while collecting in-depth demographic information, offers insights about the preference of different markets. This can also help tailor make the rubs and spreads to various preferred meats in that demographic. Conducting this type of research helps the organization tweak their business model and amplify marketing in core markets.
• Another example of where this research can be used is if a school district wishes to evaluate teachers’ attitudes about using technology in the classroom. By conducting surveys and observing their comfortableness using technology through observational methods, the researcher can gauge what they can help understand if a full-fledged implementation can face an issue. This also helps in understanding if the students are impacted in any way with this change.

Some other research problems and research questions that can lead to descriptive research are:

• Market researchers want to observe the habits of consumers.
• A company wants to evaluate the morale of its staff.
• A school district wants to understand if students will access online lessons rather than textbooks.
• To understand if its wellness questionnaire programs enhance the overall health of the employees.

LEARN MORE         FREE TRIAL

MORE LIKE THIS

QuestionPro: Leading the Charge in Customer Journey Management and Voice of the Customer Platforms

Sep 17, 2024

What is Driver Analysis? Importance and Best Practices

Was The Experience Memorable? (Part II) — Tuesday CX Thoughts

Data Discovery: What it is, Importance, Process + Use Cases

Sep 16, 2024

Other categories

• Academic Research
• Artificial Intelligence
• Assessments
• Brand Awareness
• Case Studies
• Communities
• Consumer Insights
• Customer effort score
• Customer Engagement
• Customer Experience
• Customer Loyalty
• Customer Research
• Customer Satisfaction
• Employee Benefits
• Employee Engagement
• Employee Retention
• Friday Five
• General Data Protection Regulation
• Insights Hub
• Life@QuestionPro
• Market Research
• Mobile diaries
• Mobile Surveys
• New Features
• Online Communities
• Question Types
• Questionnaire
• QuestionPro Products
• Release Notes
• Research Tools and Apps
• Revenue at Risk
• Survey Templates
• Training Tips
• Tuesday CX Thoughts (TCXT)
• Uncategorized
• What’s Coming Up
• Workforce Intelligence

• 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.

Connect to Formplus, Get Started Now - It's Free!

• descriptive research
• descriptive research method
• example of descriptive research
• types of descriptive research
• busayo.longe

You may also like:

Cross-Sectional Studies: Types, Pros, Cons & Uses

In this article, we’ll look at what cross-sectional studies are, how it applies to your research and how to use Formplus to collect...

Extrapolation in Statistical Research: Definition, Examples, Types, Applications

In this article we’ll look at the different types and characteristics of extrapolation, plus how it contrasts to interpolation.

Acceptance Sampling: Meaning, Examples, When to Use

In this post, we will discuss extensively what acceptance sampling is and when it is applied.

Type I vs Type II Errors: Causes, Examples & Prevention

This article will discuss the two different types of errors in hypothesis testing and how you can prevent them from occurring in your research

Formplus - For Seamless Data Collection

Collect data the right way with a versatile data collection tool. try formplus and transform your work productivity today..

Bridging the Gap: Overcome these 7 flaws in descriptive research design

Descriptive research design is a powerful tool used by scientists and researchers to gather information about a particular group or phenomenon. This type of research provides a detailed and accurate picture of the characteristics and behaviors of a particular population or subject. By observing and collecting data on a given topic, descriptive research helps researchers gain a deeper understanding of a specific issue and provides valuable insights that can inform future studies.

In this blog, we will explore the definition, characteristics, and common flaws in descriptive research design, and provide tips on how to avoid these pitfalls to produce high-quality results. Whether you are a seasoned researcher or a student just starting, understanding the fundamentals of descriptive research design is essential to conducting successful scientific studies.

Table of Contents

What Is Descriptive Research Design?

The descriptive research design involves observing and collecting data on a given topic without attempting to infer cause-and-effect relationships. The goal of descriptive research is to provide a comprehensive and accurate picture of the population or phenomenon being studied and to describe the relationships, patterns, and trends that exist within the data.

Descriptive research methods can include surveys, observational studies , and case studies, and the data collected can be qualitative or quantitative . The findings from descriptive research provide valuable insights and inform future research, but do not establish cause-and-effect relationships.

Importance of Descriptive Research in Scientific Studies

1. understanding of a population or phenomenon.

Descriptive research provides a comprehensive picture of the characteristics and behaviors of a particular population or phenomenon, allowing researchers to gain a deeper understanding of the topic.

2. Baseline Information

The information gathered through descriptive research can serve as a baseline for future research and provide a foundation for further studies.

3. Informative Data

Descriptive research can provide valuable information and insights into a particular topic, which can inform future research, policy decisions, and programs.

4. Sampling Validation

Descriptive research can be used to validate sampling methods and to help researchers determine the best approach for their study.

5. Cost Effective

Descriptive research is often less expensive and less time-consuming than other research methods , making it a cost-effective way to gather information about a particular population or phenomenon.

6. Easy to Replicate

Descriptive research is straightforward to replicate, making it a reliable way to gather and compare information from multiple sources.

Key Characteristics of Descriptive Research Design

The primary purpose of descriptive research is to describe the characteristics, behaviors, and attributes of a particular population or phenomenon.

2. Participants and Sampling

Descriptive research studies a particular population or sample that is representative of the larger population being studied. Furthermore, sampling methods can include convenience, stratified, or random sampling.

3. Data Collection Techniques

Descriptive research typically involves the collection of both qualitative and quantitative data through methods such as surveys, observational studies, case studies, or focus groups.

4. Data Analysis

Descriptive research data is analyzed to identify patterns, relationships, and trends within the data. Statistical techniques , such as frequency distributions and descriptive statistics, are commonly used to summarize and describe the data.

5. Focus on Description

Descriptive research is focused on describing and summarizing the characteristics of a particular population or phenomenon. It does not make causal inferences.

6. Non-Experimental

Descriptive research is non-experimental, meaning that the researcher does not manipulate variables or control conditions. The researcher simply observes and collects data on the population or phenomenon being studied.

When Can a Researcher Conduct Descriptive Research?

A researcher can conduct descriptive research in the following situations:

• To better understand a particular population or phenomenon
• To describe the relationships between variables
• To describe patterns and trends
• To validate sampling methods and determine the best approach for a study
• To compare data from multiple sources.

Types of Descriptive Research Design

1. survey research.

Surveys are a type of descriptive research that involves collecting data through self-administered or interviewer-administered questionnaires. Additionally, they can be administered in-person, by mail, or online, and can collect both qualitative and quantitative data.

2. Observational Research

Observational research involves observing and collecting data on a particular population or phenomenon without manipulating variables or controlling conditions. It can be conducted in naturalistic settings or controlled laboratory settings.

3. Case Study Research

Case study research is a type of descriptive research that focuses on a single individual, group, or event. It involves collecting detailed information on the subject through a variety of methods, including interviews, observations, and examination of documents.

4. Focus Group Research

Focus group research involves bringing together a small group of people to discuss a particular topic or product. Furthermore, the group is usually moderated by a researcher and the discussion is recorded for later analysis.

5. Ethnographic Research

Ethnographic research involves conducting detailed observations of a particular culture or community. It is often used to gain a deep understanding of the beliefs, behaviors, and practices of a particular group.

Advantages of Descriptive Research Design

1. provides a comprehensive understanding.

Descriptive research provides a comprehensive picture of the characteristics, behaviors, and attributes of a particular population or phenomenon, which can be useful in informing future research and policy decisions.

2. Non-invasive

Descriptive research is non-invasive and does not manipulate variables or control conditions, making it a suitable method for sensitive or ethical concerns.

3. Flexibility

Descriptive research allows for a wide range of data collection methods , including surveys, observational studies, case studies, and focus groups, making it a flexible and versatile research method.

4. Cost-effective

Descriptive research is often less expensive and less time-consuming than other research methods. Moreover, it gives a cost-effective option to many researchers.

5. Easy to Replicate

Descriptive research is easy to replicate, making it a reliable way to gather and compare information from multiple sources.

6. Informs Future Research

The insights gained from a descriptive research can inform future research and inform policy decisions and programs.

Disadvantages of Descriptive Research Design

1. limited scope.

Descriptive research only provides a snapshot of the current situation and cannot establish cause-and-effect relationships.

2. Dependence on Existing Data

Descriptive research relies on existing data, which may not always be comprehensive or accurate.

3. Lack of Control

Researchers have no control over the variables in descriptive research, which can limit the conclusions that can be drawn.

The researcher’s own biases and preconceptions can influence the interpretation of the data.

5. Lack of Generalizability

Descriptive research findings may not be applicable to other populations or situations.

6. Lack of Depth

Descriptive research provides a surface-level understanding of a phenomenon, rather than a deep understanding.

7. Time-consuming

Descriptive research often requires a large amount of data collection and analysis, which can be time-consuming and resource-intensive.

7 Ways to Avoid Common Flaws While Designing Descriptive Research

1. Clearly define the research question

A clearly defined research question is the foundation of any research study, and it is important to ensure that the question is both specific and relevant to the topic being studied.

2. Choose the appropriate research design

Choosing the appropriate research design for a study is crucial to the success of the study. Moreover, researchers should choose a design that best fits the research question and the type of data needed to answer it.

3. Select a representative sample

Selecting a representative sample is important to ensure that the findings of the study are generalizable to the population being studied. Researchers should use a sampling method that provides a random and representative sample of the population.

4. Use valid and reliable data collection methods

Using valid and reliable data collection methods is important to ensure that the data collected is accurate and can be used to answer the research question. Researchers should choose methods that are appropriate for the study and that can be administered consistently and systematically.

5. Minimize bias

Bias can significantly impact the validity and reliability of research findings.  Furthermore, it is important to minimize bias in all aspects of the study, from the selection of participants to the analysis of data.

6. Ensure adequate sample size

An adequate sample size is important to ensure that the results of the study are statistically significant and can be generalized to the population being studied.

7. Use appropriate data analysis techniques

The appropriate data analysis technique depends on the type of data collected and the research question being asked. Researchers should choose techniques that are appropriate for the data and the question being asked.

Have you worked on descriptive research designs? How was your experience creating a descriptive design? What challenges did you face? Do write to us or leave a comment below and share your insights on descriptive research designs!

extremely very educative

Indeed very educative and useful. Well explained. Thank you

Simple,easy to understand

Excellent and easy to understand queries and questions get answered easily. Its rather clear than any confusion. Thanks a million Shritika Sirisilla.

Easy to understand. Well written , educative and informative

Rate this article Cancel Reply

Your email address will not be published.

Enago Academy's Most Popular Articles

• Promoting Research

Graphical Abstracts Vs. Infographics: Best practices for using visual illustrations for increased research impact

Dr. Sarah Chen stared at her computer screen, her eyes staring at her recently published…

• Publishing Research

10 Tips to Prevent Research Papers From Being Retracted

Research paper retractions represent a critical event in the scientific community. When a published article…

• Industry News

Google Releases 2024 Scholar Metrics, Evaluates Impact of Scholarly Articles

Google has released its 2024 Scholar Metrics, assessing scholarly articles from 2019 to 2023. This…

Ensuring Academic Integrity and Transparency in Academic Research: A comprehensive checklist for researchers

Academic integrity is the foundation upon which the credibility and value of scientific findings are…

• Reporting Research

How to Optimize Your Research Process: A step-by-step guide

For researchers across disciplines, the path to uncovering novel findings and insights is often filled…

Choosing the Right Analytical Approach: Thematic analysis vs. content analysis for…

Comparing Cross Sectional and Longitudinal Studies: 5 steps for choosing the right…

Research Recommendations – Guiding policy-makers for evidence-based decision making

Sign-up to read more

Subscribe for free to get unrestricted access to all our resources on research writing and academic publishing including:

• 2000+ blog articles
• 50+ Webinars
• 10+ Expert podcasts
• 50+ Infographics
• 10+ Checklists
• Research Guides

We hate spam too. We promise to protect your privacy and never spam you.

• AI in Academia
• Career Corner
• Diversity and Inclusion
• Infographics
• Expert Video Library
• Other Resources
• Enago Learn
• Upcoming & On-Demand Webinars
• Peer Review Week 2024
• Open Access Week 2023
• Conference Videos
• Enago Report
• Journal Finder
• Enago Plagiarism & AI Grammar Check
• Editing Services
• Publication Support Services
• Research Impact
• Translation Services
• Publication solutions
• AI-Based Solutions
• Thought Leadership
• Call for Articles
• Call for Speakers
• Author Training
• Edit Profile

I am looking for Editing/ Proofreading services for my manuscript Tentative date of next journal submission:

Which among these features would you prefer the most in a peer review assistant?

What is Descriptive Research and How is it Used?

Introduction

What does descriptive research mean, why would you use a descriptive research design, what are the characteristics of descriptive research, examples of descriptive research, what are the data collection methods in descriptive research, how do you analyze descriptive research data, ensuring validity and reliability in the findings.

Conducting descriptive research offers researchers a way to present phenomena as they naturally occur. Rooted in an open-ended and non-experimental nature, this type of research focuses on portraying the details of specific phenomena or contexts, helping readers gain a clearer understanding of topics of interest.

From businesses gauging customer satisfaction to educators assessing classroom dynamics, the data collected from descriptive research provides invaluable insights across various fields.

This article aims to illuminate the essence, utility, characteristics, and methods associated with descriptive research, guiding those who wish to harness its potential in their respective domains.

At its core, descriptive research refers to a systematic approach used by researchers to collect, analyze, and present data about real-life phenomena to describe it in its natural context. It primarily aims to describe what exists, based on empirical observations .

Unlike experimental research, where variables are manipulated to observe outcomes, descriptive research deals with the "as-is" scenario to facilitate further research by providing a framework or new insights on which continuing studies can build.

Definition of descriptive research

Descriptive research is defined as a research method that observes and describes the characteristics of a particular group, situation, or phenomenon.

The goal is not to establish cause and effect relationships but rather to provide a detailed account of the situation.

The difference between descriptive and exploratory research

While both descriptive and exploratory research seek to provide insights into a topic or phenomenon, they differ in their focus. Exploratory research is more about investigating a topic to develop preliminary insights or to identify potential areas of interest.

In contrast, descriptive research offers detailed accounts and descriptions of the observed phenomenon, seeking to paint a full picture of what's happening.

The evolution of descriptive research in academia

Historically, descriptive research has played a foundational role in numerous academic disciplines. Anthropologists, for instance, used this approach to document cultures and societies. Psychologists have employed it to capture behaviors, emotions, and reactions.

Over time, the method has evolved, incorporating technological advancements and adapting to contemporary needs, yet its essence remains rooted in describing a phenomenon or setting as it is.

Descriptive research serves as a cornerstone in the research landscape for its ability to provide a detailed snapshot of life. Its unique qualities and methods make it an invaluable method for various research purposes. Here's why:

Benefits of obtaining a clear picture

Descriptive research captures the present state of phenomena, offering researchers a detailed reflection of situations. This unaltered representation is crucial for sectors like marketing, where understanding current consumer behavior can shape future strategies.

Facilitating data interpretation

Given its straightforward nature, descriptive research can provide data that's easier to interpret, both for researchers and their audiences. Rather than analyzing complex statistical relationships among variables, researchers present detailed descriptions of their qualitative observations . Researchers can engage in in depth analysis relating to their research question , but audiences can also draw insights from their own interpretations or reflections on potential underlying patterns.

Enhancing the clarity of the research problem

By presenting things as they are, descriptive research can help elucidate ambiguous research questions. A well-executed descriptive study can shine light on overlooked aspects of a problem, paving the way for further investigative research.

Addressing practical problems

In real-world scenarios, it's not always feasible to manipulate variables or set up controlled experiments. For instance, in social sciences, understanding cultural norms without interference is paramount. Descriptive research allows for such non-intrusive insights, ensuring genuine understanding.

Building a foundation for future research

Often, descriptive studies act as stepping stones for more complex research endeavors. By establishing baseline data and highlighting patterns, they create a platform upon which more intricate hypotheses can be built and tested in subsequent studies.

Descriptive research is distinguished by a set of hallmark characteristics that set it apart from other research methodologies . Recognizing these features can help researchers effectively design, implement , and interpret descriptive studies.

Specificity in the research question

As with all research, descriptive research starts with a well-defined research question aiming to detail a particular phenomenon. The specificity ensures that the study remains focused on gathering relevant data without unnecessary deviations.

Focus on the present situation

While some research methods aim to predict future trends or uncover historical truths, descriptive research is predominantly concerned with the present. It seeks to capture the current state of affairs, such as understanding today's consumer habits or documenting a newly observed phenomenon.

