parts of a research experiment

Experimental Design – Types, Methods, Guide

Table of Contents

Experimental Design

Experimental design is a process of planning and conducting scientific experiments to investigate a hypothesis or research question. It involves carefully designing an experiment that can test the hypothesis, and controlling for other variables that may influence the results.

Experimental design typically includes identifying the variables that will be manipulated or measured, defining the sample or population to be studied, selecting an appropriate method of sampling, choosing a method for data collection and analysis, and determining the appropriate statistical tests to use.

Types of Experimental Design

Here are the different types of experimental design:

Completely Randomized Design

In this design, participants are randomly assigned to one of two or more groups, and each group is exposed to a different treatment or condition.

Randomized Block Design

This design involves dividing participants into blocks based on a specific characteristic, such as age or gender, and then randomly assigning participants within each block to one of two or more treatment groups.

Factorial Design

In a factorial design, participants are randomly assigned to one of several groups, each of which receives a different combination of two or more independent variables.

Repeated Measures Design

In this design, each participant is exposed to all of the different treatments or conditions, either in a random order or in a predetermined order.

Crossover Design

This design involves randomly assigning participants to one of two or more treatment groups, with each group receiving one treatment during the first phase of the study and then switching to a different treatment during the second phase.

Split-plot Design

In this design, the researcher manipulates one or more variables at different levels and uses a randomized block design to control for other variables.

Nested Design

This design involves grouping participants within larger units, such as schools or households, and then randomly assigning these units to different treatment groups.

Laboratory Experiment

Laboratory experiments are conducted under controlled conditions, which allows for greater precision and accuracy. However, because laboratory conditions are not always representative of real-world conditions, the results of these experiments may not be generalizable to the population at large.

Field Experiment

Field experiments are conducted in naturalistic settings and allow for more realistic observations. However, because field experiments are not as controlled as laboratory experiments, they may be subject to more sources of error.

Experimental Design Methods

Experimental design methods refer to the techniques and procedures used to design and conduct experiments in scientific research. Here are some common experimental design methods:

Randomization

This involves randomly assigning participants to different groups or treatments to ensure that any observed differences between groups are due to the treatment and not to other factors.

Control Group

The use of a control group is an important experimental design method that involves having a group of participants that do not receive the treatment or intervention being studied. The control group is used as a baseline to compare the effects of the treatment group.

Blinding involves keeping participants, researchers, or both unaware of which treatment group participants are in, in order to reduce the risk of bias in the results.

Counterbalancing

This involves systematically varying the order in which participants receive treatments or interventions in order to control for order effects.

Replication

Replication involves conducting the same experiment with different samples or under different conditions to increase the reliability and validity of the results.

This experimental design method involves manipulating multiple independent variables simultaneously to investigate their combined effects on the dependent variable.

This involves dividing participants into subgroups or blocks based on specific characteristics, such as age or gender, in order to reduce the risk of confounding variables.

Data Collection Method

Experimental design data collection methods are techniques and procedures used to collect data in experimental research. Here are some common experimental design data collection methods:

Direct Observation

This method involves observing and recording the behavior or phenomenon of interest in real time. It may involve the use of structured or unstructured observation, and may be conducted in a laboratory or naturalistic setting.

Self-report Measures

Self-report measures involve asking participants to report their thoughts, feelings, or behaviors using questionnaires, surveys, or interviews. These measures may be administered in person or online.

Behavioral Measures

Behavioral measures involve measuring participants’ behavior directly, such as through reaction time tasks or performance tests. These measures may be administered using specialized equipment or software.

Physiological Measures

Physiological measures involve measuring participants’ physiological responses, such as heart rate, blood pressure, or brain activity, using specialized equipment. These measures may be invasive or non-invasive, and may be administered in a laboratory or clinical setting.

Archival Data

Archival data involves using existing records or data, such as medical records, administrative records, or historical documents, as a source of information. These data may be collected from public or private sources.

Computerized Measures

Computerized measures involve using software or computer programs to collect data on participants’ behavior or responses. These measures may include reaction time tasks, cognitive tests, or other types of computer-based assessments.

Video Recording

Video recording involves recording participants’ behavior or interactions using cameras or other recording equipment. This method can be used to capture detailed information about participants’ behavior or to analyze social interactions.

Data Analysis Method

Experimental design data analysis methods refer to the statistical techniques and procedures used to analyze data collected in experimental research. Here are some common experimental design data analysis methods:

Descriptive Statistics

Descriptive statistics are used to summarize and describe the data collected in the study. This includes measures such as mean, median, mode, range, and standard deviation.

Inferential Statistics

Inferential statistics are used to make inferences or generalizations about a larger population based on the data collected in the study. This includes hypothesis testing and estimation.

Analysis of Variance (ANOVA)

ANOVA is a statistical technique used to compare means across two or more groups in order to determine whether there are significant differences between the groups. There are several types of ANOVA, including one-way ANOVA, two-way ANOVA, and repeated measures ANOVA.

Regression Analysis

Regression analysis is used to model the relationship between two or more variables in order to determine the strength and direction of the relationship. There are several types of regression analysis, including linear regression, logistic regression, and multiple regression.

Factor Analysis

Factor analysis is used to identify underlying factors or dimensions in a set of variables. This can be used to reduce the complexity of the data and identify patterns in the data.

Structural Equation Modeling (SEM)

SEM is a statistical technique used to model complex relationships between variables. It can be used to test complex theories and models of causality.

Cluster Analysis

Cluster analysis is used to group similar cases or observations together based on similarities or differences in their characteristics.

Time Series Analysis

Time series analysis is used to analyze data collected over time in order to identify trends, patterns, or changes in the data.

Multilevel Modeling

Multilevel modeling is used to analyze data that is nested within multiple levels, such as students nested within schools or employees nested within companies.

Applications of Experimental Design 

Experimental design is a versatile research methodology that can be applied in many fields. Here are some applications of experimental design:

• Medical Research: Experimental design is commonly used to test new treatments or medications for various medical conditions. This includes clinical trials to evaluate the safety and effectiveness of new drugs or medical devices.
• Agriculture : Experimental design is used to test new crop varieties, fertilizers, and other agricultural practices. This includes randomized field trials to evaluate the effects of different treatments on crop yield, quality, and pest resistance.
• Environmental science: Experimental design is used to study the effects of environmental factors, such as pollution or climate change, on ecosystems and wildlife. This includes controlled experiments to study the effects of pollutants on plant growth or animal behavior.
• Psychology : Experimental design is used to study human behavior and cognitive processes. This includes experiments to test the effects of different interventions, such as therapy or medication, on mental health outcomes.
• Engineering : Experimental design is used to test new materials, designs, and manufacturing processes in engineering applications. This includes laboratory experiments to test the strength and durability of new materials, or field experiments to test the performance of new technologies.
• Education : Experimental design is used to evaluate the effectiveness of teaching methods, educational interventions, and programs. This includes randomized controlled trials to compare different teaching methods or evaluate the impact of educational programs on student outcomes.
• Marketing : Experimental design is used to test the effectiveness of marketing campaigns, pricing strategies, and product designs. This includes experiments to test the impact of different marketing messages or pricing schemes on consumer behavior.

Examples of Experimental Design 

Here are some examples of experimental design in different fields:

• Example in Medical research : A study that investigates the effectiveness of a new drug treatment for a particular condition. Patients are randomly assigned to either a treatment group or a control group, with the treatment group receiving the new drug and the control group receiving a placebo. The outcomes, such as improvement in symptoms or side effects, are measured and compared between the two groups.
• Example in Education research: A study that examines the impact of a new teaching method on student learning outcomes. Students are randomly assigned to either a group that receives the new teaching method or a group that receives the traditional teaching method. Student achievement is measured before and after the intervention, and the results are compared between the two groups.
• Example in Environmental science: A study that tests the effectiveness of a new method for reducing pollution in a river. Two sections of the river are selected, with one section treated with the new method and the other section left untreated. The water quality is measured before and after the intervention, and the results are compared between the two sections.
• Example in Marketing research: A study that investigates the impact of a new advertising campaign on consumer behavior. Participants are randomly assigned to either a group that is exposed to the new campaign or a group that is not. Their behavior, such as purchasing or product awareness, is measured and compared between the two groups.
• Example in Social psychology: A study that examines the effect of a new social intervention on reducing prejudice towards a marginalized group. Participants are randomly assigned to either a group that receives the intervention or a control group that does not. Their attitudes and behavior towards the marginalized group are measured before and after the intervention, and the results are compared between the two groups.

When to use Experimental Research Design 

Experimental research design should be used when a researcher wants to establish a cause-and-effect relationship between variables. It is particularly useful when studying the impact of an intervention or treatment on a particular outcome.

Here are some situations where experimental research design may be appropriate:

• When studying the effects of a new drug or medical treatment: Experimental research design is commonly used in medical research to test the effectiveness and safety of new drugs or medical treatments. By randomly assigning patients to treatment and control groups, researchers can determine whether the treatment is effective in improving health outcomes.
• When evaluating the effectiveness of an educational intervention: An experimental research design can be used to evaluate the impact of a new teaching method or educational program on student learning outcomes. By randomly assigning students to treatment and control groups, researchers can determine whether the intervention is effective in improving academic performance.
• When testing the effectiveness of a marketing campaign: An experimental research design can be used to test the effectiveness of different marketing messages or strategies. By randomly assigning participants to treatment and control groups, researchers can determine whether the marketing campaign is effective in changing consumer behavior.
• When studying the effects of an environmental intervention: Experimental research design can be used to study the impact of environmental interventions, such as pollution reduction programs or conservation efforts. By randomly assigning locations or areas to treatment and control groups, researchers can determine whether the intervention is effective in improving environmental outcomes.
• When testing the effects of a new technology: An experimental research design can be used to test the effectiveness and safety of new technologies or engineering designs. By randomly assigning participants or locations to treatment and control groups, researchers can determine whether the new technology is effective in achieving its intended purpose.

How to Conduct Experimental Research

Here are the steps to conduct Experimental Research:

• Identify a Research Question : Start by identifying a research question that you want to answer through the experiment. The question should be clear, specific, and testable.
• Develop a Hypothesis: Based on your research question, develop a hypothesis that predicts the relationship between the independent and dependent variables. The hypothesis should be clear and testable.
• Design the Experiment : Determine the type of experimental design you will use, such as a between-subjects design or a within-subjects design. Also, decide on the experimental conditions, such as the number of independent variables, the levels of the independent variable, and the dependent variable to be measured.
• Select Participants: Select the participants who will take part in the experiment. They should be representative of the population you are interested in studying.
• Randomly Assign Participants to Groups: If you are using a between-subjects design, randomly assign participants to groups to control for individual differences.
• Conduct the Experiment : Conduct the experiment by manipulating the independent variable(s) and measuring the dependent variable(s) across the different conditions.
• Analyze the Data: Analyze the data using appropriate statistical methods to determine if there is a significant effect of the independent variable(s) on the dependent variable(s).
• Draw Conclusions: Based on the data analysis, draw conclusions about the relationship between the independent and dependent variables. If the results support the hypothesis, then it is accepted. If the results do not support the hypothesis, then it is rejected.
• Communicate the Results: Finally, communicate the results of the experiment through a research report or presentation. Include the purpose of the study, the methods used, the results obtained, and the conclusions drawn.

Purpose of Experimental Design 

The purpose of experimental design is to control and manipulate one or more independent variables to determine their effect on a dependent variable. Experimental design allows researchers to systematically investigate causal relationships between variables, and to establish cause-and-effect relationships between the independent and dependent variables. Through experimental design, researchers can test hypotheses and make inferences about the population from which the sample was drawn.

Experimental design provides a structured approach to designing and conducting experiments, ensuring that the results are reliable and valid. By carefully controlling for extraneous variables that may affect the outcome of the study, experimental design allows researchers to isolate the effect of the independent variable(s) on the dependent variable(s), and to minimize the influence of other factors that may confound the results.

Experimental design also allows researchers to generalize their findings to the larger population from which the sample was drawn. By randomly selecting participants and using statistical techniques to analyze the data, researchers can make inferences about the larger population with a high degree of confidence.

Overall, the purpose of experimental design is to provide a rigorous, systematic, and scientific method for testing hypotheses and establishing cause-and-effect relationships between variables. Experimental design is a powerful tool for advancing scientific knowledge and informing evidence-based practice in various fields, including psychology, biology, medicine, engineering, and social sciences.

Advantages of Experimental Design 

Experimental design offers several advantages in research. Here are some of the main advantages:

• Control over extraneous variables: Experimental design allows researchers to control for extraneous variables that may affect the outcome of the study. By manipulating the independent variable and holding all other variables constant, researchers can isolate the effect of the independent variable on the dependent variable.
• Establishing causality: Experimental design allows researchers to establish causality by manipulating the independent variable and observing its effect on the dependent variable. This allows researchers to determine whether changes in the independent variable cause changes in the dependent variable.
• Replication : Experimental design allows researchers to replicate their experiments to ensure that the findings are consistent and reliable. Replication is important for establishing the validity and generalizability of the findings.
• Random assignment: Experimental design often involves randomly assigning participants to conditions. This helps to ensure that individual differences between participants are evenly distributed across conditions, which increases the internal validity of the study.
• Precision : Experimental design allows researchers to measure variables with precision, which can increase the accuracy and reliability of the data.
• Generalizability : If the study is well-designed, experimental design can increase the generalizability of the findings. By controlling for extraneous variables and using random assignment, researchers can increase the likelihood that the findings will apply to other populations and contexts.

Limitations of Experimental Design

Experimental design has some limitations that researchers should be aware of. Here are some of the main limitations:

• Artificiality : Experimental design often involves creating artificial situations that may not reflect real-world situations. This can limit the external validity of the findings, or the extent to which the findings can be generalized to real-world settings.
• Ethical concerns: Some experimental designs may raise ethical concerns, particularly if they involve manipulating variables that could cause harm to participants or if they involve deception.
• Participant bias : Participants in experimental studies may modify their behavior in response to the experiment, which can lead to participant bias.
• Limited generalizability: The conditions of the experiment may not reflect the complexities of real-world situations. As a result, the findings may not be applicable to all populations and contexts.
• Cost and time : Experimental design can be expensive and time-consuming, particularly if the experiment requires specialized equipment or if the sample size is large.
• Researcher bias : Researchers may unintentionally bias the results of the experiment if they have expectations or preferences for certain outcomes.
• Lack of feasibility : Experimental design may not be feasible in some cases, particularly if the research question involves variables that cannot be manipulated or controlled.

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

You may also like

Survey Research – Types, Methods, Examples

Focus Groups – Steps, Examples and Guide

Textual Analysis – Types, Examples and Guide

Transformative Design – Methods, Types, Guide

Ethnographic Research -Types, Methods and Guide

Mixed Methods Research – Types & Analysis

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

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

Home Market Research

Experimental Research: What it is + Types of designs

Any research conducted under scientifically acceptable conditions uses experimental methods. The success of experimental studies hinges on researchers confirming the change of a variable is based solely on the manipulation of the constant variable. The research should establish a notable cause and effect.

What is Experimental Research?

Experimental research is a study conducted with a scientific approach using two sets of variables. The first set acts as a constant, which you use to measure the differences of the second set. Quantitative research methods , for example, are experimental.

If you don’t have enough data to support your decisions, you must first determine the facts. This research gathers the data necessary to help you make better decisions.

You can conduct experimental research in the following situations:

• Time is a vital factor in establishing a relationship between cause and effect.
• Invariable behavior between cause and effect.
• You wish to understand the importance of cause and effect.

Experimental Research Design Types

The classic experimental design definition is: “The methods used to collect data in experimental studies.”

There are three primary types of experimental design:

• Pre-experimental research design
• True experimental research design
• Quasi-experimental research design

The way you classify research subjects based on conditions or groups determines the type of research design  you should use.

0 1. Pre-Experimental Design

A group, or various groups, are kept under observation after implementing cause and effect factors. You’ll conduct this research to understand whether further investigation is necessary for these particular groups.

You can break down pre-experimental research further into three types:

• One-shot Case Study Research Design
• One-group Pretest-posttest Research Design
• Static-group Comparison

0 2. True Experimental Design

It relies on statistical analysis to prove or disprove a hypothesis, making it the most accurate form of research. Of the types of experimental design, only true design can establish a cause-effect relationship within a group. In a true experiment, three factors need to be satisfied:

• There is a Control Group, which won’t be subject to changes, and an Experimental Group, which will experience the changed variables.
• A variable that can be manipulated by the researcher
• Random distribution

This experimental research method commonly occurs in the physical sciences.

0 3. Quasi-Experimental Design

The word “Quasi” indicates similarity. A quasi-experimental design is similar to an experimental one, but it is not the same. The difference between the two is the assignment of a control group. In this research, an independent variable is manipulated, but the participants of a group are not randomly assigned. Quasi-research is used in field settings where random assignment is either irrelevant or not required.

Importance of Experimental Design

Experimental research is a powerful tool for understanding cause-and-effect relationships. It allows us to manipulate variables and observe the effects, which is crucial for understanding how different factors influence the outcome of a study.

But the importance of experimental research goes beyond that. It’s a critical method for many scientific and academic studies. It allows us to test theories, develop new products, and make groundbreaking discoveries.

For example, this research is essential for developing new drugs and medical treatments. Researchers can understand how a new drug works by manipulating dosage and administration variables and identifying potential side effects.

Similarly, experimental research is used in the field of psychology to test theories and understand human behavior. By manipulating variables such as stimuli, researchers can gain insights into how the brain works and identify new treatment options for mental health disorders.

It is also widely used in the field of education. It allows educators to test new teaching methods and identify what works best. By manipulating variables such as class size, teaching style, and curriculum, researchers can understand how students learn and identify new ways to improve educational outcomes.

In addition, experimental research is a powerful tool for businesses and organizations. By manipulating variables such as marketing strategies, product design, and customer service, companies can understand what works best and identify new opportunities for growth.

Advantages of Experimental Research

When talking about this research, we can think of human life. Babies do their own rudimentary experiments (such as putting objects in their mouths) to learn about the world around them, while older children and teens do experiments at school to learn more about science.

