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Research Design – Types, Methods and Examples

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Research Design

Definition:

Research design refers to the overall strategy or plan for conducting a research study. It outlines the methods and procedures that will be used to collect and analyze data, as well as the goals and objectives of the study. Research design is important because it guides the entire research process and ensures that the study is conducted in a systematic and rigorous manner.

Types of Research Design

Types of Research Design are as follows:

Descriptive Research Design

This type of research design is used to describe a phenomenon or situation. It involves collecting data through surveys, questionnaires, interviews, and observations. The aim of descriptive research is to provide an accurate and detailed portrayal of a particular group, event, or situation. It can be useful in identifying patterns, trends, and relationships in the data.

Correlational Research Design

Correlational research design is used to determine if there is a relationship between two or more variables. This type of research design involves collecting data from participants and analyzing the relationship between the variables using statistical methods. The aim of correlational research is to identify the strength and direction of the relationship between the variables.

Experimental Research Design

Experimental research design is used to investigate cause-and-effect relationships between variables. This type of research design involves manipulating one variable and measuring the effect on another variable. It usually involves randomly assigning participants to groups and manipulating an independent variable to determine its effect on a dependent variable. The aim of experimental research is to establish causality.

Quasi-experimental Research Design

Quasi-experimental research design is similar to experimental research design, but it lacks one or more of the features of a true experiment. For example, there may not be random assignment to groups or a control group. This type of research design is used when it is not feasible or ethical to conduct a true experiment.

Case Study Research Design

Case study research design is used to investigate a single case or a small number of cases in depth. It involves collecting data through various methods, such as interviews, observations, and document analysis. The aim of case study research is to provide an in-depth understanding of a particular case or situation.

Longitudinal Research Design

Longitudinal research design is used to study changes in a particular phenomenon over time. It involves collecting data at multiple time points and analyzing the changes that occur. The aim of longitudinal research is to provide insights into the development, growth, or decline of a particular phenomenon over time.

Structure of Research Design

The format of a research design typically includes the following sections:

• Introduction : This section provides an overview of the research problem, the research questions, and the importance of the study. It also includes a brief literature review that summarizes previous research on the topic and identifies gaps in the existing knowledge.
• Research Questions or Hypotheses: This section identifies the specific research questions or hypotheses that the study will address. These questions should be clear, specific, and testable.
• Research Methods : This section describes the methods that will be used to collect and analyze data. It includes details about the study design, the sampling strategy, the data collection instruments, and the data analysis techniques.
• Data Collection: This section describes how the data will be collected, including the sample size, data collection procedures, and any ethical considerations.
• Data Analysis: This section describes how the data will be analyzed, including the statistical techniques that will be used to test the research questions or hypotheses.
• Results : This section presents the findings of the study, including descriptive statistics and statistical tests.
• Discussion and Conclusion : This section summarizes the key findings of the study, interprets the results, and discusses the implications of the findings. It also includes recommendations for future research.
• References : This section lists the sources cited in the research design.

Example of Research Design

An Example of Research Design could be:

Research question: Does the use of social media affect the academic performance of high school students?

Research design:

• Research approach : The research approach will be quantitative as it involves collecting numerical data to test the hypothesis.
• Research design : The research design will be a quasi-experimental design, with a pretest-posttest control group design.
• Sample : The sample will be 200 high school students from two schools, with 100 students in the experimental group and 100 students in the control group.
• Data collection : The data will be collected through surveys administered to the students at the beginning and end of the academic year. The surveys will include questions about their social media usage and academic performance.
• Data analysis : The data collected will be analyzed using statistical software. The mean scores of the experimental and control groups will be compared to determine whether there is a significant difference in academic performance between the two groups.
• Limitations : The limitations of the study will be acknowledged, including the fact that social media usage can vary greatly among individuals, and the study only focuses on two schools, which may not be representative of the entire population.
• Ethical considerations: Ethical considerations will be taken into account, such as obtaining informed consent from the participants and ensuring their anonymity and confidentiality.

How to Write Research Design

Writing a research design involves planning and outlining the methodology and approach that will be used to answer a research question or hypothesis. Here are some steps to help you write a research design:

• Define the research question or hypothesis : Before beginning your research design, you should clearly define your research question or hypothesis. This will guide your research design and help you select appropriate methods.
• Select a research design: There are many different research designs to choose from, including experimental, survey, case study, and qualitative designs. Choose a design that best fits your research question and objectives.
• Develop a sampling plan : If your research involves collecting data from a sample, you will need to develop a sampling plan. This should outline how you will select participants and how many participants you will include.
• Define variables: Clearly define the variables you will be measuring or manipulating in your study. This will help ensure that your results are meaningful and relevant to your research question.
• Choose data collection methods : Decide on the data collection methods you will use to gather information. This may include surveys, interviews, observations, experiments, or secondary data sources.
• Create a data analysis plan: Develop a plan for analyzing your data, including the statistical or qualitative techniques you will use.
• Consider ethical concerns : Finally, be sure to consider any ethical concerns related to your research, such as participant confidentiality or potential harm.

When to Write Research Design

Research design should be written before conducting any research study. It is an important planning phase that outlines the research methodology, data collection methods, and data analysis techniques that will be used to investigate a research question or problem. The research design helps to ensure that the research is conducted in a systematic and logical manner, and that the data collected is relevant and reliable.

Ideally, the research design should be developed as early as possible in the research process, before any data is collected. This allows the researcher to carefully consider the research question, identify the most appropriate research methodology, and plan the data collection and analysis procedures in advance. By doing so, the research can be conducted in a more efficient and effective manner, and the results are more likely to be valid and reliable.

Purpose of Research Design

The purpose of research design is to plan and structure a research study in a way that enables the researcher to achieve the desired research goals with accuracy, validity, and reliability. Research design is the blueprint or the framework for conducting a study that outlines the methods, procedures, techniques, and tools for data collection and analysis.

Some of the key purposes of research design include:

• Providing a clear and concise plan of action for the research study.
• Ensuring that the research is conducted ethically and with rigor.
• Maximizing the accuracy and reliability of the research findings.
• Minimizing the possibility of errors, biases, or confounding variables.
• Ensuring that the research is feasible, practical, and cost-effective.
• Determining the appropriate research methodology to answer the research question(s).
• Identifying the sample size, sampling method, and data collection techniques.
• Determining the data analysis method and statistical tests to be used.
• Facilitating the replication of the study by other researchers.
• Enhancing the validity and generalizability of the research findings.

Applications of Research Design

There are numerous applications of research design in various fields, some of which are:

• Social sciences: In fields such as psychology, sociology, and anthropology, research design is used to investigate human behavior and social phenomena. Researchers use various research designs, such as experimental, quasi-experimental, and correlational designs, to study different aspects of social behavior.
• Education : Research design is essential in the field of education to investigate the effectiveness of different teaching methods and learning strategies. Researchers use various designs such as experimental, quasi-experimental, and case study designs to understand how students learn and how to improve teaching practices.
• Health sciences : In the health sciences, research design is used to investigate the causes, prevention, and treatment of diseases. Researchers use various designs, such as randomized controlled trials, cohort studies, and case-control studies, to study different aspects of health and healthcare.
• Business : Research design is used in the field of business to investigate consumer behavior, marketing strategies, and the impact of different business practices. Researchers use various designs, such as survey research, experimental research, and case studies, to study different aspects of the business world.
• Engineering : In the field of engineering, research design is used to investigate the development and implementation of new technologies. Researchers use various designs, such as experimental research and case studies, to study the effectiveness of new technologies and to identify areas for improvement.

Advantages of Research Design

Here are some advantages of research design:

• Systematic and organized approach : A well-designed research plan ensures that the research is conducted in a systematic and organized manner, which makes it easier to manage and analyze the data.
• Clear objectives: The research design helps to clarify the objectives of the study, which makes it easier to identify the variables that need to be measured, and the methods that need to be used to collect and analyze data.
• Minimizes bias: A well-designed research plan minimizes the chances of bias, by ensuring that the data is collected and analyzed objectively, and that the results are not influenced by the researcher’s personal biases or preferences.
• Efficient use of resources: A well-designed research plan helps to ensure that the resources (time, money, and personnel) are used efficiently and effectively, by focusing on the most important variables and methods.
• Replicability: A well-designed research plan makes it easier for other researchers to replicate the study, which enhances the credibility and reliability of the findings.
• Validity: A well-designed research plan helps to ensure that the findings are valid, by ensuring that the methods used to collect and analyze data are appropriate for the research question.
• Generalizability : A well-designed research plan helps to ensure that the findings can be generalized to other populations, settings, or situations, which increases the external validity of the study.

Research Design Vs Research Methodology

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5 Classic Psychology Research Designs

• By Cliff Stamp, BS Psychology, MS Rehabilitation Counseling
• Published November 10, 2019
• Last Updated November 17, 2023
• Read Time 6 mins

Posted November 2019 by Clifton Stamp, B.S. Psychology; M.A. Rehabilitation Counseling, M.A. English; 10 updates since. Reading time: 5 min. Reading level: Grade 11+. Questions on psychology research designs? Email Toni at: [email protected] .

Psychology research is carried out by a variety of methods, all of which are intended to increase the fund of knowledge we have concerning human behavior. Research is a formalized, systematic way of deriving accurate and reproducible results. Research designs are the particular methods and procedures used to generate, collect and analyze information.

Research can be carried out in many different ways, but can broadly be defined as qualitative or quantitative.  Quantitative psychological research refers to research that yields outcomes that derive from statistics or mathematical modeling. Quantitative research is centered around testing objective hypotheses . It is based on empiricism and attempts to show the accuracy of a hypothesis.

Qualitative psychological research attempts to understand behavior within its natural context and setting. Qualitative psychological research uses observation, interviews, focus groups and participant observation as its most common methods.

Classic Psychology Research Designs

Research is typically focused on finding a particular answer or answers to a question or problem, logically enough called the research question. A research design is a formalized means of finding answers to a research question. Research designs create a framework for gathering and collecting information in a structured, orderly way. Five of the most common psychology research designs include descriptive, correlational, semi-experimental, experimental, review and meta-analytic designs.

Descriptive Research Designs

• Case study . Case study research involves researchers conduction a close-up look at an individual, a phenomenon, or a group in its real-world naturalistic environment. Case studies are more intrusive than naturalistic observational studies.
• Naturalistic observation . Naturalistic observation , a kind of field research, involves observing research subjects in their own environment, without any introduced external factors.  Naturalistic observation has a high degree of external validity .
• Surveys .   Everyone has taken a survey at one time or another. Surveys sample a group of individuals that are chosen to be representative of a larger population. Surveys naturally cannot research every individual in a population, thus a great deal of study is conducted to ensure that samples truly represent the populations they’re supposed to describe. Polls about public opinion, market-research surveys, public-health surveys, and government surveys are examples of mass spectrum surveys.

Correlational Research Designs

In correlational research designs, groups are studied and compared, but researchers cannot introduce variables or manipulate independent variables.

• Case-control study . A case-control study is a comparison between two groups, one of which experienced a condition while the other group did not . Case-control studies are retrospective; that is, they observe a situation that has already happened. Two groups exist that are as similar as possible, save that a hypothesized agent affected the case group. This hypothesized agent, condition or singular difference between groups is said to correlate with differences in outcomes.
• Observational study . Observational studies allow researchers to make some inferences from a group sample to an overall population. In an observational study, the independent variable cannot be controlled or modified directly. Consider a study that compares the outcomes of fetal alcohol exposure on the development of psychological disorders. It would be unethical to cause a group of fetuses to be exposed to alcohol in vivo.  Thus, two groups of individuals, as alike as possible are compared. The difference is that one group has been selected due to their exposure to alcohol during their fetal development. Researchers are not manipulating the measure of the independent variable, but they are attempting to measure its effect by group to group comparison .

Semi-Experimental Research Design

• Field experiment . A field experiment occurs in the everyday environment of the research subjects. In a field experiment, researchers manipulate an independent variable and measure changes in the tested, dependent variable. Although field experiments generalize extremely well, it’s not possible to eliminate extraneous variables. This can limit the usefulness of any conclusions.

Experimental Research Design

Experimental research is a major component of experimental psychology. In experimental psychology, researchers perform tightly controlled laboratory experiments that eliminate external, erroneous variables.  This high level of control allows experimental results to have a high degree of internal validity. Internal validity refers to the degree to which an experiment’s outcomes come from manipulations of the independent variable. On the other hand, highly controlled lab experiments may not generalize to the natural environment, precisely due to the presence of many external variables.

 Review Designs and Meta-Analysis

• Literature review . A literature review is a paper examining other experiments or research into a particular subject. Literature reviews examine research published in academic and other scholarly journals. All research starts with a search for research similar, or at least fundamentally similar, to the research question in question.
• Systematic review . A systematic review examines as much published, verified research that matches the researchers’ guidelines for a particular line of research. Systematic review involves multiple and exhaustive literature reviews. After conducting a systematic review of all other research on a topic that meets criteria, psychology researchers conduct a meta-analysis.
• Meta-analyses. Meta-analyses involve complex statistical analysis of former research to answer an overall research question.

Literature reviews and systematic reviews and meta-analyses all work together to provide psychology researchers with a big-picture view of the body of study they are investigating.

Descriptive, Correlational and Experimental Designs

All research may be thought of as having descriptive or inferential value, although there are usually aspects of both present in all research projects. Descriptive research often comes before experimental research, as examining what’s been discovered about a research topic helps guide and refine experimental research, which has a high inferential value.

Descriptive research designs include literature reviews, systematic reviews and meta-analyses. They’re able to assess and evaluate what the state of a body of knowledge is, but no experimentation is conducted. Correlational designs investigate the strength of the relationship between or among variables. Correlational studies are good for pointing out possible relationships but cannot establish causation, or a cause-and-effect relationship among variables. This leaves experimental designs. which do allow inferences to be made about cause-and-effect. Experimental designs are the most scientifically, mathematically rigorous, but that fine level of control doesn’t always extrapolate well to the world outside the lab.

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Understanding Research Design: Its Definition and Importance

Table of Contents

Have you ever wondered how data -tooltip="Professionals studying the mind and behavior">psychologists uncover the mysteries of the human mind? The answer lies within the realm of research design , the scaffold that structures the entire research process. Think of it as the blueprint for a house; without it, the structure would lack foundation and direction. Diving into the world of research design, we quickly find that it’s more than just a plan; it’s the strategic framework that guides the collection, measurement, and analysis of data in psychological research.

What is Research Design?

Research design is often described as a set of guidelines that help researchers navigate the process of conducting a study. It’s the master plan that specifies the methods and procedures for collecting and analyzing the necessary information. This systematic approach is essential for ensuring that the research question is answered as accurately as possible.

Historical Perspectives on Research Design

Renowned scholars like Winner \(1971\) and Myers \(1980\) have likened research design to an architect’s blueprint, emphasizing its critical role in research planning and structure. This comparison underscores the meticulous attention to detail and forethought required in research, similar to that needed when designing a building.

The Components of Research Design

Let’s break down the research design into its core components:

• Plan: This is the overall scheme or the roadmap for the research. It outlines what the study aims to achieve and the steps required to get there.
• Structure: Often referred to as the detailed outline of operations, structure dictates the ‘how’ of the plan. It covers the specifics of data collection, such as surveys or experiments.
• Strategy: Strategy is all about the methods for data gathering and analysis. It involves choosing the appropriate statistical techniques and tools to interpret the collected data meaningfully.

Why is Research Design Important?

Quality research design serves multiple purposes in the study of psychology. It not only facilitates the efficient achievement of research objectives but also provides a way to tackle problems encountered during the research process effectively. Thyer (1993) and Matheson (1970) have both highlighted the importance of a well-structured research design, noting that it is pivotal for the validity and reliability of a study’s findings.

Ensuring Validity and Reliability

Two of the most critical aspects of any research are its validity and reliability:

• Validity refers to the accuracy of the findings or the extent to which the research truly measures what it intends to measure.
• Reliability is about the consistency of the results. A reliable study is one that can be replicated under similar conditions with the same outcomes.

A robust research design guarantees that these aspects are thoroughly considered and addressed.

The Role of Research Design in Psychology

In the field of psychology, where the subject matter can often be abstract and complex, the importance of research design is magnified. Kerlinger \(1986\) asserted that a well-thought-out research design is the foundation upon which the entire inquiry is built. The design influences the choice of research methods, the type of data collected, and the way that data is interpreted.

Types of Research Design in Psychology

Psychological research can take many forms, each with its unique design:

• Experimental Designs : These are used to determine cause-and-effect relationships by manipulating one or more variables while controlling others.
• Correlational Designs : These designs explore the relationships between two or more variables without manipulation.
• Descriptive Designs : These include observational studies, case studies, and surveys, and are used to describe phenomena without manipulating the study environment.

Challenges in Research Design

No research design is without its challenges. Researchers must anticipate and plan for potential issues that could affect the integrity of their results. This proactive approach can include considering ethical implications, managing biases, and ensuring a representative sample.

Anticipating and Overcoming Obstacles

Effective research design involves not only planning for what is expected but also preparing for the unexpected. This could involve creating contingency plans for data collection or considering alternative interpretations of the data.

The meticulous nature of crafting a research design can be daunting, yet it is an indispensable stage in the research process. By understanding the ins and outs of research design, psychologists and researchers are better equipped to unveil the intricacies of human behavior and mental processes, contributing valuable knowledge to the field.

What do you think? How might the principles of research design apply to other areas of study? Can you think of a situation where a strong research design could make a difference in the outcome of a study?

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Research Methods in Psychology

1 Introduction to Psychological Research – Objectives and Goals, Problems, Hypothesis and Variables

• Nature of Psychological Research
• The Context of Discovery
• Context of Justification
• Characteristics of Psychological Research
• Goals and Objectives of Psychological Research

2 Introduction to Psychological Experiments and Tests

• Independent and Dependent Variables
• Extraneous Variables
• Experimental and Control Groups
• Introduction of Test
• Types of Psychological Test
• Uses of Psychological Tests

3 Steps in Research

• Research Process
• Identification of the Problem
• Review of Literature
• Formulating a Hypothesis
• Identifying Manipulating and Controlling Variables
• Formulating a Research Design
• Constructing Devices for Observation and Measurement
• Sample Selection and Data Collection
• Data Analysis and Interpretation
• Hypothesis Testing
• Drawing Conclusion

4 Types of Research and Methods of Research

• Historical Research
• Descriptive Research
• Correlational Research
• Qualitative Research
• Ex-Post Facto Research
• True Experimental Research
• Quasi-Experimental Research

5 Definition and Description Research Design, Quality of Research Design

• Research Design
• Purpose of Research Design
• Design Selection
• Criteria of Research Design
• Qualities of Research Design

6 Experimental Design (Control Group Design and Two Factor Design)

• Experimental Design
• Control Group Design
• Two Factor Design

7 Survey Design

• Survey Research Designs
• Steps in Survey Design
• Structuring and Designing the Questionnaire
• Interviewing Methodology
• Data Analysis
• Final Report

8 Single Subject Design

• Single Subject Design: Definition and Meaning
• Phases Within Single Subject Design
• Requirements of Single Subject Design
• Characteristics of Single Subject Design
• Types of Single Subject Design
• Advantages of Single Subject Design
• Disadvantages of Single Subject Design

9 Observation Method

• Definition and Meaning of Observation
• Characteristics of Observation
• Types of Observation
• Advantages and Disadvantages of Observation
• Guides for Observation Method

10 Interview and Interviewing

• Definition of Interview
• Types of Interview
• Aspects of Qualitative Research Interviews
• Interview Questions
• Convergent Interviewing as Action Research
• Research Team

11 Questionnaire Method

• Definition and Description of Questionnaires
• Types of Questionnaires
• Purpose of Questionnaire Studies
• Designing Research Questionnaires
• The Methods to Make a Questionnaire Efficient
• The Types of Questionnaire to be Included in the Questionnaire
• Advantages and Disadvantages of Questionnaire
• When to Use a Questionnaire?