Standardized and structured methodology

To ensure credibility and consistency in results, descriptive research often employs standardized methods. Whether it's using a fixed set of survey questions or adhering to specific observation protocols, this structured approach ensures that data is collected uniformly, making it easier to compare and analyze.

Non-manipulative approach in observation

One of the standout features of descriptive research is its non-invasive nature. Researchers observe and document without influencing the research subject or the environment. This passive stance ensures that the data gathered is a genuine reflection of the phenomenon under study.

Replicability and consistency in results

Due to its structured methodology, findings from descriptive research can often be replicated in different settings or with different samples. This consistency adds to the credibility of the results, reinforcing the validity of the insights drawn from the study.

Analyze data quickly and efficiently with ATLAS.ti

Download a free trial to see how you can make sense of complex qualitative data.

Numerous fields and sectors conduct descriptive research for its versatile and detailed nature. Through its focus on presenting things as they naturally occur, it provides insights into a myriad of scenarios. Here are some tangible examples from diverse domains:

Conducting market research

Businesses often turn to data analysis through descriptive research to understand the demographics of their target market. For instance, a company launching a new product might survey potential customers to understand their age, gender, income level, and purchasing habits, offering valuable data for targeted marketing strategies.

Evaluating employee behaviors

Organizations rely on descriptive research designs to assess the behavior and attitudes of their employees. By conducting observations or surveys , companies can gather data on workplace satisfaction, collaboration patterns, or the impact of a new office layout on productivity.

Understanding consumer preferences

Brands aiming to understand their consumers' likes and dislikes often use descriptive research. By observing shopping behaviors or conducting product feedback surveys , they can gauge preferences and adjust their offerings accordingly.

Documenting historical patterns

Historians and anthropologists employ descriptive research to identify patterns through analysis of events or cultural practices. For instance, a historian might detail the daily life in a particular era, while an anthropologist might document rituals and ceremonies of a specific tribe.

Assessing student performance

Educational researchers can utilize descriptive studies to understand the effectiveness of teaching methodologies. By observing classrooms or surveying students, they can measure data trends and gauge the impact of a new teaching technique or curriculum on student engagement and performance.

Descriptive research methods aim to authentically represent situations and phenomena. These techniques ensure the collection of comprehensive and reliable data about the subject of interest.

The most appropriate descriptive research method depends on the research question and resources available for your research study.

Surveys and questionnaires

One of the most familiar tools in the researcher's arsenal, surveys and questionnaires offer a structured means of collecting data from a vast audience. Through carefully designed questions, researchers can obtain standardized responses that lend themselves to straightforward comparison and analysis in quantitative and qualitative research .

Survey research can manifest in various formats, from face-to-face interactions and telephone conversations to digital platforms. While surveys can reach a broad audience and generate quantitative data ripe for statistical analysis, they also come with the challenge of potential biases in design and rely heavily on respondent honesty.

Observations and case studies

Direct or participant observation is a method wherein researchers actively watch and document behaviors or events. A researcher might, for instance, observe the dynamics within a classroom or the behaviors of shoppers in a market setting.

Case studies provide an even deeper dive, focusing on a thorough analysis of a specific individual, group, or event. These methods present the advantage of capturing real-time, detailed data, but they might also be time-intensive and can sometimes introduce observer bias .

Interviews and focus groups

Interviews , whether they follow a structured script or flow more organically, are a powerful means to extract detailed insights directly from participants. On the other hand, focus groups gather multiple participants for discussions, aiming to gather diverse and collective opinions on a particular topic or product.

These methods offer the benefit of deep insights and adaptability in data collection . However, they necessitate skilled interviewers, and focus group settings might see individual opinions being influenced by group dynamics.

Document and content analysis

Here, instead of generating new data, researchers examine existing documents or content . This can range from studying historical records and newspapers to analyzing media content or literature.

Analyzing existing content offers the advantage of accessibility and can provide insights over longer time frames. However, the reliability and relevance of the content are paramount, and researchers must approach this method with a discerning eye.

Descriptive research data, rich in details and insights, necessitates meticulous analysis to derive meaningful conclusions. The analysis process transforms raw data into structured findings that can be communicated and acted upon.

Qualitative content analysis

For data collected through interviews , focus groups , observations , or open-ended survey questions , qualitative content analysis is a popular choice. This involves examining non-numerical data to identify patterns, themes, or categories.

By coding responses or observations , researchers can identify recurring elements, making it easier to comprehend larger data sets and draw insights.

Using descriptive statistics

When dealing with quantitative data from surveys or experiments, descriptive statistics are invaluable. Measures such as mean, median, mode, standard deviation, and frequency distributions help summarize data sets, providing a snapshot of the overall patterns.

Graphical representations like histograms, pie charts, or bar graphs can further help in visualizing these statistics.

Coding and categorizing the data

Both qualitative and quantitative data often require coding. Coding involves assigning labels to specific responses or behaviors to group similar segments of data. This categorization aids in identifying patterns, especially in vast data sets.

For instance, responses to open-ended questions in a survey can be coded based on keywords or sentiments, allowing for a more structured analysis.

Visual representation through graphs and charts

Visual aids like graphs, charts, and plots can simplify complex data, making it more accessible and understandable. Whether it's showcasing frequency distributions through histograms or mapping out relationships with networks, visual representations can elucidate trends and patterns effectively.

In the realm of research , the credibility of findings is paramount. Without trustworthiness in the results, even the most meticulously gathered data can lose its value. Two cornerstones that bolster the credibility of research outcomes are validity and reliability .

Validity: Measuring the right thing

Validity addresses the accuracy of the research. It seeks to answer the question: Is the research genuinely measuring what it aims to measure? In descriptive research, where the objective is to paint an authentic picture of the current state of affairs, ensuring validity is crucial.

For instance, if a study aims to understand consumer preferences for a product category, the questions posed should genuinely reflect those preferences and not veer into unrelated territories. Multiple forms of validity, including content, criterion, and construct validity, can be examined to ensure that the research instruments and processes are aligned with the research goals.

Reliability: Consistency in findings

Reliability, on the other hand, pertains to the consistency of the research findings. When a study demonstrates reliability, this suggests that others could repeat the study and the outcomes would remain consistent across repetitions.

In descriptive research, factors like the clarity of survey questions , the training of observers , and the standardization of interview protocols play a role in enhancing reliability. Techniques such as test-retest and internal consistency measurements can be employed to assess and improve reliability.

Make your research happen with ATLAS.ti

Analyze descriptive research with our powerful data analysis interface. Download a free trial of ATLAS.ti.

• Research Process
• Manuscript Preparation
• Manuscript Review
• Publication Process
• Publication Recognition
• Language Editing Services
• Translation Services

Descriptive Research Design and Its Myriad Uses

• 3 minute read

Table of Contents

The design of a research study can be of two broad types—observational or interventional. In interventional studies, at least one variable can be controlled by the researcher. For example, drug trials that examine the efficacy of novel medicines are interventional studies. Observational studies, on the other hand, simply examine and describe uncontrollable variables¹ .   

What is descriptive research design?¹

Descriptive design is one of the simplest forms of observational study design. It can either quantify the distribution of certain variables (quantitative descriptive research) or simply report the qualities of these variables without quantifying them (qualitative descriptive research).   

When can descriptive research design be used?¹

It is useful when you wish to examine the occurrence of a phenomenon, delineate trends or patterns within the phenomenon, or describe the relationship between variables. As such, descriptive design is great for¹ :  

• A survey conducted to measure the changes in the levels of customer satisfaction among shoppers in the US is the perfect example of quantitative descriptive research.  
• Conversely, a case report detailing the experiences and perspectives of individuals living with a particular rare disease is a good example of qualitative descriptive research.  
• Cross-sectional studies : Descriptive research is ideal for cross-sectional studies that capture a snapshot of a population at a specific point in time. This approach can be used to observe the variations in risk factors and diseases in a population. Take the following examples:   
• In quantitative descriptive research: A study that measures the prevalence of heart disease among college students in the current academic year.  
• In qualitative descriptive research: A cross-sectional study exploring the cultural perceptions of mental health across different communities.  
• Ecological studies : Descriptive research design is also well-suited for studies that seek to understand relationships between variables and outcomes in specific populations. For example:  
• A study that measures the relationship between the number of police personnel and homicides in India can use quantitative descriptive research design  
• A study describing the impact of deforestation on indigenous communities’ cultural practices and beliefs can use qualitative descriptive research design.  
• Focus group discussion reports : Descriptive research can help in capturing diverse perspectives and understanding the nuances of participants’ experiences.   
• First, an example of quantitative descriptive research: A study that uses two focus groups to explore the perceptions of mental health among immigrants in London.  
• Next, an example of qualitative descriptive research: A focus group report analyzing the themes and emotions associated with different advertising campaigns.  

Benefits of descriptive research design¹  

• Easy to conduct: Due to its simplicity, descriptive research design can be employed by researchers of all experience levels.  
• Economical: Descriptive research design is not resource intensive. It is a budget-friendly approach to studying many phenomena without costly equipment.   
• Provides comprehensive and useful information: Descriptive research is a more thorough approach that can capture many different aspects of a phenomena, facilitating a wholistic understanding.  
• Aids planning of major projects or future research: As a tool for preliminary exploration, descriptive research guides can guide strategic decision-making and guide major projects.  

The Bottom Line  

Descriptive research plays a crucial role in improving our lives. Surveys help create better policies and cross-sectional studies help us understand problems affecting different populations including diseases. Used in the right context, descriptive research can advance knowledge and inform decision making¹ .  

We, at Elsevier Language Services, understand the value of your descriptive research, as well as the importance of communicating it correctly. If you have a manuscript based on a descriptive study, our experienced editors can help improve its myriad aspects. By improving the logical flow, tone, and accuracy of your writing, we ensure that your descriptive research gets published in a top tier journal and makes maximum impact in academia and beyond. Contact us for a comprehensive list of services!   

Type in wordcount for Plus Total: USD EUR JPY Follow this link if your manuscript is longer than 9,000 words. Upload

References 

• Aggarwal, R., & Ranganathan, P. (2019). Study designs: Part 2 – Descriptive studies. Perspectives in Clinical Research , 10 (1), 34. https://doi.org/10.4103/picr.picr_154_18 .  

Is The Use of AI in Manuscript Editing Feasible? Here’s Three Tips to Steer Clear of Potential Issues

Navigating “Chinglish” Errors in Academic English Writing

You may also like.

Five Common Mistakes to Avoid When Writing a Biomedical Research Paper

Making Technical Writing in Environmental Engineering Accessible

To Err is Not Human: The Dangers of AI-assisted Academic Writing

When Data Speak, Listen: Importance of Data Collection and Analysis Methods

Choosing the Right Research Methodology: A Guide for Researchers

Why is data validation important in research?

Writing a good review article

Scholarly Sources: What are They and Where can You Find Them?

Input your search keywords and press Enter.

• Subscribe to journal Subscribe
• Get new issue alerts Get alerts

Secondary Logo

Journal logo.

Colleague's E-mail is Invalid

Your message has been successfully sent to your colleague.

Save my selection

Understanding Descriptive Research Designs and Methods

Siedlecki, Sandra L. PhD, RN, APRN-CNS, FAAN

Author Affiliation: Senior Nurse Scientist and Clinical Nurse Specialist, Office of Nursing Research & Innovation, Nursing Institute, Cleveland Clinic, Ohio.

The author reports no conflicts of interest.

Correspondence: Sandra L. Siedlecki, PhD, RN, APRN-CNS, 3271 Stillwater Dr, Medina, OH 44256 ( [email protected] ).

Full Text Access for Subscribers:

Individual subscribers.

Institutional Users

Not a subscriber.

You can read the full text of this article if you:

• + Favorites
• View in Gallery

Readers Of this Article Also Read

Quasi-experimental research designs, correlation designs and analyses: what the clinical nurse specialist needs to..., conducting interviews for qualitative research studies, reporting outcomes: the time is now, maternity deserts: an ethical crisis looming.

An official website of the United States government

The .gov means it’s official. Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

The site is secure. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

• Publications
• Account settings
• My Bibliography
• Collections
• Citation manager

Save citation to file

Email citation, add to collections.

• Create a new collection
• Add to an existing collection

Add to My Bibliography

Your saved search, create a file for external citation management software, your rss feed.

• Search in PubMed
• Search in NLM Catalog
• Add to Search

Understanding Descriptive Research Designs and Methods

Affiliation.

• 1 Author Affiliation: Senior Nurse Scientist and Clinical Nurse Specialist, Office of Nursing Research & Innovation, Nursing Institute, Cleveland Clinic, Ohio.
• PMID: 31789957
• DOI: 10.1097/NUR.0000000000000493

PubMed Disclaimer

Similar articles

• Quasi-Experimental Research Designs. Siedlecki SL. Siedlecki SL. Clin Nurse Spec. 2020 Sep/Oct;34(5):198-202. doi: 10.1097/NUR.0000000000000540. Clin Nurse Spec. 2020. PMID: 32796378 No abstract available.
• Correlation Designs and Analyses: What the Clinical Nurse Specialist Needs to Know. Siedlecki SL. Siedlecki SL. Clin Nurse Spec. 2020 Jul/Aug;34(4):143-149. doi: 10.1097/NUR.0000000000000525. Clin Nurse Spec. 2020. PMID: 32541598 No abstract available.
• Pilot or Feasibility Studies: An Option for the Clinical Nurse Specialist to Consider. Siedlecki SL. Siedlecki SL. Clin Nurse Spec. 2019 Sep/Oct;33(5):205-208. doi: 10.1097/NUR.0000000000000468. Clin Nurse Spec. 2019. PMID: 31403996 No abstract available.
• Writing for publication as an advanced practice nurse. Oermann MH. Oermann MH. Nursingconnections. 1999 Fall;12(3):5-13. Nursingconnections. 1999. PMID: 10788899 Review.
• Descriptive designs: the case for case studies and case series in research. Whitney JD. Whitney JD. J Wound Ostomy Continence Nurs. 2000 Mar;27(2):76-8. doi: 10.1016/s1071-5754(00)90072-x. J Wound Ostomy Continence Nurs. 2000. PMID: 10729175 Review. No abstract available.
• Positive Association between Patients' Perception of Chronic Pain Rehabilitation as a Personally Meaningful Experience and the Flourishing Aspect of Well-Being. Liddiard KJ, Brown CA, Raynor AJ. Liddiard KJ, et al. Healthcare (Basel). 2024 Aug 20;12(16):1655. doi: 10.3390/healthcare12161655. Healthcare (Basel). 2024. PMID: 39201213 Free PMC article.
• Student nurse perceptions of family nursing practices in South Africa: A descriptive survey. Chironda G, Brysiewicz P. Chironda G, et al. Health SA. 2024 Mar 28;29:2321. doi: 10.4102/hsag.v29i0.2321. eCollection 2024. Health SA. 2024. PMID: 38628233 Free PMC article.
• Healthcare team resilience during COVID-19: a qualitative study. Ambrose JW, Catchpole K, Evans HL, Nemeth LS, Layne DM, Nichols M. Ambrose JW, et al. BMC Health Serv Res. 2024 Apr 12;24(1):459. doi: 10.1186/s12913-024-10895-3. BMC Health Serv Res. 2024. PMID: 38609968 Free PMC article.
• Implementation of an Electronic Mental Health Platform for Youth and Young Adults in a School Context Across Alberta, Canada: Thematic Analysis of the Perspectives of Stakeholders. Dimitropoulos G, Bassi EM, Bright KS, Gondziola J, Bradley J, Fersovitch M, Stamp L, LaMonica HM, Iorfino F, Gaskell T, Tomlinson S, Johnson DW. Dimitropoulos G, et al. JMIR Ment Health. 2024 Jan 17;11:e49099. doi: 10.2196/49099. JMIR Ment Health. 2024. PMID: 38231558 Free PMC article.
• How to adopt technologies in home care: a mixed methods study on user experiences and change of home care in Finland. Anttila M, Koivisto J, Luoma ML, Anttila H. Anttila M, et al. BMC Health Serv Res. 2023 Dec 2;23(1):1342. doi: 10.1186/s12913-023-10368-z. BMC Health Serv Res. 2023. PMID: 38042800 Free PMC article.
• Search in MeSH

LinkOut - more resources

Full text sources.