Ancient scientists used this research to prove that their hypotheses were correct. For example, Galileo Galilei and Antoine Lavoisier conducted various experiments to discover key concepts in physics and chemistry. The same is true of modern experts, who use this scientific method to see if new drugs are effective, discover treatments for diseases, and create new electronic devices (among others).

It’s vital to test new ideas or theories. Why put time, effort, and funding into something that may not work?

This research allows you to test your idea in a controlled environment before marketing. It also provides the best method to test your theory thanks to the following advantages:

• Researchers have a stronger hold over variables to obtain desired results.
• The subject or industry does not impact the effectiveness of experimental research. Any industry can implement it for research purposes.
• The results are specific.
• After analyzing the results, you can apply your findings to similar ideas or situations.
• You can identify the cause and effect of a hypothesis. Researchers can further analyze this relationship to determine more in-depth ideas.
• Experimental research makes an ideal starting point. The data you collect is a foundation for building more ideas and conducting more action research .

Whether you want to know how the public will react to a new product or if a certain food increases the chance of disease, experimental research is the best place to start. Begin your research by finding subjects using  QuestionPro Audience  and other tools today.

LEARN MORE         FREE TRIAL

MORE LIKE THIS

Age Gating: Effective Strategies for Online Content Control

Aug 23, 2024

Customer Experience Lessons from 13,000 Feet — Tuesday CX Thoughts

Aug 20, 2024

Insight: Definition & meaning, types and examples

Aug 19, 2024

Employee Loyalty: Strategies for Long-Term Business Success 

Other categories.

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

Have a language expert improve your writing

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

• Knowledge Base
• Methodology
• A Quick Guide to Experimental Design | 5 Steps & Examples

A Quick Guide to Experimental Design | 5 Steps & Examples

Published on 11 April 2022 by Rebecca Bevans . Revised on 5 December 2022.

Experiments are used to study causal relationships . You manipulate one or more independent variables and measure their effect on one or more dependent variables.

Experimental design means creating a set of procedures to systematically test a hypothesis . A good experimental design requires a strong understanding of the system you are studying. 

There are five key steps in designing an experiment:

• Consider your variables and how they are related
• Write a specific, testable hypothesis
• Design experimental treatments to manipulate your independent variable
• Assign subjects to groups, either between-subjects or within-subjects
• Plan how you will measure your dependent variable

For valid conclusions, you also need to select a representative sample and control any  extraneous variables that might influence your results. If if random assignment of participants to control and treatment groups is impossible, unethical, or highly difficult, consider an observational study instead.

Table of contents

Step 1: define your variables, step 2: write your hypothesis, step 3: design your experimental treatments, step 4: assign your subjects to treatment groups, step 5: measure your dependent variable, frequently asked questions about experimental design.

You should begin with a specific research question . We will work with two research question examples, one from health sciences and one from ecology:

To translate your research question into an experimental hypothesis, you need to define the main variables and make predictions about how they are related.

Start by simply listing the independent and dependent variables .

Then you need to think about possible extraneous and confounding variables and consider how you might control  them in your experiment.

Finally, you can put these variables together into a diagram. Use arrows to show the possible relationships between variables and include signs to show the expected direction of the relationships.

Here we predict that increasing temperature will increase soil respiration and decrease soil moisture, while decreasing soil moisture will lead to decreased soil respiration.

Prevent plagiarism, run a free check.

Now that you have a strong conceptual understanding of the system you are studying, you should be able to write a specific, testable hypothesis that addresses your research question.

The next steps will describe how to design a controlled experiment . In a controlled experiment, you must be able to:

• Systematically and precisely manipulate the independent variable(s).
• Precisely measure the dependent variable(s).
• Control any potential confounding variables.

If your study system doesn’t match these criteria, there are other types of research you can use to answer your research question.

How you manipulate the independent variable can affect the experiment’s external validity – that is, the extent to which the results can be generalised and applied to the broader world.

First, you may need to decide how widely to vary your independent variable.

• just slightly above the natural range for your study region.
• over a wider range of temperatures to mimic future warming.
• over an extreme range that is beyond any possible natural variation.

Second, you may need to choose how finely to vary your independent variable. Sometimes this choice is made for you by your experimental system, but often you will need to decide, and this will affect how much you can infer from your results.

• a categorical variable : either as binary (yes/no) or as levels of a factor (no phone use, low phone use, high phone use).
• a continuous variable (minutes of phone use measured every night).

How you apply your experimental treatments to your test subjects is crucial for obtaining valid and reliable results.

First, you need to consider the study size : how many individuals will be included in the experiment? In general, the more subjects you include, the greater your experiment’s statistical power , which determines how much confidence you can have in your results.

Then you need to randomly assign your subjects to treatment groups . Each group receives a different level of the treatment (e.g. no phone use, low phone use, high phone use).

You should also include a control group , which receives no treatment. The control group tells us what would have happened to your test subjects without any experimental intervention.

When assigning your subjects to groups, there are two main choices you need to make:

• A completely randomised design vs a randomised block design .
• A between-subjects design vs a within-subjects design .

Randomisation

An experiment can be completely randomised or randomised within blocks (aka strata):

• In a completely randomised design , every subject is assigned to a treatment group at random.
• In a randomised block design (aka stratified random design), subjects are first grouped according to a characteristic they share, and then randomly assigned to treatments within those groups.

Sometimes randomisation isn’t practical or ethical , so researchers create partially-random or even non-random designs. An experimental design where treatments aren’t randomly assigned is called a quasi-experimental design .

Between-subjects vs within-subjects

In a between-subjects design (also known as an independent measures design or classic ANOVA design), individuals receive only one of the possible levels of an experimental treatment.

In medical or social research, you might also use matched pairs within your between-subjects design to make sure that each treatment group contains the same variety of test subjects in the same proportions.

In a within-subjects design (also known as a repeated measures design), every individual receives each of the experimental treatments consecutively, and their responses to each treatment are measured.

Within-subjects or repeated measures can also refer to an experimental design where an effect emerges over time, and individual responses are measured over time in order to measure this effect as it emerges.

Counterbalancing (randomising or reversing the order of treatments among subjects) is often used in within-subjects designs to ensure that the order of treatment application doesn’t influence the results of the experiment.

Finally, you need to decide how you’ll collect data on your dependent variable outcomes. You should aim for reliable and valid measurements that minimise bias or error.

Some variables, like temperature, can be objectively measured with scientific instruments. Others may need to be operationalised to turn them into measurable observations.

• Ask participants to record what time they go to sleep and get up each day.
• Ask participants to wear a sleep tracker.

How precisely you measure your dependent variable also affects the kinds of statistical analysis you can use on your data.

Experiments are always context-dependent, and a good experimental design will take into account all of the unique considerations of your study system to produce information that is both valid and relevant to your research question.

Experimental designs are a set of procedures that you plan in order to examine the relationship between variables that interest you.

To design a successful experiment, first identify:

• A testable hypothesis
• One or more independent variables that you will manipulate
• One or more dependent variables that you will measure

When designing the experiment, first decide:

• How your variable(s) will be manipulated
• How you will control for any potential confounding or lurking variables
• How many subjects you will include
• How you will assign treatments to your subjects

The key difference between observational studies and experiments is that, done correctly, an observational study will never influence the responses or behaviours of participants. Experimental designs will have a treatment condition applied to at least a portion of participants.

A confounding variable , also called a confounder or confounding factor, is a third variable in a study examining a potential cause-and-effect relationship.

A confounding variable is related to both the supposed cause and the supposed effect of the study. It can be difficult to separate the true effect of the independent variable from the effect of the confounding variable.

In your research design , it’s important to identify potential confounding variables and plan how you will reduce their impact.

In a between-subjects design , every participant experiences only one condition, and researchers assess group differences between participants in various conditions.

In a within-subjects design , each participant experiences all conditions, and researchers test the same participants repeatedly for differences between conditions.

The word ‘between’ means that you’re comparing different conditions between groups, while the word ‘within’ means you’re comparing different conditions within the same group.

Cite this Scribbr article

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

Bevans, R. (2022, December 05). A Quick Guide to Experimental Design | 5 Steps & Examples. Scribbr. Retrieved 21 August 2024, from https://www.scribbr.co.uk/research-methods/guide-to-experimental-design/

Is this article helpful?

Rebecca Bevans

Scientific Method Example

Illustration by J.R. Bee. ThoughtCo. 

• Cell Biology
• Weather & Climate
• B.A., Biology, Emory University
• A.S., Nursing, Chattahoochee Technical College

The scientific method is a series of steps that scientific investigators follow to answer specific questions about the natural world. Scientists use the scientific method to make observations, formulate hypotheses , and conduct scientific experiments .

A scientific inquiry starts with an observation. Then, the formulation of a question about what has been observed follows. Next, the scientist will proceed through the remaining steps of the scientific method to end at a conclusion.

The six steps of the scientific method are as follows:

Observation

The first step of the scientific method involves making an observation about something that interests you. Taking an interest in your scientific discovery is important, for example, if you are doing a science project , because you will want to work on something that holds your attention. Your observation can be of anything from plant movement to animal behavior, as long as it is something you want to know more about.​ This step is when you will come up with an idea if you are working on a science project.

Once you have made your observation, you must formulate a question about what you observed. Your question should summarize what it is you are trying to discover or accomplish in your experiment. When stating your question, be as specific as possible.​ For example, if you are doing a project on plants , you may want to know how plants interact with microbes. Your question could be: Do plant spices inhibit bacterial growth ?

The hypothesis is a key component of the scientific process. A hypothesis is an idea that is suggested as an explanation for a natural event, a particular experience, or a specific condition that can be tested through definable experimentation. It states the purpose of your experiment, the variables used, and the predicted outcome of your experiment. It is important to note that a hypothesis must be testable. That means that you should be able to test your hypothesis through experimentation .​ Your hypothesis must either be supported or falsified by your experiment. An example of a good hypothesis is: If there is a relation between listening to music and heart rate, then listening to music will cause a person's resting heart rate to either increase or decrease.

Once you have developed a hypothesis, you must design and conduct an experiment that will test it. You should develop a procedure that states clearly how you plan to conduct your experiment. It is important you include and identify a controlled variable or dependent variable in your procedure. Controls allow us to test a single variable in an experiment because they are unchanged. We can then make observations and comparisons between our controls and our independent variables (things that change in the experiment) to develop an accurate conclusion.​

The results are where you report what happened in the experiment. That includes detailing all observations and data made during your experiment. Most people find it easier to visualize the data by charting or graphing the information.​

Developing a conclusion is the final step of the scientific method. This is where you analyze the results from the experiment and reach a determination about the hypothesis. Did the experiment support or reject your hypothesis? If your hypothesis was supported, great. If not, repeat the experiment or think of ways to improve your procedure.

• Glycolysis Steps
• Biology Prefixes and Suffixes: chrom- or chromo-
• Biology Prefixes and Suffixes: proto-
• 6 Things You Should Know About Biological Evolution
• Biology Prefixes and Suffixes: Aer- or Aero-
• Taxonomy and Organism Classification
• Homeostasis
• Biology Prefixes and Suffixes: diplo-
• The Biology Suffix -lysis
• Biology Prefixes and Suffixes Index
• Biology Prefixes and Suffixes: tel- or telo-
• Parasitism: Definition and Examples
• Biology Prefixes and Suffixes: Erythr- or Erythro-
• Biology Prefixes and Suffixes: ana-
• Biology Prefixes and Suffixes: phago- or phag-
• Biology Prefixes and Suffixes: -phyll or -phyl

• Science Notes Posts
• Contact Science Notes
• Todd Helmenstine Biography
• Anne Helmenstine Biography
• Free Printable Periodic Tables (PDF and PNG)
• Periodic Table Wallpapers
• Interactive Periodic Table
• Periodic Table Posters
• Science Experiments for Kids
• How to Grow Crystals
• Chemistry Projects
• Fire and Flames Projects
• Holiday Science
• Chemistry Problems With Answers
• Physics Problems
• Unit Conversion Example Problems
• Chemistry Worksheets
• Biology Worksheets
• Periodic Table Worksheets
• Physical Science Worksheets
• Science Lab Worksheets
• My Amazon Books

Experiment Definition in Science – What Is a Science Experiment?

In science, an experiment is simply a test of a hypothesis in the scientific method . It is a controlled examination of cause and effect. Here is a look at what a science experiment is (and is not), the key factors in an experiment, examples, and types of experiments.

Experiment Definition in Science

By definition, an experiment is a procedure that tests a hypothesis. A hypothesis, in turn, is a prediction of cause and effect or the predicted outcome of changing one factor of a situation. Both the hypothesis and experiment are components of the scientific method. The steps of the scientific method are:

• Make observations.
• Ask a question or identify a problem.
• State a hypothesis.
• Perform an experiment that tests the hypothesis.
• Based on the results of the experiment, either accept or reject the hypothesis.
• Draw conclusions and report the outcome of the experiment.

Key Parts of an Experiment

The two key parts of an experiment are the independent and dependent variables. The independent variable is the one factor that you control or change in an experiment. The dependent variable is the factor that you measure that responds to the independent variable. An experiment often includes other types of variables , but at its heart, it’s all about the relationship between the independent and dependent variable.

Examples of Experiments

Fertilizer and plant size.

For example, you think a certain fertilizer helps plants grow better. You’ve watched your plants grow and they seem to do better when they have the fertilizer compared to when they don’t. But, observations are only the beginning of science. So, you state a hypothesis: Adding fertilizer increases plant size. Note, you could have stated the hypothesis in different ways. Maybe you think the fertilizer increases plant mass or fruit production, for example. However you state the hypothesis, it includes both the independent and dependent variables. In this case, the independent variable is the presence or absence of fertilizer. The dependent variable is the response to the independent variable, which is the size of the plants.

Now that you have a hypothesis, the next step is designing an experiment that tests it. Experimental design is very important because the way you conduct an experiment influences its outcome. For example, if you use too small of an amount of fertilizer you may see no effect from the treatment. Or, if you dump an entire container of fertilizer on a plant you could kill it! So, recording the steps of the experiment help you judge the outcome of the experiment and aid others who come after you and examine your work. Other factors that might influence your results might include the species of plant and duration of the treatment. Record any conditions that might affect the outcome. Ideally, you want the only difference between your two groups of plants to be whether or not they receive fertilizer. Then, measure the height of the plants and see if there is a difference between the two groups.

Salt and Cookies

You don’t need a lab for an experiment. For example, consider a baking experiment. Let’s say you like the flavor of salt in your cookies, but you’re pretty sure the batch you made using extra salt fell a bit flat. If you double the amount of salt in a recipe, will it affect their size? Here, the independent variable is the amount of salt in the recipe and the dependent variable is cookie size.

Test this hypothesis with an experiment. Bake cookies using the normal recipe (your control group ) and bake some using twice the salt (the experimental group). Make sure it’s the exact same recipe. Bake the cookies at the same temperature and for the same time. Only change the amount of salt in the recipe. Then measure the height or diameter of the cookies and decide whether to accept or reject the hypothesis.

Examples of Things That Are Not Experiments

Based on the examples of experiments, you should see what is not an experiment:

• Making observations does not constitute an experiment. Initial observations often lead to an experiment, but are not a substitute for one.
• Making a model is not an experiment.
• Neither is making a poster.
• Just trying something to see what happens is not an experiment. You need a hypothesis or prediction about the outcome.
• Changing a lot of things at once isn’t an experiment. You only have one independent and one dependent variable. However, in an experiment, you might suspect the independent variable has an effect on a separate. So, you design a new experiment to test this.

Types of Experiments

There are three main types of experiments: controlled experiments, natural experiments, and field experiments,

• Controlled experiment : A controlled experiment compares two groups of samples that differ only in independent variable. For example, a drug trial compares the effect of a group taking a placebo (control group) against those getting the drug (the treatment group). Experiments in a lab or home generally are controlled experiments
• Natural experiment : Another name for a natural experiment is a quasi-experiment. In this type of experiment, the researcher does not directly control the independent variable, plus there may be other variables at play. Here, the goal is establishing a correlation between the independent and dependent variable. For example, in the formation of new elements a scientist hypothesizes that a certain collision between particles creates a new atom. But, other outcomes may be possible. Or, perhaps only decay products are observed that indicate the element, and not the new atom itself. Many fields of science rely on natural experiments, since controlled experiments aren’t always possible.
• Field experiment : While a controlled experiments takes place in a lab or other controlled setting, a field experiment occurs in a natural setting. Some phenomena cannot be readily studied in a lab or else the setting exerts an influence that affects the results. So, a field experiment may have higher validity. However, since the setting is not controlled, it is also subject to external factors and potential contamination. For example, if you study whether a certain plumage color affects bird mate selection, a field experiment in a natural environment eliminates the stressors of an artificial environment. Yet, other factors that could be controlled in a lab may influence results. For example, nutrition and health are controlled in a lab, but not in the field.
• Bailey, R.A. (2008). Design of Comparative Experiments . Cambridge: Cambridge University Press. ISBN 9780521683579.
• di Francia, G. Toraldo (1981). The Investigation of the Physical World . Cambridge University Press. ISBN 0-521-29925-X.
• Hinkelmann, Klaus; Kempthorne, Oscar (2008). Design and Analysis of Experiments. Volume I: Introduction to Experimental Design (2nd ed.). Wiley. ISBN 978-0-471-72756-9.
• Holland, Paul W. (December 1986). “Statistics and Causal Inference”.  Journal of the American Statistical Association . 81 (396): 945–960. doi: 10.2307/2289064
• Stohr-Hunt, Patricia (1996). “An Analysis of Frequency of Hands-on Experience and Science Achievement”. Journal of Research in Science Teaching . 33 (1): 101–109. doi: 10.1002/(SICI)1098-2736(199601)33:1<101::AID-TEA6>3.0.CO;2-Z

Related Posts

Exploring Experimental Research: Methodologies, Designs, and Applications Across Disciplines

• SSRN Electronic Journal

• The National University of Cheasim Kamchaymear

Discover the world's research

• 25+ million members
• 160+ million publication pages
• 2.3+ billion citations

• Khaoula Boussalham

• COMPUT COMMUN REV

• Debbie Rohwer

• Sorakrich Maneewan
• Ravinder Koul
• Int J Contemp Hospit Manag

• J EXP ANAL BEHAV
• Alan E. Kazdin
• Jimmie Leppink
• Keith Morrison
• Louis Cohen
• Lawrence Manion
• ACCOUNT ORG SOC
• Wim A. Van der Stede
• Recruit researchers
• Join for free
• Login Email Tip: Most researchers use their institutional email address as their ResearchGate login Password Forgot password? Keep me logged in Log in or Continue with Google Welcome back! Please log in. Email · Hint Tip: Most researchers use their institutional email address as their ResearchGate login Password Forgot password? Keep me logged in Log in or Continue with Google No account? Sign up

• Skip to secondary menu
• Skip to main content
• Skip to primary sidebar

Statistics By Jim

Making statistics intuitive

Experimental Design: Definition and Types

By Jim Frost 3 Comments

What is Experimental Design?