12 Case Study

• Definition and Description of Case Study Method
• Historical Account of Case Study Method
• Designing Case Study
• Requirements for Case Studies
• Guideline to Follow in Case Study Method
• Other Important Measures in Case Study Method
• Case Reports

13 Report Writing

• Purpose of a Report
• Writing Style of the Report
• Report Writing – the Do’s and the Don’ts
• Format for Report in Psychology Area
• Major Sections in a Report

14 Review of Literature

• Purposes of Review of Literature
• Sources of Review of Literature
• Types of Literature
• Writing Process of the Review of Literature
• Preparation of Index Card for Reviewing and Abstracting

15 Methodology

• Definition and Purpose of Methodology
• Participants (Sample)
• Apparatus and Materials

16 Result, Analysis and Discussion of the Data

• Definition and Description of Results
• Statistical Presentation
• Tables and Figures

17 Summary and Conclusion

• Summary Definition and Description
• Guidelines for Writing a Summary
• Writing the Summary and Choosing Words
• A Process for Paraphrasing and Summarising
• Summary of a Report
• Writing Conclusions

18 References in Research Report

• Reference List (the Format)
• References (Process of Writing)
• Reference List and Print Sources
• Electronic Sources
• Book on CD Tape and Movie
• Reference Specifications
• General Guidelines to Write References

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2.2 Research Designs in Psychology

Learning objectives.

• Differentiate the goals of descriptive, correlational, and experimental research designs, and explain the advantages and disadvantages of each.

Psychologists agree that if their ideas and theories about human behaviour are to be taken seriously, they must be backed up by data. Researchers have a variety of research designs available to them in testing their predictions. A research design  is the specific method a researcher uses to collect, analyze, and interpret data. Psychologists use three major types of research designs in their research, and each provides an essential avenue for scientific investigation. Descriptive research  is designed to provide a snapshot of the current state of affairs. Correlational research  is designed to discover relationships among variables. Experimental research is designed to assess cause and effect. Each of the three research designs has specific strengths and limitations, and it is important to understand how each differs. See the table below for a summary.

Descriptive research: Assessing the current state of affairs

Descriptive research is designed to create a snapshot of the current thoughts, feelings, or behaviour of individuals. This section reviews four types of descriptive research: case studies, surveys and tests, naturalistic observation, and laboratory observation.

Sometimes the data in a descriptive research project are collected from only a small set of individuals, often only one person or a single small group. These research designs are known as case studies , which are descriptive records of one or more individual’s experiences and behaviour. Sometimes case studies involve ordinary individuals, as when developmental psychologist Jean Piaget used his observation of his own children to develop his stage theory of cognitive development. More frequently, case studies are conducted on individuals who have unusual or abnormal experiences or characteristics, this may include those who find themselves in particularly difficult or stressful situations. The assumption is that carefully studying individuals can give us results that tell us something about human nature. Of course, one individual cannot necessarily represent a larger group of people who were in the same circumstances.

Sigmund Freud was a master of using the psychological difficulties of individuals to draw conclusions about basic psychological processes. Freud wrote case studies of some of his most interesting patients and used these careful examinations to develop his important theories of personality. One classic example is Freud’s description of “Little Hans,” a child whose fear of horses was interpreted in terms of repressed sexual impulses and the Oedipus complex (Freud, 1909/1964).

Another well-known case study is of Phineas Gage, a man whose thoughts and emotions were extensively studied by cognitive psychologists after a railroad spike was blasted through his skull in an accident. Although there are questions about the interpretation of this case study (Kotowicz, 2007), it did provide early evidence that the brain’s frontal lobe is involved in emotion and morality (Damasio et al., 2005). An interesting example of a case study in clinical psychology is described by Milton Rokeach (1964), who investigated in detail the beliefs of and interactions among three patients with schizophrenia, all of whom were convinced they were Jesus Christ.

Research using case studies has some unique challenges when it comes to interpreting the data. By definition, case studies are based on one or a very small number of individuals. While their situations may be unique, we cannot know how well they represent what would be found in other cases. Furthermore, the information obtained in a case study may be inaccurate or incomplete. While researchers do their best to objectively understand one case, making any generalizations to other people is problematic. Researchers can usually only speculate about cause and effect, and even then, they must do so with great caution. Case studies are particularly useful when researchers are starting out to study something about which there is not much research or as a source for generating hypotheses that can be tested using other research designs.

In other cases, the data from descriptive research projects come in the form of a survey , which is a measure administered through either an interview or a written questionnaire to get a picture of the beliefs or behaviours of a sample of people of interest. The people chosen to participate in the research, known as the sample , are selected to be representative of all the people that the researcher wishes to know about, known as the population . The representativeness of samples is enormously important. For example, a representative sample of Canadians must reflect Canada’s demographic make-up in terms of age, sex, gender orientation, socioeconomic status, ethnicity, and so on. Research based on unrepresentative samples is limited in generalizability , meaning it will not apply well to anyone who was not represented in the sample. Psychologists use surveys to measure a wide variety of behaviours, attitudes, opinions, and facts. Surveys could be used to measure the amount of exercise people get every week, eating or drinking habits, attitudes towards climate change, and so on. These days, many surveys are available online, and they tend to be aimed at a wide audience. Statistics Canada is a rich source of surveys of Canadians on a diverse array of topics. Their databases are searchable and downloadable, and many deal with topics of interest to psychologists, such as mental health, wellness, and so on. Their raw data may be used by psychologists who are able to take advantage of the fact that the data have already been collected. This is called archival research .

Related to surveys are psychological tests . These are measures developed by psychologists to assess one’s score on a psychological construct, such as extroversion, self-esteem, or aptitude for a particular career. The difference between surveys and tests is really down to what is being measured, with surveys more likely to be fact-gathering and tests more likely to provide a score on a psychological construct.

As you might imagine, respondents to surveys and psychological tests are not always accurate or truthful in their replies. Respondents may also skew their answers in the direction they think is more socially desirable or in line with what the researcher expects. Sometimes people do not have good insight into their own behaviour and are not accurate in judging themselves. Sometimes tests have built-in social desirability or lie scales that attempt to help researchers understand when someone’s scores might need to be discarded from the research because they are not accurate.

Tests and surveys are only useful if they are valid and reliable . Validity exists when an instrument actually measures what you think it measures (e.g., a test of intelligence that actually measures how many years of education you have lacks validity). Demonstrating the validity of a test or survey is the responsibility of any researcher who uses the instrument. Reliability is a related but different construct; it exists when a test or survey gives the same responses from time to time or in different situations. For example, if you took an intelligence test three times and every time it gave you a different score, that would not be a reliable test. Demonstrating the reliability of tests and surveys is another responsibility of researchers. There are different types of validity and reliability, and there is a branch of psychology devoted to understanding not only how to demonstrate that tests and surveys are valid and reliable, but also how to improve them.

An important criticism of psychological research is its reliance on so-called WEIRD samples (Henrich, Heine, & Norenzayan, 2010). WEIRD stands for Western, educated, industrialized, rich, and democratic. People fitting the WEIRD description have been over-represented in psychological research, while people from poorer, less-educated backgrounds, for example, have participated far less often. This criticism is important because in psychology we may be trying to understand something about people in general. For example, if we want to understand whether early enrichment programs can boost IQ scores later, we need to conduct this research using people from a variety of backgrounds and situations. Most of the world’s population is not WEIRD, so psychologists trying to conduct research that has broad generalizability need to expand their participant pool to include a more representative sample.

Another type of descriptive research is  naturalistic observation , which refers to research based on the observation of everyday events. For instance, a developmental psychologist who watches children on a playground and describes what they say to each other while they play is conducting naturalistic observation, as is a biopsychologist who observes animals in their natural habitats. Naturalistic observation is challenging because, in order for it to be accurate, the observer must be effectively invisible. Imagine walking onto a playground, armed with a clipboard and pencil to watch children a few feet away. The presence of an adult may change the way the children behave; if the children know they are being watched, they may not behave in the same ways as they would when no adult is present. Researchers conducting naturalistic observation studies have to find ways to recede into the background so that their presence does not cause the behaviour they are watching to change. They also must find ways to record their observations systematically and completely — not an easy task if you are watching children, for example. As such, it is common to have multiple observers working independently; their combined observations can provide a more accurate record of what occurred.

Sometimes, researchers conducting observational research move out of the natural world and into a laboratory. Laboratory observation allows much more control over the situation and setting in which the participants will be observed. The downside to moving into a laboratory is the potential artificiality of the setting; the participants may not behave the same way in the lab as they would in the natural world, so the behaviour that is observed may not be completely authentic. Consider the researcher who is interested in aggression in children. They might go to a school playground and record what occurs; however, this could be quite time-consuming if the frequency is low or if the children are playing some distance away and their behaviour is difficult to interpret. Instead, the researcher could construct a play setting in a laboratory and attempt to observe aggressive behaviours in this smaller and more controlled context; for instance, they could only provide one highly desirable toy instead of one for each child. What they gain in control, they lose in artificiality. In this example, the possibility for children to act differently in the lab than they would in the real world would create a challenge in interpreting results.

Correlational research: Seeking relationships among variables

In contrast to descriptive research — which is designed primarily to provide a snapshot of behaviour, attitudes, and so on — correlational research involves measuring the relationship between two variables. Variables can be behaviours, attitudes, and so on. Anything that can be measured is a potential variable. The key aspect of correlational research is that the researchers are not asking some of their participants to do one thing and others to do something else; all of the participants are providing scores on the same two variables. Correlational research is not about how an individual scores; rather, it seeks to understand the association between two things in a larger sample of people. The previous comments about the representativeness of the sample all apply in correlational research. Researchers try to find a sample that represents the population of interest.

An example of correlation research would be to measure the association between height and weight. We should expect that there is a relationship because taller people have more mass and therefore should weigh more than short people. We know from observation, however, that there are many tall, thin people just as there are many short, overweight people. In other words, we would expect that in a group of people, height and weight should be systematically related (i.e., correlated), but the degree of relatedness is not expected to be perfect. Imagine we repeated this study with samples representing different populations: elite athletes, women over 50, children under 5, and so on. We might make different predictions about the relationship between height and weight based on the characteristics of the sample. This highlights the importance of obtaining a representative sample.

Psychologists make frequent use of correlational research designs. Examples might be the association between shyness and number of Facebook friends, between age and conservatism, between time spent on social media and grades in school, and so on. Correlational research designs tend to be relatively less expensive because they are time-limited and can often be conducted without much equipment. Online survey platforms have made data collection easier than ever. Some correlational research does not even necessitate collecting data; researchers using archival data sets as described above simply download the raw data from another source. For example, suppose you were interested in whether or not height is related to the number of points scored in hockey players. You could extract data for both variables from nhl.com , the official National Hockey League website, and conduct archival research using the data that have already been collected.

Correlational research designs look for associations between variables. A statistic that measures that association is the correlation coefficient. Correlation coefficients can be either positive or negative, and they range in value from -1.0 through 0 to 1.0. The most common statistical measure is the Pearson correlation coefficient , which is symbolized by the letter r . Positive values of r (e.g., r = .54 or r = .67) indicate that the relationship is positive, whereas negative values of r (e.g., r = –.30 or r = –.72) indicate negative relationships. The closer the coefficient is to -1 or +1, and the further away from zero, the greater the size of the association between the two variables. For instance, r = –.54 is a stronger relationship than r = .30, and r = .72 is a stronger relationship than r = –.57. Correlations of 0 indicate no relationship between the two variables.

Examples of positive correlation coefficients would include those between height and weight, between education and income, and between age and mathematical abilities in children. In each case, people who score higher, or lower, on one of the variables also tend to score higher, or lower, on the other variable. Negative correlations occur when people score high on one variable and low on the other. Examples of negative linear relationships include those between the age of a child and the number of diapers the child uses and between time practising and errors made on a learning task. In these cases, people who score higher on one of the variables tend to score lower on the other variable. Note that the correlation coefficient does not tell you anything about one specific person’s score.

One way of organizing the data from a correlational study with two variables is to graph the values of each of the measured variables using a scatterplot. A scatterplot  is a visual image of the relationship between two variables (see Figure 2.3 ). A point is plotted for each individual at the intersection of his or her scores for the two variables. In this example, data extracted from the official National Hockey League (NHL) website of 30 randomly picked hockey players for the 2017/18 season. For each of these players, there is a dot representing player height and number of points (i.e., goals plus assists). The slope or angle of the dotted line through the middle of the scatter tells us something about the strength and direction of the correlation. In this case, the line slopes up slightly to the right, indicating a positive but small correlation. In these NHL players, there is not much of relationship between height and points. The Pearson correlation calculated for this sample is r = 0.14. It is possible that the correlation would be totally different in a different sample of players, such as a greater number, only those who played a full season, only rookies, only forwards, and so on.

For practise constructing and interpreting scatterplots, see the following:

• Interactive Quiz: Positive and Negative Associations in Scatterplots (Khan Academy, 2018)

When the association between the variables on the scatterplot can be easily approximated with a straight line, the variables are said to have a linear relationship . We are only going to consider linear relationships here. Just be aware that some pairs of variables have non-linear relationships, such as the relationship between physiological arousal and performance. Both high and low arousal are associated with sub-optimal performance, shown by a U-shaped scatterplot curve.

The most important limitation of correlational research designs is that they cannot be used to draw conclusions about the causal relationships among the measured variables; in other words, we cannot know what causes what in correlational research. Consider, for instance, a researcher who has hypothesized that viewing violent behaviour will cause increased aggressive play in children. The researcher has collected, from a sample of Grade 4 children, a measure of how many violent television shows each child views during the week as well as a measure of how aggressively each child plays on the school playground. From the data collected, the researcher discovers a positive correlation between the two measured variables.

Although this positive correlation appears to support the researcher’s hypothesis, it cannot be taken to indicate that viewing violent television causes aggressive behaviour. Although the researcher is tempted to assume that viewing violent television causes aggressive play, there are other possibilities. One alternative possibility is that the causal direction is exactly opposite of what has been hypothesized; perhaps children who have behaved aggressively at school are more likely to prefer violent television shows at home.

Still another possible explanation for the observed correlation is that it has been produced by a so-called third variable , one that is not part of the research hypothesis but that causes both of the observed variables and, thus, the correlation between them. In our example, a potential third variable is the discipline style of the children’s parents. Parents who use a harsh and punitive discipline style may allow children to watch violent television and to behave aggressively in comparison to children whose parents use less different types of discipline.

To review, whenever we have a correlation that is not zero, there are three potential pathways of cause and effect that must be acknowledged. The easiest way to practise understanding this challenge is to automatically designate the two variables X and Y. It does not matter which is which. Then, think through any ways in which X might cause Y. Then, flip the direction of cause and effect, and consider how Y might cause X. Finally, and possibly the most challenging, try to think of other variables — let’s call these C — that were not part of the original correlation, which cause both X and Y. Understanding these potential explanations for correlational research is an important aspect of scientific literacy. In the above example, we have shown how X (i.e., viewing violent TV) could cause Y (i.e., aggressive behaviour), how Y could cause X, and how C (i.e., parenting) could cause both X and Y.

Test your understanding with each example below. Find three different interpretations of cause and effect using the procedure outlined above. In each case, identify variables X, Y, and C:

• A positive correlation between dark chocolate consumption and health
• A negative correlation between sleep and smartphone use
• A positive correlation between children’s aggressiveness and time spent playing video games
• A negative association between time spent exercising and consumption of junk food

In sum, correlational research designs have both strengths and limitations. One strength is that they can be used when experimental research is not possible or when fewer resources are available. Correlational designs also have the advantage of allowing the researcher to study behaviour as it occurs in everyday life. We can also use correlational designs to make predictions, such as predicting the success of job trainees based on their test scores during training. They are also excellent sources of suggested avenues for further research, but we cannot use such correlational information to understand cause and effect. For that, researchers rely on experiments.

Experimental research: Understanding the causes of behaviour

The goal of experimental research design is to provide definitive conclusions about the causal relationships among the variables in the research hypothesis. In an experimental research design, there are independent variables and dependent variables. The independent variable  is the one manipulated by the researchers so that there is more than one condition. The dependent variable is the outcome or score on the measure of interest that is dependent on the actions of the independent variable. Let’s consider a classic drug study to illustrate the relationship between independent and dependent variables. To begin, a sample of people with a medical condition are randomly assigned to one of two conditions. In one condition, they are given a drug over a period of time. In the other condition, a placebo is given for the same period of time. To be clear, a placebo is a type of medication that looks like the real thing but is actually chemically inert, sometimes referred to as a”sugar pill.” After the testing period, the groups are compared to see if the drug condition shows better improvement in health than the placebo condition.

While the basic design of experiments is quite simple, the success of experimental research rests on meeting a number of criteria. Some important criteria are:

• Participants must be randomly assigned to the conditions so that there are no differences between the groups. In the drug study example, you could not assign the males to the drug condition and the females to the placebo condition. The groups must be demographically equivalent.
• There must be a control condition. Having a condition that does not receive treatment allows experimenters to compare the results of the drug to the results of placebo.
• The only thing that can change between the conditions is the independent variable. For example, the participants in the drug study should receive the medication at the same place, from the same person, at the same time, and so on, for both conditions. Experiments often employ double-blind procedures in which neither the experimenter nor the participants know which condition any participant is in during the experiment. In a single-blind procedure, the participants do not know which condition they are in.
• The sample size has to be large and diverse enough to represent the population of interest. For example, a pharmaceutical company should not use only men in their drug study if the drug will eventually be prescribed to women as well.
• Experimenter effects should be minimized. This means that if there is a difference in scores on the dependent variable, they should not be attributable to something the experimenter did or did not do. For example, if an experiment involved comparing a yoga condition with an exercise condition, experimenters would need to make sure that they treated the participants exactly the same in each condition. They would need to control the amount of time they spent with the participants, how much they interacted verbally, smiled at the participants, and so on. Experimenters often employ research assistants who are blind to the participants’ condition to interact with the participants.

As you can probably see, much of experimental design is about control. The experimenters have a high degree of control over who does what. All of this tight control is to try to ensure that if there is a difference between the different levels of the independent variable, it is detectable. In other words, if there is even a small difference between a drug and placebo, it is detected. Furthermore, this level of control is aimed at ensuring that the only difference between conditions is the one the experimenters are testing while making correct and accurate determinations about cause and effect.