• Ovid Technologies, Inc.
• Wolters Kluwer

• Citation Manager

NCBI Literature Resources

MeSH PMC Bookshelf Disclaimer

The PubMed wordmark and PubMed logo are registered trademarks of the U.S. Department of Health and Human Services (HHS). Unauthorized use of these marks is strictly prohibited.

Educational resources and simple solutions for your research journey

What is Descriptive Research? Definition, Methods, Types and Examples

Descriptive research is a methodological approach that seeks to depict the characteristics of a phenomenon or subject under investigation. In scientific inquiry, it serves as a foundational tool for researchers aiming to observe, record, and analyze the intricate details of a particular topic. This method provides a rich and detailed account that aids in understanding, categorizing, and interpreting the subject matter.

Descriptive research design is widely employed across diverse fields, and its primary objective is to systematically observe and document all variables and conditions influencing the phenomenon.

After this descriptive research definition, let’s look at this example. Consider a researcher working on climate change adaptation, who wants to understand water management trends in an arid village in a specific study area. She must conduct a demographic survey of the region, gather population data, and then conduct descriptive research on this demographic segment. The study will then uncover details on “what are the water management practices and trends in village X.” Note, however, that it will not cover any investigative information about “why” the patterns exist.

Table of Contents

What is descriptive research?

If you’ve been wondering “What is descriptive research,” we’ve got you covered in this post! In a nutshell, descriptive research is an exploratory research method that helps a researcher describe a population, circumstance, or phenomenon. It can help answer what , where , when and how questions, but not why questions. In other words, it does not involve changing the study variables and does not seek to establish cause-and-effect relationships.

Importance of descriptive research

Now, let’s delve into the importance of descriptive research. This research method acts as the cornerstone for various academic and applied disciplines. Its primary significance lies in its ability to provide a comprehensive overview of a phenomenon, enabling researchers to gain a nuanced understanding of the variables at play. This method aids in forming hypotheses, generating insights, and laying the groundwork for further in-depth investigations. The following points further illustrate its importance:

Provides insights into a population or phenomenon: Descriptive research furnishes a comprehensive overview of the characteristics and behaviors of a specific population or phenomenon, thereby guiding and shaping the research project.

Offers baseline data: The data acquired through this type of research acts as a reference for subsequent investigations, laying the groundwork for further studies.

Allows validation of sampling methods: Descriptive research validates sampling methods, aiding in the selection of the most effective approach for the study.

Helps reduce time and costs: It is cost-effective and time-efficient, making this an economical means of gathering information about a specific population or phenomenon.

Ensures replicability: Descriptive research is easily replicable, ensuring a reliable way to collect and compare information from various sources.

When to use descriptive research design?

Determining when to use descriptive research depends on the nature of the research question. Before diving into the reasons behind an occurrence, understanding the how, when, and where aspects is essential. Descriptive research design is a suitable option when the research objective is to discern characteristics, frequencies, trends, and categories without manipulating variables. It is therefore often employed in the initial stages of a study before progressing to more complex research designs. To put it in another way, descriptive research precedes the hypotheses of explanatory research. It is particularly valuable when there is limited existing knowledge about the subject.

Some examples are as follows, highlighting that these questions would arise before a clear outline of the research plan is established:

• In the last two decades, what changes have occurred in patterns of urban gardening in Mumbai?
• What are the differences in climate change perceptions of farmers in coastal versus inland villages in the Philippines?

Characteristics of descriptive research

Coming to the characteristics of descriptive research, this approach is characterized by its focus on observing and documenting the features of a subject. Specific characteristics are as below.

• Quantitative nature: Some descriptive research types involve quantitative research methods to gather quantifiable information for statistical analysis of the population sample.
• Qualitative nature: Some descriptive research examples include those using the qualitative research method to describe or explain the research problem.
• Observational nature: This approach is non-invasive and observational because the study variables remain untouched. Researchers merely observe and report, without introducing interventions that could impact the subject(s).
• Cross-sectional nature: In descriptive research, different sections belonging to the same group are studied, providing a “snapshot” of sorts.
• Springboard for further research: The data collected are further studied and analyzed using different research techniques. This approach helps guide the suitable research methods to be employed.

Types of descriptive research

There are various descriptive research types, each suited to different research objectives. Take a look at the different types below.

• Surveys: This involves collecting data through questionnaires or interviews to gather qualitative and quantitative data.
• Observational studies: This involves observing and collecting data on a particular population or phenomenon without influencing the study variables or manipulating the conditions. These may be further divided into cohort studies, case studies, and cross-sectional studies:
• Cohort studies: Also known as longitudinal studies, these studies involve the collection of data over an extended period, allowing researchers to track changes and trends.
• Case studies: These deal with a single individual, group, or event, which might be rare or unusual.
• Cross-sectional studies : A researcher collects data at a single point in time, in order to obtain a snapshot of a specific moment.
• Focus groups: In this approach, a small group of people are brought together to discuss a topic. The researcher moderates and records the group discussion. This can also be considered a “participatory” observational method.
• Descriptive classification: Relevant to the biological sciences, this type of approach may be used to classify living organisms.

Descriptive research methods

Several descriptive research methods can be employed, and these are more or less similar to the types of approaches mentioned above.

• Surveys: This method involves the collection of data through questionnaires or interviews. Surveys may be done online or offline, and the target subjects might be hyper-local, regional, or global.
• Observational studies: These entail the direct observation of subjects in their natural environment. These include case studies, dealing with a single case or individual, as well as cross-sectional and longitudinal studies, for a glimpse into a population or changes in trends over time, respectively. Participatory observational studies such as focus group discussions may also fall under this method.

Researchers must carefully consider descriptive research methods, types, and examples to harness their full potential in contributing to scientific knowledge.

Examples of descriptive research

Now, let’s consider some descriptive research examples.

• In social sciences, an example could be a study analyzing the demographics of a specific community to understand its socio-economic characteristics.
• In business, a market research survey aiming to describe consumer preferences would be a descriptive study.
• In ecology, a researcher might undertake a survey of all the types of monocots naturally occurring in a region and classify them up to species level.

These examples showcase the versatility of descriptive research across diverse fields.

Advantages of descriptive research

There are several advantages to this approach, which every researcher must be aware of. These are as follows:

• Owing to the numerous descriptive research methods and types, primary data can be obtained in diverse ways and be used for developing a research hypothesis .
• It is a versatile research method and allows flexibility.
• Detailed and comprehensive information can be obtained because the data collected can be qualitative or quantitative.
• It is carried out in the natural environment, which greatly minimizes certain types of bias and ethical concerns.
• It is an inexpensive and efficient approach, even with large sample sizes

Disadvantages of descriptive research

On the other hand, this design has some drawbacks as well:

• It is limited in its scope as it does not determine cause-and-effect relationships.
• The approach does not generate new information and simply depends on existing data.
• Study variables are not manipulated or controlled, and this limits the conclusions to be drawn.
• Descriptive research findings may not be generalizable to other populations.
• Finally, it offers a preliminary understanding rather than an in-depth understanding.

To reiterate, the advantages of descriptive research lie in its ability to provide a comprehensive overview, aid hypothesis generation, and serve as a preliminary step in the research process. However, its limitations include a potential lack of depth, inability to establish cause-and-effect relationships, and susceptibility to bias.

Frequently asked questions

When should researchers conduct descriptive research.

Descriptive research is most appropriate when researchers aim to portray and understand the characteristics of a phenomenon without manipulating variables. It is particularly valuable in the early stages of a study.

What is the difference between descriptive and exploratory research?

Descriptive research focuses on providing a detailed depiction of a phenomenon, while exploratory research aims to explore and generate insights into an issue where little is known.

What is the difference between descriptive and experimental research?

Descriptive research observes and documents without manipulating variables, whereas experimental research involves intentional interventions to establish cause-and-effect relationships.

Is descriptive research only for social sciences?

No, various descriptive research types may be applicable to all fields of study, including social science, humanities, physical science, and biological science.

How important is descriptive research?

The importance of descriptive research lies in its ability to provide a glimpse of the current state of a phenomenon, offering valuable insights and establishing a basic understanding. Further, the advantages of descriptive research include its capacity to offer a straightforward depiction of a situation or phenomenon, facilitate the identification of patterns or trends, and serve as a useful starting point for more in-depth investigations. Additionally, descriptive research can contribute to the development of hypotheses and guide the formulation of research questions for subsequent studies.

Editage All Access is a subscription-based platform that unifies the best AI tools and services designed to speed up, simplify, and streamline every step of a researcher’s journey. The Editage All Access Pack is a one-of-a-kind subscription that unlocks full access to an AI writing assistant, literature recommender, journal finder, scientific illustration tool, and exclusive discounts on professional publication services from Editage.  

Based on 22+ years of experience in academia, Editage All Access empowers researchers to put their best research forward and move closer to success. Explore our top AI Tools pack, AI Tools + Publication Services pack, or Build Your Own Plan. Find everything a researcher needs to succeed, all in one place –  Get All Access now starting at just $14 a month !    

Related Posts

Editage Plus: Tools and Pricing

Editage Review: Editage Services, Pricing, Editage Plus, and Editage Insights

An official website of the United States government

The .gov means it’s official. Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

The site is secure. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

• Publications
• Account settings

The PMC website is updating on October 15, 2024. Learn More or Try it out now .

• Advanced Search
• Journal List
• Perspect Clin Res
• v.9(4); Oct-Dec 2018

Study designs: Part 1 – An overview and classification

Priya ranganathan.

Department of Anaesthesiology, Tata Memorial Centre, Mumbai, Maharashtra, India

Rakesh Aggarwal

1 Department of Gastroenterology, Sanjay Gandhi Postgraduate Institute of Medical Sciences, Lucknow, Uttar Pradesh, India

There are several types of research study designs, each with its inherent strengths and flaws. The study design used to answer a particular research question depends on the nature of the question and the availability of resources. In this article, which is the first part of a series on “study designs,” we provide an overview of research study designs and their classification. The subsequent articles will focus on individual designs.

INTRODUCTION

Research study design is a framework, or the set of methods and procedures used to collect and analyze data on variables specified in a particular research problem.

Research study designs are of many types, each with its advantages and limitations. The type of study design used to answer a particular research question is determined by the nature of question, the goal of research, and the availability of resources. Since the design of a study can affect the validity of its results, it is important to understand the different types of study designs and their strengths and limitations.

There are some terms that are used frequently while classifying study designs which are described in the following sections.

A variable represents a measurable attribute that varies across study units, for example, individual participants in a study, or at times even when measured in an individual person over time. Some examples of variables include age, sex, weight, height, health status, alive/dead, diseased/healthy, annual income, smoking yes/no, and treated/untreated.

Exposure (or intervention) and outcome variables

A large proportion of research studies assess the relationship between two variables. Here, the question is whether one variable is associated with or responsible for change in the value of the other variable. Exposure (or intervention) refers to the risk factor whose effect is being studied. It is also referred to as the independent or the predictor variable. The outcome (or predicted or dependent) variable develops as a consequence of the exposure (or intervention). Typically, the term “exposure” is used when the “causative” variable is naturally determined (as in observational studies – examples include age, sex, smoking, and educational status), and the term “intervention” is preferred where the researcher assigns some or all participants to receive a particular treatment for the purpose of the study (experimental studies – e.g., administration of a drug). If a drug had been started in some individuals but not in the others, before the study started, this counts as exposure, and not as intervention – since the drug was not started specifically for the study.

Observational versus interventional (or experimental) studies

Observational studies are those where the researcher is documenting a naturally occurring relationship between the exposure and the outcome that he/she is studying. The researcher does not do any active intervention in any individual, and the exposure has already been decided naturally or by some other factor. For example, looking at the incidence of lung cancer in smokers versus nonsmokers, or comparing the antenatal dietary habits of mothers with normal and low-birth babies. In these studies, the investigator did not play any role in determining the smoking or dietary habit in individuals.

For an exposure to determine the outcome, it must precede the latter. Any variable that occurs simultaneously with or following the outcome cannot be causative, and hence is not considered as an “exposure.”

Observational studies can be either descriptive (nonanalytical) or analytical (inferential) – this is discussed later in this article.

Interventional studies are experiments where the researcher actively performs an intervention in some or all members of a group of participants. This intervention could take many forms – for example, administration of a drug or vaccine, performance of a diagnostic or therapeutic procedure, and introduction of an educational tool. For example, a study could randomly assign persons to receive aspirin or placebo for a specific duration and assess the effect on the risk of developing cerebrovascular events.

Descriptive versus analytical studies

Descriptive (or nonanalytical) studies, as the name suggests, merely try to describe the data on one or more characteristics of a group of individuals. These do not try to answer questions or establish relationships between variables. Examples of descriptive studies include case reports, case series, and cross-sectional surveys (please note that cross-sectional surveys may be analytical studies as well – this will be discussed in the next article in this series). Examples of descriptive studies include a survey of dietary habits among pregnant women or a case series of patients with an unusual reaction to a drug.

Analytical studies attempt to test a hypothesis and establish causal relationships between variables. In these studies, the researcher assesses the effect of an exposure (or intervention) on an outcome. As described earlier, analytical studies can be observational (if the exposure is naturally determined) or interventional (if the researcher actively administers the intervention).

Directionality of study designs

Based on the direction of inquiry, study designs may be classified as forward-direction or backward-direction. In forward-direction studies, the researcher starts with determining the exposure to a risk factor and then assesses whether the outcome occurs at a future time point. This design is known as a cohort study. For example, a researcher can follow a group of smokers and a group of nonsmokers to determine the incidence of lung cancer in each. In backward-direction studies, the researcher begins by determining whether the outcome is present (cases vs. noncases [also called controls]) and then traces the presence of prior exposure to a risk factor. These are known as case–control studies. For example, a researcher identifies a group of normal-weight babies and a group of low-birth weight babies and then asks the mothers about their dietary habits during the index pregnancy.

Prospective versus retrospective study designs

The terms “prospective” and “retrospective” refer to the timing of the research in relation to the development of the outcome. In retrospective studies, the outcome of interest has already occurred (or not occurred – e.g., in controls) in each individual by the time s/he is enrolled, and the data are collected either from records or by asking participants to recall exposures. There is no follow-up of participants. By contrast, in prospective studies, the outcome (and sometimes even the exposure or intervention) has not occurred when the study starts and participants are followed up over a period of time to determine the occurrence of outcomes. Typically, most cohort studies are prospective studies (though there may be retrospective cohorts), whereas case–control studies are retrospective studies. An interventional study has to be, by definition, a prospective study since the investigator determines the exposure for each study participant and then follows them to observe outcomes.

The terms “prospective” versus “retrospective” studies can be confusing. Let us think of an investigator who starts a case–control study. To him/her, the process of enrolling cases and controls over a period of several months appears prospective. Hence, the use of these terms is best avoided. Or, at the very least, one must be clear that the terms relate to work flow for each individual study participant, and not to the study as a whole.

Classification of study designs

Figure 1 depicts a simple classification of research study designs. The Centre for Evidence-based Medicine has put forward a useful three-point algorithm which can help determine the design of a research study from its methods section:[ 1 ]

Classification of research study designs

• Does the study describe the characteristics of a sample or does it attempt to analyze (or draw inferences about) the relationship between two variables? – If no, then it is a descriptive study, and if yes, it is an analytical (inferential) study
• If analytical, did the investigator determine the exposure? – If no, it is an observational study, and if yes, it is an experimental study
• If observational, when was the outcome determined? – at the start of the study (case–control study), at the end of a period of follow-up (cohort study), or simultaneously (cross sectional).

In the next few pieces in the series, we will discuss various study designs in greater detail.

Financial support and sponsorship

Conflicts of interest.

There are no conflicts of interest.

Descriptive Research in Psychology: Methods, Applications, and Importance

Picture a psychologist’s toolkit, brimming with an array of methods designed to unravel the mysteries of the human mind—among them, the unsung hero of descriptive research, a powerful lens through which we can observe, understand, and illuminate the vast landscape of human behavior and cognition. This versatile approach to psychological inquiry serves as a cornerstone in our quest to comprehend the intricacies of the human experience, offering insights that shape our understanding of everything from child development to social interactions.

Descriptive research in psychology is like a skilled artist’s sketch, capturing the essence of human behavior and mental processes with precision and depth. It’s the foundation upon which many psychological theories are built, providing a rich tapestry of observations that inform more complex studies. Unlike experimental methods that manipulate variables to establish cause-and-effect relationships, descriptive research aims to paint a vivid picture of what is, rather than what could be.