An experimental design is a detailed plan for collecting and using data to identify causal relationships. Through careful planning, the design of experiments allows your data collection efforts to have a reasonable chance of detecting effects and testing hypotheses that answer your research questions.

An experiment is a data collection procedure that occurs in controlled conditions to identify and understand causal relationships between variables. Researchers can use many potential designs. The ultimate choice depends on their research question, resources, goals, and constraints. In some fields of study, researchers refer to experimental design as the design of experiments (DOE). Both terms are synonymous.

Ultimately, the design of experiments helps ensure that your procedures and data will evaluate your research question effectively. Without an experimental design, you might waste your efforts in a process that, for many potential reasons, can’t answer your research question. In short, it helps you trust your results.

Learn more about Independent and Dependent Variables .

Design of Experiments: Goals & Settings

Experiments occur in many settings, ranging from psychology, social sciences, medicine, physics, engineering, and industrial and service sectors. Typically, experimental goals are to discover a previously unknown effect , confirm a known effect, or test a hypothesis.

Effects represent causal relationships between variables. For example, in a medical experiment, does the new medicine cause an improvement in health outcomes? If so, the medicine has a causal effect on the outcome.

An experimental design’s focus depends on the subject area and can include the following goals:

• Understanding the relationships between variables.
• Identifying the variables that have the largest impact on the outcomes.
• Finding the input variable settings that produce an optimal result.

For example, psychologists have conducted experiments to understand how conformity affects decision-making. Sociologists have performed experiments to determine whether ethnicity affects the public reaction to staged bike thefts. These experiments map out the causal relationships between variables, and their primary goal is to understand the role of various factors.

Conversely, in a manufacturing environment, the researchers might use an experimental design to find the factors that most effectively improve their product’s strength, identify the optimal manufacturing settings, and do all that while accounting for various constraints. In short, a manufacturer’s goal is often to use experiments to improve their products cost-effectively.

In a medical experiment, the goal might be to quantify the medicine’s effect and find the optimum dosage.

Developing an Experimental Design

Developing an experimental design involves planning that maximizes the potential to collect data that is both trustworthy and able to detect causal relationships. Specifically, these studies aim to see effects when they exist in the population the researchers are studying, preferentially favor causal effects, isolate each factor’s true effect from potential confounders, and produce conclusions that you can generalize to the real world.

To accomplish these goals, experimental designs carefully manage data validity and reliability , and internal and external experimental validity. When your experiment is valid and reliable, you can expect your procedures and data to produce trustworthy results.

An excellent experimental design involves the following:

• Lots of preplanning.
• Developing experimental treatments.
• Determining how to assign subjects to treatment groups.

The remainder of this article focuses on how experimental designs incorporate these essential items to accomplish their research goals.

Learn more about Data Reliability vs. Validity and Internal and External Experimental Validity .

Preplanning, Defining, and Operationalizing for Design of Experiments

This phase of the design of experiments helps you identify critical variables, know how to measure them while ensuring reliability and validity, and understand the relationships between them. The review can also help you find ways to reduce sources of variability, which increases your ability to detect treatment effects. Notably, the literature review allows you to learn how similar studies designed their experiments and the challenges they faced.

Operationalizing a study involves taking your research question, using the background information you gathered, and formulating an actionable plan.

This process should produce a specific and testable hypothesis using data that you can reasonably collect given the resources available to the experiment.

• Null hypothesis : The jumping exercise intervention does not affect bone density.
• Alternative hypothesis : The jumping exercise intervention affects bone density.

To learn more about this early phase, read Five Steps for Conducting Scientific Studies with Statistical Analyses .

Formulating Treatments in Experimental Designs

In an experimental design, treatments are variables that the researchers control. They are the primary independent variables of interest. Researchers administer the treatment to the subjects or items in the experiment and want to know whether it causes changes in the outcome.

As the name implies, a treatment can be medical in nature, such as a new medicine or vaccine. But it’s a general term that applies to other things such as training programs, manufacturing settings, teaching methods, and types of fertilizers. I helped run an experiment where the treatment was a jumping exercise intervention that we hoped would increase bone density. All these treatment examples are things that potentially influence a measurable outcome.

Even when you know your treatment generally, you must carefully consider the amount. How large of a dose? If you’re comparing three different temperatures in a manufacturing process, how far apart are they? For my bone mineral density study, we had to determine how frequently the exercise sessions would occur and how long each lasted.

How you define the treatments in the design of experiments can affect your findings and the generalizability of your results.

Assigning Subjects to Experimental Groups

A crucial decision for all experimental designs is determining how researchers assign subjects to the experimental conditions—the treatment and control groups. The control group is often, but not always, the lack of a treatment. It serves as a basis for comparison by showing outcomes for subjects who don’t receive a treatment. Learn more about Control Groups .

How your experimental design assigns subjects to the groups affects how confident you can be that the findings represent true causal effects rather than mere correlation caused by confounders. Indeed, the assignment method influences how you control for confounding variables. This is the difference between correlation and causation .

Imagine a study finds that vitamin consumption correlates with better health outcomes. As a researcher, you want to be able to say that vitamin consumption causes the improvements. However, with the wrong experimental design, you might only be able to say there is an association. A confounder, and not the vitamins, might actually cause the health benefits.

Let’s explore some of the ways to assign subjects in design of experiments.

Completely Randomized Designs

A completely randomized experimental design randomly assigns all subjects to the treatment and control groups. You simply take each participant and use a random process to determine their group assignment. You can flip coins, roll a die, or use a computer. Randomized experiments must be prospective studies because they need to be able to control group assignment.

Random assignment in the design of experiments helps ensure that the groups are roughly equivalent at the beginning of the study. This equivalence at the start increases your confidence that any differences you see at the end were caused by the treatments. The randomization tends to equalize confounders between the experimental groups and, thereby, cancels out their effects, leaving only the treatment effects.

For example, in a vitamin study, the researchers can randomly assign participants to either the control or vitamin group. Because the groups are approximately equal when the experiment starts, if the health outcomes are different at the end of the study, the researchers can be confident that the vitamins caused those improvements.

Statisticians consider randomized experimental designs to be the best for identifying causal relationships.

If you can’t randomly assign subjects but want to draw causal conclusions about an intervention, consider using a quasi-experimental design .

Learn more about Randomized Controlled Trials and Random Assignment in Experiments .

Randomized Block Designs

Nuisance factors are variables that can affect the outcome, but they are not the researcher’s primary interest. Unfortunately, they can hide or distort the treatment results. When experimenters know about specific nuisance factors, they can use a randomized block design to minimize their impact.

This experimental design takes subjects with a shared “nuisance” characteristic and groups them into blocks. The participants in each block are then randomly assigned to the experimental groups. This process allows the experiment to control for known nuisance factors.

Blocking in the design of experiments reduces the impact of nuisance factors on experimental error. The analysis assesses the effects of the treatment within each block, which removes the variability between blocks. The result is that blocked experimental designs can reduce the impact of nuisance variables, increasing the ability to detect treatment effects accurately.

Suppose you’re testing various teaching methods. Because grade level likely affects educational outcomes, you might use grade level as a blocking factor. To use a randomized block design for this scenario, divide the participants by grade level and then randomly assign the members of each grade level to the experimental groups.

A standard guideline for an experimental design is to “Block what you can, randomize what you cannot.” Use blocking for a few primary nuisance factors. Then use random assignment to distribute the unblocked nuisance factors equally between the experimental conditions.

You can also use covariates to control nuisance factors. Learn about Covariates: Definition and Uses .

Observational Studies

In some experimental designs, randomly assigning subjects to the experimental conditions is impossible or unethical. The researchers simply can’t assign participants to the experimental groups. However, they can observe them in their natural groupings, measure the essential variables, and look for correlations. These observational studies are also known as quasi-experimental designs. Retrospective studies must be observational in nature because they look back at past events.

Imagine you’re studying the effects of depression on an activity. Clearly, you can’t randomly assign participants to the depression and control groups. But you can observe participants with and without depression and see how their task performance differs.

Observational studies let you perform research when you can’t control the treatment. However, quasi-experimental designs increase the problem of confounding variables. For this design of experiments, correlation does not necessarily imply causation. While special procedures can help control confounders in an observational study, you’re ultimately less confident that the results represent causal findings.

Learn more about Observational Studies .

For a good comparison, learn about the differences and tradeoffs between Observational Studies and Randomized Experiments .

Between-Subjects vs. Within-Subjects Experimental Designs

When you think of the design of experiments, you probably picture a treatment and control group. Researchers assign participants to only one of these groups, so each group contains entirely different subjects than the other groups. Analysts compare the groups at the end of the experiment. Statisticians refer to this method as a between-subjects, or independent measures, experimental design.

In a between-subjects design , you can have more than one treatment group, but each subject is exposed to only one condition, the control group or one of the treatment groups.

A potential downside to this approach is that differences between groups at the beginning can affect the results at the end. As you’ve read earlier, random assignment can reduce those differences, but it is imperfect. There will always be some variability between the groups.

In a  within-subjects experimental design , also known as repeated measures, subjects experience all treatment conditions and are measured for each. Each subject acts as their own control, which reduces variability and increases the statistical power to detect effects.

In this experimental design, you minimize pre-existing differences between the experimental conditions because they all contain the same subjects. However, the order of treatments can affect the results. Beware of practice and fatigue effects. Learn more about Repeated Measures Designs .

Design of Experiments Examples

For example, a bone density study has three experimental groups—a control group, a stretching exercise group, and a jumping exercise group.

In a between-subjects experimental design, scientists randomly assign each participant to one of the three groups.

In a within-subjects design, all subjects experience the three conditions sequentially while the researchers measure bone density repeatedly. The procedure can switch the order of treatments for the participants to help reduce order effects.

Matched Pairs Experimental Design

A matched pairs experimental design is a between-subjects study that uses pairs of similar subjects. Researchers use this approach to reduce pre-existing differences between experimental groups. It’s yet another design of experiments method for reducing sources of variability.

Researchers identify variables likely to affect the outcome, such as demographics. When they pick a subject with a set of characteristics, they try to locate another participant with similar attributes to create a matched pair. Scientists randomly assign one member of a pair to the treatment group and the other to the control group.

On the plus side, this process creates two similar groups, and it doesn’t create treatment order effects. While matched pairs do not produce the perfectly matched groups of a within-subjects design (which uses the same subjects in all conditions), it aims to reduce variability between groups relative to a between-subjects study.

On the downside, finding matched pairs is very time-consuming. Additionally, if one member of a matched pair drops out, the other subject must leave the study too.

Learn more about Matched Pairs Design: Uses & Examples .

Another consideration is whether you’ll use a cross-sectional design (one point in time) or use a longitudinal study to track changes over time .

A case study is a research method that often serves as a precursor to a more rigorous experimental design by identifying research questions, variables, and hypotheses to test. Learn more about What is a Case Study? Definition & Examples .

In conclusion, the design of experiments is extremely sensitive to subject area concerns and the time and resources available to the researchers. Developing a suitable experimental design requires balancing a multitude of considerations. A successful design is necessary to obtain trustworthy answers to your research question and to have a reasonable chance of detecting treatment effects when they exist.

Share this:

Reader Interactions

March 23, 2024 at 2:35 pm

Dear Jim You wrote a superb document, I will use it in my Buistatistics course, along with your three books. Thank you very much! Miguel

March 23, 2024 at 5:43 pm

Thanks so much, Miguel! Glad this post was helpful and I trust the books will be as well.

April 10, 2023 at 4:36 am

What are the purpose and uses of experimental research design?

Comments and Questions Cancel reply

Sciencing_Icons_Science SCIENCE

Sciencing_icons_biology biology, sciencing_icons_cells cells, sciencing_icons_molecular molecular, sciencing_icons_microorganisms microorganisms, sciencing_icons_genetics genetics, sciencing_icons_human body human body, sciencing_icons_ecology ecology, sciencing_icons_chemistry chemistry, sciencing_icons_atomic &amp; molecular structure atomic & molecular structure, sciencing_icons_bonds bonds, sciencing_icons_reactions reactions, sciencing_icons_stoichiometry stoichiometry, sciencing_icons_solutions solutions, sciencing_icons_acids &amp; bases acids & bases, sciencing_icons_thermodynamics thermodynamics, sciencing_icons_organic chemistry organic chemistry, sciencing_icons_physics physics, sciencing_icons_fundamentals-physics fundamentals, sciencing_icons_electronics electronics, sciencing_icons_waves waves, sciencing_icons_energy energy, sciencing_icons_fluid fluid, sciencing_icons_astronomy astronomy, sciencing_icons_geology geology, sciencing_icons_fundamentals-geology fundamentals, sciencing_icons_minerals &amp; rocks minerals & rocks, sciencing_icons_earth scructure earth structure, sciencing_icons_fossils fossils, sciencing_icons_natural disasters natural disasters, sciencing_icons_nature nature, sciencing_icons_ecosystems ecosystems, sciencing_icons_environment environment, sciencing_icons_insects insects, sciencing_icons_plants &amp; mushrooms plants & mushrooms, sciencing_icons_animals animals, sciencing_icons_math math, sciencing_icons_arithmetic arithmetic, sciencing_icons_addition &amp; subtraction addition & subtraction, sciencing_icons_multiplication &amp; division multiplication & division, sciencing_icons_decimals decimals, sciencing_icons_fractions fractions, sciencing_icons_conversions conversions, sciencing_icons_algebra algebra, sciencing_icons_working with units working with units, sciencing_icons_equations &amp; expressions equations & expressions, sciencing_icons_ratios &amp; proportions ratios & proportions, sciencing_icons_inequalities inequalities, sciencing_icons_exponents &amp; logarithms exponents & logarithms, sciencing_icons_factorization factorization, sciencing_icons_functions functions, sciencing_icons_linear equations linear equations, sciencing_icons_graphs graphs, sciencing_icons_quadratics quadratics, sciencing_icons_polynomials polynomials, sciencing_icons_geometry geometry, sciencing_icons_fundamentals-geometry fundamentals, sciencing_icons_cartesian cartesian, sciencing_icons_circles circles, sciencing_icons_solids solids, sciencing_icons_trigonometry trigonometry, sciencing_icons_probability-statistics probability & statistics, sciencing_icons_mean-median-mode mean/median/mode, sciencing_icons_independent-dependent variables independent/dependent variables, sciencing_icons_deviation deviation, sciencing_icons_correlation correlation, sciencing_icons_sampling sampling, sciencing_icons_distributions distributions, sciencing_icons_probability probability, sciencing_icons_calculus calculus, sciencing_icons_differentiation-integration differentiation/integration, sciencing_icons_application application, sciencing_icons_projects projects, sciencing_icons_news news.

• Share Tweet Email Print
• Home ⋅
• Science Fair Project Ideas for Kids, Middle & High School Students ⋅

5 Components of a Well-Designed Scientific Experiment

How to Write a Summary on a Science Project

Whether in middle school or a laboratory at NASA, the scientific method is the accepted approach for conducting an experiment. The five components of the scientific method are: observations, questions, hypothesis, methods and results. Following the scientific method procedure not only ensures that the experiment can be repeated by other researchers, but also that the results garnered can be accepted.

Observations and Question

Observations allow an experimenter to gather and use background information concerning the principles being tested to better predict and understand the forthcoming outcome. A researcher or student may choose to perform independent research or look at similar experiments before making observations. The question is the aspect being tested, what is the experiment is trying to answer. For example, the question that an experiment may ask is: "Does the temperature of ice increase as it undergoes a phase change?"

The hypothesis is a prediction of the outcome, which is generally stated in a complete sentence; it uses the observations made before the experiment to make an educated assertion. At the end of the experiment, the researcher will have to use the results to decide if he can accept the hypothesis or reject it. The hypothesis must stand up to questioning during the experiment.

The method section of the scientific method lists all of the materials used in the experiment in specific detail along with the exact procedures that were taken. It is important that the methods are detailed and accurate so another researcher can repeat the experiment and expect to get similar results. It is also necessary to list the methods used because it may be useful to go back to them after the experiment to explain some of the results that occurred.

You must record the results of the experiment. Researchers must interpret the results they receive, giving explanations for the data gathered. Most importantly, they must also draw a conclusion from the results. The conclusion must decide whether to accept or reject the hypothesis made at the beginning of the experiment. It is often useful to display results with visual aids, such as graphs or charts, to help identify trends and relationships.

Related Articles

What are the 8 steps in scientific research, how to calculate experimental value, steps & procedures for conducting scientific research, difference between proposition & hypothesis, what is the next step if an experiment fails to confirm..., five characteristics of the scientific method, why should you only test for one variable at a time..., definitions of control, constant, independent and dependent..., scientific method for volcano science projects, methods of probability, what is the meaning of variables in research, 10 characteristics of a science experiment, science project ideas & the scientific method, how to write a protocol for biology experiments, why is constant temperature important in an experiment, how to do a science project step-by-step, how to write a hypothesis for correlation, the scientific method for an egg drop, difference between data & conclusion of a study.