Research Focus

Video games and aggression

Consider an experiment conducted by Craig Anderson and Karen Dill (2000). The study was designed to test the hypothesis that viewing violent video games would increase aggressive behaviour. In this research, male and female undergraduates from Iowa State University were given a chance to play with either a violent video game (e.g., Wolfenstein 3D) or a nonviolent video game (e.g., Myst). During the experimental session, the participants played their assigned video games for 15 minutes. Then, after the play, each participant played a competitive game with an opponent in which the participant could deliver blasts of white noise through the earphones of the opponent. The operational definition of the dependent variable (i.e., aggressive behaviour) was the level and duration of noise delivered to the opponent. The design of the experiment is shown below (see Figure 2.4 ).

There are two strong advantages of the experimental research design. First, there is assurance that the independent variable, also known as the experimental manipulation , occurs prior to the measured dependent variable; second, there is creation of initial equivalence between the conditions of the experiment, which is made possible by using random assignment to conditions.

Experimental designs have two very nice features. For one, they guarantee that the independent variable occurs prior to the measurement of the dependent variable. This eliminates the possibility of reverse causation. Second, the influence of common-causal variables is controlled, and thus eliminated, by creating initial equivalence among the participants in each of the experimental conditions before the manipulation occurs.

The most common method of creating equivalence among the experimental conditions is through random assignment to conditions, a procedure in which the condition that each participant is assigned to is determined through a random process, such as drawing numbers out of an envelope or using a random number table. Anderson and Dill first randomly assigned about 100 participants to each of their two groups: Group A and Group B. Since they used random assignment to conditions, they could be confident that, before the experimental manipulation occurred, the students in Group A were, on average, equivalent to the students in Group B on every possible variable, including variables that are likely to be related to aggression, such as parental discipline style, peer relationships, hormone levels, diet — and in fact everything else.

Then, after they had created initial equivalence, Anderson and Dill created the experimental manipulation; they had the participants in Group A play the violent game and the participants in Group B play the nonviolent game. Then, they compared the dependent variable (i.e., the white noise blasts) between the two groups, finding that the students who had viewed the violent video game gave significantly longer noise blasts than did the students who had played the nonviolent game.

Anderson and Dill had from the outset created initial equivalence between the groups. This initial equivalence allowed them to observe differences in the white noise levels between the two groups after the experimental manipulation, leading to the conclusion that it was the independent variable, and not some other variable, that caused these differences. The idea is that the only thing that was different between the students in the two groups was the video game they had played.

Sometimes, experimental research has a confound. A confound is a variable that has slipped unwanted into the research and potentially caused the results because it has created a systematic difference between the levels of the independent variable. In other words, the confound caused the results, not the independent variable. For example, suppose you were a researcher who wanted to know if eating sugar just before an exam was beneficial. You obtain a large sample of students, divide them randomly into two groups, give everyone the same material to study, and then give half of the sample a chocolate bar containing high levels of sugar and the other half a glass of water before they write their test. Lo and behold, you find the chocolate bar group does better. However, the chocolate bar also contains caffeine, fat and other ingredients. These other substances besides sugar are potential confounds; for example, perhaps caffeine rather than sugar caused the group to perform better. Confounds introduce a systematic difference between levels of the independent variable such that it is impossible to distinguish between effects due to the independent variable and effects due to the confound.

Despite the advantage of determining causation, experiments do have limitations. One is that they are often conducted in laboratory situations rather than in the everyday lives of people. Therefore, we do not know whether results that we find in a laboratory setting will necessarily hold up in everyday life. Do people act the same in a laboratory as they do in real life? Often researchers are forced to balance the need for experimental control with the use of laboratory conditions that can only approximate real life.

Additionally, it is very important to understand that many of the variables that psychologists are interested in are not things that can be manipulated experimentally. For example, psychologists interested in sex differences cannot randomly assign participants to be men or women. If a researcher wants to know if early attachments to parents are important for the development of empathy, or in the formation of adult romantic relationships, the participants cannot be randomly assigned to childhood attachments. Thus, a large number of human characteristics cannot be manipulated or assigned. This means that research may look experimental because it has different conditions (e.g., men or women, rich or poor, highly intelligent or not so intelligent, etc.); however, it is quasi-experimental . The challenge in interpreting quasi-experimental research is that the inability to randomly assign the participants to condition results in uncertainty about cause and effect. For example, if you find that men and women differ in some ability, it could be biology that is the cause, but it is equally likely it could be the societal experience of being male or female that is responsible.

Of particular note, while experiments are the gold standard for understanding cause and effect, a large proportion of psychology research is not experimental for a variety of practical and ethical reasons.

Key Takeaways

• Descriptive, correlational, and experimental research designs are used to collect and analyze data.
• Descriptive designs include case studies, surveys, psychological tests, naturalistic observation, and laboratory observation. The goal of these designs is to get a picture of the participants’ current thoughts, feelings, or behaviours.
• Correlational research designs measure the relationship between two or more variables. The variables may be presented on a scatterplot to visually show the relationships. The Pearson correlation coefficient is a measure of the strength of linear relationship between two variables. Correlations have three potential pathways for interpreting cause and effect.
• Experimental research involves the manipulation of an independent variable and the measurement of a dependent variable. Done correctly, experiments allow researchers to make conclusions about cause and effect. There are a number of criteria that must be met in experimental design. Not everything can be studied experimentally, and laboratory experiments may not replicate real-life conditions well.

Exercises and Critical Thinking

• There is a negative correlation between how close students sit to the front of the classroom and their final grade in the class. Explain some possible reasons for this.
• Imagine you are tasked with creating a survey of online habits of Canadian teenagers. What questions would you ask and why? How valid and reliable would your test be?
• Imagine a researcher wants to test the hypothesis that participating in psychotherapy will cause a decrease in reported anxiety. Describe the type of research design the investigator might use to draw this conclusion. What would be the independent and dependent variables in the research?

Image Attributions

Figure 2.2. This Might Be Me in a Few Years by Frank Kovalchek is used under a CC BY 2.0 license.

Figure 2.3. Used under a CC BY-NC-SA 4.0 license.

Figure 2.4. Used under a CC BY-NC-SA 4.0 license.

Anderson, C. A., & Dill, K. E. (2000). Video games and aggressive thoughts, feelings, and behavior in the laboratory and in life.  Journal of Personality and Social Psychology, 78 (4), 772–790.

Damasio, H., Grabowski, T., Frank, R., Galaburda, A. M., Damasio, A. R., Cacioppo, J. T., & Berntson, G. G. (2005). The return of Phineas Gage: Clues about the brain from the skull of a famous patient. In  Social neuroscience: Key readings (pp. 21–28). New York, NY: Psychology Press.

Freud, S. (1909/1964). Analysis of phobia in a five-year-old boy. In E. A. Southwell & M. Merbaum (Eds.),  Personality: Readings in theory and research (pp. 3–32). Belmont, CA: Wadsworth. (Original work published 1909)

Henrich, J., Heine, S. J., & Norenzaya, A. (2010). The weirdest people in the world? Behavioral and Brain Sciences, 33 , 61–83.

Kotowicz, Z. (2007). The strange case of Phineas Gage.  History of the Human Sciences, 20 (1), 115–131.

Rokeach, M. (1964).  The three Christs of Ypsilanti: A psychological study . New York, NY: Knopf.

Stangor, C. (2011). Research methods for the behavioral sciences (4th ed.) . Mountain View, CA: Cengage.

Psychology - 1st Canadian Edition Copyright © 2020 by Sally Walters is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

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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.

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Overview of the Scientific Method

11 Designing a Research Study

Learning objectives.

• Define the concept of a variable, distinguish quantitative from categorical variables, and give examples of variables that might be of interest to psychologists.
• Explain the difference between a population and a sample.
• Distinguish between experimental and non-experimental research.
• Distinguish between lab studies, field studies, and field experiments.

Identifying and Defining the Variables and Population

Variables and operational definitions.

Part of generating a hypothesis involves identifying the variables that you want to study and operationally defining those variables so that they can be measured. Research questions in psychology are about variables. A  variable  is a quantity or quality that varies across people or situations. For example, the height of the students enrolled in a university course is a variable because it varies from student to student. The chosen major of the students is also a variable as long as not everyone in the class has declared the same major. Almost everything in our world varies and as such thinking of examples of constants (things that don’t vary) is far more difficult. A rare example of a constant is the speed of light. Variables can be either quantitative or categorical. A  quantitative variable  is a quantity, such as height, that is typically measured by assigning a number to each individual. Other examples of quantitative variables include people’s level of talkativeness, how depressed they are, and the number of siblings they have. A categorical variable  is a quality, such as chosen major, and is typically measured by assigning a category label to each individual (e.g., Psychology, English, Nursing, etc.). Other examples include people’s nationality, their occupation, and whether they are receiving psychotherapy.

After the researcher generates their hypothesis and selects the variables they want to manipulate and measure, the researcher needs to find ways to actually measure the variables of interest. This requires an  operational definition —a definition of the variable in terms of precisely how it is to be measured. Most variables that researchers are interested in studying cannot be directly observed or measured and this poses a problem because empiricism (observation) is at the heart of the scientific method. Operationally defining a variable involves taking an abstract construct like depression that cannot be directly observed and transforming it into something that can be directly observed and measured. Most variables can be operationally defined in many different ways. For example, depression can be operationally defined as people’s scores on a paper-and-pencil depression scale such as the Beck Depression Inventory, the number of depressive symptoms they are experiencing, or whether they have been diagnosed with major depressive disorder. Researchers are wise to choose an operational definition that has been used extensively in the research literature.

Sampling and Measurement

In addition to identifying which variables to manipulate and measure, and operationally defining those variables, researchers need to identify the population of interest. Researchers in psychology are usually interested in drawing conclusions about some very large group of people. This is called the  population . It could be all American teenagers, children with autism, professional athletes, or even just human beings—depending on the interests and goals of the researcher. But they usually study only a small subset or  sample  of the population. For example, a researcher might measure the talkativeness of a few hundred university students with the intention of drawing conclusions about the talkativeness of men and women in general. It is important, therefore, for researchers to use a representative sample—one that is similar to the population in important respects.

One method of obtaining a sample is simple random sampling , in which every member of the population has an equal chance of being selected for the sample. For example, a pollster could start with a list of all the registered voters in a city (the population), randomly select 100 of them from the list (the sample), and ask those 100 whom they intend to vote for. Unfortunately, random sampling is difficult or impossible in most psychological research because the populations are less clearly defined than the registered voters in a city. How could a researcher give all American teenagers or all children with autism an equal chance of being selected for a sample? The most common alternative to random sampling is convenience sampling , in which the sample consists of individuals who happen to be nearby and willing to participate (such as introductory psychology students). Of course, the obvious problem with convenience sampling is that the sample might not be representative of the population and therefore it may be less appropriate to generalize the results from the sample to that population.

Experimental vs. Non-Experimental Research

The next step a researcher must take is to decide which type of approach they will use to collect the data. As you will learn in your research methods course there are many different approaches to research that can be divided in many different ways. One of the most fundamental distinctions is between experimental and non-experimental research.

Experimental Research

Researchers who want to test hypotheses about causal relationships between variables (i.e., their goal is to explain) need to use an experimental method. This is because the experimental method is the only method that allows us to determine causal relationships. Using the experimental approach, researchers first manipulate one or more variables while attempting to control extraneous variables, and then they measure how the manipulated variables affect participants’ responses.

The terms independent variable and dependent variable are used in the context of experimental research. The independent variable is the variable the experimenter manipulates (it is the presumed cause) and the dependent variable is the variable the experimenter measures (it is the presumed effect).

Extraneous variables  are any variable other than the dependent variable. Confounds are a specific type of extraneous variable that systematically varies along with the variables under investigation and therefore provides an alternative explanation for the results. When researchers design an experiment they need to ensure that they control for confounds; they need to ensure that extraneous variables don’t become confounding variables because in order to make a causal conclusion they need to make sure alternative explanations for the results have been ruled out.

As an example, if we manipulate the lighting in the room and examine the effects of that manipulation on workers’ productivity, then the lighting conditions (bright lights vs. dim lights) would be considered the independent variable and the workers’ productivity would be considered the dependent variable. If the bright lights are noisy then that noise would be a confound since the noise would be present whenever the lights are bright and the noise would be absent when the lights are dim. If noise is varying systematically with light then we wouldn’t know if a difference in worker productivity across the two lighting conditions is due to noise or light. So confounds are bad, they disrupt our ability to make causal conclusions about the nature of the relationship between variables. However, if there is noise in the room both when the lights are on and when the lights are off then noise is merely an extraneous variable (it is a variable other than the independent or dependent variable) and we don’t worry much about extraneous variables. This is because unless a variable varies systematically with the manipulated independent variable it cannot be a competing explanation for the results.

Non-Experimental Research

Researchers who are simply interested in describing characteristics of people, describing relationships between variables, and using those relationships to make predictions can use non-experimental research. Using the non-experimental approach, the researcher simply measures variables as they naturally occur, but they do not manipulate them. For instance, if I just measured the number of traffic fatalities in America last year that involved the use of a cell phone but I did not actually manipulate cell phone use then this would be categorized as non-experimental research. Alternatively, if I stood at a busy intersection and recorded drivers’ genders and whether or not they were using a cell phone when they passed through the intersection to see whether men or women are more likely to use a cell phone when driving, then this would be non-experimental research. It is important to point out that non-experimental does not mean nonscientific. Non-experimental research is scientific in nature. It can be used to fulfill two of the three goals of science (to describe and to predict). However, unlike with experimental research, we cannot make causal conclusions using this method; we cannot say that one variable causes another variable using this method.

Laboratory vs. Field Research

The next major distinction between research methods is between laboratory and field studies. A laboratory study is a study that is conducted in the laboratory environment. In contrast, a field study is a study that is conducted in the real-world, in a natural environment.

Laboratory experiments typically have high  internal validity . Internal validity refers to the degree to which we can confidently infer a causal relationship between variables. When we conduct an experimental study in a laboratory environment we have very high internal validity because we manipulate one variable while controlling all other outside extraneous variables. When we manipulate an independent variable and observe an effect on a dependent variable and we control for everything else so that the only difference between our experimental groups or conditions is the one manipulated variable then we can be quite confident that it is the independent variable that is causing the change in the dependent variable. In contrast, because field studies are conducted in the real-world, the experimenter typically has less control over the environment and potential extraneous variables, and this decreases internal validity, making it less appropriate to arrive at causal conclusions.

But there is typically a trade-off between internal and external validity. External validity simply refers to the degree to which we can generalize the findings to other circumstances or settings, like the real-world environment. When internal validity is high, external validity tends to be low; and when internal validity is low, external validity tends to be high. So laboratory studies are typically low in external validity, while field studies are typically high in external validity. Since field studies are conducted in the real-world environment it is far more appropriate to generalize the findings to that real-world environment than when the research is conducted in the more artificial sterile laboratory.

Finally, there are field studies which are non-experimental in nature because nothing is manipulated. But there are also field experiment s where an independent variable is manipulated in a natural setting and extraneous variables are controlled. Depending on their overall quality and the level of control of extraneous variables, such field experiments can have high external and high internal validity.

A quantity or quality that varies across people or situations.

A quantity, such as height, that is typically measured by assigning a number to each individual.

A variable that represents a characteristic of an individual, such as chosen major, and is typically measured by assigning each individual's response to one of several categories (e.g., Psychology, English, Nursing, Engineering, etc.).

A definition of the variable in terms of precisely how it is to be measured.

A large group of people about whom researchers in psychology are usually interested in drawing conclusions, and from whom the sample is drawn.

A smaller portion of the population the researcher would like to study.

A common method of non-probability sampling in which the sample consists of individuals who happen to be easily available and willing to participate (such as introductory psychology students).

The variable the experimenter manipulates.

The variable the experimenter measures (it is the presumed effect).

Any variable other than the dependent and independent variable.

A specific type of extraneous variable that systematically varies along with the variables under investigation and therefore provides an alternative explanation for the results.

A study that is conducted in the laboratory environment.

A study that is conducted in a "real world" environment outside the laboratory.

Refers to the degree to which we can confidently infer a causal relationship between variables.

Refers to the degree to which we can generalize the findings to other circumstances or settings, like the real-world environment.

A type of field study where an independent variable is manipulated in a natural setting and extraneous variables are controlled as much as possible.

Research Methods in Psychology Copyright © 2019 by Rajiv S. Jhangiani, I-Chant A. Chiang, Carrie Cuttler, & Dana C. Leighton is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

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2.2 Psychologists Use Descriptive, Correlational, and Experimental Research Designs to Understand Behavior

Learning objectives.

• Differentiate the goals of descriptive, correlational, and experimental research designs and explain the advantages and disadvantages of each.
• Explain the goals of descriptive research and the statistical techniques used to interpret it.
• Summarize the uses of correlational research and describe why correlational research cannot be used to infer causality.
• Review the procedures of experimental research and explain how it can be used to draw causal inferences.

Psychologists agree that if their ideas and theories about human behavior are to be taken seriously, they must be backed up by data. However, the research of different psychologists is designed with different goals in mind, and the different goals require different approaches. These varying approaches, summarized in Table 2.2 “Characteristics of the Three Research Designs” , are known as research designs . A research design is the specific method a researcher uses to collect, analyze, and interpret data . Psychologists use three major types of research designs in their research, and each provides an essential avenue for scientific investigation. Descriptive research is research designed to provide a snapshot of the current state of affairs . Correlational research is research designed to discover relationships among variables and to allow the prediction of future events from present knowledge . Experimental research is research in which initial equivalence among research participants in more than one group is created, followed by a manipulation of a given experience for these groups and a measurement of the influence of the manipulation . Each of the three research designs varies according to its strengths and limitations, and it is important to understand how each differs.

Table 2.2 Characteristics of the Three Research Designs

Stangor, C. (2011). Research methods for the behavioral sciences (4th ed.). Mountain View, CA: Cengage.

Descriptive Research: Assessing the Current State of Affairs

Descriptive research is designed to create a snapshot of the current thoughts, feelings, or behavior of individuals. This section reviews three types of descriptive research: case studies , surveys , and naturalistic observation .

Sometimes the data in a descriptive research project are based on only a small set of individuals, often only one person or a single small group. These research designs are known as case studies — descriptive records of one or more individual’s experiences and behavior . Sometimes case studies involve ordinary individuals, as when developmental psychologist Jean Piaget used his observation of his own children to develop his stage theory of cognitive development. More frequently, case studies are conducted on individuals who have unusual or abnormal experiences or characteristics or who find themselves in particularly difficult or stressful situations. The assumption is that by carefully studying individuals who are socially marginal, who are experiencing unusual situations, or who are going through a difficult phase in their lives, we can learn something about human nature.

Sigmund Freud was a master of using the psychological difficulties of individuals to draw conclusions about basic psychological processes. Freud wrote case studies of some of his most interesting patients and used these careful examinations to develop his important theories of personality. One classic example is Freud’s description of “Little Hans,” a child whose fear of horses the psychoanalyst interpreted in terms of repressed sexual impulses and the Oedipus complex (Freud (1909/1964).