Defining Descriptive Research in Psychology: More Than Meets the Eye

At its core, descriptive research in psychology is a systematic approach to observing and cataloging human behavior, thoughts, and emotions in their natural context. It’s the scientific equivalent of people-watching, but with a structured methodology and a keen eye for detail. This type of research doesn’t just scratch the surface; it dives deep into the nuances of human experience, capturing the subtleties that might otherwise go unnoticed.

The beauty of descriptive research lies in its versatility. It can take many forms, each offering a unique perspective on the human psyche. From participant observation in psychology , where researchers immerse themselves in the world they’re studying, to meticulous case studies that explore individual experiences in depth, descriptive research adapts to the questions at hand.

One of the primary goals of descriptive research is to provide a comprehensive account of a phenomenon. It’s not about proving or disproving hypotheses; instead, it’s about gathering rich, detailed information that can later inform more targeted inquiries. This approach is particularly valuable when exploring new or understudied areas of psychology, serving as a springboard for future research.

Methods and Techniques: The Descriptive Researcher’s Toolkit

The methods employed in descriptive research are as diverse as the questions they seek to answer. Let’s take a closer look at some of the key tools in the descriptive researcher’s arsenal:

1. Observational methods: Picture a researcher sitting quietly in a playground, noting how children interact. This direct observation can yield invaluable insights into social development and behavior patterns.

2. Case studies: These in-depth explorations of individual experiences can shed light on rare psychological phenomena or provide detailed accounts of therapeutic interventions.

3. Surveys and questionnaires: By tapping into the thoughts and opinions of large groups, researchers can identify trends and patterns in attitudes and behaviors.

4. Archival research in psychology : Delving into historical records and existing datasets can uncover long-term trends and provide context for current psychological phenomena.

5. Naturalistic observation: This method involves studying behavior in its natural environment, without interference from the researcher. It’s like being a fly on the wall, capturing authentic human interactions.

Each of these methods has its strengths and limitations, and skilled researchers often combine multiple approaches to gain a more comprehensive understanding of their subject matter.

Applications: Descriptive Research in Action

The applications of descriptive research in psychology are as varied as human behavior itself. Let’s explore how this approach illuminates different areas of psychological study:

In developmental psychology, descriptive research plays a crucial role in understanding how children grow and change over time. Longitudinal studies in psychology , which follow the same group of individuals over an extended period, provide invaluable insights into the trajectory of human development.

Social psychology relies heavily on descriptive methods to explore how people interact and influence one another. For instance, observational studies in public spaces can reveal patterns of nonverbal communication or group dynamics that might be difficult to capture in a laboratory setting.

Clinical psychology often employs case studies to delve into the complexities of mental health disorders. These detailed accounts can provide rich, contextual information about the lived experiences of individuals dealing with psychological challenges.

In educational psychology, descriptive research helps identify effective teaching strategies and learning patterns. Classroom observations and student surveys can inform educational policies and practices, ultimately improving learning outcomes.

Real-world examples of descriptive studies abound. Consider the famous “Bobo doll” experiments by Albert Bandura, which used observational methods to explore how children learn aggressive behaviors. While not strictly descriptive in nature, these studies incorporated descriptive elements that provided crucial insights into social learning theory.

Strengths and Limitations: A Balanced View

Like any research method, descriptive research has its strengths and limitations. On the plus side, it offers a level of ecological validity that’s hard to match in controlled experiments. By studying behavior in natural settings, researchers can capture the complexity and nuance of real-world phenomena.

Descriptive research is also particularly adept at identifying patterns and generating hypotheses. It’s often the first step in a longer research process, providing the foundation for more targeted experimental studies. This approach can be especially valuable when dealing with sensitive topics or populations that might be difficult to study in more controlled settings.

However, it’s important to acknowledge the limitations of descriptive research. One of the primary challenges is the directionality problem in psychology . While descriptive studies can identify relationships between variables, they can’t establish causation. This limitation can sometimes lead to misinterpretation of results or overreaching conclusions.

Another potential pitfall is researcher bias. The subjective nature of some descriptive methods, particularly observational studies, can introduce unintended biases into the data collection and interpretation process. Researchers must be vigilant in maintaining objectivity and employing strategies to minimize bias.

When compared to experimental research, descriptive studies may seem less rigorous or definitive. However, this perception overlooks the unique value that descriptive research brings to the table. While experiments are excellent for testing specific hypotheses and establishing causal relationships, they often lack the richness and contextual detail that descriptive methods provide.

Conducting a Descriptive Study: From Planning to Publication

Embarking on a descriptive research project requires careful planning and execution. Here’s a roadmap for aspiring researchers:

1. Define your research question: Start with a clear, focused question that guides your inquiry. What specific aspect of human behavior or cognition do you want to explore?

2. Choose your method: Select the descriptive technique(s) best suited to answer your research question. Will you be conducting surveys, observing behavior, or delving into case studies?

3. Develop your data collection tools: Create robust instruments for gathering information, whether it’s a well-designed questionnaire or a structured observation protocol.

4. Recruit participants: If your study involves human subjects, ensure you have a representative sample and obtain proper informed consent.

5. Collect data: Implement your chosen method(s) with consistency and attention to detail. Remember, the quality of your data will directly impact the value of your findings.

6. Analyze and interpret: Once you’ve gathered your data, it’s time to make sense of it. Look for patterns, themes, and relationships within your observations.

7. Draw conclusions: Based on your analysis, what can you say about the phenomenon you’ve studied? Be careful not to overstate your findings or imply causation where none has been established.

Throughout this process, it’s crucial to keep ethical considerations at the forefront. Respect for participants’ privacy, confidentiality, and well-being should guide every step of your research.

The Future of Descriptive Research: Evolving Methods and New Frontiers

As we look to the future, descriptive research in psychology continues to evolve and adapt to new challenges and opportunities. Emerging technologies are opening up exciting possibilities for data collection and analysis. For instance, wearable devices and smartphone apps are enabling researchers to gather real-time data on behavior and physiological responses in natural settings.

The rise of big data and advanced analytics is also transforming descriptive research. By analyzing vast datasets of human behavior online, researchers can identify patterns and trends on a scale previously unimaginable. However, this new frontier also brings ethical challenges, particularly around privacy and consent.

Another promising direction is the integration of descriptive methods with other research approaches. Quasi-experiments in psychology , which combine elements of descriptive and experimental research, offer a middle ground that can leverage the strengths of both approaches.

As we continue to unravel the complexities of the human mind, descriptive research will undoubtedly play a crucial role. Its ability to capture the richness and diversity of human experience makes it an indispensable tool in the psychologist’s toolkit.

In conclusion, descriptive research in psychology is far more than just a preliminary step in the scientific process. It’s a powerful approach that provides the foundation for our understanding of human behavior and mental processes. By offering detailed, contextual insights into the human experience, descriptive research helps us identify patterns, generate hypotheses, and ultimately advance our knowledge of psychology.

From exploring the intricacies of child development to unraveling the dynamics of social interactions, descriptive research continues to illuminate the vast landscape of human psychology. As we move forward, the challenge for researchers will be to harness new technologies and methodologies while maintaining the core strengths of descriptive approaches – their ability to capture the nuance, complexity, and diversity of human experience.

In the end, it’s this deep, rich understanding of human behavior that drives psychological science forward, informing theories, shaping interventions, and ultimately helping us to better understand ourselves and others. As we continue to explore the fascinating world of the human mind, descriptive research will remain an essential tool, helping us to see the world through the eyes of those we study and to tell their stories with clarity, empathy, and scientific rigor.

References:

1. Coolican, H. (2014). Research methods and statistics in psychology. Psychology Press.

2. Creswell, J. W., & Creswell, J. D. (2017). Research design: Qualitative, quantitative, and mixed methods approaches. Sage publications.

3. Goodwin, C. J., & Goodwin, K. A. (2016). Research in psychology: Methods and design. John Wiley & Sons.

4. Kazdin, A. E. (2011). Single-case research designs: Methods for clinical and applied settings. Oxford University Press.

5. Leedy, P. D., & Ormrod, J. E. (2015). Practical research: Planning and design. Pearson.

6. Marczyk, G., DeMatteo, D., & Festinger, D. (2005). Essentials of research design and methodology. John Wiley & Sons.

7. Mertens, D. M. (2014). Research and evaluation in education and psychology: Integrating diversity with quantitative, qualitative, and mixed methods. Sage publications.

8. Rosenthal, R., & Rosnow, R. L. (2008). Essentials of behavioral research: Methods and data analysis. McGraw-Hill.

9. Shaughnessy, J. J., Zechmeister, E. B., & Zechmeister, J. S. (2015). Research methods in psychology. McGraw-Hill Education.

10. Willig, C., & Rogers, W. S. (Eds.). (2017). The SAGE handbook of qualitative research in psychology. Sage.

Similar Posts

Chaining Psychology: Unlocking Behavior Modification Techniques

Chaining psychology, a powerful yet often overlooked behavior modification tool, holds the key to unlocking the potential for lasting change and skill acquisition in various domains of life. This fascinating approach to learning and behavior modification has been quietly revolutionizing the way we understand and implement personal growth strategies. But what exactly is chaining psychology,…

Dark Humor Psychology: Unraveling the Appeal of Morbid Jokes

A morbid chuckle, a guilty grin—dark humor has long been a fascinating enigma, captivating minds and sparking debates about the fine line between wit and impropriety. It’s that uncomfortable laughter that bubbles up when we least expect it, often leaving us wondering, “Should I really be laughing at this?” But what is it about dark…

Congruence Psychology: Exploring Authenticity and Self-Alignment in Mental Health

Authenticity—the elusive yet powerful force that shapes our mental well-being, relationships, and personal growth—lies at the heart of congruence psychology, a field that delves into the profound impact of aligning our inner selves with our outward expressions. It’s a concept that resonates deeply with many of us, as we navigate the complexities of modern life…

Spoon Theory: A Psychological Framework for Understanding Chronic Illness and Energy Management

For those living with chronic illness or managing mental health conditions, every day can feel like a careful balancing act of rationing out a limited supply of energy, akin to counting precious spoons. This seemingly odd comparison is the foundation of a powerful psychological framework known as Spoon Theory, which has revolutionized how we understand…

Psychology of Repeating Yourself: Causes, Consequences, and Coping Strategies

From the annoying friend who won’t stop telling the same story to the politician rehashing talking points, repetitive speech patterns are a pervasive phenomenon that can strain even the most patient listener. We’ve all been there, caught in the verbal quicksand of someone who seems to be stuck on repeat. But what drives this behavior?…

Triangle Technique in Psychology: A Powerful Tool for Understanding Relationships

The Triangle Technique, a powerful psychological tool, illuminates the intricate dance of relationships, revealing the hidden dynamics that shape our connections with others. It’s a fascinating concept that has captivated the minds of psychologists and relationship experts for decades. But what exactly is this mysterious triangle, and how can it help us navigate the complex…

Leave a Reply Cancel reply

Your email address will not be published. Required fields are marked *

Save my name, email, and website in this browser for the next time I comment.

Advertisement

Computational thinking with game design: An action research study with middle school students

• Open access
• Published: 17 September 2024

Cite this article

You have full access to this open access article

• Lorien Cafarella 1 &
• Lucas Vasconcelos   ORCID: orcid.org/0000-0001-9074-203X 2  

Middle school students often enter Computer Science (CS) classes without previous CS or Computational Thinking (CT) instruction. This study evaluated how Code.org’s block-based programming curriculum affects middle school students’ CT skills and attitudes toward CT and CS. Sixteen students participated in the study. This was a mixed methods action research study that used pre- and post-tests, surveys, artifacts, and interviews as data sources. Descriptive statistics, paired samples t-tests, and inductive thematic analysis were administered. Findings showed a statistically significant increase in participants’ algorithmic thinking, debugging, and pattern recognition skills but not in abstraction skills. Attitudes toward CT and CS improved but the difference was not statistically significant. Qualitative themes revealed benefits of game-based learning to promote CT skills, collaboration to promote successful error debugging, and enjoyment of programming resulting from a balance between structured guidance and creative freedom. Findings emphasize the importance of low-threshold and engaging strategies to introduce novice learners to CT and CS.

Explore related subjects

• Artificial Intelligence
• Digital Education and Educational Technology

Avoid common mistakes on your manuscript.

1 Introduction

Students have limited exposure to Computer Science (CS) education in K-12 schools (Brown et al., 2014 ; Google & Gallup, 2020 ), which hampers their ability to develop Computational Thinking (CT) skills. CT includes problem-solving skills that students need to acquire in order to flourish in a digital era in which computer software drives several aspects of our lives (Román-González et al., 2017 ; Wing, 2006 ). As such, CT is an essential part of learning for all ages and should be incorporated into K-12 curricula (Runciman, 2011 ) so that students can not only learn how to use computers and consume technology, but also create technologies and use them for innovative problem solving (Kafai, 2016 ; Runciman, 2011 ).

Students’ limited access to CT and CS education is partly due to the decline in the number of qualified CS teachers in the past two decades (Kafai, 2016 ; Runciman, 2011 ). Several recommendations to address the CS teacher shortage include the creation of pathways for teachers to become CS endorsed and using funds for CS professional development (Computer Science Teachers Association, 2019 ). Lack of teacher preparation leads to inadequate CS instruction, which may prevent students from developing positive attitudes toward CT and CS. Very often, K-12 students think CS is unattainable, difficult to learn, and not enjoyable. It is imperative to teach CS to students in ways that integrate positive learning opportunities that not only promote CT skills but also improve their attitudes toward CT and CS.

One way to foster positive student attitudes toward CS education is by adopting fun, engaging, and lower-threshold computational activities that are part of an already established curriculum. Specifically, we propose a combination of game design and block-based programming to foster CT skills among young learners. Using block-based programming to design games (Akcaoglu, 2014 ; Akcaoglu & Kale, 2016 ) is a promising approach that allows students to practice CT skills while constructing a personally relevant artifact (Kafai & Burke, 2015 ; Ketenci et al., 2019 ). This study focuses on Code.org’s block-based programming curriculum for game design. To our knowledge, empirical studies seeking to investigate the impact of that curriculum on students’ CT skills and attitudes toward CT and CS are lacking. The present study addresses this literature gap.

2 Related literature

2.1 computational thinking.

The term computational thinking (CT) has received various definitions over the years. Wing ( 2006 ) explained that CT “involves solving problems, designing systems, and understanding human behavior by drawing on the concepts fundamental to CS” (p. 33). CT was further defined by Dagli and Tokmak ( 2022 ) as a problem-solving process aimed to understand how computers work. Brennan and Resnick ( 2012 ) proposed a CT framework that includes three dimensions: computational and programming concepts, computational practices, and computational perspectives. The computational and programming concepts focused on sequences, loops, events, conditionals, operators, and data; computational practices included incremental and iterative programming, testing and debugging, reusing and remixing, and abstracting and modularizing; and computational perspectives emerge regarding the world and oneself through programming (Brennan & Resnick, 2012 ). CT has also been described as the cognitive processes needed to frame problems so that the solutions manifest as computational and algorithmic steps (Aho, 2012 ). For this study, we adopt Shute et al.’s ( 2017 ) definition of CT “as the conceptual foundation required to solve problems effectively and efficiently (i.e., algorithmically, with or without the assistance of computers) with solutions that are reusable in different contexts” (p. 1). This definition was chosen because it emphasizes the concepts of problem solving and creation of solutions which are central to CS education.

The literature on CT includes several skills that are associated with CT. Wing ( 2006 ) described the skills as problem-solving, recursive thinking, abstraction and decomposition, preparing for error correction, and heuristic reasoning. Shute et al. ( 2017 ) believed that the skills included in CT were decomposition, abstraction, algorithms, debugging, iteration, and generalization. Dagli and Tokmak ( 2022 ) defined CT skills as “interpreting and understanding the digital data, algorithmic thinking, critical thinking, and decision making” (p. 513). There is significant overlap in definitions of CT skills. For this study, CT is divided into four skills: algorithmic thinking, abstraction, debugging, and pattern recognition. These four skills are the basis for understanding computational activities whether these involve programming or not.

2.1.1 Algorithmic thinking

An algorithm is a sequence of steps (Peel & Friedrichsen, 2018 ). Algorithmic thinking is a “logical, organized way of thinking used to break down a complicated goal into a series of (ordered) steps using available tools” (Lockwood et al., 2016 , p. 1591). Algorithmic thinking entails creating a sequential order of actions that are logically organized and can be used to guide a machine or a human through the process of solving a complex problem (Chuechote et al., 2020 ). The processes of creating a recipe, designing a flowchart to guide decision making, and writing code that triggers an alarm when a home door is broken into are real applications of algorithms and algorithmic thinking.