• University of Rochester Department of Physics and Astronomy; Introduction to the Scientific Method; Prof. Frank L. H. Wolfs

About the Author

Clayton Yuetter has worked as a professional writer since 1999. His writing has appeared in many journals and websites such as The Milk House, The Country Folks, Progressive Dairyman and Three Times Daily. He received a Master of Arts in writing at the National University of Ireland, Galway.

Photo Credits

Photos.com/AbleStock.com/Getty Images

Find Your Next Great Science Fair Project! GO

Want to create or adapt books like this? Learn more about how Pressbooks supports open publishing practices.

10 Experimental research

Experimental research—often considered to be the ‘gold standard’ in research designs—is one of the most rigorous of all research designs. In this design, one or more independent variables are manipulated by the researcher (as treatments), subjects are randomly assigned to different treatment levels (random assignment), and the results of the treatments on outcomes (dependent variables) are observed. The unique strength of experimental research is its internal validity (causality) due to its ability to link cause and effect through treatment manipulation, while controlling for the spurious effect of extraneous variable.

Experimental research is best suited for explanatory research—rather than for descriptive or exploratory research—where the goal of the study is to examine cause-effect relationships. It also works well for research that involves a relatively limited and well-defined set of independent variables that can either be manipulated or controlled. Experimental research can be conducted in laboratory or field settings. Laboratory experiments , conducted in laboratory (artificial) settings, tend to be high in internal validity, but this comes at the cost of low external validity (generalisability), because the artificial (laboratory) setting in which the study is conducted may not reflect the real world. Field experiments are conducted in field settings such as in a real organisation, and are high in both internal and external validity. But such experiments are relatively rare, because of the difficulties associated with manipulating treatments and controlling for extraneous effects in a field setting.

Experimental research can be grouped into two broad categories: true experimental designs and quasi-experimental designs. Both designs require treatment manipulation, but while true experiments also require random assignment, quasi-experiments do not. Sometimes, we also refer to non-experimental research, which is not really a research design, but an all-inclusive term that includes all types of research that do not employ treatment manipulation or random assignment, such as survey research, observational research, and correlational studies.

Basic concepts

Treatment and control groups. In experimental research, some subjects are administered one or more experimental stimulus called a treatment (the treatment group ) while other subjects are not given such a stimulus (the control group ). The treatment may be considered successful if subjects in the treatment group rate more favourably on outcome variables than control group subjects. Multiple levels of experimental stimulus may be administered, in which case, there may be more than one treatment group. For example, in order to test the effects of a new drug intended to treat a certain medical condition like dementia, if a sample of dementia patients is randomly divided into three groups, with the first group receiving a high dosage of the drug, the second group receiving a low dosage, and the third group receiving a placebo such as a sugar pill (control group), then the first two groups are experimental groups and the third group is a control group. After administering the drug for a period of time, if the condition of the experimental group subjects improved significantly more than the control group subjects, we can say that the drug is effective. We can also compare the conditions of the high and low dosage experimental groups to determine if the high dose is more effective than the low dose.

Treatment manipulation. Treatments are the unique feature of experimental research that sets this design apart from all other research methods. Treatment manipulation helps control for the ‘cause’ in cause-effect relationships. Naturally, the validity of experimental research depends on how well the treatment was manipulated. Treatment manipulation must be checked using pretests and pilot tests prior to the experimental study. Any measurements conducted before the treatment is administered are called pretest measures , while those conducted after the treatment are posttest measures .

Random selection and assignment. Random selection is the process of randomly drawing a sample from a population or a sampling frame. This approach is typically employed in survey research, and ensures that each unit in the population has a positive chance of being selected into the sample. Random assignment, however, is a process of randomly assigning subjects to experimental or control groups. This is a standard practice in true experimental research to ensure that treatment groups are similar (equivalent) to each other and to the control group prior to treatment administration. Random selection is related to sampling, and is therefore more closely related to the external validity (generalisability) of findings. However, random assignment is related to design, and is therefore most related to internal validity. It is possible to have both random selection and random assignment in well-designed experimental research, but quasi-experimental research involves neither random selection nor random assignment.

Threats to internal validity. Although experimental designs are considered more rigorous than other research methods in terms of the internal validity of their inferences (by virtue of their ability to control causes through treatment manipulation), they are not immune to internal validity threats. Some of these threats to internal validity are described below, within the context of a study of the impact of a special remedial math tutoring program for improving the math abilities of high school students.

History threat is the possibility that the observed effects (dependent variables) are caused by extraneous or historical events rather than by the experimental treatment. For instance, students’ post-remedial math score improvement may have been caused by their preparation for a math exam at their school, rather than the remedial math program.

Maturation threat refers to the possibility that observed effects are caused by natural maturation of subjects (e.g., a general improvement in their intellectual ability to understand complex concepts) rather than the experimental treatment.

Testing threat is a threat in pre-post designs where subjects’ posttest responses are conditioned by their pretest responses. For instance, if students remember their answers from the pretest evaluation, they may tend to repeat them in the posttest exam.

Not conducting a pretest can help avoid this threat.

Instrumentation threat , which also occurs in pre-post designs, refers to the possibility that the difference between pretest and posttest scores is not due to the remedial math program, but due to changes in the administered test, such as the posttest having a higher or lower degree of difficulty than the pretest.

Mortality threat refers to the possibility that subjects may be dropping out of the study at differential rates between the treatment and control groups due to a systematic reason, such that the dropouts were mostly students who scored low on the pretest. If the low-performing students drop out, the results of the posttest will be artificially inflated by the preponderance of high-performing students.

Regression threat —also called a regression to the mean—refers to the statistical tendency of a group’s overall performance to regress toward the mean during a posttest rather than in the anticipated direction. For instance, if subjects scored high on a pretest, they will have a tendency to score lower on the posttest (closer to the mean) because their high scores (away from the mean) during the pretest were possibly a statistical aberration. This problem tends to be more prevalent in non-random samples and when the two measures are imperfectly correlated.

Two-group experimental designs

Pretest-posttest control group design . In this design, subjects are randomly assigned to treatment and control groups, subjected to an initial (pretest) measurement of the dependent variables of interest, the treatment group is administered a treatment (representing the independent variable of interest), and the dependent variables measured again (posttest). The notation of this design is shown in Figure 10.1.

Statistical analysis of this design involves a simple analysis of variance (ANOVA) between the treatment and control groups. The pretest-posttest design handles several threats to internal validity, such as maturation, testing, and regression, since these threats can be expected to influence both treatment and control groups in a similar (random) manner. The selection threat is controlled via random assignment. However, additional threats to internal validity may exist. For instance, mortality can be a problem if there are differential dropout rates between the two groups, and the pretest measurement may bias the posttest measurement—especially if the pretest introduces unusual topics or content.

Posttest -only control group design . This design is a simpler version of the pretest-posttest design where pretest measurements are omitted. The design notation is shown in Figure 10.2.

The treatment effect is measured simply as the difference in the posttest scores between the two groups:

The appropriate statistical analysis of this design is also a two-group analysis of variance (ANOVA). The simplicity of this design makes it more attractive than the pretest-posttest design in terms of internal validity. This design controls for maturation, testing, regression, selection, and pretest-posttest interaction, though the mortality threat may continue to exist.

Because the pretest measure is not a measurement of the dependent variable, but rather a covariate, the treatment effect is measured as the difference in the posttest scores between the treatment and control groups as:

Due to the presence of covariates, the right statistical analysis of this design is a two-group analysis of covariance (ANCOVA). This design has all the advantages of posttest-only design, but with internal validity due to the controlling of covariates. Covariance designs can also be extended to pretest-posttest control group design.

Factorial designs

Two-group designs are inadequate if your research requires manipulation of two or more independent variables (treatments). In such cases, you would need four or higher-group designs. Such designs, quite popular in experimental research, are commonly called factorial designs. Each independent variable in this design is called a factor , and each subdivision of a factor is called a level . Factorial designs enable the researcher to examine not only the individual effect of each treatment on the dependent variables (called main effects), but also their joint effect (called interaction effects).

In a factorial design, a main effect is said to exist if the dependent variable shows a significant difference between multiple levels of one factor, at all levels of other factors. No change in the dependent variable across factor levels is the null case (baseline), from which main effects are evaluated. In the above example, you may see a main effect of instructional type, instructional time, or both on learning outcomes. An interaction effect exists when the effect of differences in one factor depends upon the level of a second factor. In our example, if the effect of instructional type on learning outcomes is greater for three hours/week of instructional time than for one and a half hours/week, then we can say that there is an interaction effect between instructional type and instructional time on learning outcomes. Note that the presence of interaction effects dominate and make main effects irrelevant, and it is not meaningful to interpret main effects if interaction effects are significant.

Hybrid experimental designs

Hybrid designs are those that are formed by combining features of more established designs. Three such hybrid designs are randomised bocks design, Solomon four-group design, and switched replications design.

Randomised block design. This is a variation of the posttest-only or pretest-posttest control group design where the subject population can be grouped into relatively homogeneous subgroups (called blocks ) within which the experiment is replicated. For instance, if you want to replicate the same posttest-only design among university students and full-time working professionals (two homogeneous blocks), subjects in both blocks are randomly split between the treatment group (receiving the same treatment) and the control group (see Figure 10.5). The purpose of this design is to reduce the ‘noise’ or variance in data that may be attributable to differences between the blocks so that the actual effect of interest can be detected more accurately.

Solomon four-group design . In this design, the sample is divided into two treatment groups and two control groups. One treatment group and one control group receive the pretest, and the other two groups do not. This design represents a combination of posttest-only and pretest-posttest control group design, and is intended to test for the potential biasing effect of pretest measurement on posttest measures that tends to occur in pretest-posttest designs, but not in posttest-only designs. The design notation is shown in Figure 10.6.

Switched replication design . This is a two-group design implemented in two phases with three waves of measurement. The treatment group in the first phase serves as the control group in the second phase, and the control group in the first phase becomes the treatment group in the second phase, as illustrated in Figure 10.7. In other words, the original design is repeated or replicated temporally with treatment/control roles switched between the two groups. By the end of the study, all participants will have received the treatment either during the first or the second phase. This design is most feasible in organisational contexts where organisational programs (e.g., employee training) are implemented in a phased manner or are repeated at regular intervals.

Quasi-experimental designs

Quasi-experimental designs are almost identical to true experimental designs, but lacking one key ingredient: random assignment. For instance, one entire class section or one organisation is used as the treatment group, while another section of the same class or a different organisation in the same industry is used as the control group. This lack of random assignment potentially results in groups that are non-equivalent, such as one group possessing greater mastery of certain content than the other group, say by virtue of having a better teacher in a previous semester, which introduces the possibility of selection bias . Quasi-experimental designs are therefore inferior to true experimental designs in interval validity due to the presence of a variety of selection related threats such as selection-maturation threat (the treatment and control groups maturing at different rates), selection-history threat (the treatment and control groups being differentially impacted by extraneous or historical events), selection-regression threat (the treatment and control groups regressing toward the mean between pretest and posttest at different rates), selection-instrumentation threat (the treatment and control groups responding differently to the measurement), selection-testing (the treatment and control groups responding differently to the pretest), and selection-mortality (the treatment and control groups demonstrating differential dropout rates). Given these selection threats, it is generally preferable to avoid quasi-experimental designs to the greatest extent possible.

In addition, there are quite a few unique non-equivalent designs without corresponding true experimental design cousins. Some of the more useful of these designs are discussed next.

Regression discontinuity (RD) design . This is a non-equivalent pretest-posttest design where subjects are assigned to the treatment or control group based on a cut-off score on a preprogram measure. For instance, patients who are severely ill may be assigned to a treatment group to test the efficacy of a new drug or treatment protocol and those who are mildly ill are assigned to the control group. In another example, students who are lagging behind on standardised test scores may be selected for a remedial curriculum program intended to improve their performance, while those who score high on such tests are not selected from the remedial program.

Because of the use of a cut-off score, it is possible that the observed results may be a function of the cut-off score rather than the treatment, which introduces a new threat to internal validity. However, using the cut-off score also ensures that limited or costly resources are distributed to people who need them the most, rather than randomly across a population, while simultaneously allowing a quasi-experimental treatment. The control group scores in the RD design do not serve as a benchmark for comparing treatment group scores, given the systematic non-equivalence between the two groups. Rather, if there is no discontinuity between pretest and posttest scores in the control group, but such a discontinuity persists in the treatment group, then this discontinuity is viewed as evidence of the treatment effect.

Proxy pretest design . This design, shown in Figure 10.11, looks very similar to the standard NEGD (pretest-posttest) design, with one critical difference: the pretest score is collected after the treatment is administered. A typical application of this design is when a researcher is brought in to test the efficacy of a program (e.g., an educational program) after the program has already started and pretest data is not available. Under such circumstances, the best option for the researcher is often to use a different prerecorded measure, such as students’ grade point average before the start of the program, as a proxy for pretest data. A variation of the proxy pretest design is to use subjects’ posttest recollection of pretest data, which may be subject to recall bias, but nevertheless may provide a measure of perceived gain or change in the dependent variable.

Separate pretest-posttest samples design . This design is useful if it is not possible to collect pretest and posttest data from the same subjects for some reason. As shown in Figure 10.12, there are four groups in this design, but two groups come from a single non-equivalent group, while the other two groups come from a different non-equivalent group. For instance, say you want to test customer satisfaction with a new online service that is implemented in one city but not in another. In this case, customers in the first city serve as the treatment group and those in the second city constitute the control group. If it is not possible to obtain pretest and posttest measures from the same customers, you can measure customer satisfaction at one point in time, implement the new service program, and measure customer satisfaction (with a different set of customers) after the program is implemented. Customer satisfaction is also measured in the control group at the same times as in the treatment group, but without the new program implementation. The design is not particularly strong, because you cannot examine the changes in any specific customer’s satisfaction score before and after the implementation, but you can only examine average customer satisfaction scores. Despite the lower internal validity, this design may still be a useful way of collecting quasi-experimental data when pretest and posttest data is not available from the same subjects.

An interesting variation of the NEDV design is a pattern-matching NEDV design , which employs multiple outcome variables and a theory that explains how much each variable will be affected by the treatment. The researcher can then examine if the theoretical prediction is matched in actual observations. This pattern-matching technique—based on the degree of correspondence between theoretical and observed patterns—is a powerful way of alleviating internal validity concerns in the original NEDV design.

Perils of experimental research

Experimental research is one of the most difficult of research designs, and should not be taken lightly. This type of research is often best with a multitude of methodological problems. First, though experimental research requires theories for framing hypotheses for testing, much of current experimental research is atheoretical. Without theories, the hypotheses being tested tend to be ad hoc, possibly illogical, and meaningless. Second, many of the measurement instruments used in experimental research are not tested for reliability and validity, and are incomparable across studies. Consequently, results generated using such instruments are also incomparable. Third, often experimental research uses inappropriate research designs, such as irrelevant dependent variables, no interaction effects, no experimental controls, and non-equivalent stimulus across treatment groups. Findings from such studies tend to lack internal validity and are highly suspect. Fourth, the treatments (tasks) used in experimental research may be diverse, incomparable, and inconsistent across studies, and sometimes inappropriate for the subject population. For instance, undergraduate student subjects are often asked to pretend that they are marketing managers and asked to perform a complex budget allocation task in which they have no experience or expertise. The use of such inappropriate tasks, introduces new threats to internal validity (i.e., subject’s performance may be an artefact of the content or difficulty of the task setting), generates findings that are non-interpretable and meaningless, and makes integration of findings across studies impossible.

The design of proper experimental treatments is a very important task in experimental design, because the treatment is the raison d’etre of the experimental method, and must never be rushed or neglected. To design an adequate and appropriate task, researchers should use prevalidated tasks if available, conduct treatment manipulation checks to check for the adequacy of such tasks (by debriefing subjects after performing the assigned task), conduct pilot tests (repeatedly, if necessary), and if in doubt, use tasks that are simple and familiar for the respondent sample rather than tasks that are complex or unfamiliar.

In summary, this chapter introduced key concepts in the experimental design research method and introduced a variety of true experimental and quasi-experimental designs. Although these designs vary widely in internal validity, designs with less internal validity should not be overlooked and may sometimes be useful under specific circumstances and empirical contingencies.

Social Science Research: Principles, Methods and Practices (Revised edition) Copyright © 2019 by Anol Bhattacherjee is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

Share This Book

Experimental Research Design — 6 mistakes you should never make!

Since school days’ students perform scientific experiments that provide results that define and prove the laws and theorems in science. These experiments are laid on a strong foundation of experimental research designs.

An experimental research design helps researchers execute their research objectives with more clarity and transparency.

In this article, we will not only discuss the key aspects of experimental research designs but also the issues to avoid and problems to resolve while designing your research study.

Table of Contents

What Is Experimental Research Design?

Experimental research design is a framework of protocols and procedures created to conduct experimental research with a scientific approach using two sets of variables. Herein, the first set of variables acts as a constant, used to measure the differences of the second set. The best example of experimental research methods is quantitative research .

Experimental research helps a researcher gather the necessary data for making better research decisions and determining the facts of a research study.

When Can a Researcher Conduct Experimental Research?

A researcher can conduct experimental research in the following situations —

• When time is an important factor in establishing a relationship between the cause and effect.
• When there is an invariable or never-changing behavior between the cause and effect.
• Finally, when the researcher wishes to understand the importance of the cause and effect.

Importance of Experimental Research Design

To publish significant results, choosing a quality research design forms the foundation to build the research study. Moreover, effective research design helps establish quality decision-making procedures, structures the research to lead to easier data analysis, and addresses the main research question. Therefore, it is essential to cater undivided attention and time to create an experimental research design before beginning the practical experiment.

By creating a research design, a researcher is also giving oneself time to organize the research, set up relevant boundaries for the study, and increase the reliability of the results. Through all these efforts, one could also avoid inconclusive results. If any part of the research design is flawed, it will reflect on the quality of the results derived.

Types of Experimental Research Designs

Based on the methods used to collect data in experimental studies, the experimental research designs are of three primary types:

1. Pre-experimental Research Design

A research study could conduct pre-experimental research design when a group or many groups are under observation after implementing factors of cause and effect of the research. The pre-experimental design will help researchers understand whether further investigation is necessary for the groups under observation.