Political polls reported in newspapers and on the Internet are descriptive research designs that provide snapshots of the likely voting behavior of a population.

Another well-known case study is Phineas Gage, a man whose thoughts and emotions were extensively studied by cognitive psychologists after a railroad spike was blasted through his skull in an accident. Although there is question about the interpretation of this case study (Kotowicz, 2007), it did provide early evidence that the brain’s frontal lobe is involved in emotion and morality (Damasio et al., 2005). An interesting example of a case study in clinical psychology is described by Rokeach (1964), who investigated in detail the beliefs and interactions among three patients with schizophrenia, all of whom were convinced they were Jesus Christ.

In other cases the data from descriptive research projects come in the form of a survey — a measure administered through either an interview or a written questionnaire to get a picture of the beliefs or behaviors of a sample of people of interest . The people chosen to participate in the research (known as the sample ) are selected to be representative of all the people that the researcher wishes to know about (the population ). In election polls, for instance, a sample is taken from the population of all “likely voters” in the upcoming elections.

The results of surveys may sometimes be rather mundane, such as “Nine out of ten doctors prefer Tymenocin,” or “The median income in Montgomery County is $36,712.” Yet other times (particularly in discussions of social behavior), the results can be shocking: “More than 40,000 people are killed by gunfire in the United States every year,” or “More than 60% of women between the ages of 50 and 60 suffer from depression.” Descriptive research is frequently used by psychologists to get an estimate of the prevalence (or incidence ) of psychological disorders.

A final type of descriptive research—known as naturalistic observation —is research based on the observation of everyday events . For instance, a developmental psychologist who watches children on a playground and describes what they say to each other while they play is conducting descriptive research, as is a biopsychologist who observes animals in their natural habitats. One example of observational research involves a systematic procedure known as the strange situation , used to get a picture of how adults and young children interact. The data that are collected in the strange situation are systematically coded in a coding sheet such as that shown in Table 2.3 “Sample Coding Form Used to Assess Child’s and Mother’s Behavior in the Strange Situation” .

Table 2.3 Sample Coding Form Used to Assess Child’s and Mother’s Behavior in the Strange Situation

The results of descriptive research projects are analyzed using descriptive statistics — numbers that summarize the distribution of scores on a measured variable . Most variables have distributions similar to that shown in Figure 2.5 “Height Distribution” , where most of the scores are located near the center of the distribution, and the distribution is symmetrical and bell-shaped. A data distribution that is shaped like a bell is known as a normal distribution .

Table 2.4 Height and Family Income for 25 Students

Figure 2.5 Height Distribution

The distribution of the heights of the students in a class will form a normal distribution. In this sample the mean ( M ) = 67.12 and the standard deviation ( s ) = 2.74.

A distribution can be described in terms of its central tendency —that is, the point in the distribution around which the data are centered—and its dispersion , or spread. The arithmetic average, or arithmetic mean , is the most commonly used measure of central tendency . It is computed by calculating the sum of all the scores of the variable and dividing this sum by the number of participants in the distribution (denoted by the letter N ). In the data presented in Figure 2.5 “Height Distribution” , the mean height of the students is 67.12 inches. The sample mean is usually indicated by the letter M .

In some cases, however, the data distribution is not symmetrical. This occurs when there are one or more extreme scores (known as outliers ) at one end of the distribution. Consider, for instance, the variable of family income (see Figure 2.6 “Family Income Distribution” ), which includes an outlier (a value of $3,800,000). In this case the mean is not a good measure of central tendency. Although it appears from Figure 2.6 “Family Income Distribution” that the central tendency of the family income variable should be around $70,000, the mean family income is actually $223,960. The single very extreme income has a disproportionate impact on the mean, resulting in a value that does not well represent the central tendency.

The median is used as an alternative measure of central tendency when distributions are not symmetrical. The median is the score in the center of the distribution, meaning that 50% of the scores are greater than the median and 50% of the scores are less than the median . In our case, the median household income ($73,000) is a much better indication of central tendency than is the mean household income ($223,960).

Figure 2.6 Family Income Distribution

The distribution of family incomes is likely to be nonsymmetrical because some incomes can be very large in comparison to most incomes. In this case the median or the mode is a better indicator of central tendency than is the mean.

A final measure of central tendency, known as the mode , represents the value that occurs most frequently in the distribution . You can see from Figure 2.6 “Family Income Distribution” that the mode for the family income variable is $93,000 (it occurs four times).

In addition to summarizing the central tendency of a distribution, descriptive statistics convey information about how the scores of the variable are spread around the central tendency. Dispersion refers to the extent to which the scores are all tightly clustered around the central tendency, like this:

Or they may be more spread out away from it, like this:

One simple measure of dispersion is to find the largest (the maximum ) and the smallest (the minimum ) observed values of the variable and to compute the range of the variable as the maximum observed score minus the minimum observed score. You can check that the range of the height variable in Figure 2.5 “Height Distribution” is 72 – 62 = 10. The standard deviation , symbolized as s , is the most commonly used measure of dispersion . Distributions with a larger standard deviation have more spread. The standard deviation of the height variable is s = 2.74, and the standard deviation of the family income variable is s = $745,337.

An advantage of descriptive research is that it attempts to capture the complexity of everyday behavior. Case studies provide detailed information about a single person or a small group of people, surveys capture the thoughts or reported behaviors of a large population of people, and naturalistic observation objectively records the behavior of people or animals as it occurs naturally. Thus descriptive research is used to provide a relatively complete understanding of what is currently happening.

Despite these advantages, descriptive research has a distinct disadvantage in that, although it allows us to get an idea of what is currently happening, it is usually limited to static pictures. Although descriptions of particular experiences may be interesting, they are not always transferable to other individuals in other situations, nor do they tell us exactly why specific behaviors or events occurred. For instance, descriptions of individuals who have suffered a stressful event, such as a war or an earthquake, can be used to understand the individuals’ reactions to the event but cannot tell us anything about the long-term effects of the stress. And because there is no comparison group that did not experience the stressful situation, we cannot know what these individuals would be like if they hadn’t had the stressful experience.

Correlational Research: Seeking Relationships Among Variables

In contrast to descriptive research, which is designed primarily to provide static pictures, correlational research involves the measurement of two or more relevant variables and an assessment of the relationship between or among those variables. For instance, the variables of height and weight are systematically related (correlated) because taller people generally weigh more than shorter people. In the same way, study time and memory errors are also related, because the more time a person is given to study a list of words, the fewer errors he or she will make. When there are two variables in the research design, one of them is called the predictor variable and the other the outcome variable . The research design can be visualized like this, where the curved arrow represents the expected correlation between the two variables:

Figure 2.2.2

One way of organizing the data from a correlational study with two variables is to graph the values of each of the measured variables using a scatter plot . As you can see in Figure 2.10 “Examples of Scatter Plots” , a scatter plot is a visual image of the relationship between two variables . A point is plotted for each individual at the intersection of his or her scores for the two variables. When the association between the variables on the scatter plot can be easily approximated with a straight line, as in parts (a) and (b) of Figure 2.10 “Examples of Scatter Plots” , the variables are said to have a linear relationship .

When the straight line indicates that individuals who have above-average values for one variable also tend to have above-average values for the other variable, as in part (a), the relationship is said to be positive linear . Examples of positive linear relationships include those between height and weight, between education and income, and between age and mathematical abilities in children. In each case people who score higher on one of the variables also tend to score higher on the other variable. Negative linear relationships , in contrast, as shown in part (b), occur when above-average values for one variable tend to be associated with below-average values for the other variable. Examples of negative linear relationships include those between the age of a child and the number of diapers the child uses, and between practice on and errors made on a learning task. In these cases people who score higher on one of the variables tend to score lower on the other variable.

Relationships between variables that cannot be described with a straight line are known as nonlinear relationships . Part (c) of Figure 2.10 “Examples of Scatter Plots” shows a common pattern in which the distribution of the points is essentially random. In this case there is no relationship at all between the two variables, and they are said to be independent . Parts (d) and (e) of Figure 2.10 “Examples of Scatter Plots” show patterns of association in which, although there is an association, the points are not well described by a single straight line. For instance, part (d) shows the type of relationship that frequently occurs between anxiety and performance. Increases in anxiety from low to moderate levels are associated with performance increases, whereas increases in anxiety from moderate to high levels are associated with decreases in performance. Relationships that change in direction and thus are not described by a single straight line are called curvilinear relationships .

Figure 2.10 Examples of Scatter Plots

Some examples of relationships between two variables as shown in scatter plots. Note that the Pearson correlation coefficient ( r ) between variables that have curvilinear relationships will likely be close to zero.

Adapted from Stangor, C. (2011). Research methods for the behavioral sciences (4th ed.). Mountain View, CA: Cengage.

The most common statistical measure of the strength of linear relationships among variables is the Pearson correlation coefficient , which is symbolized by the letter r . The value of the correlation coefficient ranges from r = –1.00 to r = +1.00. The direction of the linear relationship is indicated by the sign of the correlation coefficient. Positive values of r (such as r = .54 or r = .67) indicate that the relationship is positive linear (i.e., the pattern of the dots on the scatter plot runs from the lower left to the upper right), whereas negative values of r (such as r = –.30 or r = –.72) indicate negative linear relationships (i.e., the dots run from the upper left to the lower right). The strength of the linear relationship is indexed by the distance of the correlation coefficient from zero (its absolute value). For instance, r = –.54 is a stronger relationship than r = .30, and r = .72 is a stronger relationship than r = –.57. Because the Pearson correlation coefficient only measures linear relationships, variables that have curvilinear relationships are not well described by r , and the observed correlation will be close to zero.

It is also possible to study relationships among more than two measures at the same time. A research design in which more than one predictor variable is used to predict a single outcome variable is analyzed through multiple regression (Aiken & West, 1991). Multiple regression is a statistical technique, based on correlation coefficients among variables, that allows predicting a single outcome variable from more than one predictor variable . For instance, Figure 2.11 “Prediction of Job Performance From Three Predictor Variables” shows a multiple regression analysis in which three predictor variables are used to predict a single outcome. The use of multiple regression analysis shows an important advantage of correlational research designs—they can be used to make predictions about a person’s likely score on an outcome variable (e.g., job performance) based on knowledge of other variables.

Figure 2.11 Prediction of Job Performance From Three Predictor Variables

Multiple regression allows scientists to predict the scores on a single outcome variable using more than one predictor variable.

An important limitation of correlational research designs is that they cannot be used to draw conclusions about the causal relationships among the measured variables. Consider, for instance, a researcher who has hypothesized that viewing violent behavior will cause increased aggressive play in children. He has collected, from a sample of fourth-grade children, a measure of how many violent television shows each child views during the week, as well as a measure of how aggressively each child plays on the school playground. From his collected data, the researcher discovers a positive correlation between the two measured variables.

Although this positive correlation appears to support the researcher’s hypothesis, it cannot be taken to indicate that viewing violent television causes aggressive behavior. Although the researcher is tempted to assume that viewing violent television causes aggressive play,

there are other possibilities. One alternate possibility is that the causal direction is exactly opposite from what has been hypothesized. Perhaps children who have behaved aggressively at school develop residual excitement that leads them to want to watch violent television shows at home:

Although this possibility may seem less likely, there is no way to rule out the possibility of such reverse causation on the basis of this observed correlation. It is also possible that both causal directions are operating and that the two variables cause each other:

Still another possible explanation for the observed correlation is that it has been produced by the presence of a common-causal variable (also known as a third variable ). A common-causal variable is a variable that is not part of the research hypothesis but that causes both the predictor and the outcome variable and thus produces the observed correlation between them . In our example a potential common-causal variable is the discipline style of the children’s parents. Parents who use a harsh and punitive discipline style may produce children who both like to watch violent television and who behave aggressively in comparison to children whose parents use less harsh discipline:

In this case, television viewing and aggressive play would be positively correlated (as indicated by the curved arrow between them), even though neither one caused the other but they were both caused by the discipline style of the parents (the straight arrows). When the predictor and outcome variables are both caused by a common-causal variable, the observed relationship between them is said to be spurious . A spurious relationship is a relationship between two variables in which a common-causal variable produces and “explains away” the relationship . If effects of the common-causal variable were taken away, or controlled for, the relationship between the predictor and outcome variables would disappear. In the example the relationship between aggression and television viewing might be spurious because by controlling for the effect of the parents’ disciplining style, the relationship between television viewing and aggressive behavior might go away.

Common-causal variables in correlational research designs can be thought of as “mystery” variables because, as they have not been measured, their presence and identity are usually unknown to the researcher. Since it is not possible to measure every variable that could cause both the predictor and outcome variables, the existence of an unknown common-causal variable is always a possibility. For this reason, we are left with the basic limitation of correlational research: Correlation does not demonstrate causation. It is important that when you read about correlational research projects, you keep in mind the possibility of spurious relationships, and be sure to interpret the findings appropriately. Although correlational research is sometimes reported as demonstrating causality without any mention being made of the possibility of reverse causation or common-causal variables, informed consumers of research, like you, are aware of these interpretational problems.

In sum, correlational research designs have both strengths and limitations. One strength is that they can be used when experimental research is not possible because the predictor variables cannot be manipulated. Correlational designs also have the advantage of allowing the researcher to study behavior as it occurs in everyday life. And we can also use correlational designs to make predictions—for instance, to predict from the scores on their battery of tests the success of job trainees during a training session. But we cannot use such correlational information to determine whether the training caused better job performance. For that, researchers rely on experiments.

Experimental Research: Understanding the Causes of Behavior

The goal of experimental research design is to provide more definitive conclusions about the causal relationships among the variables in the research hypothesis than is available from correlational designs. In an experimental research design, the variables of interest are called the independent variable (or variables ) and the dependent variable . The independent variable in an experiment is the causing variable that is created (manipulated) by the experimenter . The dependent variable in an experiment is a measured variable that is expected to be influenced by the experimental manipulation . The research hypothesis suggests that the manipulated independent variable or variables will cause changes in the measured dependent variables. We can diagram the research hypothesis by using an arrow that points in one direction. This demonstrates the expected direction of causality:

Figure 2.2.3

Research Focus: Video Games and Aggression

Consider an experiment conducted by Anderson and Dill (2000). The study was designed to test the hypothesis that viewing violent video games would increase aggressive behavior. In this research, male and female undergraduates from Iowa State University were given a chance to play with either a violent video game (Wolfenstein 3D) or a nonviolent video game (Myst). During the experimental session, the participants played their assigned video games for 15 minutes. Then, after the play, each participant played a competitive game with an opponent in which the participant could deliver blasts of white noise through the earphones of the opponent. The operational definition of the dependent variable (aggressive behavior) was the level and duration of noise delivered to the opponent. The design of the experiment is shown in Figure 2.17 “An Experimental Research Design” .

Figure 2.17 An Experimental Research Design

Two advantages of the experimental research design are (1) the assurance that the independent variable (also known as the experimental manipulation) occurs prior to the measured dependent variable, and (2) the creation of initial equivalence between the conditions of the experiment (in this case by using random assignment to conditions).

Experimental designs have two very nice features. For one, they guarantee that the independent variable occurs prior to the measurement of the dependent variable. This eliminates the possibility of reverse causation. Second, the influence of common-causal variables is controlled, and thus eliminated, by creating initial equivalence among the participants in each of the experimental conditions before the manipulation occurs.

The most common method of creating equivalence among the experimental conditions is through random assignment to conditions , a procedure in which the condition that each participant is assigned to is determined through a random process, such as drawing numbers out of an envelope or using a random number table . Anderson and Dill first randomly assigned about 100 participants to each of their two groups (Group A and Group B). Because they used random assignment to conditions, they could be confident that, before the experimental manipulation occurred, the students in Group A were, on average, equivalent to the students in Group B on every possible variable, including variables that are likely to be related to aggression, such as parental discipline style, peer relationships, hormone levels, diet—and in fact everything else.

Then, after they had created initial equivalence, Anderson and Dill created the experimental manipulation—they had the participants in Group A play the violent game and the participants in Group B play the nonviolent game. Then they compared the dependent variable (the white noise blasts) between the two groups, finding that the students who had viewed the violent video game gave significantly longer noise blasts than did the students who had played the nonviolent game.

Anderson and Dill had from the outset created initial equivalence between the groups. This initial equivalence allowed them to observe differences in the white noise levels between the two groups after the experimental manipulation, leading to the conclusion that it was the independent variable (and not some other variable) that caused these differences. The idea is that the only thing that was different between the students in the two groups was the video game they had played.

Despite the advantage of determining causation, experiments do have limitations. One is that they are often conducted in laboratory situations rather than in the everyday lives of people. Therefore, we do not know whether results that we find in a laboratory setting will necessarily hold up in everyday life. Second, and more important, is that some of the most interesting and key social variables cannot be experimentally manipulated. If we want to study the influence of the size of a mob on the destructiveness of its behavior, or to compare the personality characteristics of people who join suicide cults with those of people who do not join such cults, these relationships must be assessed using correlational designs, because it is simply not possible to experimentally manipulate these variables.

Key Takeaways

• Descriptive, correlational, and experimental research designs are used to collect and analyze data.
• Descriptive designs include case studies, surveys, and naturalistic observation. The goal of these designs is to get a picture of the current thoughts, feelings, or behaviors in a given group of people. Descriptive research is summarized using descriptive statistics.
• Correlational research designs measure two or more relevant variables and assess a relationship between or among them. The variables may be presented on a scatter plot to visually show the relationships. The Pearson Correlation Coefficient ( r ) is a measure of the strength of linear relationship between two variables.
• Common-causal variables may cause both the predictor and outcome variable in a correlational design, producing a spurious relationship. The possibility of common-causal variables makes it impossible to draw causal conclusions from correlational research designs.
• Experimental research involves the manipulation of an independent variable and the measurement of a dependent variable. Random assignment to conditions is normally used to create initial equivalence between the groups, allowing researchers to draw causal conclusions.

Exercises and Critical Thinking

• There is a negative correlation between the row that a student sits in in a large class (when the rows are numbered from front to back) and his or her final grade in the class. Do you think this represents a causal relationship or a spurious relationship, and why?
• Think of two variables (other than those mentioned in this book) that are likely to be correlated, but in which the correlation is probably spurious. What is the likely common-causal variable that is producing the relationship?
• Imagine a researcher wants to test the hypothesis that participating in psychotherapy will cause a decrease in reported anxiety. Describe the type of research design the investigator might use to draw this conclusion. What would be the independent and dependent variables in the research?

Aiken, L., & West, S. (1991). Multiple regression: Testing and interpreting interactions . Newbury Park, CA: Sage.

Ainsworth, M. S., Blehar, M. C., Waters, E., & Wall, S. (1978). Patterns of attachment: A psychological study of the strange situation . Hillsdale, NJ: Lawrence Erlbaum Associates.

Anderson, C. A., & Dill, K. E. (2000). Video games and aggressive thoughts, feelings, and behavior in the laboratory and in life. Journal of Personality and Social Psychology, 78 (4), 772–790.