2.1.2 Abstraction

Abstraction is the process of simplifying complex content or conveying only the important information that is needed for a given context or audience (Peel & Friedrichsen, 2018 ; Taub et al., 2014 ). In other words, abstraction involves gathering relevant information, discarding unrelated data to develop patterns, and discovering commonalities across different scenarios (Shute et al., 2017 ). In CS, practicing abstraction entails handling complexity by hiding unnecessary details (Cetin & Dubinsky, 2017 ) to hide chunks of an algorithmic sequence that can be accessed later if needed.

2.1.3 Debugging

Debugging is the process of error identification and correction when a solution does not work as expected (Dagli & Tokmak, 2022 ; Kim et al., 2022 ; Shute et al., 2017 ). Programs and other algorithmic sequences rarely work on a first attempt (Brennan & Resnick, 2012 ; Vasconcelos & Kim, 2020 ). When an error or bug is identified, one needs to read the program lines, locate the error, test a hypothesized solution, and evaluate the outcome. Hence, it is crucial for one to develop systematic strategies for dealing with problems and persisting through iterative rounds of debugging until the problem is addressed (Peel & Friedrichsen, 2018 ).

2.1.4 Pattern recognition

Pattern recognition is the ability to “identify patterns/rules underlying the data/information structure” (Shute et al., 2017 , p. 153). These patterns include specific programming concepts that are linked to events and interactions in the algorithmic sequence. To use pattern recognition, one must recognize patterns or sequences in previously written algorithms and then effectively apply them to a situation (Chalmers, 2018 ) to solve a different problem. So, the ability to identify patterns in a program and reuse or remix them is what pattern recognition entails (Prextová et al., 2018 ).

3 Attitudes toward CS and programming

Students often hold stereotypical beliefs about CS. These stereotypes include that CS is just about coding, it is only for smart people, it is boring, or requires a tremendous amount of work (Carter, 2006 ; Lewis et al., 2010 ; Taub et al., 2012 ; Vasconcelos et al., 2022 , 2023 ). Students also hold misconceptions about careers in the computing industry. They assume that programming is the foundation of computing jobs when in reality the foundation of CS is problem solving (Grover et al., 2015 ; Taub et al., 2012 ) regardless of whether it involves programming or not. Inaccurate perceptions about CS may lead to negative attitudes and lowered self-efficacy, which is a predicting factor of academic performance and future involvement in CS (Gunbatar & Karalar, 2018 ). Inaccurate perceptions of CS and the level of challenge in computational tasks lead to negative attitudes toward CS education and careers.

Another factor that may lead to negative attitudes is the nature of CT activities themselves (Gunbatar & Karalar, 2018 ). A study that introduced middle schoolers to unplugged CT activities without programming tasks found no improvement on students’ attitudes and intentions toward studying CS (Taub et al., 2012 ). Alternatively, students who perceive the programming task as too challenging or far beyond their abilities are more likely to disengage and give up (Durak, 2020 ). This may happen with text-based programming environments, which are too complex for novice learners who would need to type up commands and follow syntax rules. Students often perceive text-based programming as time consuming and not fun (Carter, 2006 ; Weintrop & Wilensky, 2018 ). Negative attitudes due to a high level of difficulty in programming may cause disengagement in CT activities, which in turn hinders development of CT skills (Zhao & Shute, 2019 ).

4 Game design to teach CT

4.1 game-based learning and digital games.

Game-based learning is broadly defined as an approach that uses games to promote playful and fun learning (Barman & Kjällander, 2022 ; Homer et al., 2020 ) while also promoting interaction with target educational content (Banihashem et al., 2023 ; De Freitas, 2006 ; Dehghanzadeh et al., 2024 ; Karakoç et al., 2022 ; Lamb et al., 2018 ; Noroozi et al., 2020 ). This approach has been extensively adopted to improve learners’ motivation and engagement (Barreto et al., 2018 ; Leonard et al., 2016 ; Partovi & Razavi, 2019 ) across a variety of contexts. Game-based learning often features digital games, which are interactive and complex systems that immerse one in a scenario or virtual world as one uses gameplay mechanics, follows pre-determined rules, overcomes challenges, and interacts with other characters or environmental elements to achieve certain goals (Akcaoglu & Green, 2019 ; Barreto et al., 2018 ; Chen et al., 2020 ). Digital games may be designed purely for leisure and entertainment, and these are known as commercial games, or for educational purposes by exposing players to content that is embedded within their structure, and these are called educational or serious games (Dahalan et al., 2024 ; De Freitas, 2006 ; Dehghanzadeh et al., 2024 ; Karakoç et al., 2022 ). While digital games can be played with mobile devices, computers, and consoles, this study focuses on browser-based games that can be designed and played with block-based programming languages.

4.2 Game design

Game design is a specific type of game-based learning in which students lead the process of designing and developing digital games using a set of tools and/or online platforms (Akcaoglu, 2014 ; Cheng et al., 2023 ; Kafai & Burke, 2015 ). Game design is inherently a constructionist approach (Kafai & Burke, 2015 ; Papert, 1980 ) as it involves construction of knowledge through designing, developing, and playing a digital game. In CS education, game design offers an open-ended opportunity to apply CT skills through creation of unique, interactive, and functional user interfaces (Jiang et al., 2022 ; Kafai & Burke, 2015 ; Repenning et al., 2010 ; Wang et al., 2023 ) using a block-based programming language. Game design grounds abstract CT concepts into concrete game play practices (Lu et al., 2023 ; Wang et al., 2023 ). For instance, designing a game engages learners in various levels of CT that involve identifying a problem and scenario to serve as the game foundation, breaking down the game problem into smaller parts that can be represented on the screen, abstracting and generalizing algorithmic sequences to control similar behaviors across multiple game elements, creating an algorithmic sequence of steps that encapsulate target concepts/commands to be used as the problem solution (that is, to win the game), and practicing analytical reasoning that goes with the iterative process of testing and debugging algorithms that control a game. Research has shown that game design can promote higher academic achievement in programming courses (Topalli & Cagiltay, 2018 ), greater motivation to use and understand CT (Ouahbi et al., 2015 ), and improved CT skills to apply concepts such as variables, loops, and if–then statements (Hsu & Tsai, 2023 ; Kafai & Burke, 2015 ; Mladenović et al., 2018 ).

Game design offers fun and engaging opportunities to manipulate game elements (e.g., a storyline, rewards) (Filippou et al., 2018 ; Noroozi et al., 2020 ) and develop CT skills, which may positively influence students’ attitudes toward CS. Moreover, reducing the challenge in programming tasks by adopting a block-based programming language can boost confidence and promote positive attitudes (Grover et al., 2015 ; Gunbatar & Karalar, 2018 ). Rather than typing up text to create commands, which requires knowledge of programming syntax, students just need to stack blocks to sequentially combine a series of commands that lead to a desired output (Vasconcelos & Kim, 2020 , 2022 ). Further, students argue that block-based programming environments are fun and more interactive than text-based programming (Weintrop, 2019 ; Weintrop & Wilensky, 2017 ). One study showed encouraging effects of video games and block-based programming through boosted confidence toward programming and participants’ developed self-identification as a programmer (Zhao & Shute, 2019 ). In addition, studies show promising results from using block-based programming through improved understanding of CT concepts and enhanced CT skills (Hsu & Tsai, 2023 ; Mladenović et al., 2018 ; Zur-Bargury et al. ( 2013 ). The literature about attitudes toward CS and CT (e.g., Choi, 2022 ; Mason & Rich, 2019 , 2020 ) and about Code.org’s block-based programming curriculum (e.g., Choi, 2022 ; Kale & Yuan, 2021 ; Kale et al., 2023 ; Lahullier, 2019 ) is prolific, but no study has assessed the impact of Code.org’s block-based programming curriculum on middle schooler’s CT skills and attitudes toward CT and CS. This is the literature gap that the present study addresses.

5 Purpose statement

The purpose of this action research study was to assess how Code.org’s block-based programming curriculum in game design affects middle school students’ CT skills and their attitudes toward CT and CS. The following research questions guided the study:

How and to what degree does a block-based programming curriculum in game design affect middle school students’ CT skills?

How and to what degree does a block-based programming curriculum in game design affect middle school students’ attitudes toward CT and CS?

6 The code.org CS discoveries curriculum

Code.org’s CS Discoveries curriculum is engaging, challenging, and developmentally appropriate for middle school students. Code.org is a platform that contains lessons on how to design and create games with block-based programming and discover CT and CS concepts without the burden of memorizing syntax (Kalelioglu, 2015 ). The lessons target the four CT skills focused on this study: algorithmic thinking, abstraction, debugging, and pattern recognition. Lesson activities rely on guided discovery to promote “a level of freedom for learners so that they explore the problem, identify patterns, and discover the underlying principles on the problem” (Kale & Yuan, 2021 , p. 622). Guided discovery is an approach to teaching small ideas or program structures that students can gradually connect to prior knowledge, which allows CS teachers to scaffold content learning. Activities are organized with an increasing level of challenge. Teacher lesson plans provide detailed notes about the content to be taught, suggested scripting to start class discussions, and answer keys.

The Code.org platform allows teachers to monitor student progress through a dashboard, provide feedback, and grade student-created programs (Kalelioglu, 2015 ). The teacher can see errors students make, identify the concepts students struggle with, and provide targeted feedback for program improvement. Furthermore, the platform has a keep working tag that shows students outstanding tasks and errors to be addressed.

6.1 Implementation timeline

This was a 9-week module that used Code.org’s Animations & Games Unit to teach CT. This class met every day face-to-face, Monday through Friday, for 45 min for the entire school year. All assignments were completed individually on students’ assigned Chromebooks. Participants were allowed to collaborate with each other, provide insights into other participants’ programs, help find errors, and encourage others to create fun and interactive games. The curriculum has a game design unit that is divided into two chapters. Chapter one contained lessons 1 through 17 where students learn to use sprites, variables, the draw-loop, conditionals, and user input. For example, participants learned how to order the code to create different elements in a scene (Fig.  1 ). Chapter two contains lessons 18 through 27 where students learn velocity, sprite collisions, functions, and game design process. For instance, participants learned how to program user input to create an interactive character and generate a visual outcome (Fig.  2 ). Each lesson has multiple steps where participants completed tasks focused on content, skill building, assessments, practice, and challenges. An overview of CS topics, duration, target blocks, and deliverables is provided in Table  1 .

Lesson 9 instructional example

Mini project lesson 17 student example

7.1 Research design

This was an action research study, which consists of a “systematic inquiry conducted by educators for the purpose of gathering information about how their particular schools operate, how they teach, and how their students learn” (Mertler, 2020 , p. 29). Action research seeks to address a problem of practice within the scope of an educator’s professional practice (Anderson et al., 2001 ; Arslan-Ari et al., 2020 ; Johnson, 2008 ). Findings of action research lead to evidence-based changes to improve processes of teaching and learning. Action research was an appropriate design for this study, which aimed to address a problem of practice related to CS education within the first author’s instructional setting. Specifically, this action research study was designed to collect standardized data from middle school students to investigate the impact of Code.org’s curriculum on their CT skills and their attitudes toward CT and CS. The ultimate goal was to make data-driven decisions to enhance CS education for these students based on study findings. This action research used a triangulation mixed methods design by combining qualitative and quantitative data sources. The integration of both types of data produces insights beyond the information provided by one type of data alone (Creswell & Creswell, 2018 ; Mertler, 2020 ).

7.2 Setting and participants

This study was conducted in an urban Title 1 middle school in the Southeast of the United States. Participants were recruited from a CS course taught to 25 middle school students enrolled in 8th grade and attending the CS course in the spring of 2022. This was an advanced course that offered high school credit to middle schoolers. A total of 16 students agreed to join the study. They were 13.75 years old on average. Nine were female and seven were male. Eight identified as African American, four as White, one as Asian, and three as multiracial. One was an English as Second Language student. A total of eight participants were considered gifted students in the course. Eight participants had previously enrolled in a CS course.

7.3 Data collection

Institutional Review Board approval, school district approval, parental consent, and participant assent were obtained prior to data collection. Four different data sources were adopted to assess participants’ CT skills and attitudes toward CT and CS: an attitudinal survey, pre- and post-tests, participant artifacts, and participant interviews.

7.3.1 Pre- and post-survey

To assess participants’ attitudes toward CT and CS before the intervention, a 5-point Likert scale attitudinal survey with 19 items was administered (Appendix A). Eight items were borrowed from three of the six subscales in Korkmaz et al.’s ( 2017 ) Computational Thinking Scale: creativity (8 items), algorithmic thinking (6 items), and critical thinking (5 items). Sample items include I like Computer Science and It is fun to try to solve complex problems . Minor adjustments were performed to adapt survey items to lower grade students. For example, the item I believe that I can easily catch the relation between the two pictures was adapted to I believe that I can easily catch the relation between the two pictures or two programs. The added excerpt was for improved clarity. Korkmaz et al. ( 2017 ) confirmed that the items had adequate or good reliability ranging from 0.79 to 0.87.

7.3.2 Pre- and post-tests

The pre-test was administered to 16 participants to establish their baseline CT skills prior to the intervention. The test had 29 questions (Appendix A). Three questions were selected from Rachmatullah et al.’s ( 2020 ) Middle Grades Computer Science Concept Inventory, and all questions presented an internal consistency higher than 0.80. Minor changes were made to these questions: a screenshot of a block-based program was added to the question so participants could visualize the target programming concepts. Seventeen questions were designed by the CS teacher. Construct validity and reliability for the teacher-created questions were established by piloting them with a similar population of middle school students enrolled in the same course prior to data collection. The questions were also reviewed by an expert on CS education and research methods. Minor changes were performed to the questions. Moreover, nine questions were adopted from the Code.org curriculum (see Table  2 ), and these have been created and widely used by CS educators. Twenty-one questions were multiple-choice, two were true/false, and six were short answer. The test assessed algorithmic thinking (14 questions), abstraction (five questions), debugging (five questions), and pattern recognition (five questions). The test was delivered via Google Forms. An identical post-test was administered after the intervention.

7.3.3 Participant artifacts

Participants used block-based programming to create games in Code.org’s online platform at the end of each lesson throughout the intervention. Artifacts, or any type of performance assessment or student projects, are used to systematically evaluate the attainment of learning targets (McMillan, 2013 ). These block-based programming games served as a tool to assess participants’ CT skills based on the extent to which they were able to apply such skills into game design.

7.3.4 Participant interviews

A semi-structured interview protocol (Appendix B) was designed to collect qualitative data on participants’ CT skills and attitudes toward CT and CS to allow triangulation of findings with quantitative data sources. Four participants were randomly chosen for a 20-min, in-person, one-on-one interview after the intervention. Sample questions include When you encountered a problem in your code without an obvious answer, what steps did you take to solve it? (debugging skill) and Do you see yourself as a computer scientist? (attitudes toward CS). The code that participants used to create games was demonstrated during the interview to prompt descriptions about their “strategy on designing video games using block-based programming” (Tang et al., 2020 , p. 4). Follow-up prompts were included to encourage elaboration on responses (Chalmers, 2018 ). Interviews were audio recorded.

8 Data analysis

This study included quantitative and qualitative data analysis methods to develop a better understanding of the phenomenon being investigated (Creswell & Creswell, 2018 ). These methods consisted of descriptive statistics, paired samples t-tests, inductive and thematic analysis as displayed in the research matrix (Table  3 ).

8.1 Descriptive statistics

Data from pre- and post-tests as well as pre- and post-surveys was descriptively analyzed with JASP, a free computer-based statistics software. Measures of central tendency (mean) were used to summarize the central position of the quantitative data set distribution, and measures of dispersion (standard deviation) were used to assess the variability within the same data set (Hanneman et al., 2012 ; Mertler, 2020 ). Descriptive statistics were important to synthesize a large amount of quantitative data and facilitate interpretations about trends and patterns in participants’ CT skills and attitudes towards CT and CS.

8.2 Paired-samples T-tests

Pre- and post-survey data was first computed with Microsoft Excel, then JASP to analyze composite scores for participant attitudes. All reliability coefficients for the composite subscales fall within the range of 0.74 to 0.86. According to DeVellis ( 2016 ), reliability coefficients of 0.70 and above have acceptable reliability. Subsequently, the Shapiro–Wilk test for normality revealed that all subscales from the pre- and post-tests as well as pre- and post-surveys were normally distributed. Thus, the parametric paired samples t-tests (Hanneman et al., 2012 ) were performed to test the hypothesis of a statistically significant increase in CT skills and attitudes towards CT and CS.