Pre-experimental research is of three types —

• One-shot Case Study Research Design
• One-group Pretest-posttest Research Design
• Static-group Comparison

2. True Experimental Research Design

A true experimental research design relies on statistical analysis to prove or disprove a researcher’s hypothesis. It is one of the most accurate forms of research because it provides specific scientific evidence. Furthermore, out of all the types of experimental designs, only a true experimental design can establish a cause-effect relationship within a group. However, in a true experiment, a researcher must satisfy these three factors —

• There is a control group that is not subjected to changes and an experimental group that will experience the changed variables
• A variable that can be manipulated by the researcher
• Random distribution of the variables

This type of experimental research is commonly observed in the physical sciences.

3. Quasi-experimental Research Design

The word “Quasi” means similarity. A quasi-experimental design is similar to a true experimental design. However, the difference between the two is the assignment of the control group. In this research design, an independent variable is manipulated, but the participants of a group are not randomly assigned. This type of research design is used in field settings where random assignment is either irrelevant or not required.

The classification of the research subjects, conditions, or groups determines the type of research design to be used.

Advantages of Experimental Research

Experimental research allows you to test your idea in a controlled environment before taking the research to clinical trials. Moreover, it provides the best method to test your theory because of the following advantages:

• Researchers have firm control over variables to obtain results.
• The subject does not impact the effectiveness of experimental research. Anyone can implement it for research purposes.
• The results are specific.
• Post results analysis, research findings from the same dataset can be repurposed for similar research ideas.
• Researchers can identify the cause and effect of the hypothesis and further analyze this relationship to determine in-depth ideas.
• Experimental research makes an ideal starting point. The collected data could be used as a foundation to build new research ideas for further studies.

6 Mistakes to Avoid While Designing Your Research

There is no order to this list, and any one of these issues can seriously compromise the quality of your research. You could refer to the list as a checklist of what to avoid while designing your research.

1. Invalid Theoretical Framework

Usually, researchers miss out on checking if their hypothesis is logical to be tested. If your research design does not have basic assumptions or postulates, then it is fundamentally flawed and you need to rework on your research framework.

2. Inadequate Literature Study

Without a comprehensive research literature review , it is difficult to identify and fill the knowledge and information gaps. Furthermore, you need to clearly state how your research will contribute to the research field, either by adding value to the pertinent literature or challenging previous findings and assumptions.

3. Insufficient or Incorrect Statistical Analysis

Statistical results are one of the most trusted scientific evidence. The ultimate goal of a research experiment is to gain valid and sustainable evidence. Therefore, incorrect statistical analysis could affect the quality of any quantitative research.

4. Undefined Research Problem

This is one of the most basic aspects of research design. The research problem statement must be clear and to do that, you must set the framework for the development of research questions that address the core problems.

5. Research Limitations

Every study has some type of limitations . You should anticipate and incorporate those limitations into your conclusion, as well as the basic research design. Include a statement in your manuscript about any perceived limitations, and how you considered them while designing your experiment and drawing the conclusion.

6. Ethical Implications

The most important yet less talked about topic is the ethical issue. Your research design must include ways to minimize any risk for your participants and also address the research problem or question at hand. If you cannot manage the ethical norms along with your research study, your research objectives and validity could be questioned.

Experimental Research Design Example

In an experimental design, a researcher gathers plant samples and then randomly assigns half the samples to photosynthesize in sunlight and the other half to be kept in a dark box without sunlight, while controlling all the other variables (nutrients, water, soil, etc.)

By comparing their outcomes in biochemical tests, the researcher can confirm that the changes in the plants were due to the sunlight and not the other variables.

Experimental research is often the final form of a study conducted in the research process which is considered to provide conclusive and specific results. But it is not meant for every research. It involves a lot of resources, time, and money and is not easy to conduct, unless a foundation of research is built. Yet it is widely used in research institutes and commercial industries, for its most conclusive results in the scientific approach.

Have you worked on research designs? How was your experience creating an experimental design? What difficulties did you face? Do write to us or comment below and share your insights on experimental research designs!

Frequently Asked Questions

Randomization is important in an experimental research because it ensures unbiased results of the experiment. It also measures the cause-effect relationship on a particular group of interest.

Experimental research design lay the foundation of a research and structures the research to establish quality decision making process.

There are 3 types of experimental research designs. These are pre-experimental research design, true experimental research design, and quasi experimental research design.

The difference between an experimental and a quasi-experimental design are: 1. The assignment of the control group in quasi experimental research is non-random, unlike true experimental design, which is randomly assigned. 2. Experimental research group always has a control group; on the other hand, it may not be always present in quasi experimental research.

Experimental research establishes a cause-effect relationship by testing a theory or hypothesis using experimental groups or control variables. In contrast, descriptive research describes a study or a topic by defining the variables under it and answering the questions related to the same.

good and valuable

Very very good

Good presentation.

Rate this article Cancel Reply

Your email address will not be published.

Enago Academy's Most Popular Articles

• Publishing Research

10 Tips to Prevent Research Papers From Being Retracted

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

• Industry News

Google Releases 2024 Scholar Metrics, Evaluates Impact of Scholarly Articles

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

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

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

• Reporting Research

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

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

• Trending Now

Breaking Barriers: Sony and Nature unveil “Women in Technology Award”

Sony Group Corporation and the prestigious scientific journal Nature have collaborated to launch the inaugural…

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

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

Sign-up to read more

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

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

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

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

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

In your opinion, what is the most effective way to improve integrity in the peer review process?

• How it works

A Complete Guide to Experimental Research

Published by Carmen Troy at August 14th, 2021 , Revised On August 25, 2023

A Quick Guide to Experimental Research

Experimental research refers to the experiments conducted in the laboratory or observation under controlled conditions. Researchers try to find out the cause-and-effect relationship between two or more variables. 

The subjects/participants in the experiment are selected and observed. They receive treatments such as changes in room temperature, diet, atmosphere, or given a new drug to observe the changes. Experiments can vary from personal and informal natural comparisons. It includes three  types of variables ;

• Independent variable
• Dependent variable
• Controlled variable

Before conducting experimental research, you need to have a clear understanding of the experimental design. A true experimental design includes  identifying a problem , formulating a  hypothesis , determining the number of variables, selecting and assigning the participants,  types of research designs , meeting ethical values, etc.

There are many  types of research  methods that can be classified based on:

• The nature of the problem to be studied
• Number of participants (individual or groups)
• Number of groups involved (Single group or multiple groups)
• Types of data collection methods (Qualitative/Quantitative/Mixed methods)
• Number of variables (single independent variable/ factorial two independent variables)
• The experimental design

Types of Experimental Research

Laboratory Experiment  

It is also called experimental research. This type of research is conducted in the laboratory. A researcher can manipulate and control the variables of the experiment.

Example: Milgram’s experiment on obedience.

Field Experiment

Field experiments are conducted in the participants’ open field and the environment by incorporating a few artificial changes. Researchers do not have control over variables under measurement. Participants know that they are taking part in the experiment.

Natural Experiments

The experiment is conducted in the natural environment of the participants. The participants are generally not informed about the experiment being conducted on them.

Examples: Estimating the health condition of the population. Did the increase in tobacco prices decrease the sale of tobacco? Did the usage of helmets decrease the number of head injuries of the bikers?

Quasi-Experiments

A quasi-experiment is an experiment that takes advantage of natural occurrences. Researchers cannot assign random participants to groups.

Example: Comparing the academic performance of the two schools.

Does your Research Methodology Have the Following?

• Great Research/Sources
• Perfect Language
• Accurate Sources

If not, we can help. Our panel of experts makes sure to keep the 3 pillars of Research Methodology strong.

How to Conduct Experimental Research?

Step 1. identify and define the problem.

You need to identify a problem as per your field of study and describe your  research question .

Example: You want to know about the effects of social media on the behavior of youngsters. It would help if you found out how much time students spend on the internet daily.

Example: You want to find out the adverse effects of junk food on human health. It would help if you found out how junk food frequent consumption can affect an individual’s health.

Step 2. Determine the Number of Levels of Variables

You need to determine the number of  variables . The independent variable is the predictor and manipulated by the researcher. At the same time, the dependent variable is the result of the independent variable.

In the first example, we predicted that increased social media usage negatively correlates with youngsters’ negative behaviour.

In the second example, we predicted the positive correlation between a balanced diet and a good healthy and negative relationship between junk food consumption and multiple health issues.

Step 3. Formulate the Hypothesis

One of the essential aspects of experimental research is formulating a hypothesis . A researcher studies the cause and effect between the independent and dependent variables and eliminates the confounding variables. A  null hypothesis is when there is no significant relationship between the dependent variable and the participants’ independent variables. A researcher aims to disprove the theory. H0 denotes it.  The  Alternative hypothesis  is the theory that a researcher seeks to prove.  H1or HA denotes it. 

Why should you use a Plagiarism Detector for your Paper?

It ensures:

• Original work
• Structure and Clarity
• Zero Spelling Errors
• No Punctuation Faults

Step 4. Selection and Assignment of the Subjects

It’s an essential feature that differentiates the experimental design from other research designs . You need to select the number of participants based on the requirements of your experiment. Then the participants are assigned to the treatment group. There should be a control group without any treatment to study the outcomes without applying any changes compared to the experimental group.

Randomisation:  The participants are selected randomly and assigned to the experimental group. It is known as probability sampling. If the selection is not random, it’s considered non-probability sampling.

Stratified sampling : It’s a type of random selection of the participants by dividing them into strata and randomly selecting them from each level. 

Matching:   Even though participants are selected randomly, they can be assigned to the various comparison groups. Another procedure for selecting the participants is ‘matching.’ The participants are selected from the controlled group to match the experimental groups’ participants in all aspects based on the dependent variables.  

What is Replicability?

When a researcher uses the same methodology  and subject groups to carry out the experiments, it’s called ‘replicability.’ The  results will be similar each time. Researchers usually replicate their own work to strengthen external validity.

Step 5. Select a Research Design

You need to select a  research design  according to the requirements of your experiment. There are many types of experimental designs as follows.

Step 6. Meet Ethical and Legal Requirements

• Participants of the research should not be harmed.
• The dignity and confidentiality of the research should be maintained.
• The consent of the participants should be taken before experimenting.
• The privacy of the participants should be ensured.
• Research data should remain confidential.
• The anonymity of the participants should be ensured.
• The rules and objectives of the experiments should be followed strictly.
• Any wrong information or data should be avoided.

Tips for Meeting the Ethical Considerations

To meet the ethical considerations, you need to ensure that.

• Participants have the right to withdraw from the experiment.
• They should be aware of the required information about the experiment.
• It would help if you avoided offensive or unacceptable language while framing the questions of interviews, questionnaires, or Focus groups.
• You should ensure the privacy and anonymity of the participants.
• You should acknowledge the sources and authors in your dissertation using any referencing styles such as APA/MLA/Harvard referencing style.

Step 7. Collect and Analyse Data.

Collect the data  by using suitable data collection according to your experiment’s requirement, such as observations,  case studies ,  surveys ,  interviews , questionnaires, etc. Analyse the obtained information.

Step 8. Present and Conclude the Findings of the Study.

Write the report of your research. Present, conclude, and explain the outcomes of your study .  

Frequently Asked Questions

What is the first step in conducting an experimental research.

The first step in conducting experimental research is to define your research question or hypothesis. Clearly outline the purpose and expectations of your experiment to guide the entire research process.

You May Also Like

Blinding in experimental research is the process in which participants, data analysts, and data collectors are kept unaware of the experiment or study.

This comprehensive guide introduces what median is, how it’s calculated and represented and its importance, along with some simple examples.

One way ANOVA test is a kind of ANOVA that aims to find if there is a significant statistical difference among the means of two or more independent groups.

USEFUL LINKS

LEARNING RESOURCES

COMPANY DETAILS

• How It Works

• Voxco Online
• Voxco Panel Management
• Voxco Panel Portal
• Voxco Audience
• Voxco Mobile Offline
• Voxco Dialer Cloud
• Voxco Dialer On-premise
• Voxco TCPA Connect
• Voxco Analytics
• Voxco Text & Sentiment Analysis

• 40+ question types
• Drag-and-drop interface
• Skip logic and branching
• Multi-lingual survey
• Text piping
• Question library
• CSS customization
• White-label surveys
• Customizable ‘Thank You’ page
• Customizable survey theme
• Reminder send-outs
• Survey rewards
• Social media
• Website surveys
• Correlation analysis
• Cross-tabulation analysis
• Trend analysis
• Real-time dashboard
• Customizable report
• Email address validation
• Recaptcha validation
• SSL security

Take a peek at our powerful survey features to design surveys that scale discoveries.

Download feature sheet.

• Hospitality
• Academic Research
• Customer Experience
• Employee Experience
• Product Experience
• Market Research
• Social Research
• Data Analysis

Explore Voxco 

Need to map Voxco’s features & offerings? We can help!

Watch a Demo 

Download Brochures 

Get a Quote

• NPS Calculator
• CES Calculator
• A/B Testing Calculator
• Margin of Error Calculator
• Sample Size Calculator
• CX Strategy & Management Hub
• Market Research Hub
• Patient Experience Hub
• Employee Experience Hub
• NPS Knowledge Hub
• Market Research Guide
• Customer Experience Guide
• Survey Research Guides
• Survey Template Library
• Webinars and Events
• Feature Sheets
• Try a sample survey
• Professional Services

Get exclusive insights into research trends and best practices from top experts! Access Voxco’s ‘State of Research Report 2024 edition’ .

We’ve been avid users of the Voxco platform now for over 20 years. It gives us the flexibility to routinely enhance our survey toolkit and provides our clients with a more robust dataset and story to tell their clients.

VP Innovation & Strategic Partnerships, The Logit Group

• Client Stories
• Voxco Reviews
• Why Voxco Research?
• Careers at Voxco
• Vulnerabilities and Ethical Hacking

Explore Regional Offices

• Survey Software The world’s leading omnichannel survey software
• Online Survey Tools Create sophisticated surveys with ease.
• Mobile Offline Conduct efficient field surveys.
• Text Analysis
• Close The Loop
• Automated Translations
• NPS Dashboard
• CATI Manage high volume phone surveys efficiently
• Cloud/On-premise Dialer TCPA compliant Cloud on-premise dialer
• IVR Survey Software Boost productivity with automated call workflows.
• Analytics Analyze survey data with visual dashboards
• Panel Manager Nurture a loyal community of respondents.
• Survey Portal Best-in-class user friendly survey portal.
• Voxco Audience Conduct targeted sample research in hours.
• Predictive Analytics
• Customer 360
• Customer Loyalty
• Fraud & Risk Management
• AI/ML Enablement Services
• Credit Underwriting

Find the best survey software for you! (Along with a checklist to compare platforms)

Get Buyer’s Guide

• 100+ question types
• SMS surveys
• Financial Services
• Banking & Financial Services
• Retail Solution
• Risk Management
• Customer Lifecycle Solutions
• Net Promoter Score
• Customer Behaviour Analytics
• Customer Segmentation
• Data Unification

Explore Voxco 

Watch a Demo 

Download Brochures 

• CX Strategy & Management Hub
• The Voxco Guide to Customer Experience
• Professional services
• Blogs & White papers
• Case Studies

Find the best customer experience platform

Uncover customer pain points, analyze feedback and run successful CX programs with the best CX platform for your team.

Get the Guide Now

VP Innovation & Strategic Partnerships, The Logit Group

• Why Voxco Intelligence?
• Our clients
• Client stories
• Featuresheets

5 components of experimental design you need to know

• October 15, 2021

SHARE THE ARTICLE ON

What are the 5 components of experimental design?

As we looked at the overview of the experimental design, here 5 major components of the experimental design that we have to pay attention to while conducting our research through experimental design approach:

Observations

We have heard this word many times. Observation is basically the first step towards any scientific research. It is a way for gathering data through observing the subjects. The researcher has to go to the participants’ environment and observe the way they behave, react and respond to the natural phenomenon.

Structured observations are conducted with respect to pre-defined variables and schedule, whereas unstructured observations are conducted in a free manner with no pre-defined variables and schedule. 

Observational approach allows you to have a direct access to the phenomena and helps you have a long term record regarding the same. That being said, there is a high chance that the observation will be influenced by the biases of the observer. Or in other sense, the presence of an observer himself might change the behaviour of the subjects. 

Example: to study the above topic, the researcher will observe the kids who play violent video games on regular basis to study their behaviour and if they show any signs of aggression or impatience. 

Questions are the important way to gather primary data. Researcher asks questions to the participants about particular topics or points that he wants to cover while studying the research problem. 

Questions can be asked through surveys – which are sent to the participants through various online and offline channels, interviews – where the researcher himself asks questions to the participants on one-on-one basis.

There are two main types of questions asked to the participants:

• Close-ended question – These questions give quantitative data as result. The questions asked are closed-ended, meaning there is no scope for the respondent to elaborate their answers. They are used when the researcher wants to study a large sample and get numerical data to statistically analyze it. 

For example : On a scale of 1-5, how much did you like our event? 

• Can be better
• Open-ended questions – these kinds of questions are asked to get qualitative data on hand. The data gathered from asking open-ended questions is very long and cannot be gathered from a large sample. The researcher will have to code and label the data since it will be very long and descriptive. 

For example: Can you tell us how to improve?

Hypothesis 

When a researcher picks up a topic to research, he formulates a hypothesis. A hypothesis is nothing but an assumption statement that defines the cause-effect relationship between two or more variables. This statement can be proved true or false, depending on the result of the research. 

A researcher will put forth this hypothesis regarding his research topic and will begin to conduct the research. The prime benefit of formulating a hypothesis is, it sets out a guideline for how the research is to be conducted and within what bounds. Hence, the researcher will gather the information that is enough to prove the hypothesis true or false. 

For the same reason, it is important to know how to write a hypothesis that appropriately covers all the concepts in your research topic. Apart from that, there is a fair chance that the researcher’s bias will interfere with the study. This happens when the researcher is personally in favour of a hypothesis being either true or false. 