Damasio, H., Grabowski, T., Frank, R., Galaburda, A. M., Damasio, A. R., Cacioppo, J. T., & Berntson, G. G. (2005). The return of Phineas Gage: Clues about the brain from the skull of a famous patient. In Social neuroscience: Key readings. (pp. 21–28). New York, NY: Psychology Press.

Freud, S. (1964). Analysis of phobia in a five-year-old boy. In E. A. Southwell & M. Merbaum (Eds.), Personality: Readings in theory and research (pp. 3–32). Belmont, CA: Wadsworth. (Original work published 1909)

Kotowicz, Z. (2007). The strange case of Phineas Gage. History of the Human Sciences, 20 (1), 115–131.

Rokeach, M. (1964). The three Christs of Ypsilanti: A psychological study . New York, NY: Knopf.

Introduction to Psychology Copyright © 2015 by University of Minnesota is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

5.2 Experimental Design

Learning objectives.

• Explain the difference between between-subjects and within-subjects experiments, list some of the pros and cons of each approach, and decide which approach to use to answer a particular research question.
• Define random assignment, distinguish it from random sampling, explain its purpose in experimental research, and use some simple strategies to implement it
• Define several types of carryover effect, give examples of each, and explain how counterbalancing helps to deal with them.

In this section, we look at some different ways to design an experiment. The primary distinction we will make is between approaches in which each participant experiences one level of the independent variable and approaches in which each participant experiences all levels of the independent variable. The former are called between-subjects experiments and the latter are called within-subjects experiments.

Between-Subjects Experiments

In a  between-subjects experiment , each participant is tested in only one condition. For example, a researcher with a sample of 100 university  students might assign half of them to write about a traumatic event and the other half write about a neutral event. Or a researcher with a sample of 60 people with severe agoraphobia (fear of open spaces) might assign 20 of them to receive each of three different treatments for that disorder. It is essential in a between-subjects experiment that the researcher assigns participants to conditions so that the different groups are, on average, highly similar to each other. Those in a trauma condition and a neutral condition, for example, should include a similar proportion of men and women, and they should have similar average intelligence quotients (IQs), similar average levels of motivation, similar average numbers of health problems, and so on. This matching is a matter of controlling these extraneous participant variables across conditions so that they do not become confounding variables.

Random Assignment

The primary way that researchers accomplish this kind of control of extraneous variables across conditions is called  random assignment , which means using a random process to decide which participants are tested in which conditions. Do not confuse random assignment with random sampling. Random sampling is a method for selecting a sample from a population, and it is rarely used in psychological research. Random assignment is a method for assigning participants in a sample to the different conditions, and it is an important element of all experimental research in psychology and other fields too.

In its strictest sense, random assignment should meet two criteria. One is that each participant has an equal chance of being assigned to each condition (e.g., a 50% chance of being assigned to each of two conditions). The second is that each participant is assigned to a condition independently of other participants. Thus one way to assign participants to two conditions would be to flip a coin for each one. If the coin lands heads, the participant is assigned to Condition A, and if it lands tails, the participant is assigned to Condition B. For three conditions, one could use a computer to generate a random integer from 1 to 3 for each participant. If the integer is 1, the participant is assigned to Condition A; if it is 2, the participant is assigned to Condition B; and if it is 3, the participant is assigned to Condition C. In practice, a full sequence of conditions—one for each participant expected to be in the experiment—is usually created ahead of time, and each new participant is assigned to the next condition in the sequence as he or she is tested. When the procedure is computerized, the computer program often handles the random assignment.

One problem with coin flipping and other strict procedures for random assignment is that they are likely to result in unequal sample sizes in the different conditions. Unequal sample sizes are generally not a serious problem, and you should never throw away data you have already collected to achieve equal sample sizes. However, for a fixed number of participants, it is statistically most efficient to divide them into equal-sized groups. It is standard practice, therefore, to use a kind of modified random assignment that keeps the number of participants in each group as similar as possible. One approach is block randomization . In block randomization, all the conditions occur once in the sequence before any of them is repeated. Then they all occur again before any of them is repeated again. Within each of these “blocks,” the conditions occur in a random order. Again, the sequence of conditions is usually generated before any participants are tested, and each new participant is assigned to the next condition in the sequence.  Table 5.2  shows such a sequence for assigning nine participants to three conditions. The Research Randomizer website ( http://www.randomizer.org ) will generate block randomization sequences for any number of participants and conditions. Again, when the procedure is computerized, the computer program often handles the block randomization.

Random assignment is not guaranteed to control all extraneous variables across conditions. The process is random, so it is always possible that just by chance, the participants in one condition might turn out to be substantially older, less tired, more motivated, or less depressed on average than the participants in another condition. However, there are some reasons that this possibility is not a major concern. One is that random assignment works better than one might expect, especially for large samples. Another is that the inferential statistics that researchers use to decide whether a difference between groups reflects a difference in the population takes the “fallibility” of random assignment into account. Yet another reason is that even if random assignment does result in a confounding variable and therefore produces misleading results, this confound is likely to be detected when the experiment is replicated. The upshot is that random assignment to conditions—although not infallible in terms of controlling extraneous variables—is always considered a strength of a research design.

Matched Groups

An alternative to simple random assignment of participants to conditions is the use of a matched-groups design . Using this design, participants in the various conditions are matched on the dependent variable or on some extraneous variable(s) prior the manipulation of the independent variable. This guarantees that these variables will not be confounded across the experimental conditions. For instance, if we want to determine whether expressive writing affects people’s health then we could start by measuring various health-related variables in our prospective research participants. We could then use that information to rank-order participants according to how healthy or unhealthy they are. Next, the two healthiest participants would be randomly assigned to complete different conditions (one would be randomly assigned to the traumatic experiences writing condition and the other to the neutral writing condition). The next two healthiest participants would then be randomly assigned to complete different conditions, and so on until the two least healthy participants. This method would ensure that participants in the traumatic experiences writing condition are matched to participants in the neutral writing condition with respect to health at the beginning of the study. If at the end of the experiment, a difference in health was detected across the two conditions, then we would know that it is due to the writing manipulation and not to pre-existing differences in health.

Within-Subjects Experiments

In a  within-subjects experiment , each participant is tested under all conditions. Consider an experiment on the effect of a defendant’s physical attractiveness on judgments of his guilt. Again, in a between-subjects experiment, one group of participants would be shown an attractive defendant and asked to judge his guilt, and another group of participants would be shown an unattractive defendant and asked to judge his guilt. In a within-subjects experiment, however, the same group of participants would judge the guilt of both an attractive  and  an unattractive defendant.

The primary advantage of this approach is that it provides maximum control of extraneous participant variables. Participants in all conditions have the same mean IQ, same socioeconomic status, same number of siblings, and so on—because they are the very same people. Within-subjects experiments also make it possible to use statistical procedures that remove the effect of these extraneous participant variables on the dependent variable and therefore make the data less “noisy” and the effect of the independent variable easier to detect. We will look more closely at this idea later in the book .  However, not all experiments can use a within-subjects design nor would it be desirable to do so.

One disadvantage of within-subjects experiments is that they make it easier for participants to guess the hypothesis. For example, a participant who is asked to judge the guilt of an attractive defendant and then is asked to judge the guilt of an unattractive defendant is likely to guess that the hypothesis is that defendant attractiveness affects judgments of guilt. This  knowledge could  lead the participant to judge the unattractive defendant more harshly because he thinks this is what he is expected to do. Or it could make participants judge the two defendants similarly in an effort to be “fair.”

Carryover Effects and Counterbalancing

The primary disadvantage of within-subjects designs is that they can result in order effects. An order effect  occurs when participants’ responses in the various conditions are affected by the order of conditions to which they were exposed. One type of order effect is a carryover effect. A  carryover effect  is an effect of being tested in one condition on participants’ behavior in later conditions. One type of carryover effect is a  practice effect , where participants perform a task better in later conditions because they have had a chance to practice it. Another type is a fatigue effect , where participants perform a task worse in later conditions because they become tired or bored. Being tested in one condition can also change how participants perceive stimuli or interpret their task in later conditions. This  type of effect is called a  context effect (or contrast effect) . For example, an average-looking defendant might be judged more harshly when participants have just judged an attractive defendant than when they have just judged an unattractive defendant. Within-subjects experiments also make it easier for participants to guess the hypothesis. For example, a participant who is asked to judge the guilt of an attractive defendant and then is asked to judge the guilt of an unattractive defendant is likely to guess that the hypothesis is that defendant attractiveness affects judgments of guilt. 

Carryover effects can be interesting in their own right. (Does the attractiveness of one person depend on the attractiveness of other people that we have seen recently?) But when they are not the focus of the research, carryover effects can be problematic. Imagine, for example, that participants judge the guilt of an attractive defendant and then judge the guilt of an unattractive defendant. If they judge the unattractive defendant more harshly, this might be because of his unattractiveness. But it could be instead that they judge him more harshly because they are becoming bored or tired. In other words, the order of the conditions is a confounding variable. The attractive condition is always the first condition and the unattractive condition the second. Thus any difference between the conditions in terms of the dependent variable could be caused by the order of the conditions and not the independent variable itself.

There is a solution to the problem of order effects, however, that can be used in many situations. It is  counterbalancing , which means testing different participants in different orders. The best method of counterbalancing is complete counterbalancing  in which an equal number of participants complete each possible order of conditions. For example, half of the participants would be tested in the attractive defendant condition followed by the unattractive defendant condition, and others half would be tested in the unattractive condition followed by the attractive condition. With three conditions, there would be six different orders (ABC, ACB, BAC, BCA, CAB, and CBA), so some participants would be tested in each of the six orders. With four conditions, there would be 24 different orders; with five conditions there would be 120 possible orders. With counterbalancing, participants are assigned to orders randomly, using the techniques we have already discussed. Thus, random assignment plays an important role in within-subjects designs just as in between-subjects designs. Here, instead of randomly assigning to conditions, they are randomly assigned to different orders of conditions. In fact, it can safely be said that if a study does not involve random assignment in one form or another, it is not an experiment.

A more efficient way of counterbalancing is through a Latin square design which randomizes through having equal rows and columns. For example, if you have four treatments, you must have four versions. Like a Sudoku puzzle, no treatment can repeat in a row or column. For four versions of four treatments, the Latin square design would look like:

You can see in the diagram above that the square has been constructed to ensure that each condition appears at each ordinal position (A appears first once, second once, third once, and fourth once) and each condition preceded and follows each other condition one time. A Latin square for an experiment with 6 conditions would by 6 x 6 in dimension, one for an experiment with 8 conditions would be 8 x 8 in dimension, and so on. So while complete counterbalancing of 6 conditions would require 720 orders, a Latin square would only require 6 orders.

Finally, when the number of conditions is large experiments can use  random counterbalancing  in which the order of the conditions is randomly determined for each participant. Using this technique every possible order of conditions is determined and then one of these orders is randomly selected for each participant. This is not as powerful a technique as complete counterbalancing or partial counterbalancing using a Latin squares design. Use of random counterbalancing will result in more random error, but if order effects are likely to be small and the number of conditions is large, this is an option available to researchers.

There are two ways to think about what counterbalancing accomplishes. One is that it controls the order of conditions so that it is no longer a confounding variable. Instead of the attractive condition always being first and the unattractive condition always being second, the attractive condition comes first for some participants and second for others. Likewise, the unattractive condition comes first for some participants and second for others. Thus any overall difference in the dependent variable between the two conditions cannot have been caused by the order of conditions. A second way to think about what counterbalancing accomplishes is that if there are carryover effects, it makes it possible to detect them. One can analyze the data separately for each order to see whether it had an effect.

When 9 Is “Larger” Than 221

Researcher Michael Birnbaum has argued that the  lack  of context provided by between-subjects designs is often a bigger problem than the context effects created by within-subjects designs. To demonstrate this problem, he asked participants to rate two numbers on how large they were on a scale of 1-to-10 where 1 was “very very small” and 10 was “very very large”.  One group of participants were asked to rate the number 9 and another group was asked to rate the number 221 (Birnbaum, 1999) [1] . Participants in this between-subjects design gave the number 9 a mean rating of 5.13 and the number 221 a mean rating of 3.10. In other words, they rated 9 as larger than 221! According to Birnbaum, this  difference  is because participants spontaneously compared 9 with other one-digit numbers (in which case it is  relatively large) and compared 221 with other three-digit numbers (in which case it is relatively  small).

Simultaneous Within-Subjects Designs

So far, we have discussed an approach to within-subjects designs in which participants are tested in one condition at a time. There is another approach, however, that is often used when participants make multiple responses in each condition. Imagine, for example, that participants judge the guilt of 10 attractive defendants and 10 unattractive defendants. Instead of having people make judgments about all 10 defendants of one type followed by all 10 defendants of the other type, the researcher could present all 20 defendants in a sequence that mixed the two types. The researcher could then compute each participant’s mean rating for each type of defendant. Or imagine an experiment designed to see whether people with social anxiety disorder remember negative adjectives (e.g., “stupid,” “incompetent”) better than positive ones (e.g., “happy,” “productive”). The researcher could have participants study a single list that includes both kinds of words and then have them try to recall as many words as possible. The researcher could then count the number of each type of word that was recalled. 

Between-Subjects or Within-Subjects?

Almost every experiment can be conducted using either a between-subjects design or a within-subjects design. This possibility means that researchers must choose between the two approaches based on their relative merits for the particular situation.

Between-subjects experiments have the advantage of being conceptually simpler and requiring less testing time per participant. They also avoid carryover effects without the need for counterbalancing. Within-subjects experiments have the advantage of controlling extraneous participant variables, which generally reduces noise in the data and makes it easier to detect a relationship between the independent and dependent variables.

A good rule of thumb, then, is that if it is possible to conduct a within-subjects experiment (with proper counterbalancing) in the time that is available per participant—and you have no serious concerns about carryover effects—this design is probably the best option. If a within-subjects design would be difficult or impossible to carry out, then you should consider a between-subjects design instead. For example, if you were testing participants in a doctor’s waiting room or shoppers in line at a grocery store, you might not have enough time to test each participant in all conditions and therefore would opt for a between-subjects design. Or imagine you were trying to reduce people’s level of prejudice by having them interact with someone of another race. A within-subjects design with counterbalancing would require testing some participants in the treatment condition first and then in a control condition. But if the treatment works and reduces people’s level of prejudice, then they would no longer be suitable for testing in the control condition. This difficulty is true for many designs that involve a treatment meant to produce long-term change in participants’ behavior (e.g., studies testing the effectiveness of psychotherapy). Clearly, a between-subjects design would be necessary here.

Remember also that using one type of design does not preclude using the other type in a different study. There is no reason that a researcher could not use both a between-subjects design and a within-subjects design to answer the same research question. In fact, professional researchers often take exactly this type of mixed methods approach.

Key Takeaways

• Experiments can be conducted using either between-subjects or within-subjects designs. Deciding which to use in a particular situation requires careful consideration of the pros and cons of each approach.
• Random assignment to conditions in between-subjects experiments or counterbalancing of orders of conditions in within-subjects experiments is a fundamental element of experimental research. The purpose of these techniques is to control extraneous variables so that they do not become confounding variables.
• You want to test the relative effectiveness of two training programs for running a marathon.
• Using photographs of people as stimuli, you want to see if smiling people are perceived as more intelligent than people who are not smiling.
• In a field experiment, you want to see if the way a panhandler is dressed (neatly vs. sloppily) affects whether or not passersby give him any money.
• You want to see if concrete nouns (e.g.,  dog ) are recalled better than abstract nouns (e.g.,  truth).
• Birnbaum, M.H. (1999). How to show that 9>221: Collect judgments in a between-subjects design. Psychological Methods, 4 (3), 243-249. ↵

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What Is a Factorial Design? Definition and Examples

Categories Dictionary

A factorial design is a type of experiment that involves manipulating two or more variables. While simple psychology experiments look at how one independent variable affects one dependent variable, researchers often want to know more about the effects of multiple independent variables.

Table of Contents

How a Factorial Design Works

Let’s take a closer look at how a factorial design might work in a psychology experiment:

• The independent variable is the variable of interest that the experimenter will manipulate.
• The dependent variable is the variable that the researcher then measures.

By doing this, psychologists can see if changing the independent variable results in some type of change in the dependent variable.

For example, imagine that a researcher wants to do an experiment looking at whether sleep deprivation hurts reaction times during a driving test. If she were only to perform the experiment using these variables–the sleep deprivation being the independent variable and the performance on the driving test being the dependent variable–it would be an example of a simple experiment.

However, let’s imagine that she is also interested in learning if sleep deprivation impacts the driving abilities of men and women differently. She has just added a second independent variable of interest (sex of the driver) into her study, which now makes it a factorial design.

Types of Factorial Designs

One common type of experiment is known as a 2×2 factorial design. In this type of study, there are two factors (or independent variables), each with two levels.

The number of digits tells you how many independent variables (IVs) there are in an experiment, while the value of each number tells you how many levels there are for each independent variable.

So, for example, a 4×3 factorial design would involve two independent variables with four levels for one IV and three levels for the other IV.

Advantages of a Factorial Design

One of the big advantages of factorial designs is that they allow researchers to look for interactions between independent variables.

An interaction is a result in which the effects of one experimental manipulation depends upon the experimental manipulation of another independent variable.

Example of a Factorial Design

For example, imagine that researchers want to test the effects of a memory-enhancing drug. Participants are given one of three different drug doses, and then asked to either complete a simple or complex memory task.

The researchers note that the effects of the memory drug are more pronounced with the simple memory tasks, but not as apparent when it comes to the complex tasks. In this 3×2 factorial design, there is an interaction effect between the drug dosage and the complexity of the memory task.

Understanding Variable Effects in Factorial Designs

So if researchers are manipulating two or more independent variables, how exactly do they know which effects are linked to which variables?

“It is true that when two manipulations are operating simultaneously, it is impossible to disentangle their effects completely,” explain authors Breckler, Olson, and Wiggins in their book Social Psychology Alive .

“Nevertheless, the researchers can explore the effects of each independent variable separately by averaging across all levels of the other independent variable . This procedure is called looking at the main effect.”

Examples of Factorial Designs

A university wants to assess the starting salaries of their MBA graduates. The study looks at graduates working in four different employment areas: accounting, management, finance, and marketing.

In addition to looking at the employment sector, the researchers also look at gender. In this example, the employment sector and gender of the graduates are the independent variables, and the starting salaries are the dependent variables. This would be considered a 4×2 factorial design.

Researchers want to determine how the amount of sleep a person gets the night before an exam impacts performance on a math test the next day. But the experimenters also know that many people like to have a cup of coffee (or two) in the morning to help them get going.

So, the researchers decided to look at how the amount of sleep and caffeine influence test performance. 

The researchers then decided to look at three levels of sleep (4 hours, 6 hours, and 8 hours) and only two levels of caffeine consumption (2 cups versus no coffee). In this case, the study is a 3×2 factorial design.