8.3 Participant artifacts

A rubric created by Code.org was used to assess the four CT skills (algorithmic thinking, debugging, abstraction, and pattern recognition) using participants’ artifacts, that is, the games created with block-based programs. The rubric contains 7 criteria: program development, program readability, use of functions, background and variables, interactions and controls, position and movement, and variables. Each criterion is assessed based on four levels of achievement ranging from no evidence (0 points) to extensive evidence (7–10 possible points) as presented in Table  4 . The first author, who individually coded participant artifacts, had used the rubric previously to assess student artifacts with a similar population attending the same class. Results from artifact scoring were entered into an Excel spreadsheet and then analyzed with JASP to generate descriptive statistics about participants’ performance. The full rubric with descriptions about each level of achievement is provided in Appendix C.

8.4 Inductive thematic analysis

Inductive thematic analysis was used to identify and organize data into codes and categories to construct a framework to present qualitative findings (Mertler, 2020 ). Open coding and in vivo coding were administered to identify patterns or similarities in the data set (Saldaña, 2016 ). Iterative rounds of coding were performed using the computer-based qualitative data analysis tool Delve. The first author read the coded data multiple times and used Delve’s retrieval features to select and visualize all excerpts assigned the same code. This facilitated visualization of patterns across participants. Then authors held peer debriefing meetings to assign codes into categories and jointly craft qualitative themes that describe participants’ experiences (Braun & Clarke, 2006 ; Clarke & Braun, 2017 ). Themes were probed against coded excerpts for relevance. Thick and rich descriptions with quotes were used to support themes (Mertler, 2020 ).

9.1 Quantitative results

9.1.1 computational thinking skills.

Results showed a noticeable increase in participants’ CT skills between the overall pre-test ( M  = 11.38, SD  = 3.32) and post-test scores ( M  = 18.69, SD  = 4.81) (see Table  5 ). The largest improvement was in algorithmic thinking ( M  = 9.25, SD  = 2.38) followed by pattern recognition ( M  = 3.31, SD  = 1.40) and debugging ( M  = 3.06, SD  = 0.77). The smallest increase was in abstraction ( M  = 3.38, SD  = 0.96). The average artifact score was ( M  = 75.06, SD  = 22.63), with subscale scores for algorithmic thinking ( M  = 24.88, SD  = 8.84), debugging ( M  = 14.75, SD  = 3.43), abstraction ( M  = 20.31, SD  = 6.48) and pattern recognition ( M  = 15.38, SD  = 4.78). Roughly 87% of participants scored 60 or higher in their game designs (see Fig.  3 ).

Artifact scores

The Shapiro–Wilk test for normality (Gibbons & Chakraborti, 2021 ) revealed that all subscales were normally distributed. To account for type 1 errors, the Bonferroni Correction was used to lower the p -value threshold (Armstrong, 2014 ), hence p  < 0.0125 was the new threshold. Paired samples t-tests revealed a statistically significant improvement in three CT skills: algorithmic thinking ( M  = 6.20, SD  = 2.65, t (14) = -3.11, p  = 0.004), debugging ( M  = 3.06, SD  = 0.77, t (14) = -4.22, p  < 0.001), and pattern recognition ( M  = 1.40, SD  = 0.74, t (14) = -4.50, p  < 0.001) (see Table  6 ). Participants’ abstraction skills were not statistically significantly different ( M  = 3.38, SD  = 0.96, t (14) = -1.20, p  = 0.035). A large effect size was found for algorithmic thinking ( d  = 0.80), debugging ( d  = 1.09), pattern recognition ( d  = 1.16), and the overall test ( d  = 1.20), while a medium effect size was found for abstraction skills ( d  = 0.51).

9.1.2 Participant attitudes

There was a marginal increase in participants’ attitudes toward CT and CS from the pre-survey ( M  = 25.31, SD  = 4.90) to the post-survey ( M  = 25.75, SD  = 5.46) (Table  7 ). Moreover, there was a small positive increase in participants’ CT beliefs from the pre-survey ( M  = 24.25, SD  = 3.98) to the post-survey ( M  = 25.95, SD  = 6.01). The overall survey scores showed a similar increase from the pre-survey ( M  = 49.56, SD  = 8.51) to the post-survey ( M  = 51.69, SD  = 11.23).

The Shapiro–Wilk test for normality (Gibbons & Chakraborti, 2021 ) revealed that all subscales were normally distributed. To account for type 1 errors, the Bonferroni Correction was used to lower the p -value threshold (Armstrong, 2014 ), hence p  < 0.0125 was the new threshold. It was determined that paired-samples t-tests would be the most appropriate method to analyze the data inferentially (Gibbons & Chakraborti, 2021 ). Paired samples t-tests revealed no statistically significant differences between the pre- and the post-surveys for CS attitudes ( M  = 25.75, SD  = 5.46, t (15) = -0.43, p  = 0.34), CT beliefs ( M  = 25.94, SD  = 6.01, t (15) = -1.41, p  = 0.09), and the overall survey ( M  = 51.69, SD  = 11.23, t (15) = -1.05, p  = 0.16) (see Table  7 ). A small effect size was found for the attitudes ( d  = 0.11), CT beliefs ( d  = 0.35), and overall survey ( d  = 0.26).

9.2 Qualitative results

9.2.1 configuration of game elements as the foundation to understand and apply ct.

Designing and programming games exposed participants to basic programming concepts which in turn supported learning of CT skills. Participants in this study grounded application of CT skills on their experience with video game elements. Particularly, participants used configuration of game elements such as rewards and points, character movement around the scene, player-character interactions, character-character interactions, and games rules as the foundation to understand and apply CT skills.

Participants who were interviewed mentioned several elements of games to explain programming concepts. For example, when Joe was asked about what he wanted to happen if the score was below zero, he said “I wanted the game to end because if somebody has something like negative one and if they continue playing, then the game will never stop”. Similarly, when May was asked why she looped her sprites she stated, “the loop I used is that I wanted when the player touches the sprites, the sprites had to go in a different place.” Participants also referred to game scores to discuss variables, counter pattern, and character interactions. When asked about how variables work, participants mentioned scores as a variable which can be added or subtracted to for winning or losing a game. Mary stated that a “variable is a number that is subject to change. So, um, in scoring, you could say every time your sprite touches an object, you will gain a point”. May similarly said, “if the player touches the enemy, the [player’s] health would go down, but if you [the player] touch[es] like a candy, your scoring will go up.”

When prompted to define conditional statements, several participants described them as a tool that allows the game designer to create situations and outcomes. For instance, Connie said “if the score is higher than 10”, or “if the right arrow is pressed.” Along these lines, Mary added that “a conditional statement asks if a certain, uh, aspect is true or false and based off of whether that aspect is true or false, it will perform a certain action”. Participants connected game characters with sprites, which are two-dimensional images that represent a character and/or background element. Participants liked the word sprite, thought it was funny, and used it to describe all images adopted in their programs. Further, participants learned how to move sprites with the arrow keys using if statements, the counter pattern, and loops. John stated, “the loop will allow the fish sprite to reset back to the left of the screen”. In summary, most interviewed participants relied on game elements to articulate an understanding of CT concepts and explain how they applied CT for game design.

9.2.2 Collaboration promoted debugging of participants’ own programs

Collaboration during block-based programming involves working with peers to plan, revise, and complete a program. Participants were encouraged to collaborate with their peers if they were stuck and did not know what to do before asking the teacher for help. This allowed participants to take ownership of their work and understand that collaboration is not cheating. During interviews, participants emphasized that collaboration was beneficial. May stated, “I asked one of my friends, because she's very helpful for me. She helped me a bit for the velocity stuff. And then the rest I was able to figure out on my own.” Along these lines, Mary stated that “it was very helpful” collaborating with a classmate. Being able to work with peers and review others’ programs helped participants feel at ease, identify errors in the program, and understand CT concepts more independently without the instructor.

Throughout the module, participants were given faulty programs in which they had to identify and fix the bug. When asked about the debugging process, Joe stated that “I try to look back at the code and make sure that there's no spelling errors.” This is a first step in the debugging process to find the bug location. However, when participants were not successful, they were encouraged to work with peers. When prompted to talk about peer collaboration, May explained, “what she did is she showed me a bit of hers and that showed me how to deal with mine.” These statements indicate that reviewing a peer’s program and comparing it against their own program helped participants create insights about where the bug was without asking the teacher for help.

9.2.3 Balancing creative freedom and structure led to enjoyment of programming

The block-based programming module offered an optimal combination of structure and creative freedom so participants could design their own game. The structure comes in the form of guided planning for using required programs and CT skills while the freedom comes in the form of making decisions about the game environment, characters, and rules in ways that were personally relevant. This balance ensured participants mastered target CT skills but also enjoyed taking ownership over the design of game content or characters. As May said, “I liked choosing my sprites and then deciding how they would interact”. Similarly, John showed excitement when asked about his game sprites. He said “you see, I made that sprite, I drew it and it looks so good.”

The balance between creative freedom and structure made programming a fun activity, which appears to have promoted positive attitudes. Participants seemed to enjoy programming their own games which led them to being very engaged and working hard on game design. May was curious about creating games prior to the study, and then game design helped her perceive programming as a fun activity. May stated “I'd just say creating your own games and websites is very fun. I've always wanted to learn how to create those (…) I thought it wasn't gonna be, but it's very, I'm having a lot of fun doing it.” Along these lines, Joe confirmed that he likes CS and that the experience helped him understand the mechanisms that make programmed artifacts function. Joe said “I just like coding in general. It helps me understand how like video games and stuff like that work.” Understanding the functionality of games was also something Mary enjoyed. She stated, “I like to be able to create code and then watch it actually work”, and “I enjoy creating games once I know how to make them.”

10 Discussion

CT is a fundamental skill for the twenty-first century workforce within and beyond the computing industry (Wing, 2019 ). And yet, middle schoolers in the U.S. have limited exposure to CS education (Google & Gallup, 2020 ), which prevents them from developing CT skills (Brown et al., 2014 ). CS education made the leap from teaching students to consume technology to teaching students to be technology creators within the past 10 years (Kafai, 2016 ; Runciman, 2011 ). But despite the development of many new initiatives and curricula, students still think CS concepts are difficult to learn (Mladenović et al., 2018 ). One way to develop middle school students’ CT skills is to use Code.org’s CS Discoveries game design curriculum. The first author, who is a CS school teacher, led this action research study by collecting standardized data in the form of pre- and post-tests, interviews, and participant artifacts to assess the impact of the curriculum on middle school students’ CT skills and attitudes towards CT and CS. A discussion of results follows.

10.1 CT skills

Quantitative results showed a statistically significant improvement in three of the four CT skills: algorithmic thinking, debugging, and pattern recognition. A total of 14 out of 16 participants had higher overall post-test scores. Participants’ games also showed development of CT skills. These results align with previous research that found the use of game design to benefit learning to program and development of CT skills (Ouahbi et al., 2015 ; Scherer et al., 2020 ).

Algorithmic thinking is the ability to think in steps to solve problems (Chuechote et al., 2020 ). Both quantitative and qualitative results showed improved algorithmic thinking skills. Participants were able to “solve tasks demanding thinking, not only using the rules and algorithms they had learned” (Harangus & Kátai, 2018 , p. 1037) but also creating solutions for complex problems (Durak, 2020 ) related to their own game design. Previous studies that adopted the Code.org curriculum also showed an improvement in participants’ algorithmic thinking skills (Chuechote et al., 2020 ; Lockwood et al., 2016 ; Oluk & Çakir, 2021 ; Peel & Friedrichsen, 2018 ; Tonbuloğlu & Tonbuloğlu, 2019 ). Block-based programming in Code.org allows one to build a plan and think algorithmically as part of their CT learning experience (Kale et al., 2023 ). Further, block-based programming is an effective tool to prepare students for future CS courses as it helps students focus on the process of learning CS concepts without the syntax from text-based programming (Weintrop, 2019 ). The use of block-based programs to control game mechanics offered an “interactive learning environment centered on problem-solving” (Wang et al., 2023 , p. 1506) which encouraged participants to break down character behaviors and game animations into smaller, more manageable tasks. Hence, the game design approach was conducive of algorithmic thinking throughout the multiple lessons implemented in this study.

Debugging is very much part of problem solving within and beyond CS. In this study, finding and fixing program errors was critical for successful game design. Practicing error debugging can lead to improved understanding of programming concepts (Kim et al., 2018 ). Both quantitative and qualitative data showed that participants improved debugging skills. The CS teacher who taught the module encouraged participants to engage in collaborative debugging by reviewing each other’s code and helping each other find and fix errors, which has been found beneficial in the CS education literature. In Papavlasopoulou et al.’ ( 2019 ) study, participants who collaborated more on programming tasks “had a higher level of shared understanding and could communicate better during the coding activity” (p. 421). Kim et al. ( 2022 ) reported that students who were given scaffolds to help debug errors and students who worked with collaborators were more successful at programming. Collaborative debugging seems to promote increased persistence and engagement in programming and problem solving, which in turn contributes to the development of CT skills (Margulieux et al., 2020 ; Tonbuloğlu & Tonbuloğlu, 2019 ; Turchi et al., 2019 ). From a game design standpoint, participants in this study improved their debugging skills due to the required creation and testing of algorithmic sequences to refine the actions to occur within their game and based on visual feedback. In fact, visual feedback has been identified as one of the most effective factors leading to positive learning outcomes in game-based learning (Dehghanzadeh et al., 2024 ; Noroozi et al., 2016 , 2023 ; Sailer & Sailer, 2021 ). In this study, visual feedback helped participants identify a discrepancy between the intended and the actual performance (Carless, 2006 ) of game characters on the screen, which prompted debugging.

In pattern recognition, one sees similarities between program elements that can be matched with previous tasks (Qian & Choi, 2023 ) and then apply those patterns from one problem to the next (Barrón-Estrada et al., 2022 ; Yasin & Nusantara, 2023 ). Results from this study showed an improvement in participants’ pattern recognition. The Code.org curriculum repeatedly referred participants back to previous tasks so they could identify similarities and develop a plan for reusing parts of a program. For example, after learning to program a game score, participants reused that program chunk in every lesson afterwards in increasingly challenging activities. Improved pattern recognition through block-based programming was also found in other studies in which participants successfully synthesized multiple repeated blocks into a more efficient use of loops (Hernández-Zavaleta et al., 2021 ). We argue that the game design curriculum supported study pattern recognition as “participants transferred their learning of block-based programming from previous coding challenges to the new ones when they located any similarities (…) across the challenges” (Umutlu, 2022 , p. 761). The game design approach was crucial in engaging participants in programming similar patterns to control game character behaviors.

Abstraction, or the process of simplifying information, is applicable through the use of functions to hide parts of the program that are reused and reactivated when needed. For example, it is not necessary to display the actual code that controls the addition of points to the game score. This function can be found when needed, but it was often hidden in the code. Results of this study showed an improvement in abstraction skills, but the difference was not statistically significant. One possible explanation for the lack of statistical significance is that the tasks may have been too advanced for participants at this age. In another study with children enrolled in first through sixth grade, “older students were found to do better on the abstraction task than students in the youngest age group” (Rijke et al., 2018 , p. 86). Jean Piaget’s theory of cognitive development states that children are still forming schemas until the age of 12, which makes abstract reasoning difficult for them (Piaget & Cook, 1952 ). Another possibility is that abstraction is not an explicit focus of the Code.org curriculum and perhaps it should be integrated earlier into those lessons. Kale and Yuan’s ( 2021 ) study implemented the same curriculum and found that improvement in participants’ abstraction was lower than in pattern recognition and algorithmic thinking, which aligns with findings from this study. While it was expected that game design with block-based programming would be beneficial for abstraction skills, perhaps participants in this study needed more support. One possibility for future research is the inclusion of causal maps so participants can produce visual illustrations that show relationships among variables of interest (Akcaoglu & Green, 2019 ; Öllinger et al., 2015 ) and relationships between planned programming concepts and game character behaviors.