Example: For the above research topic, its formulated hypothesis can be “Overuse of violent video games affects the behaviour of new generation.”

Explore all the survey question types possible on Voxco

Once the hypothesis is ready, the next challenge for the researcher is to choose a proper research design method to run the entire study through. This will depend on how he wants his research to be conducted. Whether the research wants his sample to be assigned randomly, or not, whether there are any control variables, matters a lot while selecting an approach for the research. 

Depending on their use, there are three main experimental design types:

Pre-experimental design

As the name suggests, pre-experimental design is carried out before a true experiment is conducted. In this, one or more groups are kept under observation after giving them a treatment related to the research study. Depending on the number of groups involved and the performed pre-test post-test techniques, pre-experimental design is further classified into three: Static group comparison, one group pretest-postest design and one-shot case study. 

True experimental design

This is the perfect form of experimental design whose purpose is to test the hypothesis and prove it true or false. It is the most commonly used method of experimental design and its characteristics include random sample assignment, presence of a control group against a treatment group, variable manipulation.

Quasi-experimental design

This method is similar to the true experimental design. Except it doesn’t have randomization of the sample. It has a treatment and control group which the researcher observes to derive the causal relationship between the variables.

The final component that defines an experimental design is, of course, the results. After the observations, surveys and interviews and running the research process through any one of the above-mentioned types of research design, the researcher will have the result of the hypothesis testing. 

This result will be either for or against the hypothesis.

Example: In our example, the researcher observes the behaviour of the children who has a habit of playing violent video games excessively, and he then conducts a survey or interview with their parents regarding their in general behaviour in the family and friends. On conducting the needed research, he found out that the hypothesis that he framed was true. 

Conclusion: The hypothesis “Overuse of violent video games affects the behaviour of new generation” is true. 

What Components Are Necessary for an Experiment to Be Valid?

Researchers carry out an experiment to research a certain decision or result. In order to achieve that, they take their study through various phases of an experimental design right from observation to treating experimental groups and data analysis. While doing so, various things affect the credibility of the conducted research and make the researcher questions the results. 

We are here to help you with some of the components that will ensure the validity of your experiment and its results: 

Control group 

As we know, there are two groups in an experiment namely the treatment group and the control group. A control group is a group that does not receive any treatment concerning the research. This group is then compared to the treatment group which went through the experiment. The results will show how whether the experiment is a fail or a success. 

Example: the treatment group is of people who have a weak vocabulary and are made to read books, while the control group were never made to do so. When the experiment ended, the results show that the treatment group did way better in the post-test than in the pre-tests while the control group were on the same level. 

Independent variable

It is a variable in a hypothesis that affect a dependent variable. This variable is controlled and manipulated by the researcher to see its effect on the experiment. In our example, the independent variable would be the number of books the treatment group was made to read. And to see if the number of books will affect the vocabulary in a significant way. 

Dependent variable

This is a variable that is dependent on the independent variable. As the researcher manipulated the independent variable, and if its result is significant, then it is bound to make the respective changes in the dependent variable as well. In our example , the dependent variable would be the level of the vocabulary of the treatment group and how it differs depending on the manipulation of the independent variable. 

Constant variables

While experimenting, there might be some external variables that influence the dependent variable to change other than the independent variable. In our example , it can be gender, age, grasping ability, etc. Holding these variables constant across the research will minimize the effects they have on the dependent variables. 

See Voxco survey software in action with a Free demo.

The components of experimental design are control, independent variable and dependent variable, constant variables, random assignment and manipulation . These are the components that also help you define if the experiment is valid.

The 5 steps of designing an experiment are literature history, observation, hypothesis, experiment methodology and conclusion . The researcher follows these steps to get the conclusions regarding the research study. 

The experiments are meant to be defect and bias-free when their results come out. Hence the components that ensure these things are control, independent variables, dependent variables and constant variables. 

The experimental questions are supposed to be short, clear, concise, and focused on the purpose of the research study . These questions will be the footing for the entire research process and are treated as guidelines for the same. 

A good and well-conducted experiment design always has these components that define them: Observation, questions, hypothesis formulation, methodology, results . The researcher has to look for any interventions or biases in any of those phases to ensure a defect-free result. 

The four basic principles of experimental design are:

• Control – Control over the independent variable to examine its effects on the dependent variable.
• Randomize – random assignment of the participants to ensure they all have an equal chance of getting into the experimental groups. 
• Replicate – repeating the experiment by applying the treatment to various experimental groups. 

Block – block the external variables that might affect the results of the experiment like age, gender, genetics, etc.

Exclusive Research Guide

See how Amazon,Uber and Apple enhance customer experience at scale.

Explore Voxco Survey Software

+ Omnichannel Survey Software 

+ Online Survey Software 

+ CATI Survey Software 

+ IVR Survey Software 

+ Market Research Tool

+ Customer Experience Tool 

+ Product Experience Software 

+ Enterprise Survey Software 

Understanding Credit Risk Underwriting: Importance, Models, and FAQs

Understanding Credit Risk Underwriting: Importance, Models, and FAQs SHARE THE ARTICLE ON Table of Contents When applying for a loan or credit card, you may

How to Measure Price Sensitivity

How to Measure Price Sensitivity SHARE THE ARTICLE ON Table of Contents Introduction What is Price Sensitivity?  Price sensitivity is a critical aspect of consumer

Getting meaningful returns from employee experience programs beyond 2020

The global economy is undergoing a forced recession, and it has thrown into chaos traditional working environments as well employee experience programs. However, this turmoil can be just the breather companies need to evaluate their Employee Experience (EX) programs

How a Market Research Tool Helps Solve Top Market Research Challenges

Top 5 Market Research Challenges and How to Solve Them With Voxco Audience SHARE THE ARTICLE ON Table of Contents The past two years have

What are the different types of IVR systems

Different Types of IVR System SHARE THE ARTICLE ON Table of Contents According to a Finance Online survey, if a customer gets a response on

A Rundown of Top Market Research Analysis Tools – Essential Tool for Global Brands and Enterprises

A Rundown of Top Market Research Analysis Tools – Essential Tools for Global Brands and Enterprises SHARE THE ARTICLE ON Table of Contents In the

We use cookies in our website to give you the best browsing experience and to tailor advertising. By continuing to use our website, you give us consent to the use of cookies. Read More

• Foundations
• Write Paper

Search form

• Experiments
• Anthropology
• Self-Esteem
• Social Anxiety

• Research Paper >

Parts of a Research Paper

One of the most important aspects of science is ensuring that you get all the parts of the written research paper in the right order.

This article is a part of the guide:

• Outline Examples
• Example of a Paper
• Write a Hypothesis
• Introduction

Browse Full Outline

• 1 Write a Research Paper
• 2 Writing a Paper
• 3.1 Write an Outline
• 3.2 Outline Examples
• 4.1 Thesis Statement
• 4.2 Write a Hypothesis
• 5.2 Abstract
• 5.3 Introduction
• 5.4 Methods
• 5.5 Results
• 5.6 Discussion
• 5.7 Conclusion
• 5.8 Bibliography
• 6.1 Table of Contents
• 6.2 Acknowledgements
• 6.3 Appendix
• 7.1 In Text Citations
• 7.2 Footnotes
• 7.3.1 Floating Blocks
• 7.4 Example of a Paper
• 7.5 Example of a Paper 2
• 7.6.1 Citations
• 7.7.1 Writing Style
• 7.7.2 Citations
• 8.1.1 Sham Peer Review
• 8.1.2 Advantages
• 8.1.3 Disadvantages
• 8.2 Publication Bias
• 8.3.1 Journal Rejection
• 9.1 Article Writing
• 9.2 Ideas for Topics

You may have finished the best research project on earth but, if you do not write an interesting and well laid out paper, then nobody is going to take your findings seriously.

The main thing to remember with any research paper is that it is based on an hourglass structure. It begins with general information and undertaking a literature review , and becomes more specific as you nail down a research problem and hypothesis .

Finally, it again becomes more general as you try to apply your findings to the world at general.

Whilst there are a few differences between the various disciplines, with some fields placing more emphasis on certain parts than others, there is a basic underlying structure.

These steps are the building blocks of constructing a good research paper. This section outline how to lay out the parts of a research paper, including the various experimental methods and designs.

The principles for literature review and essays of all types follow the same basic principles.

Reference List

For many students, writing the introduction is the first part of the process, setting down the direction of the paper and laying out exactly what the research paper is trying to achieve.

For others, the introduction is the last thing written, acting as a quick summary of the paper. As long as you have planned a good structure for the parts of a research paper, both approaches are acceptable and it is a matter of preference.

A good introduction generally consists of three distinct parts:

• You should first give a general presentation of the research problem.
• You should then lay out exactly what you are trying to achieve with this particular research project.
• You should then state your own position.

Ideally, you should try to give each section its own paragraph, but this will vary given the overall length of the paper.

1) General Presentation

Look at the benefits to be gained by the research or why the problem has not been solved yet. Perhaps nobody has thought about it, or maybe previous research threw up some interesting leads that the previous researchers did not follow up.

Another researcher may have uncovered some interesting trends, but did not manage to reach the significance level , due to experimental error or small sample sizes .

2) Purpose of the Paper

The research problem does not have to be a statement, but must at least imply what you are trying to find.

Many writers prefer to place the thesis statement or hypothesis here, which is perfectly acceptable, but most include it in the last sentences of the introduction, to give the reader a fuller picture.

3) A Statement of Intent From the Writer

The idea is that somebody will be able to gain an overall view of the paper without needing to read the whole thing. Literature reviews are time-consuming enough, so give the reader a concise idea of your intention before they commit to wading through pages of background.

In this section, you look to give a context to the research, including any relevant information learned during your literature review. You are also trying to explain why you chose this area of research, attempting to highlight why it is necessary. The second part should state the purpose of the experiment and should include the research problem. The third part should give the reader a quick summary of the form that the parts of the research paper is going to take and should include a condensed version of the discussion.

This should be the easiest part of the paper to write, as it is a run-down of the exact design and methodology used to perform the research. Obviously, the exact methodology varies depending upon the exact field and type of experiment .

There is a big methodological difference between the apparatus based research of the physical sciences and the methods and observation methods of social sciences. However, the key is to ensure that another researcher would be able to replicate the experiment to match yours as closely as possible, but still keeping the section concise.

You can assume that anybody reading your paper is familiar with the basic methods, so try not to explain every last detail. For example, an organic chemist or biochemist will be familiar with chromatography, so you only need to highlight the type of equipment used rather than explaining the whole process in detail.

In the case of a survey , if you have too many questions to cover in the method, you can always include a copy of the questionnaire in the appendix . In this case, make sure that you refer to it.

This is probably the most variable part of any research paper, and depends on the results and aims of the experiment.

For quantitative research , it is a presentation of the numerical results and data, whereas for qualitative research it should be a broader discussion of trends, without going into too much detail.

For research generating a lot of results , then it is better to include tables or graphs of the analyzed data and leave the raw data in the appendix, so that a researcher can follow up and check your calculations.

A commentary is essential to linking the results together, rather than just displaying isolated and unconnected charts and figures.

It can be quite difficult to find a good balance between the results and the discussion section, because some findings, especially in a quantitative or descriptive experiment , will fall into a grey area. Try to avoid repeating yourself too often.

It is best to try to find a middle path, where you give a general overview of the data and then expand on it in the discussion - you should try to keep your own opinions and interpretations out of the results section, saving that for the discussion later on.

This is where you elaborate on your findings, and explain what you found, adding your own personal interpretations.

Ideally, you should link the discussion back to the introduction, addressing each point individually.

It’s important to make sure that every piece of information in your discussion is directly related to the thesis statement , or you risk cluttering your findings. In keeping with the hourglass principle, you can expand on the topic later in the conclusion .

The conclusion is where you build on your discussion and try to relate your findings to other research and to the world at large.

In a short research paper, it may be a paragraph or two, or even a few lines.

In a dissertation, it may well be the most important part of the entire paper - not only does it describe the results and discussion in detail, it emphasizes the importance of the results in the field, and ties it in with the previous research.

Some research papers require a recommendations section, postulating the further directions of the research, as well as highlighting how any flaws affected the results. In this case, you should suggest any improvements that could be made to the research design .

No paper is complete without a reference list , documenting all the sources that you used for your research. This should be laid out according to APA , MLA or other specified format, allowing any interested researcher to follow up on the research.

One habit that is becoming more common, especially with online papers, is to include a reference to your own paper on the final page. Lay this out in MLA, APA and Chicago format, allowing anybody referencing your paper to copy and paste it.

• Psychology 101
• Flags and Countries
• Capitals and Countries

Martyn Shuttleworth (Jun 5, 2009). Parts of a Research Paper. Retrieved Aug 25, 2024 from Explorable.com: https://explorable.com/parts-of-a-research-paper

You Are Allowed To Copy The Text

The text in this article is licensed under the Creative Commons-License Attribution 4.0 International (CC BY 4.0) .

This means you're free to copy, share and adapt any parts (or all) of the text in the article, as long as you give appropriate credit and provide a link/reference to this page.

That is it. You don't need our permission to copy the article; just include a link/reference back to this page. You can use it freely (with some kind of link), and we're also okay with people reprinting in publications like books, blogs, newsletters, course-material, papers, wikipedia and presentations (with clear attribution).

Want to stay up to date? Follow us!

Check out the official book.

Learn how to construct, style and format an Academic paper and take your skills to the next level.

(also available as ebook )

Save this course for later

Don't have time for it all now? No problem, save it as a course and come back to it later.

Footer bottom

• Privacy Policy

• Subscribe to our RSS Feed
• Like us on Facebook
• Follow us on Twitter

Have a language expert improve your writing

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

• Knowledge Base

Methodology

• Types of Research Designs Compared | Guide & Examples

Types of Research Designs Compared | Guide & Examples

Published on June 20, 2019 by Shona McCombes . Revised on June 22, 2023.

When you start planning a research project, developing research questions and creating a  research design , you will have to make various decisions about the type of research you want to do.

There are many ways to categorize different types of research. The words you use to describe your research depend on your discipline and field. In general, though, the form your research design takes will be shaped by:

• The type of knowledge you aim to produce
• The type of data you will collect and analyze
• The sampling methods , timescale and location of the research

This article takes a look at some common distinctions made between different types of research and outlines the key differences between them.

Table of contents

Types of research aims, types of research data, types of sampling, timescale, and location, other interesting articles.

The first thing to consider is what kind of knowledge your research aims to contribute.

Here's why students love Scribbr's proofreading services

Discover proofreading & editing

The next thing to consider is what type of data you will collect. Each kind of data is associated with a range of specific research methods and procedures.

Finally, you have to consider three closely related questions: how will you select the subjects or participants of the research? When and how often will you collect data from your subjects? And where will the research take place?

Keep in mind that the methods that you choose bring with them different risk factors and types of research bias . Biases aren’t completely avoidable, but can heavily impact the validity and reliability of your findings if left unchecked.

Choosing between all these different research types is part of the process of creating your research design , which determines exactly how your research will be conducted. But the type of research is only the first step: next, you have to make more concrete decisions about your research methods and the details of the study.

Read more about creating a research design

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

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

Research bias

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

Cite this Scribbr article

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

McCombes, S. (2023, June 22). Types of Research Designs Compared | Guide & Examples. Scribbr. Retrieved August 24, 2024, from https://www.scribbr.com/methodology/types-of-research/

Is this article helpful?

Shona McCombes

Other students also liked, what is a research design | types, guide & examples, qualitative vs. quantitative research | differences, examples & methods, what is a research methodology | steps & tips, get unlimited documents corrected.

✔ Free APA citation check included ✔ Unlimited document corrections ✔ Specialized in correcting academic texts

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

• View all journals
• Explore content
• About the journal
• Publish with us
• Sign up for alerts
• Open access
• Published: 23 August 2024

Examining the Proteus effect on misogynistic behavior induced by a sports mascot avatar in virtual reality

• Rabindra Ratan 1 ,
• Josephine Boumis 2 ,
• George McNeill 1 ,
• Ann Desrochers 1 ,
• Stefani Taskas 1 ,
• Dayeoun Jang 1 &
• Taj Makki 1  

Scientific Reports volume  14 , Article number:  19659 ( 2024 ) Cite this article

Metrics details

• Communication and replication
• Human behaviour

The Proteus effect is a phenomenon found in over 60 studies where people tend to conform behaviorally to their avatars’ identity characteristics, especially in virtual reality. This study extends research on the Proteus effect to consider organization-representing avatars and misogynistic behavioral outcomes. Male participants ( N  = 141) in a lab experiment embodied a set of pretested avatars which varied in level of association with a university mascot (i.e., color and body type) in a bespoke virtual reality simulation designed to elicit misogynistic behavior. Namely, participants were directed to place a hand on virtual agents’ body parts, including the buttocks (i.e., a transgressive misogynistic act). Time delay in complying with directions to touch the agents’ buttocks served as an implicit measure of resistance to this misogynistic behavior. Results suggest that within moderately masculine body-size avatar users, those who embodied a university-color-associated avatar exhibited more misogynistic behaviors (i.e., faster buttocks-touching). Unexpectedly, this effect of avatar color was not apparent within the hypermasculine body-size avatars, and within the university-associated color condition, hypermasculine body-type was associated with less misogynistic behavior. These findings suggest that organization-representing avatars may induce behavioral conformity to implicit attitudes associated with the organization, such as misogyny.

Similar content being viewed by others

Embodying the avatar of an omnipotent agent modulates the perception of one’s own abilities and enhances feelings of invulnerability

Sport–gender stereotypes and their impact on impression evaluations

An Embodied Perspective as a Victim of Sexual Harassment in Virtual Reality Reduces Action Conformity in a Later Milgram Obedience Scenario

Avatars are usually thought to represent individuals 1 , but groups or organizations also utilize digital self-representations akin to avatars, such as mascots or virtual influencers 2 . The present exploratory research examines this notion of organization-representing avatars within the university sports-culture context in order to extend theorization on the Proteus effect, the phenomenon that people tend to conform behaviorally to their avatars’ identity characteristics 3 . Building on the logic that mascots represent university sports communities, this study tests if a university’s hypermasculine sports mascot used as an avatar induces misogynistic behaviors via the Proteus effect. This research extends theorization on the Proteus effect to include organization-representing avatars in the context of anti-social (i.e., misogynistic) effects.