Baker TB, Smith SS, Bolt DM, et al. Implementing clinical research using factorial designs: A primer .  Behav Ther . 2017;48(4):567-580. doi:10.1016/j.beth.2016.12.005

Collins LM, Dziak JJ, Li R. Design of experiments with multiple independent variables: a resource management perspective on complete and reduced factorial designs .  Psychol Methods . 2009;14(3):202-224. doi:10.1037/a0015826

Haerling Adamson K, Prion S. Two-by-two factorial design .  Clin Simul Nurs . 2020;49:90-91. doi:10.1016/j.ecns.2020.06.004

Watkins ER, Newbold A. Factorial designs help to understand how psychological therapy works .  Front Psychiatry . 2020;11:429. doi:10.3389/fpsyt.2020.00429

Research Design in Psychology

• First Online: 28 August 2019

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• J. P. Verma 2  

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Psychological research can be broadly classified into experimental or non-experimental. In experimental research, independent variable is manipulated to see its impact on some variable of interest. On the other hand, in non-experimental research, existing association is used to study the cause-and-effect relationship. A detailed discussion has been made in this chapter on various considerations in developing an empirical study. To ensure internal validity, one needs to maximize the systematic variance, control extraneous variance, and minimize the error variance. This has been discussed by means of an illustration. After going through this chapter, the readers can understand different methods of psychological research and use appropriate research designs so that the internal validity of findings can be enhanced. To control the variability in sample, blocking principle has been discussed in detail. Further, the readers will also understand the situation where the factorial experiment can be planned.

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Experimental Design

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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.

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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?

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

Survey research allows you to gather large volumes of data that can be 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

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Research Methodology

Methodology refers to the overarching strategy and rationale of your research. Developing your methodology involves studying the research methods used in your field and the theories or principles that underpin them, in order to choose the approach that best matches your research objectives. Methodology is the first step in planning a research project.

Scoping Review vs Systematic Review

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Doing a meta-analysis: a practical, step-by-step guide, scientific method, qualitative research, experiments.

The scientific method is a step-by-step process used by researchers and scientists to determine if there is a relationship between two or more variables. Psychologists use this method to conduct psychological research, gather data, process information, and describe behaviors.

Learn More: Steps of the Scientific Method

Variables apply to experimental investigations. The independent variable is the variable the experimenter manipulates or changes. The dependent variable is the variable being tested and measured in an experiment, and is 'dependent' on the independent variable.

Learn More: Independent and Dependent Variables

When you perform a statistical test a p-value helps you determine the significance of your results in relation to the null hypothesis. A p-value less than 0.05 (typically ≤ 0.05) is statistically significant.

Learn More: P-Value and Statistical Significance

Qualitative research is a process used for the systematic collection, analysis, and interpretation of non-numerical data. Qualitative research can be used to gain a deep contextual understanding of the subjective social reality of individuals.

The experimental method involves the manipulation of variables to establish cause-and-effect relationships. The key features are controlled methods and the random allocation of participants into controlled and experimental groups.

Learn More: How the Experimental Method Works in Psychology

Frequent Asked Questions

What does p-value of 0.05 mean?

A p-value less than 0.05 (typically ≤ 0.05) is statistically significant. It indicates strong evidence against the null hypothesis, as there is less than a 5% probability the results have occurred by random chance rather than a real effect. Therefore, we reject the null hypothesis and accept the alternative hypothesis.

However, it is important to note that the p-value is not the only factor that should be considered when interpreting the results of a hypothesis test. Other factors, such as effect size, should also be considered.

Learn More: What A p-Value Tells You About Statistical Significance

What does z-score tell you?

A  z-score  describes the position of a raw score in terms of its distance from the mean when measured in standard deviation units. It is also known as a standard score because it allows the comparison of scores on different variables by standardizing the distribution. The z-score is positive if the value lies above the mean and negative if it lies below the mean.

Learn More: Z-Score: Definition, Calculation, Formula, & Interpretation

What is an independent vs dependent variable?

The independent variable is the variable the experimenter manipulates or changes and is assumed to have a direct effect on the dependent variable. For example, allocating participants to either drug or placebo conditions (independent variable) to measure any changes in the intensity of their anxiety (dependent variable).

Learn More : What are Independent and Dependent Variables?

What is the difference between qualitative and quantitative?

Quantitative data is numerical information about quantities and qualitative data is descriptive and regards phenomena that can be observed but not measured, such as language.

Learn More: What’s the difference between qualitative and quantitative research?

Explore Research Methodology

Critical Discourse Analysis

Mixed Methods Research

Conversation Analysis

Discourse Analysis

Phenomenology In Qualitative Research

Ethnography In Qualitative Research

Narrative Analysis In Qualitative Research

Thematic Analysis: A Step by Step Guide

Metasynthesis Of Qualitative Research

Grounded Theory In Qualitative Research: A Practical Guide

Qualitative Data Coding

What Is a Focus Group?

Cross-Cultural Research Methodology In Psychology

What Is Internal Validity In Research?

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REVIEW article

Analysis of the causes, psychological mechanisms, and coping strategies of short video addiction in china.

• Business School, Southwest University of Political Science & Law, Chongqing, China

Short video addiction refers to the uncontrollable desire of users to watch short videos, leading to significant behavioral loss of control or attention disorders, which in turn result in difficulties in social interaction, learning, and work adaptation. With the “invasion” of short videos into people’s daily lives and their spread among underage groups, the issue of short video addiction has attracted widespread social attention. Firstly, based on the causes of short video addiction, this study analyzes it from four levels: algorithm design, content services, platform control, and user experience. Secondly, combining relevant scientific theories, the psychological mechanisms of short video addiction are explained from four levels: cognition, emotion, motivation, and social factors. Finally, in terms of coping strategies, on the theoretical level, further research on the occurrence mechanism of short video addiction should be deepened, and attention should be paid to the influence of recommendation algorithms on short video addiction. On the practical level, the obligations and responsibilities of relevant stakeholders such as short video producers, platforms, and regulators in preventing short video addiction should be clarified, aiming to promote prevention and management of short video addiction.

1 Introduction

With the rapid development of digital technology, people’s lifestyles have undergone revolutionary changes, giving rise to a new form of social media – short videos. Short videos refer to online videos with durations ranging from a few seconds to a few minutes, which are published and shared through social media platforms, video-sharing websites, mobile applications, and more. Short video apps such as Douyin (TikTok), Kuaishou, and Tencent Weishi have formed a “individual-short video-society” structure in the current social relationship chain. Due to their rich content, concise interaction, light-hearted humor, and wide age appeal, short videos have gained popularity among a large number of users. The phrase “Five minutes on Douyin, one hour in the real world” humorously captures the empathetic feeling of people getting immersed in short videos without realizing it. According to the 52nd Statistical Report on Internet Development in China published by the China Internet Network Information Center, as of June 2023, the number of short video users reached 1.026 billion, with a user penetration rate of 95.2% ( China Internet Network Information Center, 2023a ). Nearly one-fourth of new internet users were introduced to the internet through short videos, and the average daily viewing time of short videos exceeded 2.5 h per person ( Media Review, 2023 ). The 5th National Survey on Internet Use by Minors shows that the proportion of underage internet users who frequently watch short videos has increased from 40.5% in 2018 to 54.1% in 2022, making it an important channel for minors to obtain information (see Figure 1 ; China Internet Network Information Center, 2023b ). Hormonal changes caused by brain growth and development can affect the self-control of minors and expose them to higher risks of mental health issues ( Casey, 2013 ). Due to the “invasion” of short videos into users’ daily lives and their spread among underage groups, short video addiction has led to numerous negative incidents, severely impacting the healthy development of addicts and even disrupting normal social order.

Figure 1 . Scale and usage rate of short video users from June 2018 to June 2023. Data Source: Statistical Survey on Internet Development in China.

As a result, the issue of short video addiction has gradually attracted the attention and even resistance of the public.

Critics have compared short videos to electronic fentanyl, electronic opium, and electronic cocaine, elaborate slot machines and a potential catalyst for a new era of opium wars. From their perspective, short videos are perceived as a relentless time sink, and individuals find themselves unable to break free from its addictive grip. In recent years, there has been a growing body of research on the addiction to short videos. In related research, short videos are often considered a form of network social media, and their unique impact on users has not been given sufficient attention. As network social services continue to diversify, various online platforms offer different functions, cater to distinct user groups, and operate under varying social modes ( Cinelli et al., 2021 ). Leung and Chen (2021) proposed in their literature review that future research on Internet addiction should concentrate on specific behaviors or types of content. Some scholars also advocate for paying attention to the distinctions between short videos and traditional social services in order to thoroughly investigate their impact on users ( Masciantonio et al., 2021 ). In addition, in terms of research subjects, scholars have conducted cross-group studies on various populations, including adolescents ( He and Zhou, 2021 ), college students ( Wang, 2021 ), elderly individuals ( Jia et al., 2023 ), and rural left-behind children ( Liang, 2023 ). On the whole, minors have received the most attention, and researchers tend to attribute the causes of addiction to intrinsic psychological states, media literacy, and external factors such as social and family structures, as well as the content and usage of short videos ( Meng and Leung, 2021 ). In terms of research methods, researchers often employ empirical methods to explore the mediating effects of specific factors such as social-technical factors ( Xie and Du, 2023 ). This paper focuses on short videos as a medium of information. Firstly, based on a review of relevant literature and the characteristics of internet addiction and short videos, the concept of short video addiction is defined. Secondly, the causes of short video addiction are explored from four levels: algorithm design, content services, platform control, and user experience. Finally, drawing on relevant psychological theories, the psychological mechanisms of short video addiction are deeply explored from four levels: cognition, emotion, motivation, and social factors, aiming to provide a systematic understanding of this issue.

2 Conceptual definition of short video addiction

There is no consensus in the academic community regarding the definition of short video addiction. “Addiction” stems from the term “dependence” and refers to uncontrollable usage behavior of social media, which is often considered to have addictive tendencies ( Ryan et al., 2014 ). In fact, phenomena such as internet addiction and gaming addiction have long existed and have gradually expanded from the media field to the realm of mental health. The term “internet addiction” was first proposed by American psychiatrist Ivan Goldberg, but there is still debate as to whether internet addiction is a disease or a behavioral dependency. In China, the Ministry of Health denied the classification of “internet addiction” as a clinical diagnosis in 2009 ( Southern Metropolis Daily, 2009 ). Currently, there is no specific research indicating the symptoms or clinical diagnostic criteria for short video addiction. To standardize terminology and reduce conceptual confusion, this article chooses the relatively neutral term “addiction.” In 2018, the National Health Commission of China released the “Core Information and Definitions of Health Education for Chinese Adolescents,” which defines addiction as “the impulsive behavior of internet use without the influence of addictive substances ( China Health Education Center, 2018 ).” Undoubtedly, the usage patterns of short video platforms play a significant role in the formation and maintenance of short video addiction. Short video addiction refers to a chronic or cyclical state of obsession caused by repeated use of short video apps such as Douyin (TikTok), accompanied by intense and persistent cravings and a sense of dependence ( Li et al., 2021 ). Some studies have focused on short video addiction but did not provide specific definitions ( Nong et al., 2023 ). Take Instagram addiction as an example. In recent years, there have been numerous relevant studies, including the use of addiction as moderator to investigate the relationship between Instagram overuse and stress and emotional fatigue ( Sanz-Blas et al., 2019 ). Other factors were utilized as mediating variables to examine the drivers and outcomes of Instagram addiction ( Ponnusamy et al., 2020 ), and the relationship between Instagram addiction and personality ( Kircaburun and Griffith, 2018 ). None of these studies have provided a clear definition of addiction to short videos, but these controversies do not hinder the continuous progress of related research and even serve as a driving force for scholars in the field to conduct further studies. The field of short video research is no exception, and the controversy surrounding the concept may persist for some time, but it does not impede the gradual emergence of research on short video addiction. In fact, existing studies on short video addiction almost entirely draw on concepts from related fields such as internet addiction, lacking unique considerations for short video addiction behavior ( Zhang et al., 2019 ). The ambiguity of the concept indicates that current research on short video addiction is still in its early stages.

The article argues that defining the concept of short video addiction requires a combination of research on “internet addiction” and the unique characteristics of “short videos” themselves. On one hand, it is necessary to revisit the exploration of “internet addiction.” In 1980, pathological gambling was included in the Diagnostic and Statistical Manual of Mental Disorders (DSM-III), opening the theoretical possibility for the study of non-substance addictions such as internet addiction ( Griffiths, 2000 ). Subsequently, academic attention on internet addiction has increased. In 2014, a study compared different diagnostic criteria for internet addiction, such as the components model and the Internet Addiction Test and identified three common features: (1) lack of control over internet use, (2) resulting psychological, social, or occupational conflicts or problems, and (3) mental distress ( van Rooij and Prause, 2014 ). Furthermore, they called for attention to specific addictive behaviors. On the other hand, it is important to enhance the recognition of “short video characteristics.” The concept of internet addiction is too broad and vague for various specific forms of inappropriate internet use. Defining short video addiction requires focusing on its unique characteristics. Given that each short video platform has a characteristic structure, unique features, varied use habits, and different gratifications and motives underlying its use, it is necessary to investigate each platform addiction alongside potentially related factors. However, this paper focuses solely on the overall level of research into short video addiction and does not take into account the specific factors of each individual platform. In terms of content, short videos share the general characteristic of internet content—abundance. The vast amount of short video content makes it more appealing to users. In terms of form, short videos have distinct features: (1) short duration and fast-paced, (2) rich and varied audiovisual symbols, and (3) strong social nature, as short videos were inherently endowed with social attributes from their development ( He and Zhou, 2021 ). Based on these considerations, this article proposes a descriptive concept of short video addiction by combining the three features of internet addiction and the characteristics of short video content and form: short video addiction refers to a user’s uncontrollable desire to watch short videos, leading to evident behavioral loss of control or attention disorders, resulting in difficulties in interpersonal relationships, learning, work, and other aspects of adaptation. The most tangible aspect of addiction can be reflected in the duration or frequency of daily short video consumption, but the deeper level of “uncontrollable desire” requires careful examination of individual users. For example, being deeply immersed while watching short videos or experiencing feelings of loss or sadness when not watching them for an extended period. It is necessary to clarify that this descriptive concept aims to promote academic attention and research on short video addiction and does not have diagnostic significance.

3 Analysis of the causes of short video addiction

Short video addiction is a possible outcome of user interaction with short video services. Design ethicist Tristan Harris believes that the problem of user addiction is not due to a lack of willpower but rather because thousands of people behind the screen are working hard to undermine your self-control ( Alter, 2018 ). Short video platforms manipulate users subtly through the artistic treatment of information dissemination technology and audiovisual product design. The increase in user addiction, user stickiness, and behavioral data are important capital for such applications. This article will analyze the causes of short video addiction from four aspects: algorithm design, content services, platform control, and user experience.

3.1 Algorithm design

One of the main reasons for the rapid popularity of short videos is the algorithm’s “feed.” Personalized algorithms can collect users’ interests, preferences, and behavioral records, and accurately push short videos to users’ mobile devices using various techniques, such as recommendation techniques based on previously selected content or friend relationships ( Geschke et al., 2019 ). Personalized algorithms represent a significant change for social media because they free people from a large amount of irrelevant information, allowing users to be entertained based on algorithm-recommended content without the need for active searching or selection.

In China, the early application of algorithmic recommendation technology was seen in the “Toutiao” app launched in 2012. At that time, the news field was fiercely competitive among the four major portal websites: Sina, Sohu, Netease, and Tencent. Within 3 months of its launch, “Toutiao” gained 10 million users and quickly disrupted the landscape of internet news and information. The subsequent “Douyin” app successfully replicated the algorithmic recommendation technology. It can be said that the platform, utilizing artificial intelligence and big data technology through algorithm models, tracks users’ reading preferences, matches user behavioral data with content information data, achieves highly aggregated content, and delivers it precisely. The application of algorithms has transformed the process from “people searching for information” to “information finding people,” rapidly iterating recommended content in seconds, and dynamically adapting to users’ moods and interests.

This model significantly reduces the cost of accessing information and changes people’s habits and experiences of obtaining information on the internet. With the accumulation of user data, algorithms can continue to optimize and refine the information delivery mechanism, making it more personalized and precise ( Zhang and Liu, 2021 ). Following the cycle of “personalized algorithm push – user demand satisfaction – accumulation of user behavioral data – optimization of personalized algorithms – personalized algorithm push – …” (as shown in Figure 2 ), users may become unable to make independent choices and become immersed in this effortless and enjoyable way of receiving information, unaware of the invisible control behind the scenes, ultimately leading to short video addiction or even addiction ( Qin et al., 2022 ).

Figure 2 . Algorithm “closed loop”.

Algorithms, as neutral tools, are inherently neither good nor evil. However, the algorithm models constructed by commercial companies are imbued with a profit-oriented value system, essentially being “pseudo-neutral” technologies filled with commercial logic ( Li, 2018 ). Research has also shown that watching personalized recommended short videos continuously stimulates the ventral tegmental area (VTA) of the brain, which is the neural circuit responsible for pleasure and reinforcement motivation. Prolonged activation of this area can lead to cravings and addiction in individuals ( Su et al., 2021 ). It can be said that short video apps utilize AI to provide targeted recommendations based on big data and individual browsing habits. The longer the usage time, the clearer the user’s profile becomes. Users repeatedly reinforce their usage habits through the algorithm’s “precise feeding,” leading to a state of addiction.

3.2 Content services

Short videos are brief online videos typically ranging from a few seconds to a few minutes in length. The compression of video duration increases the accessibility of information entertainment for people living in a fast-paced society, and as a result, short videos have gained widespread user traffic. Through case studies, scholars have found that short videos better align with people’s busy lifestyles as they can provide sufficient rich information stimulation within limited time ( Zea and Jung, 2019 ). Research has also found that the fragmented dissemination mode of short videos perfectly meets users’ need for mental pleasure during fragmented time ( Qin et al., 2022 ).

Although most short videos are only a few seconds to a few tens of seconds long, they contain sufficient elements. This richness can be seen in two aspects: presentation style and content of information. Taking Douyin as an example, in terms of presentation style, short videos are typically composed of lively melodies, eye-catching text, and corresponding video clips. This “multichannel” sensory stimulation enhances users’ experiential sense of information content, further reinforcing their short video usage behavior. Research has shown that when users interact with a system, pleasant or stress-relieving experiences encourage them to repeat such interactive behavior ( Liu and Chang, 2016 ), and the same applies to using short videos. The rich stimuli in short videos activate users’ sense of pleasure, alleviate negative emotions, and lead to users increasing their frequency and duration of use to reinforce this behavior.