10.2 Attitudes toward CT and CS

Participants’ attitudes toward CT and CS were moderately high after the study, but they did not statistically significantly differ. We argue that these results could be due to several factors. First, these participants were taking an elective CS course, so perhaps they already had positive attitudes toward CT and CS prior to the study. Second, some programming concepts, such as conditionals, are quite challenging for middle schoolers. In Brennan and Resnick’s ( 2012 ) study, not every student could correctly describe how a conditional statement works or why it was used in the program. One of the interviewees in this study had difficulty answering questions about conditionals even though they knew how to complete the task. Challenges with more advanced programming concepts such as conditionals may have contributed to lowered self-efficacy. Limited ability to apply CT skills can be the reason for less positive attitudes toward programming and CT. Similar to this study, Lambić et al. ( 2021 ) found no significant difference in participants’ attitudes after implementing Code.org’s curriculum while Hsu and Tsai’s ( 2023 ) study found no significant difference in participants’ attitudes after an intervention that combined block-based programming, physical computing, and game design.

Despite the lack of statistical significance, qualitative data suggests improvements in CT skills through game design and revealed that participants enjoyed block-based programming. This aligns with Kalelioglu’s ( 2015 ) study, in which participants reported that they liked using the Code.org site and desired to learn more about programming. Other studies have found that block-based programming contributed to positive attitudes such as interest in taking more programming courses (Weintrop & Wilensky, 2017 ), and positive attitudes towards CS (Bastug & Kircaburun, 2017 ; Kalelioglu, 2015 ). As Gunbatar and Karalar ( 2018 ) stated, “visual programming environments can increase students’ self-efficacy perceptions and attitudes toward programming” (p. 931) because those environments offer a lower level of challenge but also the possibility to increase the level of complexity in the program. Previous research has found that integrating block-based programming into game design yielded positive attitudes such as higher interest in programming (Hromkovic & Staub, 2019 ) and enhanced self-efficacy (Tsai et al., 2023 ).

Participants’ moderately positive attitudes may also be due to the use of games for educational purposes. Game-based learning is an approach through which students learn either by playing games or creating them (Denner et al., 2012 ; Kafai & Burke, 2015 ; Topalli & Cagiltay, 2018 ; Turchi et al., 2019 ). Research has identified the positive effects of game-based learning on student motivation and engagement (e.g., Barreto et al., 2018 ; Breien & Wasson, 2021 ; Hwang et al., 2014 ; Partovi & Razavi, 2019 ; Sharma et al., 2021 ). In this study, participants not only played games, but they were also engaged in game design. We argue that two key components of the constructionist game design experience, personalization and collaboration, contributed to participants’ enjoyment of block-based programming as well as positive attitudes toward CT and CS. Specifically, participants were free to design a game that was personally relevant and they were encouraged to help each other during programming, and these are critical to foster student motivation and engagement in CT (Barman & Kjällander, 2022 ; Hava & Ünlü, 2021 ; Sharma et al., 2021 ; Turchi et al., 2019 ).

11 Limitations

Study limitations should be considered. First, a larger sample size would have been beneficial to achieve more robust quantitative results. Future research should consider implementing the study across multiple middle school classrooms to increase the sample size. Second, the study did not provide insights on fluctuations in participants’ attitudes over time. Adding more timepoints of data collection about attitudes toward CT and CS throughout such a long intervention is recommended. Third, relying exclusively on self-reported data from surveys and interviews with young participants presents issues related to response accuracy and social desirability bias because participants’ self-reported perceptions may not accurately reflect their actual learning experience (Noroozi et al., 2024 ). Strategies to minimize bias from self-reported data should be included in future research. For example, alternative data sources such as participant observations, user data from the game design environment (Banihashem et al., 2023 ), and alternative approaches such as a longitudinal study of attitudes and engagement over time. Finally, only the first author scored the participant artifacts. In the future, inviting another CS teacher to individually score the artifacts and compare results would enhance the validity of results.

12 Conclusion and implications

K-12 students need meaningful CS learning opportunities that foster development of CT skills and positive attitudes toward CT and CS. Significant gains were found in participants’ algorithmic thinking, pattern recognition, and debugging skills but not in abstraction. These results are partly explained by the use of a block-based programming language that lowers that difficulty to learn programming concepts, the use of game design as a strategy to promote freedom of expression and creativity, and the incentive for collaborative problem solving. These elements of the module should be taken into consideration for CS education that aims to teach CT skills.

Student attitudes towards CT and CS did not improve as expected. And yet, students stated that they enjoyed designing and programming their own game. Strategies for motivational support that extend beyond the adopted curriculum and the adopted programming software should be included. Specifically, future initiatives can normalize struggle as part of problem solving and CT, expose students to career paths and tasks that do not necessarily involve programing, and introduce them to successful role models in the field that they can identify with.

The design of this study focused on middle school students, so it is important to consider the results with caution before generalizing them to other contexts and populations. The intervention, which combines game design with block-based programming to foster CT, can and should be implemented and evaluated across other contexts of CS education (e.g., informal learning environments) and other levels of education (e.g., high school students, preservice teacher preparation).

Data availability

Data collected from this study are available from the corresponding author on reasonable request.

Aho, A. V. (2012). Computation and computational thinking. Computer Journal, 55 (7), 833–835. https://doi.org/10.1093/comjnl/bxs074

Google Scholar  

Akcaoglu, M. (2014). Learning problem-solving through making games at the game design and learning summer program. Educational Technology Research and Development, 62 (5), 583–600. https://doi.org/10.1007/s11423-014-9347-4

Akcaoglu, M., & Green, L. S. (2019). Teaching systems thinking through game design. Educational Technology Research and Development, 67 (1), 1–19. https://doi.org/10.1007/s11423-018-9596-8

Akcaoglu, M., & Kale, U. (2016). Teaching to teach (with) game design: Game design and learning workshops for preservice teachers. Contemporary Issues in Technology and Teacher Education, 16 (1), 60–81.

Anderson, G. L., Nihlen, A. S., & Herr, K. (2001). Studying your own school: An educator’s guide to practitioner action research. Corwin Press.

Armstrong, R. A. (2014). When to use the Bonferroni Correction . 34 (5), 502–508. https://doi.org/10.1111/opo.12131

Arslan-Ari, I., Ari, F., Grant, M. M., Vasconcelos, L., Tang, H., & Morris, W. S. (2020). Becoming action researchers: Crafting the curriculum and learning experiences for scholarly practitioners in educational technology. In E. Romero-Hall (Ed.), Research Methods in Learning Design and Technology (pp. 78–93). Routledge.

Banihashem, S. K., Dehghanzadeh, H., Clark, D., Noroozi, O., & Biemans, H. J. A. (2023). Learning analytics for online game-Based learning: A systematic literature review. Behaviour & Information Technology , 1–28. https://doi.org/10.1080/0144929X.2023.2255301

Barman, L., & Kjällander, S. (2022). Playful and meaningful learning of programming. What does it take to integrate an app-based game promoting digital mathematics into early childhood education? Designs for Learning, 14 (1), 165–178. https://doi.org/10.16993/dfl.203

Barreto, D., Vasconcelos, L., & Orey, M. (2018). Motivation and learning engagement through playing math video games. Malaysian Journal of Learning and Instruction, 14 (2), Article 2. https://doi.org/10.32890/mjli2017.14.2.1

Barrón-Estrada, M. L., Zatarain-Cabada, R., Romero-Polo, J. A., & Monroy, J. N. (2022). Patrony: A mobile application for pattern recognition learning. Education and Information Technologies, 27 (1), 1237–1260. https://doi.org/10.1007/s10639-021-10636-7

Bastug, I., & Kircaburun, K. (2017). Effect of Javascript on middle schoolers’ attitude toward computer course. Journal of Pedagogical Research, 1 (1), 43–53.

Braun, V., & Clarke, V. (2006). Using thematic analysis in psychology. Qualitative Research in Psychology, 3 (2), 77–101. https://doi.org/10.1191/1478088706qp063oa

Breien, F. S., & Wasson, B. (2021). Narrative categorization in digital game-based learning: Engagement, motivation & learning. British Journal of Educational Technology, 52 (1), 91–111. https://doi.org/10.1111/bjet.13004

Brennan, K., & Resnick, M. (2012). New frameworks for studying and assessing the development of computational thinking. In  Proceedings of the 2012 Annual Meeting of the American Educational Research Association, Vancouver, Canada , 1–25. Retrieved from  http://scratched.gse.harvard.edu/ct/files/AERA2012.pdf . Accessed 10 Aug 2023.

Brown, N. C. C., Sentance, S., Crick, T., & Humphreys, S. (2014). Restart: The resurgence of computer science in UK schools. ACM Transactions on Computing Education , 14 (2). https://doi.org/10.1145/2602484

Carless, D. (2006). Differing perceptions in the feedback process. Studies in Higher Education, 31 (2), 219–233. https://doi.org/10.1080/03075070600572132

Carter, L. (2006). Why students with an apparent aptitude for computer science don’t choose to major in computer science. ACM SIGCSE Bulletin, 38 (1), 27–31. https://doi.org/10.1145/1124706.1121352

Cetin, I., & Dubinsky, E. (2017). Reflective abstraction in computational thinking. Journal of Mathematical Behavior, 47 , 70–80. https://doi.org/10.1016/j.jmathb.2017.06.004

Chalmers, C. (2018). Robotics and computational thinking in primary school. International Journal of Child-Computer Interaction, 17 , 93–100. https://doi.org/10.1016/j.ijcci.2018.06.005

Chen, C., Shih, C., & Law, V. (2020). The effects of competition in digital game-based learning (DGBL): A meta-analysis. Educational Technology Research and Development, 68 (4), 1855–1873. https://doi.org/10.1007/s11423-020-09794-1

Cheng, Y., Lai, C., Chen, Y., Wang, W., Huang, Y., & Wu, T. (2023). Enhancing student’s computational thinking skills with student-generated questions strategy in a game-based learning platform. Computers & Education, 200 , 104794. https://doi.org/10.1016/j.compedu.2023.104794

Choi, W. C. (2022). The influence of Code.org on computational thinking and learning attitude in block-based programming education. Proceedings of the 2022 6th International Conference on Education and E-Learning , 235–241. https://doi.org/10.1145/3578837.3578871

Chuechote, S., Nokkaew, A., Phongsasithorn, A., & Laosinchai, P. (2020). A neo-piagetian analysis of algorithmic thinking development through the “sorted” digital game. Contemporary Educational Technology , 12 (1), 1–16. https://doi.org/10.30935/cet.685959

Clarke, V., & Braun, V. (2017). Thematic analysis. The Journal of Positive Psychology, 12 (3), 297–298. https://doi.org/10.1080/17439760.2016.1262613

Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Lawrence Erlbahm.

Computer Science Teachers Association. (2019). 2019 State of computer science education equity and diversity . Retrieved from: https://advocacy.code.org/2019_state_of_cs.pdf . Accessed 10 Aug 2023.

Creswell, J., & Creswell, J. D. (2018). Research design qualitative, quantitative and mixed methods approaches (7th ed.). Sage Publications.

Dagli, Z., & Tokmak, H. (2022). Exploring high school computer science course teachers’ instructional design processes for improving students’ “computational thinking” skills. Journal of Research on Technology in Education, 54 (4), 511–534. https://doi.org/10.1080/15391523.2021.1881844

Dahalan, F., Alias, N., & Shaharom, M. S. N. (2024). Gamification and game based learning for vocational education and training: A systematic literature review. Education and Information Technologies, 29 , 1279–1317. https://doi.org/10.1007/s10639-022-11548-w

de Freitas, S. I. (2006). Using games and simulations for supporting learning. Learning, Media and Technology, 31 (4), 343–358. https://doi.org/10.1080/17439880601021967

Dehghanzadeh, H., Farrokhnia, M., Dehghanzadeh, H., Taghipour, K., & Noroozi, O. (2024). Using gamification to support learning in K-12 education: A systematic literature review. British Journal of Educational Technology, 55 (1), 34–70. https://doi.org/10.1111/bjet.13335

Denner, J., Werner, L., & Ortiz, E. (2012). Computer games created by middle school girls: Can They be used to measure understanding of computer science concepts? Computers and Education, 58 (1), 240–249. https://doi.org/10.1016/j.compedu.2011.08.006

DeVellis, R. F. (2016). Scale development theory and applications (4th ed.). Sage Publications.

Durak, H. Y. (2020). The effects of using different tools in programming teaching of secondary school students on engagement, computational thinking and reflective thinking skills for problem solving. Technology, Knowledge and Learning, 25 (1), 179–195. https://doi.org/10.1007/s10758-018-9391-y

Filippou, J., Cheong, C., & Cheong, F. (2018). A model to investigate preference for use of gamification in a learning activity. Australasian Journal of Information Systems, 22 , 1–23. https://doi.org/10.3127/ajis.v22i0.1397

Gibbons, J. D., & Chakraborti, S. (2021). Nonparametric statistical inference . CRC Press.

Google & Gallup (2020). Current perspectives and continuing challenges in computer science education in U.S. K-12 schools . Retrieved from: https://csedu.gallup.com/home.aspx . Accessed 10 Aug 2023.

Grover, S., Pea, R., & Cooper, S. (2015). Designing for deeper learning in a blended computer science course for middle school students. Computer Science Education, 25 (2), 199–237. https://doi.org/10.1080/08993408.2015.1033142

Gunbatar, M. S., & Karalar, H. (2018). Gender differences in middle school students’ attitudes and self-efficacy perceptions towards MBlock programming. European Journal of Educational Research, 7 (4), 925–933. https://doi.org/10.12973/eu-jer.7.4.923

Hanneman, R. A., Kposowa, A. J., & Riddle, M. D. (2012). Basic statistics for social research . John Wiley & Sons.

Harangus, K., & Kátai, Z. (2018). Algorithmic thinking vs. text comprehension. Procedia Manufacturing, 22 , 1031–1037. https://doi.org/10.1016/j.promfg.2018.03.146

Hava, K., & Koyunlu Ünlü, Z. (2021). Investigation of the relationship between middle school students’ computational thinking skills and their STEM career interest and attitudes toward inquiry. Journal of Science Education and Technology, 30 (4), 484–495. https://doi.org/10.1007/s10956-020-09892-y

Hernández-Zavaleta, J. E., Becker, S., Clark, D., Brady, C., & Major, N. (2021). Students’ computational thinking in two mathematics block-based programming environments: Research during covid-19. Proceedings of the International STEM in Education Conference (STEM 2021) , 208–215. https://par.nsf.gov/biblio/10311218 . Accessed 10 Aug 2023.

Homer, B. D., Raffaele, C., & Henderson, H. (2020). Games and playful learning: Implications of developmental theory for game-based learning. In J. L. Plass, R. E. Mayer, & B. D. Homer (Eds.), Handbook of game-based learning (pp. 25–52). The MIT Press.

Hromkovic, J., & Staub, J. (2019). Constructing computational thinking using CS unplugged. Constructivis Foundationst , 14 (3), 353–355. https://constructivist.info/14/3/353 . Accessed 10 Aug 2023.

Hsu, I. Y., & Tsai, F. H. (2023). Development and evaluation of a physical computing game-design project for students’ computational thinking. Educational Technology & Society, 26 (4), 38–50. https://doi.org/10.30191/ETS.202310_26(4).0003

Hwang, G. J., Hung, C. M., & Chen, N. S. (2014). Improving learning achievements, motivations and problem-solving skills through a peer assessment-based game development approach. Educational Technology Research and Development, 62 , 129–145. https://doi.org/10.1007/s11423-013-9320-7

Jiang, B., Zhao, W., Zhang, N., & Qiu, F. (2022). Programming trajectories analytics in block-based programming language learning. Interactive Learning Environments, 30 (1), 113–126. https://doi.org/10.1080/10494820.2019.1643741

Johnson, A. (2008). A short guide to action research (4th ed.). Allyn and Bacon.