The Proteus effect

Research on the Proteus effect suggests that when people use avatars, they conform behaviorally to their associations with the avatars’ identity characteristics 3 . The Proteus effect has been found in over 60 studies, and recent meta-analyses suggest that the effect size is relatively robust ( r  = 0.24), especially in studies conducted in virtual reality ( r  = 0.30) 4 , 5 . The Proteus effect has been studied across multiple domains, often with positive connotations for the outcome behaviors examined. For example, perceived avatar height, intelligence, and body shape have been found to influence negotiation prowess, creativity while brainstorming, and exercise activity, respectively 6 , 7 , 8 . Some previous work has explored anti-social behavioral outcomes of the Proteus effect, such as KKK-associated avatars leading to more aggressive intentions 9 , sexualized avatars leading to more self-objectification and rape myth acceptance 10 , 11 , and gendered avatars triggering stereotype threat 12 , 13 . However, no previous research of which we are aware has examined how avatars might induce misogynistic behavior , perhaps because of challenges in designing and conducting such a study. The present research addresses this gap, examining the impacts of hypermasculine, organization-representing avatars (i.e., mascots) on misogynistic behaviors in a virtual sports environment via the Proteus effect.

Early research on the Proteus effect was inspired by studies of similar phenomena—such as the finding that wearing black (compared to light) jerseys during sports induced more aggressive behavior because of color stereotypes (e.g., black symbolizing aggression) 14 —and offered multiple theoretical explanations of the Proteus effect. Combining these explanations, we infer that the phenomenon occurs because users associate avatar characteristics with their self-perception 7 , especially when they experience a sense of embodiment 15 or deindividuation (i.e., less attention on inward, differentiating characteristics) in an avatar 16 . Put another way, avatar use primes avatar-associated characteristics 9 , causing users to temporarily incorporate these avatar characteristics into user self-concept, thereby influencing user behavior 4 , 17 , 18 . Further, as predicted by the Social Identity model of Deindividuation Effects (SIDE), avatars potentially diminish individuating (i.e., personally relevant, differentiating) information 19 , making people more susceptible to the influence of group norms 3 , 7 , though recent findings suggest that social identification can hinder the Proteus effect if those social cues are misaligned with the avatar’s identity characteristics 16 .

Regardless of the specific mechanism, stereotypes are a central component of the Proteus effect. When avatar appearance is associated with stereotypical schemas, such as masculine avatars with dominance and decisiveness, using such avatars leads to adopting or reinforcing the stereotypes both attitudinally and behaviorally 20 , 21 . From this perspective, previous Proteus effect findings, including the influence on negotiation style, creativity, and physical activity performance 6 , 7 , 8 , can all be seen as the participants adopting latent stereotypical schemas they had in mind based on their perception of the avatars’ characteristics. Tall people are stereotypically stronger negotiators. Inventors are seen as more intelligent. Body weight is associated with exercise. Such minor, latent stereotypes linked to avatars can lead to pronounced Proteus effects. However, little research has been conducted on avatars representing organizational or cultural stereotypes.

Mascots as avatars

This research extends the literature to consider organization-representing avatars, such as university mascots, which potentially induce the Proteus effect through associations between the organization (e.g., university) and avatar (e.g., mascot). Research suggests that university students associate themselves more with the university mentally and behaviorally through “BIRGing, or basking in reflected glory after a team victory” 22 . When a university sports team wins, students and other community members tend to feel socially connected to the sports teams and sports culture, expressing this identity connection by displaying iconography (e.g., on clothing, bumper stickers, etc.) associated with the university, such as mascots 23 , 24 . In other words, mascots are embodiments of university sports culture and are largely visible on university-associated merchandise, so when people display a mascot as a personal identity symbol, they are treating the mascot as a sort of avatar that represents university sports culture. We posit that this interpretation of mascots as avatars of universities can be extended to other types of organizations (e.g., companies, government agencies, brands) that use fictional characters to represent or embody the organization’s identity.

Given that avatars can influence users' real-world behaviors and attitudes based on the Proteus effect, the need to understand avatars’ social impact becomes increasingly important as avatar-mediated communication becomes more common and interest in the metaverse continues to grow. Gaining more insight into how avatars that represent organizational and cultural stereotypes can influence social behaviors and attitudes is important for societal development in the metaverse era.

Mascots and masculinity

Although university mascots are not necessarily hypermasculine, university sports culture is often associated with hypermasculinity, so hypermasculine mascots likely reinforce this association. This is potentially problematic because hypermasculinity is associated with sexually aggressive attitudes toward women, “macho” culture, and misogyny among college males 25 , 26 , 27 , 28 , 29 , 30 . Misogyny, defined as norms which promote dehumanization and objectification of women 31 , 32 , pervades sports culture and not only discourages women from participating in sports 33 but also creates a hostile environment for female sports fans, which often leads to attacks against them 34 , 35 .

University mascots that are depicted as hypermasculine (e.g., with exaggerated upper body muscle) might be stereotypically associated with misogynistic attitudes. Further, even if a mascot is not hypermasculine but the university sports culture is associated with hypermasculinity, then the university mascot may also be associated with misogyny. In that sense, the more an avatar resembles the impression of a university mascot, the more readily the avatar could be associated with the university sports culture and, in turn, misogynistic attitudes. Therefore, the current study examines two major elements regarding the resemblance of a character, the general body type (e.g., hypermasculine) and the color (e.g., university-associated).

Extrapolating from previous research on the Proteus effect, embodying a university mascot as an avatar should lead the user to exhibit misogynistic behaviors either if the mascot is hypermasculine or the mascot displays the university’s colors, assuming the university’s sports culture is associated with hypermasculinity. Further, we expect an additive effect of hypermasculinity and the matching color of the mascot avatar on users’ misogynistic behaviors. Thus, we propose the following hypotheses to further reflect our reasoning:

Hypothesis 1

Using an avatar with a university mascot’s hypermasculine body type, compared to a moderately masculine body type, will lead to more misogynistic behaviors.

Hypothesis 2

Using an avatar with a university mascot’s colors, compared to other colors, will lead to more misogynistic behaviors.

Hypothesis 3

There is an interaction effect between avatar color and body type such that the greatest misogyny will result from mascot avatars with a university mascot’s colors and hypermasculine body type.

This research took place in two parts: an online pre-test to assess and guide the design of the university mascot avatars for use in an experimental study and then the experimental study designed to test our hypotheses.

Avatar design pretest

Our research team, which included a faculty member, graduate students, and undergraduate students focusing on game design, collaboratively and iteratively designed four avatars. One reflected the body shape of the contemporary Michigan State University (MSU) mascot figure, with broad shoulders and thick arms, which we reasoned would be perceived as highly masculine given that such cues tend to influence masculinity perceptions 36 , 37 . The other avatar body type, which we expected to be perceived as less masculine, was thinner and less muscular. Our research team drew 2-dimensional images of both body types in two colors, green (MSU’s mascot colors) and red (for comparison). See Fig.  1 for the green images (the red images are not shown for trademark policy reasons).

Avatar designs in the university-associated color (green) used in the pre-test. The non-university associated color (red) avatars are not shown here for trademark reasons. Sparty is a registered trademark of Michigan State University, all rights reserved.

We then recruited 161 participants from the MSU student population to complete an Institutional Review Board (IRB)-approved online survey to assess these avatars. Responses from 157 participants were used for the analysis after removing incomplete responses. Each participant was randomly assigned to rate one of the four avatars (2 body types × 2 colors) on a variety of items, including “This character looks masculine,” “This character looks dominant,” and “This character reminds me of MSU.” Responses on the first two items measuring masculinity were positively correlated ( r  = 0.59), so we used a mean of the two as a composite metric of perceived masculinity. According to an analysis of variance (ANOVA) with avatar body type and color as the independent variables, perceived masculinity was significantly influenced by avatar body types [ F (1, 153) = 5.977, p  = 0.016, η p 2  = 0.038] but not by color nor the interaction between size and color. However, association with MSU was significantly influenced by color [ F (1, 153) = 5.695, p  = 0.018, η p 2  = 0.036], not size nor the interaction. In other words, the larger body mascot avatar was perceived as more masculine than the moderately masculine avatars regardless of color, while green mascots were perceived as more associated with the university, regardless of size. Hence, we designed similarly shaped 3-dimensional avatars for use in our lab-based experiment. However, based on qualitative feedback, we chose purple instead of red as the contrasting color for the lab-based experiment in order to avoid associations with any of the university's main rivalries (i.e., blue, yellow, and red).

Lab experiment

Our experiment—approved by and consistent with the guidelines of the IRB at Michigan State University’s Human Research Protection Program—was conducted at Michigan State University approximately 2 years after the university had been the subject of widely discussed news reports about misogynistic abusive practices related to university sports. Despite the two-year delay (in part a result of the time required to design and develop this study), the concerns about misogynistic culture at the university were still frequently discussed by the university administration and in community news (e.g., the university president resigned around this time as a result of the exposed abusive practices). The study followed a 2 (avatar color: own university-associated—green—or not-associated—purple) × 2 (avatar body: hypermasculine mascot or moderately masculine mascot) between-subjects design. A third body type (non-athletic) was designed as a control condition but not included in this analysis because its appearance differed too significantly from the main body types of interest in this study.

A university research sampling pool was used to recruit anonymous adult men living in the university community area, and a total of 250 people participated in the study. Participants’ age ( M  = 22.2; SD  = 6.27) and race (62% White, 24% Asian, 8% Other, and 6% Black or African American) were consistent with the local area’s demographics. Given that this study is designed based on heteronormative assumptions about men being attracted to women and thus engaging in misogynistic behaviors, we asked participants the extent to which they identify as “opposite-sex attracted.” Participants who responded with a 1 (“not at all”) or 2 (“slightly”) or did not respond were not considered in the analyses, leaving participants who responded with 3–5 (“somewhat,” “strongly,” or “very much) in the study sample, N  = 141. This metric of participant sexuality was evenly distributed across conditions [χ 2 (6, N  = 250) = 10, p  = 0.123].

According to power analysis using the R package pwrss version 0.3.1 38 —with a significance criterion of α = 0.05, power = 0.80, and effect size = 0.29 5 , the minimum sample size needed to detect a meaningful effect with the desired level of power was 22 (5.5 per group). Thus, the obtained sample size of N  = 141 was more than adequate to test the study hypotheses.

All recruited participants read and signed an informed consent form before completing the study. Upon arrival, participants were given an HTC Vive virtual reality (VR) headset and two hand controllers, which, together with two base stations, precisely tracked participants’ head and hand locations and movements in six degrees of freedom. Participants were randomly assigned to avatar conditions and placed inside a model football stadium built in Unity version 2018.3.6f1. Participants then engaged in the task of taking “selfies” with 3D models (“virtual agents”) of attractive young women, posing with them in the virtual mirror (Fig.  2 ). During the entire experience, the participants stood in front of a virtual mirror, which helped make the selfie-taking task more believable while also serving to remind participants of their avatar’s identity, thereby reinforcing the experimental manipulation. After a 30-s orientation to the environment, participants completed a brief tutorial in which they were instructed to pose with one hand held in a translucent target box. After practicing on five boxes, a female virtual agent appeared along with the prompt, “Can we take a picture?” above the mirror, followed by a target box on a specific part of a virtual agent’s body, either her shoulders, upper back, lower back, or buttocks (see examples in Fig.  2 ). Before completing the final questionnaire, participants were asked to pose twice with eight different virtual models each (16 total trials). The order of body parts across trials was consistent between participants, with the box placed at the buttocks for trials 4, 6, 8, 9, 11, 13, 15, and 16.

Left top: first-person view of an upper back trial; Right top: an example of the photo-taking task; Left bottom: hypermasculine university-color avatar, Right bottom: moderately masculine university-color avatar.

For successful trial completion (indicated by a flash and camera noise), participants were required to hold their hands in the target boxes for five consecutive seconds within a 10-s period. In order to ensure a consistent experience and timing between participants, a limit was placed on the maximum time per trial. Trials in which participants did not place their hands in the target box within 10 s were considered “failed” trials, with no data recorded for that trial. Following failed trials, the virtual environment would automatically advance to the next trial, meaning participants never completed the failed trials. Trials in which participants entered the target box but then removed their hand before the required five seconds had elapsed were considered “flawed” trials. Participants were given an additional five s to return their hands to the target and complete the trial (i.e., by holding their hand in the box for five additional seconds). If they did so, the trial could be completed within 10 s from the start, but it was considered flawed. This approach allowed participants to experience the same general pattern between trials. However, the data from such flawed trials were inherently different from those of successful trials, with no clear way to calculate a comparison between the two. Hence, we chose to remove flawed (in addition to failed) trials from the analysis in order to increase data reliability. Note that the number of failed ( M  = 0.606, SD  = 1.35) and flawed ( M  = 0.568, SD  = 0.875) trials per participant was rare compared to completed trials ( M  = 14.8, SD  = 1.72). Further, according to Fisher’s exact test, the number of flawed ( p  = 0.476) and failed trials ( p  = 0.523) was not influenced by condition before the screening, and there were no differences found in observed proportions after screening (failed: p  = 0.898, flawed: p  = 0.924). Note also that there were 9 participants who did not succeed in completing any buttocks trial (i.e., all trials were failed or flawed), so they were removed entirely from the analysis, leaving an effective sample size of n  = 132, which was still adequate according to our power analysis.

Time-to-touch virtual body parts

The amount of time elapsed was measured for each trial. Time-to-touch buttocks trials were used as an implicit measure of misogynistic behavior, with more time indicating greater buttocks-touching resistance (i.e., less misogynistic behavior). People generally recognize that a man touching a woman’s buttocks is socially inappropriate and misogynistic 39 . Given that studies find that people respond to media agents as they respond to other humans 40 , 41 and that touching a robot’s buttocks led to more physiological arousal than other body parts 42 , touching a virtual agent’s buttocks should also be understood as inappropriate. In other words, the act of virtual-agent buttocks touching can be interpreted as misogynistic and transgressive because it represents a violation of a woman’s autonomy 43 . Thus, we reasoned that men influenced to behave misogynistically—through the Proteus effect based on their avatar’s appearance—would place their hand on the virtual agent’s buttocks more quickly. We constructed a mean time-to-touch metric from all of the eight buttocks-touching trials ( M  = 6.47, SD  = 0.634, min = 5.05, max = 8.70, skewness = 0.67), which exhibited a sufficiently normal distribution. The reliability across these eight measures was somewhat low (Cronbach’s α = 0.68), but we considered this acceptable given that the metric is based on behavior with high variability (i.e., hand movement in virtual reality).

Avatar-university association

The perception of the avatar as representing the university was included as a manipulation check. This composite measure was constructed from an average of responses to “The character I controlled reminded me of Michigan State University” and “The character I controlled represents Michigan State University” on 5-point scales of agreement ( r  = 0.68, p  = 0.000).

A number of additional items were given in the post-survey but not included in the present analysis because this study’s primary goal was to focus on behavioral outcomes.

Manipulation check

A manipulation check was conducted to test the assumption that using an avatar with the university’s colors and with the university mascot’s hypermasculine body type would be perceived as representing the university to a greater extent than an avatar with non-university colors and a moderately masculine body type. Because the homogeneity of variance assumption was not satisfied, a linear model using generalized least squares was performed with avatar color and avatar body type as the independent variables and perception of the avatar as reflecting the university as the dependent variable 44 . The main effect of avatar color was not significant (b = − 0.07, t  = − 0.39, p  = 0.69), but the main effect of avatar body type was significant (b = − 0.92, t  = − 3.21, p  = 0.00). Additionally, the interaction effect between the two was marginally significant, α = 0.9 (b = 0.62, t  = 1.75, p  = 0.08). A graph of this test (Fig.  3 ) suggests that avatar-university association was highest in the hypermasculine (mascot-like) body type condition, regardless of avatar color, while in the moderately masculine body type condition, perceived university reflection was higher in the university-color condition. To confirm this interpretation, we conducted two simple effects tests. Within the hypermasculine body type condition, avatar-university reflection did not differ significantly by condition ( p  = 0.70). Within the moderately masculine body type condition, avatar-university reflection was indeed higher in the university-color condition, t (45.13) = 2.09, p  < 0.05. These results suggest that the manipulation was successful as intended.

Manipulation checks for avatar-university association by avatar color and body type.

Main analyses

We then conducted an ANOVA with avatar color and avatar body type as the independent variables and the measure of misogynistic behavior, time-to-touch buttocks trials, as the dependent variable in order to test Hypotheses 1–3. Across color and body type conditions, normality and homogeneity of variance for time-to-touch buttocks were assessed by the Shapiro–Wilk test and Levene's test, but no violations were found. The main effect of mascot body type was not significant, F (1, 128) = 0.90, p  = 0.344, η p 2  = 0.01, which is not consistent with H1. The main effect of mascot color was significant, F (1, 128) = 6.37, p  = 0.013, η p 2  = 0.05, consistent with H2. Finally, the 2-way interaction between mascot color and mascot body type was also significant, F (1, 128) = 4.02, p  = 0.047, η p 2  = 0.03. Results (Fig.  4 ) suggest that in the moderately masculine mascot condition, using an avatar with university-associated colors led to the lowest buttocks-touching delay—meaning more misogynistic behavior (consistent with H2)—but in the hypermasculine body type condition, the avatar color did not appear to make a difference (providing limited support for H2 and no support for H3).

Impact of avatar color and body type on misogynistic behavior.