In terms of information content, the Douyin short video platform is highly diverse, covering various themes such as creative humor, music and dance, life sharing, children’s education, emotional stories, fashion and beauty, talent skills, news highlights, dramatic performances, food Mukbang, and sports and fitness. The greater the variety, the more it can attract people with different preferences to join Douyin and meet the information needs of different users. For example, emotional story short videos are short films with emotions as the main theme, usually including romantic, touching, and inspirational content. These types of short videos are suitable for young female users and those who enjoy a romantic and artistic atmosphere (as shown in Table 1 ). If the information content is further divided, short videos can be categorized into functional and hedonic types. Functional short videos typically feature news, policies, knowledge and other informative content, aiming to provide users with practical efficiency and meet their functional needs. On the other hand, hedonic short videos usually revolve around humorous or cute pets, appearances, song and dance performances as their main content, offering users hedonic pleasure and meeting their entertainment needs. Some scholars have conducted research to explore the relationship between two types of videos and users’ usage behavior. It has been observed that the continuous use of functional short videos by users is inadequate, while hedonic short videos captivate a large number of users due to their appeal in terms of interest, novelty, stimulation and aesthetics. This phenomenon has led to significant social consequences ( Tian, 2023 ). Therefore, the focus of this paper is mainly on the issue of addiction to hedonic short videos.

Table 1 . Information content of Douyin short videos and user groups.

Additionally, Douyin was officially launched in China in September 2016, marking the beginning of the explosive growth of short videos. However, due to its relatively brief history, the lack of government regulation has created a “lawless land” for short videos. This has led to the proliferation of low-brow, borderline violent, bloody, wealth-worshipping, and gluttonous content with misguided value orientation and bad guidance easily accessible. As a result, children may be curious about these videos and thus subjected to significant psychological impact. Furthermore, even adults may succumb to addiction to these negative videos under the pressures of work and life.

3.3 Platform control level

The concise human-computer interaction mode and rich content information experience are the advantages and characteristics of short videos ( Tian et al., 2023 ). The attribution of addiction cannot be limited to the tension of audiovisual art in short videos, the lack of self-control in users, and social environmental factors, while ignoring the platform entity ( Zhang and Liang, 2023 ). In theory, the platform economy is characterized by “double supervision,” where effective oversight can help mitigate the risk of users becoming addicted to short videos. Firstly, the government supervises platforms and merchants through the implementation of laws and regulations. Secondly, platforms themselves regulate merchants and users through access qualification reviews, monitoring transaction behavior, as well as accumulating behavior and credit data. However, the absence of government oversight has somewhat encouraged platforms to prioritize economic gain to a certain extent. This profit-driven mindset tends to undermine the regulatory willingness of platforms and may even actively entice users, especially teenagers, into excessive indulgence. In the commercial logic of the platform entity, inducing user addiction is to further obtain user labor and promote the development and growth of the platform by leveraging the creativity of the masses. Because internet technology activates individuals as information nodes, the idle resources of the masses become an important content treasure trove for the platform, providing an inexhaustible source of creation ( Yu et al., 2015 ). In fact, platforms can induce user addiction through methods such as full-screen design and time perception interference.

In terms of page design, short video apps predominantly feature full-screen playback mode, which serves two purposes: firstly, users do not need to make choices during usage, so they are not interrupted during the process and can immerse themselves in the content pushed by the algorithm, obtaining a sense of satisfaction and joy; secondly, the visually impactful and immersive nature of full-screen design makes it difficult for people to anticipate the content of the next video, further stimulating their desire to constantly chase after information. However, long-term exposure to highly accessible stimuli can potentially lead to addiction risks, as evidenced by research in the field of addiction. For example, the high accessibility of stimuli increases the risk of addiction ( Volkow et al., 2019 ). The portability, immediacy, and ease of use of mobile electronic games can lead to more severe gaming addiction risks ( Wang et al., 2019 ).

In terms of time perception, short videos, due to their varying and fragmented durations, increase the difficulty for users to perceive time. The accumulation of fragmented time results in a longer passage of time. It can be said that elements such as full-screen playback, algorithmic recommendations, and fragmented lengths are all intended to disrupt the concept of time and interfere with users’ estimation of time. Over time, the experience of constantly encountering freshness and quickly accessing core content within a minute makes it difficult for users to accept long videos. This altered viewing behavior, designed by the platform, is continuously reinforced as a form of dependence, weakening users’ critical thinking, and even causing restlessness and emptiness. It is evident that short video platforms put considerable effort into disrupting users’ perception of time as much as possible.

Media scholar Shrum proposed a formula to explain people’s choices of different media: Probability of choice = Potential rewards/Effort required ( Wilbur, 1984 ). In other words, when the potential rewards from using a particular medium are greater while the effort required is lower, the probability of users choosing that channel will be higher. For short video users, all they need to do is “swipe up” to access a wealth of information flow, significantly reducing the cost of obtaining information satisfaction. If we consider cognitive investment as the cost and the obtained stimulus satisfaction as the benefit, compared to activities that require higher cognitive participation, such as playing games, or activities that require long periods of focused attention, such as watching movies, the use of short videos is clearly a “low investment, high return” resource exchange activity. This high profitability will continuously reinforce people’s use of short videos, thereby triggering addiction risks.

3.4 User experience level

Previous research on internet addiction has shown that the sense of immersion individuals experience during use is one of the key driving factors of addiction ( Seo and Ray, 2019 ). Immersion leads individuals to experience a high sense of control, heightened awareness, and focused attention, while disregarding their surrounding environment and the passage of time ( Michailidis et al., 2018 ). When users interact with short videos, which are “actively recommended, information-rich, and interactively simple” media, they tend to focus more on the current stimuli and pay less attention to the past or future. This is known as “immersion.” Short videos can create a sense of immersion for users through first-person perspective content, and when individuals watch algorithmically recommended short videos on personal accounts, the brain’s cognitive control regions are inhibited, making it easy to enter an immersive state ( Su et al., 2021 ). This low-cost immersion can pose significant addiction risks for short video users.

It should be noted that the flow theory, which is like immersion, is often used to explain the reasons why individuals experience immersion. In fact, many scholars have used the concept of mental flow to explain the internal mechanisms of addiction in studies on internet addiction or gaming addiction ( Chou and Ting, 2003 ; Liu and Chang, 2016 ). However, the state of addiction to short videos may not be the same. Flow and immersion have certain differences, and this distinction is a key point in differentiating short video addiction from general internet addiction. Specifically, flow refers to a holistic sense of complete engagement in an activity, representing an optimal experience ( Leung, 2020 ). This state includes high levels of competence and control, high levels of challenge and arousal, focused attention, internal enjoyment, and a sense of purpose and achievement. Therefore, one condition for achieving flow is to strike a balance between an individual’s abilities and the challenge level of the task ( Yang et al., 2014 ; Liu and Chang, 2016 ). When using short videos, users can obtain rich information stimuli with low interaction costs, which does not meet the prerequisites for a flow experience. In fact, immersion leans more toward a “sense of presence,” a subjective experience that allows the subject to generate certain associations and have a strong sense of involvement. Flow, on the other hand, emphasizes a state of “total concentration” that requires strong operational skills. Furthermore, in terms of perceptual and cognitive experiences, immersion leans more toward perceptual experiences, where sensory stimuli prompt users to immerse themselves more in the current environment. Flow, on the other hand, leans more toward cognitive experiences, manifested as a mental state resulting from strong cognitive behaviors after receiving timely feedback. In summary, although immersion and flow have slight structural differences, they represent different psychological phenomena ( Michailidis et al., 2018 ). Immersion is not an extreme state and does not require a high level of interactive balance or perceptual enjoyment. Compared to flow, immersion may be more suitable for describing the focused state of individuals when using short videos.

It is important to note that while technology plays a role in promoting addiction to short videos, it would be incorrect to solely attribute the problem to technical factors. Not all users become addicted due to algorithm technology, and whether a user becomes addicted to short videos is closely related to their own psychological factors. From an experiential perspective, the inability of users to satisfy their psychological needs offline is a significant contributing factor to their addiction to short videos. On one hand, psychologist Carl Jung introduced the concept of the inner child, proposing that within each individual resides a vulnerable, wounded, and dependent child in need of nurturing care. The reason why many users are addicted to short videos, or even unable to extricate themselves, is because it invisibly meets the psychological needs of their “inner children.” Wonderful short videos not only create a virtual entertainment space but also provide a variety of emotional values for the audience, satisfying their spiritual needs that are desired but not available offline. On the other hand, according to Maslow’s hierarchy of needs theory, needs are categorized into five levels: physiological needs, safety needs, love and belongingness, esteem and self-actualization. With the advancement of the social economy, people’s basic needs have been largely satisfied, leading to a growing emphasis on spiritual fulfillment such as a sense of belonging, respect, and self-actualization. The rapid growth of short videos caters to the higher-level demands of people. In comparison to the tightly connected social patterns in real-life society, short videos establish a more gentle and convenient pattern of interpersonal interaction. This social model of weak association caters to the tendency of current users, particularly young users, to seek escape from strong interpersonal relationships in real life. Additionally, it facilitates the identification of similar groups and the development of self-identity.

4 Psychological mechanisms of short video addiction

To gain a deeper understanding of the psychological mechanisms behind short video addiction, it is necessary to analyze it from a scientific theoretical perspective. The following sections will provide theoretical explanations of short video addiction from cognitive, emotional, motivational, and social dimensions, aiming to enhance public awareness of short video use and provide a scientific basis for research and intervention regarding short video addiction.

4.1 Cognitive dimension

In the cognitive dimension, the Dual Process Theory can be used to explain short video addiction. The Dual Process Theory, proposed by psychologists Shiffrin and Schneider (1977) , suggests that cognitive processing consists of two systems: “automatic processing” and “controlled processing.” “Automatic processing” refers to well-practiced behavioral patterns, where specific steps and instructions have become nearly “unconscious” actions. “Controlled processing,” on the other hand, is limited by cognitive resources and requires conscious attention and adjustment based on the environment. According to the Dual Process Theory, due to limited cognitive resources, when the controlled processing system is weak or impaired, the influence of the “automatic processing” system on behavior is greater. Conversely, when the “controlled processing” system is strong or intact, the influence of the “automatic processing” system is smaller. To make rational and effective decisions to achieve goals, individuals must ensure that the controlled processing system can exert its supervisory and control functions to suppress impulsive behavior driven by the automatic processing system ( Saunders and Over, 2009 ).

In recent years, the Dual Process Theory has been frequently used to explain the internal mechanisms of addiction. Studies have found that the formation of addictive behaviors is related to the enhancement of the “automatic processing” system and the weakening of the “controlled processing” system ( Yan et al., 2021 ), which is supported by neurophysiological research ( Koob and Volkow, 2010 ). Another study showed that during the process of watching personalized short videos, the default mode network (DMN) in the brain was activated and coupled with visual and auditory pathways but coupled less with the prefrontal cortex and cingulate cortex. This indicates that attentional resources are highly focused on visual and auditory information processing, while difficulties arise in attention regulation. At the same time, regions involved in cognitive control are inhibited, which may lead to a loss of control over short video use ( Su et al., 2021 ). This study reveals the cognitive processing patterns of the brain during short video use from a neurobiological perspective. Based on this, due to the low cognitive engagement when interacting with short video media, the “automatic processing” system continues to be reinforced while the “controlled processing” system is restricted. As a result, even if the individual is aware that their behavior deviates from the overall goal, they may struggle to resist the impulse to repeat the behavior, ultimately leading to short video addiction.

4.2 Emotional dimension

In the emotional dimension, the Opponent Process Theory can be used to explain users’ addiction to short videos. The Opponent Process Theory originally explains how we perceive colors through opposing neural processes. It suggests that certain colors are linked, forming pairs that inhibit each other. When one color in a pair is stimulated, the other is suppressed, creating a balance in our visual experience. Emotions are reactions within the human inner world and play an important role in people’s daily lives. Emotions arise from individuals’ evaluations of things, where positive evaluations lead to positive emotions and attraction toward the object, while negative evaluations lead to negative emotions and avoidance of the object ( Strongman, 2006 ). Emotions have a peculiar characteristic that when a strong emotion subsides, it is often naturally accompanied by an opposite emotion. Opponent process theory is an explanation of how the experiences of certain sensory and neurological phenomena are linked together. Put simply, the body efficiently processes opposing experiences, such as fear and pleasure, at the same site, making it difficult for people to experience both at once. When stimulation at such a site evokes one experience, a person may experience an “afterimage” of the opposite experience after the stimulation is over. The Opponent Process Theory has been widely used to study addiction, child attachment, and sensation-seeking behaviors ( Lee et al., 2014 ). The theory describes the underlying positive reinforcement and negative reinforcement mechanisms and intuitively explains why specific systems can lead to addiction through the opposing processes of positive and negative emotions ( Tian et al., 2023 ).

Research has shown that the stimulation from short videos activates the brain’s reward pathway. The cerebellum, as part of the reward pathway, is directly connected to the ventral tegmental area, which is one of the core regions for processing rewards ( Carta et al., 2019 ). Personalized short videos, compared to regular short videos, more significantly activate the ventral tegmental area because the algorithm-recommended videos have higher reward value for users and can produce a greater pleasure effect. As a relaxed and enjoyable form of entertainment, short videos provide users with a temporary escape from busy work or study. In other words, watching short videos has a pleasurable effect. At this point, users’ positive emotions are activated. However, in real-life situations, due to psychological or external factors, users often must stop or temporarily pause their use of short videos. Once the stimulation stops or decreases, positive emotions are lost, triggering negative emotions such as anger and stress. To maintain positive emotions and reduce negative emotions, users must repeatedly activate the behavior of using short videos to maintain the sense of pleasure, ultimately leading to short video addiction ( Tian et al., 2023 ). Therefore, systematically discussing the psychological needs of short video users under the Opponent Process Theory, clarifying the usage mechanisms driven by different emotions, can provide scientific guidance for the rational use and management of short videos.

4.3 Motivational dimension

In the motivational dimension, the Social Shaping of Technology theory can be used to explain users’ addiction to short videos. The Social Shaping of Technology theory adopts a constructivist approach to study the social formation process of technology. It suggests that technology is shaped by social factors and should be open to sociological investigations. It applies sociological methods to examine how social, political, economic, and cultural forces influence the formation of technology, providing a new perspective for understanding the relationship between technology and society. This theory emerged as a reflection and critique of “technological determinism” and differs from traditional perspectives that solely focus on the outcomes of technological progress. Instead, it shifts the research focus to social factors beyond the technology itself and examines the specific processes involved in the content and innovation of technology. Specifically, the key point of the Social Shaping of Technology theory is that each stage of the emergence and implementation of new technology involves choices between different technological options, and each choice is directly or indirectly influenced by society or individuals, which in turn has corresponding impacts on individuals and society ( Williams and Edge, 1996 ). In other words, technology is not in a “black box” state. Only by opening the “black box” of technology and analyzing the socio-economic patterns in the process of technological innovation can we truly understand the origins of technology and its impacts.

The implementation of personalized recommendations for short video content relies on recommendation algorithms. However, the continuous “evolution” of recommendation algorithms is not solely the work of engineers but gradually achieved under the driving force of user feedback. As the level of internet application services continues to improve, users have more specific and precise demands for internet content. Personalized recommendation algorithms have emerged and developed under the impetus of user behavior data accumulation. However, when facing addiction to short videos, it is not appropriate to simply attribute the responsibility to recommendation algorithms or short video platforms. Instead, a deeper reflection on technological products such as short videos should be conducted from the perspectives of social development and user needs. For example, why do different users have different attitudes toward short videos? If the shaping of technology by society or individuals is ignored and the impact of short videos on individuals and society is analyzed unilaterally, the measures taken to prevent short video addiction may lag or be ineffective.

5 Recommendations for addressing short video addiction

Short video addiction is a new hot topic in current research. How can we address the negative impacts of short video addiction? Specific recommendations can be made from both theoretical research and practical applications.

5.1 Research outlook

At the theoretical research level, there are three areas that deserve future attention. Firstly, research methods and ideas need to be further developed and enhanced. Different research approaches can address the methodological needs of a particular scientific problem from various research perspectives. As a result, these approaches have the potential to uncover multiple facets of the problem. In summary, the majority of existing studies on short video addiction have relied on research data obtained through participants’ questionnaire reports (e.g., Feng et al., 2022 ), and the specificity of short video addiction was not fully considered. Some scholars have utilized qualitative research to propose a targeted measurement method for short video addiction ( Luo, 2022 ). However, this approach also encompasses users’ engagement with other content on short video platforms, such as collecting gold coins. This expansion has extended beyond the realm of short videos, resembling more of an addiction assessment for a short video platform. Indeed, a similar situation has previously occurred when certain scholars developed measurement schemes for Internet addiction ( Lortie and Guitton, 2013 ). Future research could seek to enhance the exploration of short video addiction through the following approaches: (1) Develop a brief video addiction scale through interviews or expert assessment (refer to Hsu et al., 2015 ) to evaluate whether users are addicted to short videos; (2) Conduct behavioral experiments, including randomized controlled trials and delay discounting tasks, to investigate users’ choice preferences, decision-making ability, self-regulation strategies, and other aspects under different conditions. This approach will help in understanding the behavioral characteristics and basic needs associated with short video addiction; (3) Utilize neuroimaging techniques (refer to Su et al., 2021 ), such as functional magnetic resonance imaging and electroencephalography, to observe the brain activity of individuals while they are watching short videos. By analyzing the neural mechanisms underlying user attention, emotion, and reward systems, it is beneficial to uncover the neural basis of addiction to short videos.

Secondly, there is a need to deepen the understanding of the mechanisms behind short video addiction. The key to addressing the issue lies in gaining a profound insight into its nature. However, existing research primarily focuses on the harms of short video addiction while neglecting the mechanisms of addiction, resulting in a limited understanding of the problem within the academic community and the inability to propose practical and effective governance measures. As mentioned earlier, algorithm design, content services, platform control, and user experience are the four key aspects involved in short video usage and are also potential causes of addiction. The intelligent technology, diverse content, convenience of platforms, and user experience in short video services differ from traditional social media. These characteristics may intertwine with user needs and psychological traits, leading to the occurrence of short video addiction. In future research, it is necessary to not only delve into the external triggering mechanisms of short video addiction but also further explore the psychological and behavioral factors of addiction among short video users. It can be said that the analysis of external triggering mechanisms and the exploration of internal mechanisms of short video addiction will jointly contribute to clarifying the origins of the problematic behavior, thereby providing scientific guidance for the development of preventive and intervention measures. Furthermore, by raising public awareness of the internal mechanisms of short video addiction, we can enhance societal understanding and recognition of this potential risk, achieving a fundamental effect. For example, from the perspective of short videos, personalized recommendation algorithms are one of the key factors that contribute to short video addiction ( Zhao, 2021 ). Algorithm literacy, which safeguards users’ autonomy and decision-making rights, may become an important knowledge reserve ( Bakshy et al., 2015 ). Therefore, disseminating knowledge about algorithms and their operating principles can enhance users’ rational thinking and coping abilities, enabling them to resist the negative impacts of algorithms and achieve collective prevention of short video addiction.