Kafai, Y. B. (2016). From computational thinking to computational participation in k-12 education. Communications of the ACM, 59 (8), 26–27. https://doi.org/10.1145/2955114

Kafai, Y. B., & Burke, Q. (2015). Constructionist gaming: Understanding the benefits of making games for learning. Educational Psychologist, 50 (4), 313–334. https://doi.org/10.1080/00461520.2015.1124022

Kale, U., & Yuan, J. (2021). Still a new kid on the block? computational thinking as problem solving in Code.org. Journal of Educational Computing Research, 59 (4), 620–644. https://doi.org/10.1177/0735633120972050

Kale, U., Yuan, J., & Roy, A. (2023). Thinking processes in code.org: A relational analysis approach to computational thinking. Computer Science Education, 33 (4), 545–566. https://doi.org/10.1080/08993408.2022.2145549

Kalelioglu, F. (2015). A new way of teaching programming skills to k-12 students: Code.org. Computers in Human Behavior, 52 , 200–210. https://doi.org/10.1016/j.chb.2015.05.047

Karakoç, B., Eryılmaz, K., Özpolat, E. T., & Yıldırım, İ. (2022). The effect of game-based learning on student achievement: A meta-analysis study. Technology, Knowledge and Learning, 27 (1), 207–222. https://doi.org/10.1007/s10758-020-09471-5

Ketenci, T., Calandra, B., Margulieux, L., & Cohen, J. (2019). The relationship between learner characteristics and student outcomes in a middle school computing course: An exploratory analysis using structural equation modeling. Journal of Research on Technology in Education, 51 (1), 63–76. https://doi.org/10.1080/15391523.2018.1553024

Kim, C. M., Yuan, J., Vasconcelos, L., Shin, M., & Hill, R. B. (2018). Debugging during block-based programming. Instructional Science, 46 (5), 767–787. https://doi.org/10.1007/s11251-018-9453-5

Kim, C. M., Vasconcelos, L., Belland, B. R., Umutlu, D., & Gleasman, C. (2022). Debugging behaviors of early childhood teacher candidates with or without scaffolding. International Journal of Educational Technology in Higher Education , 19 (1). https://doi.org/10.1186/s41239-022-00319-9

Korkmaz, Ö., Çakir, R., & Özden, M. Y. (2017). A validity and reliability study of the Computational Thinking Scales (CTS). Computers in Human Behavior, 72 , 558–569. https://doi.org/10.1016/j.chb.2017.01.005

Lahullier, S. C. (2019).  Code.Org and Computational Thinking: A Mixed-Methods Study on Fifth-Grade Elementary School Students  (Order No. 28150971). Available from ProQuest Dissertations & Theses Global. (2454660044). https://www.proquest.com/dissertations-theses/code-org-computational-thinking-mixed-methods/docview/2454660044/se-2 . Accessed 10 Aug 2023.

Lamb, R. L., Annetta, L., Firestone, J., & Etopio, E. (2018). A meta-analysis with examination of moderators of student cognition, affect, and learning outcomes while using serious educational games, serious games, and simulations. Computers in Human Behavior, 80 , 158–167. https://doi.org/10.1016/J.CHB.2017.10.040

Lambić, D., Đorić, B., & Ivakić, S. (2021). Investigating the effect of the use of code.org on younger elementary school students’ attitudes towards programming. Behaviour and Information Technology, 40 (16), 1784–1795. https://doi.org/10.1080/0144929X.2020.17819316

Leonard, J., Buss, A., Gamboa, R., Mitchell, M., Fashola, O. S., Hubert, T., & Almughyirah, S. (2016). Using robotics and game design to enhance children’s self-efficacy, STEM attitudes, and computational thinking skills. Journal of Science Education and Technology, 25 (6), 860–876. https://doi.org/10.1007/s10956-016-9628-2

Lewis, C., Jackson, M. H., & Waite, W. M. (2010). Student and faculty attitudes and beliefs about computer science. Communications of the ACM, 53 (5), 78–85. https://doi.org/10.1145/1735223.1735244

Lockwood, E., Asay, A., Dejarnette, A. F., & Thomas, M. (2016). Algorithmic thinking: an initial characterization of computational thinking in mathematics. In M. B. Wood, E. E. Turner, M. Civil, & J. A. Eli (Eds.), Proceedings of the 38th Annual Meeting of the North American Chapter of the International Group for the Psychology of Mathematics Education (pp. 1588–1596). The University of Arizona.

Lu, Z., Chiu, M. M., Cui, Y., Mao, W., & Lei, H. (2023). Effects of game-based learning on students’ computational thinking: A meta-analysis. Journal of Educational Computing Research, 61 (1), 235–256. https://doi.org/10.1177/07356331221100740

Margulieux, L. E., Morrison, B. B., Franke, B., & Ramilison, H. (2020). Effect of implementing subgoals in code.org’s intro to programming unit in computer science principles. ACM Transactions on Computing Education , 20 (4). https://doi.org/10.1145/3415594

Mason, S. L., & Rich, P. J. (2019). Preparing elementary school teachers to teach computing, coding, and computational thinking. Contemporary Issues in Technology and Teacher Education, 19 (4), 790–824.

Mason, S. L., & Rich, P. J. (2020). Development and analysis of the elementary student coding attitudes survey. Computers & Education, 153 , 103898. https://doi.org/10.1016/j.compedu.2020.103898

McMillan, J. H. (2013). Research on classroom assessment . Sage Publications.

Mertler, C. (2020). Action research improving schools and empowering educators (6th ed.). Sage Publications.

Mladenović, M., Boljat, I., & Žanko, Ž. (2018). Comparing loops misconceptions in block-based and text-based programming languages at the K-12 level. Education and Information Technologies, 23 (4), 1483–1500. https://doi.org/10.1007/s10639-017-9673-3

Noroozi, O., Biemans, H., & Mulder, M. (2016). Relations between scripted online peer feedback processes and quality of written argumentative essay. Internet and Higher Education, 31 , 20–31. https://doi.org/10.1016/j.iheduc.2016.05.002

Noroozi, O., Dehghanzadeh, H., & Talaee, E. (2020). A systematic review on the impacts of game-based learning on argumentation skills. Entertainment Computing, 35 , 100369. https://doi.org/10.1016/j.entcom.2020.100369

Noroozi, O., Banihashem, S. K., Biemans, H. J. A., Smits, M., Vervoort, M. T. W., & Verbaan, C. (2023). Design, implementation, and evaluation of an online supported peer feedback module to enhance students’ argumentative essay quality. Education and Information Technologies, 28 , 12757–12784. https://doi.org/10.1007/s10639-023-11683-y

Noroozi, O., Alqassab, M., Taghizadeh Kerman, N., Banihashem, S. K., & Panadero, E. (2024). Does perception mean learning? Insights from an online peer feedback setting. Assessment & Evaluation in Higher Education, 1–15. https://doi.org/10.1080/02602938.2024.2345669

Öllinger, M., Hammon, S., von Grundherr, M., & Funke, J. (2015). Does visualization enhance complex problem solving? The effect of causal mapping on performance in the computer-based microworld Tailorshop. Educational Technology Research and Development, 63 (4), 621–637. https://doi.org/10.1007/s11423-015-9393-6

Oluk, A., & Çakir, R. (2021). The effect of code.org activities on computational thinking and algorithm development skills. Journal of Teacher Education and Lifelong Learning, 3 (2), 32–40. https://doi.org/10.51535/tell.960476

Ouahbi, I., Kaddari, F., Darhmaoui, H., Elachqar, A., & Lahmine, S. (2015). Learning basic programming concepts by creating games with scratch programming environment. Procedia - Social and Behavioral Sciences, 191 , 1479–1482. https://doi.org/10.1016/j.sbspro.2015.04.224

Papavlasopoulou, S., Giannakos, M. N., & Jaccheri, L. (2019). Exploring children’s learning experience in constructionism-based coding activities through design-based research. Computers in Human Behavior, 99 (7491), 415–427. https://doi.org/10.1016/j.chb.2019.01.008

Papert, S. (1980). Mindstorms: Children, computers, and powerful ideas . Basic Books.

Partovi, T., & Razavi, M. R. (2019). The effect of game-based learning on academic achievement motivation of elementary school students. Learning and Motivation, 68 , 101592. https://doi.org/10.1016/j.lmot.2019.101592

Peel, A., & Friedrichsen, P. (2018). Algorithms, abstractions, and iterations: Teaching computational thinking using protein synthesis translation. The American Biology Teacher, 80 (1), 21–28. https://doi.org/10.1525/abt.2018.80.1.21

Piaget, J., & Cook, M. T. (1952). The origins of intelligence in children . International University Press.

Prextová, T., Homanová, Z., & Kostolányová, K. (2018). Activities for developing explain computational thinking. Proceedings of the European Conference on E-Learning (ECEL) , 474–480.

Qian, Y., & Choi, I. (2023). Tracing the essence: Ways to develop abstraction in computational thinking. Educational Technology Research and Development, 71 , 1055–1078. https://doi.org/10.1007/s11423-022-10182-0

Rachmatullah, A., Akram, B., Boulden, D., Mott, B., Boyer, K., Lester, J., & Wiebe, E. (2020). Development and validation of the middle grades computer science concept inventory (MG-CSCI) assessment. Eurasia Journal of Mathematics, Science and Technology Education , 16 (5). https://doi.org/10.29333/ejmste/116600

Repenning, A., Webb, D., & Ioannidou, A. (2010). Scalable game design and the development of a checklist for getting computational thinking into public schools. In Proceedings of the 41st ACM Technical Symposium on Computer Science Education (SIGCSE ’10) , 265–269. ACM Press.

Rijke, W. J., Bollen, L., Eysink, T. H. S., & Tolboom, J. L. J. (2018). Computational thinking in primary school: An examination of abstraction and decomposition in different age groups. Informatics in Education, 17 (1), 77–92. https://doi.org/10.15388/infedu.2018.05

Román-González, M., Pérez-González, J. C., & Jiménez-Fernández, C. (2017). Which cognitive abilities underlie computational thinking? Criterion validity of the computational thinking test. Computers in Human Behavior, 72 , 678–691. https://doi.org/10.1016/j.chb.2016.08.047

Runciman, B. (2011). The future of computer science in schools. IT Now, 53 (6), 10–11. https://doi.org/10.1093/itnow/bwr050

Sailer, M., & Sailer, M. (2021). Gamification of in-class activities in flipped classroom lectures. British Journal of Educational Technology, 52 (1), 75–90. https://doi.org/10.1111/bjet.12948

Saldaña, J. (2016). The coding manual for qualitative researchers (2nd ed.). SAGE.

Scherer, R., Siddiq, F., & Viveros, B. S. (2020). A meta-analysis of teaching and learning computer programming: Effective instructional approaches and conditions. Computers in Human Behavior , 109 . https://doi.org/10.1016/j.chb.2020.106349

Sharma, K., Torrado, J. C., Gómez, J., & Jaccheri, L. (2021). Improving girls’ perception of computer science as a viable career option through game playing and design: Lessons from a systematic literature review. Entertainment Computing, 36 , 100387. https://doi.org/10.1016/j.entcom.2020.100387

Shute, V. J., Sun, C., & Asbell-Clarke, J. (2017). Demystifying computational thinking. Educational Research Review, 22 , 142–158. https://doi.org/10.1016/j.edurev.2017.09.003

Tang, X., Yin, Y., Lin, Q., Hadad, R., & Zhai, X. (2020). Assessing computational thinking: A systematic review of empirical studies. Computers and Education, 148 . https://doi.org/10.1016/j.compedu.2019.103798

Taub, R., Armoni, M., & Ben-Ari, M. (2012). CS unplugged and middle-school students’ views, attitudes, and intentions regarding CS. ACM Transactions on Computing Education , 12 (2). https://doi.org/10.1145/2160547.2160551

Taub, R., Armoni, M., & Ben-Ari, M. (2014). Abstraction as a bridging concept between computer science and physics. WiPSCE ’14: Proceedings of the 9th Workshop in Primary and Secondary Computing Education , 16–19. https://doi.org/10.1145/2670757.2670777

Tonbuloğlu, B., & Tonbuloğlu, I. (2019). The effect of unplugged coding activities on computational thinking skills of middle school students. Informatics in Education, 18 (2), 403–426. https://doi.org/10.15388/infedu.2019.19

Topalli, D., & Cagiltay, N. E. (2018). Improving programming skills in engineering education through problem-based game projects with Scratch. Computers and Education, 120 , 64–74. https://doi.org/10.1016/j.compedu.2018.01.011

Tsai, C., Chen, Y., Hsieh, F., Chuang, M., & Lin, C. (2023). Effects of a programming course using the GAME model on undergraduates’ self-efficacy and basic programming concepts. Journal of Educational Computing Research . https://doi.org/10.1177/07356331231206071

Turchi, T., Fogli, D., & Malizia, A. (2019). Fostering computational thinking through collaborative game-based learning. Multimedia Tools and Applications, 78 (10), 13649–13673. https://doi.org/10.1007/s11042-019-7229-9

Umutlu, D. (2022). An exploratory study of pre-service teachers’ computational thinking and programming skills. Journal of Research on Technology in Education, 54 (5), 754–768. https://doi.org/10.1080/15391523.2021.1922105

Vasconcelos, L., & Kim, C. (2020). Coding in scientific modeling lessons (CS-ModeL). Educational Technology Research and Development, 68 (3), 1247–1273. https://doi.org/10.1007/s11423-019-09724-w

Vasconcelos, L., & Kim, C. (2022). Preservice science teachers coding science simulations: Epistemological understanding, coding skills, and lesson design. Educational Technology Research and Development, 70 , 1517–1549. https://doi.org/10.1007/s11423-022-10119-7

Vasconcelos, L., Ari, F., Arslan-Ari, I., & Lamb, L. (2023). Do stereotypical vs. counter-stereotypical role models affect teacher candidates’ stereotypes and attitudes toward teaching computer science? International Journal of Computer Science Education in Schools, 6 (2), 1–12. https://doi.org/10.21585/ijcses.v6i2.174

Vasconcelos, L., Ari, F., Arslan-Ari, I., & Lamb, L. (2022). Female preservice teachers stereotype computer scientists as intelligent and overworked White individuals wearing glasses. Computers & Education , 187. https://doi.org/10.1016/j.compedu.2022.104563

Wang, X., Cheng, M., & Li, X. (2023). Teaching and learning computational thinking through game-based learning: A systematic review. Journal of Educational Computing Research, 61 (7), 1505–1536. https://doi.org/10.1177/07356331231180951

Weintrop, D. (2019). Block-based programming in computer science education. Communications of the ACM, 62 (8), 22–25. https://doi.org/10.1145/3341221

Weintrop, D., & Wilensky, U. (2018). How block-based, text-based, and hybrid block/text modalities shape novice programming practices. International Journal of Child-Computer Interaction, 17 , 83–92. https://doi.org/10.1016/j.ijcci.2018.04.005

Weintrop, D., & Wilensky, U. (2017). Comparing block-based and text-based programming in high school computer science classrooms. ACM Transactions on Computing Education , 18 (1). https://doi.org/10.1145/3089799

Wing, J. (2006). Computational Thinking., 49 (3), 33–35. https://doi.org/10.1145/1999747.1999811

Wing, J. M. (2019). A conversation about computational thinking . Retrieved from: https://education.nsw.gov.au/content/dam/main-education/teaching-and-learning/education-for-a-changing-world/media/documents/Computational-Conversation_1_A.pdf . Accessed 10 Aug 2023.

Yasin, M., & Nusantara, T. (2023). Characteristics of pattern recognition to solve mathematics problems in computational thinking. AIP Conference Proceedings , 2569 (1). https://doi.org/10.1063/5.0112171

Zhao, W., & Shute, V. J. (2019). Can playing a video game foster computational thinking skills? Computers & Education , 141 . https://doi.org/10.1016/j.compedu.2019.103633

Zur-Bargury, I., Pârv, B., & Lanzberg, D. (2013). A nationwide exam as a tool for improving a new curriculum. In Proceedings of the 18th ACM conference on Innovation and technology in computer science education (pp. 267–272). ACM Press. https://doi.org/10.1145/2462476.2462479

Download references

Open access funding provided by the Carolinas Consortium. This study did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

Author information

Authors and affiliations.

West Ashley High School, Charleston, SC, USA

Lorien Cafarella

Department of Leadership, Learning Design, and Inquiry, University of South Carolina, Columbia, SC, USA

Lucas Vasconcelos

You can also search for this author in PubMed   Google Scholar

Corresponding author

Correspondence to Lucas Vasconcelos .

Ethics declarations

Ethical approval.

This research was approved by the ethics committee of the authors’ institution and by the school district.

Informed consent

Parental consent and participants’ assent were obtained prior to data collection.

Competing interests

The authors report there are no competing interests to declare.

Additional information

Publisher's note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

CS attitudinal survey and assessment test.

Semi-structured interview protocol.

Game creation project rubric.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ .

Reprints and permissions

About this article

Cafarella, L., Vasconcelos, L. Computational thinking with game design: An action research study with middle school students. Educ Inf Technol (2024). https://doi.org/10.1007/s10639-024-13010-5

Download citation

Received : 13 November 2023

Accepted : 15 August 2024

Published : 17 September 2024

DOI : https://doi.org/10.1007/s10639-024-13010-5

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Computational thinking
• Computer science
• Game design
• Block-based programming
• Middle school
• Find a journal
• Publish with us
• Track your research