Thirdly, the impact of recommendation algorithms on short video addiction should be considered. Issues such as gaming addiction and problematic use of social networking sites have been widely studied, but few scholars have explored the causes of addiction from a technological or device perspective. Short video addiction, as a newly emerging problem triggered by technology, is particularly relevant in this regard. It is well known that the development of algorithms has improved work efficiency in various industries and brought about new opportunities. However, while algorithms bring convenience to people, the risks and challenges they pose have also raised concerns in various fields. The recommendation algorithms of short video platforms are constantly evolving based on market feedback, and the specific parameters of the algorithms evolve based on changes in users’ personal information and usage behavior. The more users engage with short videos, the more the recommendation algorithm caters to their preferences, leading to deeper involvement ( Zhang and Liu, 2021 ). Unlike general social networking platforms, the key interaction in short video usage is not between users and their social networks, but between users and the so-called algorithm itself ( Bhandari and Bimo, 2020 ). This suggests that there may be a closed loop between short video usage and algorithm optimization, and if this effect continues, it may lead to more users becoming addicted and have a greater social impact. Therefore, simply treating the technological support behind short video services as a black box is not a long-term solution, as neglecting personalized algorithms in studying the impact of short videos on users may not truly address the core issue. Considering this, it is worth considering the “supply-side” perspective: if platforms blindly pursue rich user profiles and precise content recommendations, it will only lead to more users becoming addicted.

5.2 Practical measures

On the practical application level, short videos are a double-edged sword for users, especially minors. When directed toward correct, meaningful, and valuable content, short videos can provide knowledge and education. However, negative content in short videos can easily lead users, especially minors, to become addicted to shallow pleasures, which can harm their physical and mental health and even affect the formation of their correct values. It is evident that the problem of short video addiction is a complex social issue that requires the joint efforts of society. Therefore, we can clarify the obligations and responsibilities of relevant parties such as short video producers, platforms, and regulators in preventing short video addiction through legislation.

Firstly, from the perspective of short video producers, influencers are the backbone of maintaining the popularity of short video platforms. The “Douyin Research Report” shows that influencers who are similar to the respondents account for only 4.7% of the total number of platform users, but they have a staggering 97.7% of the total number of fans, and the top 2.7% of their videos attract more than 80% of the platform users’ attention and participation ( Douyin Research Report, 2018 ). In practice, influencers or other content creators often adhere to the rule of “traffic first.” While they may gain immediate benefits, in the long run, it can deplete their vitality. After experiencing explosive growth, short video platforms must inevitably face long-term production and consumption ( Peng, 2019 ). Attracting users relies on high-quality content. Therefore, as the source of content, short video producers should improve their own literacy, adhere to the principle of “content first,” and produce more high-quality short videos that guide users correctly, are rich in content, and provide educational and entertaining value.

Secondly, from the perspective of platforms, the issue of addiction reflects the commercial nature of short video platforms, which prioritize profitability. The power wielded by these platforms poses a potential threat to public communication, ideological security, and social aesthetics ( Zhang and Liang, 2023 ). Therefore, platforms should reflect on the entire process of their commercial operations and fulfill their responsibilities as entities, avoiding the control of public resources by commercial capital. In fact, it is not unreasonable to expect short video platforms to take on more regulatory responsibility for the frequent chaos in the field of short videos. Many large platforms have a large user base and a significant impact on minors. This requires platforms to first refrain from using algorithmic recommendation services to induce minors to become addicted to the internet, and it is strictly prohibited to promote violent, pornographic, or other inappropriate videos to them. Then, short video platforms should upgrade their “anti-addiction systems” using identity recognition and big data analysis and improve the short video rating system to prevent harmful content that affects the physical and mental health of users, especially minors, from being published on the platform. As a result, platforms must accurately classify, manage, and rigorously audit short videos to ensure that they are more suitable for users within specific age groups. For instance, platforms can enhance and strengthen the short video anti-addiction system through technical improvements such as interface design, content distribution, content push and time management. These measures will contribute to a more equitable balance between commercial interests and social responsibility. Therefore, in addition to legal obligations, platforms should also take on more social and moral responsibilities. In this process, short video platforms need to consider various issues, such as the potential increase in costs. However, at the same time, platforms will gain a good reputation, and society will recognize their business practices, leading to a corresponding market share. It is evident that assuming more responsibility can help short video platforms enter a virtuous cycle of development.

Thirdly, from the perspective of regulators, an efficient regulatory system is crucial to addressing the problem of short video addiction. Since regulation plays a major role in China, and the impact of government regulation on the occurrence and development of short video addiction can be hardly ignored, all sectors of society hope to see a resolution to the issue of short video addiction. However, relying solely on the efforts of platforms is not sufficient; it also requires the government to establish a scientific and efficient regulatory system. In fact, the Chinese government has taken a series of measures to solve the problem of short video addiction. In 2023, the special campaign, named “Qinglang Operation and Rectify the Problem of Poor Guidance of Short Video Information,” was launched by Chinese government, focusing on three prominent problems of spreading false information, displaying improper behavior and spreading wrong ideas. Among them, short videos displaying improper behavior, such as “pornographic edge” behavior, creating vulgar people, malicious marketing of Internet celebrities and displaying high-risk behaviors, are most likely to cause short video addiction and become the focus of regulation. In the future, China should continue to build upon the existing measures and further improve the normalized short video supervision measures to promote the standardized development of the short video industry. First of all, China should strengthen legislation and real-name system management, especially to constrain the identity of minors, and strive to urge platforms to standardize content review and functional operation, and promote the fundamental transformation from “management” to “governance.” Therefore, the government could impose restrictions on the content released by short video platforms, their operational mechanisms, user access and online time in order to ensure that platforms are compliant with regulations and operate legally. Then, regulatory authorities need to strengthen supervision and implement comprehensive regulation through measures such as online review and application control. Specifically, we can establish the position of the Cyberspace Administration as the primary regulator, clarifying its responsibilities and division of labor with the Public Security Bureau, Education Bureau, Culture Bureau, Health Commission, and Market Supervision Administration. This includes information sharing and coordinated law enforcement to improve regulatory efficiency. Meanwhile, China should strengthen regulatory oversight and implement a rigorous system of accountability at all levels of government to effectively control the issue of short video addiction. This will be essential in achieving successful prevention and control measures. Finally, we can fully leverage the proactive role of industry organizations in the internet sector to fulfill their regulatory functions, serving as an effective complement to government oversight. For instance, with regards to capital supervision, China should allocate resources toward the construction of public platform network services. It is important to establish a non-exploitative platform that prioritizes user service and ensures the protection of the legitimate rights and interests of short video users, particularly minors. Only in this way can we maximize the positive effects of multi-party governance, promote the healthy development of short video platforms, and make them a cultural home for the masses.

Author contributions

ML: Conceptualization, Formal analysis, Investigation, Writing – original draft, Writing – review & editing.

The author(s) declare that no financial support was received for the research, authorship, and/or publication of this article.

Conflict of interest

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

Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Alter, A. (2018). Irresistible: the rise of addictive technology and the business of keeping us hooked . (J. Lu, Trans.). Beijing: Machinery Industry Press.

Google Scholar

Bakshy, E., Messing, S., and Adamic, L. A. (2015). Exposure to ideologically diverse news and opinion on Facebook. Science 348, 1130–1132. doi: 10.1126/science.aaa1160

PubMed Abstract | Crossref Full Text | Google Scholar

Bhandari, A., and Bimo, S. (2020). TikTok and the “algorithmized self”: a new model of online interaction. AoIR Sel. Pap. Internet Res. doi: 10.5210/spir.v2020i0.11172

Crossref Full Text | Google Scholar

Carta, I., Chen, C. H., Schott, A. L., Dorizan, S., and Khodakhah, K. (2019). Cerebellar modulation of the reward circuitry and social behavior. Science 363:eaav0581. doi: 10.1126/science.aav0581

Casey, B. J. (2013). The teenage brain: an overview. Curr. Dir. Psychol. Sci. 22, 80–81. doi: 10.1177/0963721413486971

China Health Education Center (2018). The core messages and interpretation of health education for Chinese adolescents (2018 edition). Health Guide 6:46.

China Internet Network Information Center. (2023a). Statistical report on internet development in China, August 28, 2023. Available at: https://www.cnnic.net.cn/n4/2023/0828/c88-10829.html

China Internet Network Information Center. (2023b). Statistical survey on internet development in China, December 25, 2023. Available at: https://www.cnnic.cn/n4/2023/1225/c116-10908.html

Chou, T. J., and Ting, C. C. (2003). The role of flow experience in cyber-game addiction. Cyber Psychol. Behav. 6, 663–675. doi: 10.1089/109493103322725469

Cinelli, M., de Francisci Morales, G., Galeazzi, A., Quattrociocchi, W., and Starnini, M. (2021). The echo chamber effect on social media. Proc. Natl. Acad. Sci. 118:e2023301118. doi: 10.1073/pnas.2023301118

Douyin Research Report. (2018). Miao Zhen & Haimayun. Available at: http://www.199it.com/archives/769843.html (Accessed September 11, 2018).

Feng, Y., Li, L., and Zhao, A. (2022). A cognitive-emotional model from mobile short-form video addiction to intermittent discontinuance: the moderating role of neutralization. Int. J. Hum. Comput. Interact. 40, 1505–1517. doi: 10.1080/10447318.2022.2147714

Geschke, D., Lorenz, J., and Holtz, P. (2019). The triple-filter bubble: using agent-based modelling to test a metatheoretical framework for the emergence of filter bubbles and echo chambers. Br. J. Soc. Psychol. 58, 129–149. doi: 10.1111/bjso.12286

Griffiths, M. (2000). Internet addiction-time to be taken seriously? Addict. Res. 8, 413–418. doi: 10.3109/16066350009005587

He, Q., and Zhou, H. (2021). Investigating the phenomenon of short video addiction among adolescents. Contemp. Commun. 2021, 102–104.

Hsu, W. Y., Lin, S. S., Chang, S. M., Tseng, Y. H., and Chiu, N. Y. (2015). Examining the diagnostic criteria for Internet addiction: Expert validation. Journal of the Formosan Medical Association 114, 504–508.

Jia, Y., Liu, T. Y., and Yang, Y. (2023). Trapped in the smartphone: intergenerational relationships and internet addiction among older adults. J. Bimonthly 10, 31–44.

Kircaburun, K., and Griffiths, M. D. (2018). Instagram addiction and the big five of personality: the mediating role of self-liking. J. Behav. Addict. 7, 158–170. doi: 10.1556/2006.7.2018.15

Koob, G. F., and Volkow, N. D. (2010). Neurocircuitry of addiction. Neuropsychopharmacology 35, 217–238. doi: 10.1038/npp.2009.110

Lee, Z. W., Cheung, C. M., and Chan, T. K. (2014). Explaining the development of the excessive use of massively multiplayer online games: a positive-negative reinforcement perspective. 2014 47th Hawaii International Conference on System Sciences (pp. 668–677). IEEE.

Leung, L. (2020). Exploring the relationship between smartphone activities, flow experience, and boredom in free time. Comput. Hum. Behav. 103, 130–139. doi: 10.1016/j.chb.2019.09.030

Leung, L., and Chen, C. (2021). A review of media addiction research from 1991 to 2016. Soc. Sci. Comput. Rev. 39, 648–665. doi: 10.1177/0894439318791770

Li, L. (2018). Analysis of the “Pseudo-neutrality” of online intelligent recommendation algorithms. Modern Commun. 8:84.

Li, X., Qin, H. X., Zeng, M. H., He, Y. X., and Ma, M. Z. (2021). Relationship between symptoms of short video addiction and personality traits among college students. Chinese J. Mental Health 35, 925–928.

Liang, Q. (2023). Guide bias and bidirectional inversion: a study of short video addiction among rural left-behind children. New Theory Tianfu 4, 118–131.

Liu, C. C., and Chang, I. C. (2016). Model of online game addiction: the role of computer-mediated communication motives. Telematics Inform. 33, 904–915. doi: 10.1016/j.tele.2016.02.002

Lortie, C. L., and Guitton, M. J. (2013). Internet addiction assessment tools: dimensional structure and methodological status. Addiction 108, 1207–1216. doi: 10.1111/add.12202

Luo, G. F. (2022). Development and application of a scale for college students’ addiction to short videos . Hubei: Changjiang University.

Masciantonio, A., Bourguignon, D., Bouchat, P., Balty, M., and Rimé, B. (2021). Don’t put all social network sites in one basket: Facebook, Instagram, Twitter, TikTok, and their relations with well-being during the COVID-19 pandemic. PLoS One 16:e0248384. doi: 10.1371/journal.pone.0248384

Media Review. (2023). Research report on the development of internet audiovisual in China 2023, sarft.net, March 29, 2023. Available at: https://www.sarft.net/a/214357.aspx

Meng, K. S., and Leung, L. (2021). Factors influencing TikTok engagement behaviors in China: an examination of gratifications sought, narcissism, and the big five personality traits. Telecommun. Policy 45:102172. doi: 10.1016/j.telpol.2021.102172

Michailidis, L., Balaguer-Ballester, E., and He, X. (2018). Flow and immersion in video games: the aftermath of a conceptual challenge. Front. Psychol. 9:393107. doi: 10.3389/fpsyg.2018.01682

Nong, W., He, Z., Ye, J. H., Wu, Y. F., Wu, Y. T., Ye, J. N., et al. (2023). The relationship between short video flow, addiction, serendipity, and achievement motivation among Chinese vocational school students: the post-epidemic era context. Healthcare 11:462. doi: 10.3390/healthcare11040462

Peng, L. (2019). Short videos: “transgenic” and re-cultivation of video productivity. Journalism :36.

Ponnusamy, S., Iranmanesh, M., Foroughi, B., and Hyun, S. S. (2020). Drivers and outcomes of Instagram Addiction: Psychological well-being as moderator. Computers in human behavior 107:106294.

Qin, Y., Omar, B., and Musetti, A. (2022). The addiction behavior of short-form video app TikTok: the information quality and system quality perspective. Front. Psychol. 13:932805. doi: 10.3389/fpsyg.2022.932805

Ryan, T., Chester, A., Reece, J., and Xenos, S. (2014). The uses and abuses of Facebook: a review of Facebook addiction. J. Behav. Addict. 3, 133–148. doi: 10.1556/JBA.3.2014.016

Sanz-Blas, S., Buzova, D., and Miquel-Romero, M. J. (2019). From Instagram overuse to instastress and emotional fatigue: the mediation of addiction. Spanish J. Market. ESIC 23, 143–161. doi: 10.1108/SJME-12-2018-0059

Saunders, C., and Over, D. E. (2009). “In two minds about rationality” in In two minds: dual processes and beyond . eds. J. S. B. T. Evans and K. Frankish (New York: Oxford), 317–334.

Seo, D., and Ray, S. (2019). Habit and addiction in the use of social networking sites: their nature, antecedents, and consequences. Comput. Hum. Behav. 99, 109–125. doi: 10.1016/j.chb.2019.05.018

Shiffrin, R. M., and Schneider, W. (1977). Controlled and automatic human information processing: II. Perceptual learning, automatic attending and a general theory. Psychological review, 84:127.

Southern Metropolis Daily. (2009). Ministry of Health denies concept of “internet addiction,” advises against making minors quit the internet, China News Network. Available at: https://www.chinanews.com.cn/it/it-itxw/news/2009/11-05/1948561.shtml (Accessed November 5, 2009).

Strongman, K. T. (2006). The psychology of emotion from everyday life to theory (the 5th edition) . Wang Li, Trans. Beijing: China Light Industry Press 7–18, 69–92.

Su, C., Zhou, H., Gong, L., Teng, B., Geng, F., and Hu, Y. (2021). Viewing personalized video clips recommended by TikTok activates default mode network and ventral tegmental area. NeuroImage 237:118136. doi: 10.1016/j.neuroimage.2021.118136

Tian, X. (2023). Research on short video user behavior from the perspective of experience value . Jilin: Jilin University.

Tian, X., Bi, X., and Chen, H. (2023). How short-form video features influence addiction behavior? Empirical research from the opponent process theory perspective. Inf. Technol. People 36, 387–408. doi: 10.1108/ITP-04-2020-0186

van Rooij, A., and Prause, N. (2014). A critical review of “internet addiction” criteria with suggestions for the future. J. Behav. Addict. 3, 203–213. doi: 10.1556/JBA.3.2014.4.1

Volkow, N. D., Michaelides, M., and Baler, R. (2019). The neuroscience of drug reward and addiction. Physiol. Rev. 99, 2115–2140. doi: 10.1152/physrev.00014.2018

Wang, X. (2021). Analysis of the causes, potential risks and countermeasures of the phenomenon of short video fever in college students. Ideol. Theor. Educ. 1, 93–97.

Wang, J., Sheng, J., and Wang, H. (2019). The association between mobile game addiction and depression, social anxiety, and loneliness. Front. Public Health 7:476493. doi: 10.3389/fpubh.2019.00247

Wilbur, S. (1984). A look at human communication (Translated by Chen Liang et al.): Xinhua Publishing House, 114.

Williams, R., and Edge, D. (1996). The social shaping of technology. Res. Policy 25, 865–899. doi: 10.1016/0048-7333(96)00885-2

Xie, X., and Du, Y. (2023). An empirical study on the influence of socio-technical factors on short video addiction. Moderat. Effect Recomm. Algorith. 1, 72–75.

Yan, W., Liu, S., Zhang, R., and Xu, P. (2021). Susceptibility of compulsivity traits and the neural basis of prefrontal-Antireward system in drug addiction behavior. Adv. Psychol. Sci. 29, 1345–1357. doi: 10.3724/SP.J.1042.2021.01345

Yang, S., Lu, Y., Wang, B., and Zhao, L. (2014). The benefits and dangers of flow experience in high school students’ internet usage: the role of parental support. Comput. Hum. Behav. 41, 504–513. doi: 10.1016/j.chb.2014.09.039

Yu, G., Jiao, J., and Zhang, X. (2015). The origin, theory, and operational key of “platform media”. J. Renmin Univ. China 6:121.

Zea, Q., and Jung, H. (2019). Learning and sharing creative skills with short videos: A case study of user behavior in Tiktok and Bilibili . Manchester, UK: International Association of Societies of Design Research (IASDR), Design Revolution.

Zhang, G., and Liang, X. (2023). Control and capture: the role of platforms in the phenomenon of short video addiction. Modern Commun. 11, 81–84.

Zhang, M., and Liu, Y. (2021). A commentary of TikTok recommendation algorithms in MIT technology review 2021. Fundam. Res. 1, 846–847. doi: 10.1016/j.fmre.2021.11.015

Zhang, X., Wu, Y., and Liu, S. (2019). Exploring short-form video application addiction: socio-technical and attachment perspectives. Telematics Inform. 42:101243. doi: 10.1016/j.tele.2019.101243

Zhao, Z. (2021). Analysis on the “Douyin (Tiktok) mania” phenomenon based on recommendation algorithms. E3S Web Conf. 235:03029.

Keywords: short video addiction, psychological mechanisms, algorithms, algorithmic loop, government supervision

Citation: Liao M (2024) Analysis of the causes, psychological mechanisms, and coping strategies of short video addiction in China. Front. Psychol . 15:1391204. doi: 10.3389/fpsyg.2024.1391204

Received: 13 March 2024; Accepted: 02 July 2024; Published: 06 August 2024.

Reviewed by:

Copyright © 2024 Liao. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Mingyue Liao, [email protected]

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

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