A series of simple effects follow-up tests were then conducted to confirm this interpretation of the data. Within the moderately masculine mascot condition, using an avatar with university-associated colors led to significantly lower buttocks-touching delay—more misogynistic behavior—than non-university-associated colors with a notably large effect size, F (1, 61) = 11.76, p  < 0.01, η p 2  = 0.16, which is also consistent with H2 along with the aforementioned main effect. Within the university color-associated condition, hypermasculine body type led to significantly longer buttocks-touching delays— less misogynistic behavior—than the average-size mascot, also with a notably large effect size, F (1, 72) = 7.78, p  < 0.01, η p 2  = 0.10, contradicting H1. No significant differences were found within the non-university-associated color condition between body types [ F (1, 57) = 0.39 , p  = 0.52, η p 2  = 0.01] or other the hypermasculine-body-type condition between color conditions [ F (1, 67) = 0.12, p  = 0.73, η p 2  = 0.00]. Together, these results contradict H1, provide partial support for H2 (i.e., in the moderately-masculine condition only), and do not support H3.

This exploratory study extends Proteus effect research to consider how organization-representing avatars (i.e., mascots) might induce behaviors that reflect cultural associations with the organization, in this case, misogyny. Participants (opposite-sex attracted men from the community) who used an avatar that was more closely associated with their university (as signaled by color) exhibited more misogynistic behavior, as reflected by lower time delays (i.e., less resistance) in a task requiring them to touch a virtual agent’s buttocks with their hand in the virtual world. However, this difference was driven by an interaction effect, and upon further analysis was found to only be significant when the avatar had a moderately masculine body shape. In other words, no difference was found by the university-color association for participants who used hypermasculine-sized avatars. Within the university-associated color condition, participants who used the hypermasculine body-type avatar exhibited less misogynistic behavior (greater resistance to buttocks touching) than those who used the moderately masculine mascot, which contradicted expectations. No differences were found by avatar body type within the non-university-associated color condition. Overall, these findings extend theorization on the Proteus effect, providing evidence that organization-representing avatars can induce behavioral conformity to attitudes associated with the organization, such as misogyny.

The finding that participants who used the university-associated color (compared to non-university-associated color) avatar exhibited more misogynistic behavior—but only for moderately masculine (not hypermasculine) avatars—might suggest that when the body type was less recognizable as the university mascot, the color was the main indication of a connection to university sports culture and implicit misogyny. Hence, controlling this avatar induced more misogynistic behavior via the Proteus effect. It is also possible that “wearing” this university-associated color had a significant impact on their behavior regardless of the avatar. For example, Frank and Gilovich 14 found wearing black jerseys increased participants’ aggressive behavior. However, the avatar’s clothing and the avatar itself were intrinsically linked in our study. Given the evidence that embodying (i.e., controlling) an avatar leads to stronger Proteus effects than viewing (without controlling) the avatar 4 , 5 , 15 , we interpret this finding as a reflection of the Proteus effect. Namely, controlling an organization-representing avatar (i.e., mascot) created associations between organization-related schema (i.e., sports culture and misogyny) and self-perception—especially in the moderately masculine condition—inducing conformity with related (i.e., misogynistic) behavior.

The simple effects tests also found that the hypermasculine avatar led to significantly less misogynistic behavior than the moderately masculine avatar within the university-associated color condition. This unexpected finding may have resulted from the hypermasculine avatar being easily recognizable as a specific social other, namely, the university mascot. Instead of priming schema related to sports culture as a whole, this may have primed associations with the specific (fictional) individual mascot. Despite being hypermasculine, this mascot also serves as a family-friendly community member (e.g., posing for pictures with families at public events), so such associations may have counteracted antisocial (e.g., misogynistic) associations with university sports culture. It is also possible that participants attributed their actions to the avatar, a symbol of the university, and acted in accordance with the instructions in order to avoid tarnishing the university’s reputation. In other words, we infer that initial expectations regarding associations with the avatar were incorrect—the misogynistic associations of hypermasculinity were less salient than the prosocial associations with the mascot, perhaps because the mascot was highly recognizable in this community. This interpretation would explain why our results did not confirm our hypotheses about avatar body size. Future research in another social context could potentially support the predicted effect of hypermasculine avatar types. Overall, this finding highlights the challenge of predicting user associations with avatar characteristics, contributing to the body of research on limitations in Proteus effect, such as when social cues and individual identity cues are misaligned 16 .

Theoretical and practical implications

This study extends Proteus effect research to consider organization-representing avatars and anti-social (misogynistic) behavioral outcomes of the phenomenon. Regarding the former, although the psychological mechanisms of the phenomenon are presumably similar, mascots and virtual influencers 2 that are associated with entire communities or brands can be used by individuals as avatars, thereby influencing user behavior via those associations. This represents a novel paradigm for mediated interaction in virtual reality that may have implications for numerous psychological outcomes (e.g., sense of self and identification, motivation, and well-being).

Regarding this study’s context of misogynistic behavior, these findings contribute to a line of research on antisocial or negative Proteus effect outcomes, such as aggression, self-objectification, and stereotype threat 9 , 10 , 11 , 12 , 13 . Although we may attempt to design avatars—organization-representing or otherwise—to induce more educational engagement, healthier behavior, or greater well-being, avatars may also cause psychological, social, or even physical harm via the Proteus effect. Although we may assume (or hope) that such outcomes are not implemented intentionally within virtual environments, the potential for inadvertent harm via the Proteus effect is quite real. People who use an avatar associated with misogynistic or other anti-social behaviors may act in misogynistic or anti-social ways, even outside of the virtual environment.

The present findings can also be interpreted in the context of the proposed theoretical underpinnings of the Proteus effect. Namely, avatar embodiment 15 facilitates deindividualization 16 and primes avatar-associated characteristics 9 which are incorporated into self-concept 4 , 17 , 18 , leading to changes in self-perception 3 , 7 and thus avatar-user behavior. Participants were randomly assigned to control in VR (i.e., embody) an avatar mascot that reflected no personal characteristics (i.e., deindividualizing) and potentially signaled a group norm associated with the organization (i.e., priming) represented by the mascot. The avatar’s social identity (i.e., university sports-culture association) potentially became salient to participants’ self-concept and self-perception, thereby influencing their behavior in ways that were consistent with perceptions of that identity (e.g., misogynistic acts). Unfortunately, the present study was not designed to test these specific mechanisms of the Proteus, but they remain plausible explanations of the patterns found.

Limitations and future directions

We offer some future directions for Proteus effect research based on this study’s limitations, including the study timing and location. The study was conducted shortly after a number of incidents that led to public scrutiny of misogyny within sports culture at multiple universities. Although these events prompted and reinforced the timeliness of the current study, this research was conducted at a university that changed dramatically in the following years in response to those incidents. The association between the university sports culture and misogyny likely varies depending on publicized news items, events, and cultural initiatives, hindering the potential for replicability in the study as originally designed. Hence, future research on organization-representing avatars should carefully consider what organizational associations are likely to induce the Proteus effect.

Another limitation is the current sample, which was composed only of “opposite-sex attracted” men. Although this decision was purposeful, based on the misogynistic context of the research, future studies should sample from wider populations to increase external validity and better support claims.

The current study only used one mascot that was connected to one specific university. It is possible that the Proteus effect found in this study was specific to this mascot, and thus the phenomenon should be tested at other universities or organizations, with other cues to organizational associations besides color—as well as additional colors beyond those tested here—to enhance external validity.

There was a potential confound in avatar style, with the hypermasculine avatars appearing more cartoonish than the moderately masculine avatars. While we do not believe this difference accounts for the present findings, future research should aim to achieve stylistic consistency across avatars to better isolate Proteus effects.

The present study is limited in its reliance on a single type of implicit measurement of virtual misogynistic behavior. Corroborating this approach with direct measurement of participants’ perceived association between organizational culture and misogyny would have strengthened the behavioral metric’s validity, but such self-report measures about sensitive topics like misogyny are susceptible to social desirability bias. Another approach could have been to add a survey question asking participants about the extent to which they viewed the buttocks-touching as a transgression. We did not think of this approach at the time, but future research could easily institute this validity check.

Future research could support behavioral measurement validity by including physiological measures or implicit association tests (IAT) of attitudes 45 . Only a few avatar-effects studies have utilized physiological measures [e.g., arousal; 18 , 46 ] or the IAT [e.g., to assess racial bias; 47 , 48 ], while most Proteus effect research has utilized implicit behavioral metrics besides the IAT 4 . Future research could triangulate virtual behavioral effects with physiological or implicit attitudinal measures to bolster internal validity and elucidate psychological mechanisms behind the effects.

Also related to the behavioral metric, we limited the maximum duration of each trial to 10 s, which may have hindered the internal validity of our data by removing the possibility of more extreme data points. Although failed trials and flawed trials were relatively rare, future research should consider avoiding this issue by using naturalistic behavioral metrics that require less experimental constraint.

Another possible critique is that our findings might not have resulted from the Proteus effect, but instead on compliance to the body-touching task in general, not just for the buttocks trials. To address this, we replicated our main analyses, conducting an ANOVA with avatar color and avatar body type as the independent variables and the time-to-touch non-buttocks trials as the dependent variable. There were no significant effects found for mascot body type [F(1, 128) = 0.86, p  = 0.26, η p 2  = 0.01] color [F(1, 128) = 0.32, p  = 0.81, η p 2  = 0.00], or the interaction [F(1, 128) = 1.12, p  = 0.09, η p 2  = 0.01]. Hence, this analysis does not contradict our interpretation of these findings as a Proteus effect.

These limitations to measurement validity notwithstanding, the present research illustrates how VR is an ideal platform for observing behaviors that closely mirror real-world dynamics, facilitating analysis of rich, contextually nuanced data that is often challenging to capture through traditional methods 49 . In particular, the study exemplifies an examination of a sensitive topic (i.e., misogynistic behavior) that would be difficult ethically to conduct in a non-virtual environment and less valid if conducted in a less immersive, less naturalistic media environment. Future research should follow this paradigm to ethically measure antisocial effects (Proteus or otherwise) via virtual behavior.

Lastly, the present research was largely exploratory in its approach to examining how a mascot might serve as an avatar and induce the Proteus effect. Future research should continue this line of inquiry on how organization-representing avatars (e.g., mascots, virtual influencers) influence user behaviors in ways that are consistent with stereotypes about organizational culture, for better or in this case, worse. Such research will contribute new theoretical extensions of the Proteus effect as well as practical inferences that are important for technological and societal development, especially as interest in avatar-mediated communication platforms and the metaverse continue to grow.

Data availability

An anonymized, cleaned dataset with all variables analyzed in this study, including the raw time-elapsed data for all 16 agent-touching trials, can be found in this OSF repository: https://osf.io/smf7u/?view_only=01c5b63452fd46b19da7b0176175dcbb . A video example of the study procedure from the participant’s perspective in VR can be found here: https://youtu.be/LTDrSx6Y_oQ .

Nowak, K. L. & Fox, J. Avatars and computer-mediated communication: A review of the definitions, uses, and effects of digital representations. Rev. Commun. Res. 6 , 30–53 (2018).

Article   Google Scholar  

Kadekova, Z. & Holienčinova, M. Influencer marketing as a modern phenomenon creating a new frontier of virtual opportunities. Commun. Today 9 , 901–105 (2018).

Google Scholar  

Yee, N. & Bailenson, J. The Proteus effect: The effect of transformed self-representation on behavior. Hum. Commun. Res. 33 , 271–290. https://doi.org/10.1111/j.1468-2958.2007.00299.x (2007).

Ratan, R., Beyea, D., Li, B. & Graciano, L. Avatar characteristics induce users’ behavioral conformity with small-to-medium effect sizes: A meta-analysis of the Proteus effect. Media Psychol. https://doi.org/10.1080/15213269.2019.1623698 (2019).

Beyea, D. et al. A new meta-analysis of the Proteus effect: Studies in VR find stronger effect sizes. Presence Virtual Augment. Real. 31 , 189–202. https://doi.org/10.1162/pres_a_00392 (2022).

Guegan, J., Buisine, S., Mantelet, F., Maranzana, N. & Segonds, F. Avatar-mediated creativity: When embodying inventors makes engineers more creative. Comput. Hum. Behav. 61 , 165–175. https://doi.org/10.1016/j.chb.2016.03.024 (2016).

Yee, N., Bailenson, J. N. & Ducheneaut, N. The Proteus effect: Implications of transformed digital self-representation on online and offline behavior. Commun. Res. 36 , 285–312. https://doi.org/10.1177/0093650208330254 (2009).

Li, B. J., Lwin, M. O. & Jung, Y. Wii, myself, and size: The influence of Proteus effect and stereotype threat on overweight children’s exercise motivation and behavior in exergames. Games Health J. 3 , 40–48. https://doi.org/10.1089/g4h.2013.0081 (2014).

Article   PubMed   Google Scholar  

Peña, J., Hancock, J. T. & Merola, N. A. The priming effects of avatars in virtual settings. Commun. Res. 36 , 838–856. https://doi.org/10.1177/0093650209346802 (2009).

Fox, J., Bailenson, J. N. & Tricase, L. The embodiment of sexualized virtual selves: The Proteus effect and experiences of self-objectification via avatars. Comput. Hum. Behav. 29 , 930–938. https://doi.org/10.1016/j.chb.2012.12.027 (2013).

Fox, J., Ralston, R. A., Cooper, C. K. & Jones, K. A. Sexualized avatars lead to women’s self-objectification and acceptance of rape myths. Psychol. Women Q. 39 , 349–362. https://doi.org/10.1177/0361684314553578 (2015).

Ratan, R. & Sah, Y. J. Leveling up on stereotype threat: The role of avatar customization and avatar embodiment. Comput. Hum. Behav. 50 , 367–374. https://doi.org/10.1016/j.chb.2015.04.010 (2015).

Peck, T. C., Good, J. J. & Bourne, K. A. Inducing and mitigating stereotype threat through gendered virtual body-swap illusions. In Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems 1–13 (Association for Computing Machinery, 2020). https://doi.org/10.1145/3313831.3376419 .

Frank, M. G. & Gilovich, T. The dark side of self- and social perception: Black uniforms and aggression in professional sports. J. Pers. Soc. Psychol. 54 , 74–85 (1988).

Article   CAS   PubMed   Google Scholar  

Yee, N. & Bailenson, J. N. The difference between being and seeing: The relative contribution of self-perception and priming to behavioral changes via digital self-representation. Media Psychol. 12 , 195–209. https://doi.org/10.1080/15213260902849943 (2009).

Buisine, S. & Guegan, J. Proteus vs. social identity effects on virtual brainstorming. Behav. Inf. Technol. 39 , 594–606. https://doi.org/10.1080/0144929X.2019.1605408 (2020).

Sah, Y. J., Ratan, R., Sandy Tsai, H.-Y., Peng, W. & Sarinopoulos, I. Are you what your avatar eats? Health-behavior effects of avatar-manifested self-concept. Media Psychol. https://doi.org/10.1080/15213269.2016.1234397 (2016).

Ratan, R. & Dawson, M. When Mii is me: A psychophysiological examination of avatar self-relevance. Commun. Res. 43 , 1065–1093. https://doi.org/10.1177/0093650215570652 (2016).

Reicher, S. D., Spears, R. & Postmes, T. A social identity model of deindividuation phenomena. Eur. Rev. Soc. Psychol. 6 , 161–198. https://doi.org/10.1080/14792779443000049 (1995).

Lee-Won, R. J., Tang, W. Y. & Kibbe, M. R. When virtual muscularity enhances physical endurance: Masculinity threat and compensatory avatar customization among young male adults. Cyberpsychol. Behav. Soc. Netw. 20 , 10–16. https://doi.org/10.1089/cyber.2016.0418 (2017).

Gawlik-Kobylińska, M. The Proteus effect on security and safety exergamers. Interact. Learn. Environ. https://doi.org/10.1080/10494820.2021.1898988 (2021).

Cialdini, R. B. Basking in reflected glory: Three (football) field studies. J. Pers. Soc. Psychol. 34 , 366–375 (1976).

Hirshon, N. Social identity theory in sports fandom research. In Multidisciplinary Perspectives on Media Fandom 172–191 (IGI Global, 2020).

Chapter   Google Scholar  

Wann, D. L. & Robinson, T. N. The relationship between sport team identification and integration into and perceptions of a university. Int. Sports J. 6 , 36–44 (2002).

Corprew, C. S. III. & Mitchell, A. D. Keeping it frat: Exploring the interaction among fraternity membership, disinhibition, and hypermasculinity on sexually aggressive attitudes in college-aged males. J Coll. Stud. Dev. 55 , 548–562. https://doi.org/10.1353/csd.2014.0062 (2014).

Schwartz, M. D. Masculinities, sport, and violence against women: The contribution of male peer support theory. Violence Against Women https://doi.org/10.1177/1077801220958493 (2020).

Miller, K. E. Sport-related identities and the “toxic jock”. J. Sport Behav. 32 , 69–91 (2009).

PubMed   PubMed Central   Google Scholar  

Muir, K. B. & Seitz, T. machismo, misogyny, and homophobia in a male athletic subculture: A participant-observation study of deviant rituals in collegiate rugby. Deviant Behav. 25 , 303–327. https://doi.org/10.1080/01639620490267294 (2004).

Robertson, S. “If I let a goal in, I”ll get beat up’: Contradictions in masculinity, sport and health. Health Educ. Res. 18 , 706–716. https://doi.org/10.1093/her/cyf054 (2003).

Vechiu, C. The role of hypermasculinity as a risk factor in sexual assault perpetration. In Handbook of Sexual Assault and Sexual Assault Prevention (eds O’Donohue, W. T. & Schewe, P. A.) 257–273 (Springer International Publishing, 2019).

Manne, K. Down Girl: The Logic of Misogyny (Oxford University Press, 2017).

Book   Google Scholar  

Richardson-Self, L. Woman-hating: On misogyny, sexism, and hate speech. Hypatia 33 , 256–272. https://doi.org/10.1111/hypa.12398 (2018).

Barnes, S. & Adams, M. L. A large and troubling iceberg: Sexism and misogyny in women’s work as sport coaches. Sports Coach. Rev. 11 , 127–146. https://doi.org/10.1080/21640629.2021.1975940 (2022).

Chiweshe, M. One of the boys: Female fans’ responses to the masculine and phallocentric nature of football stadiums in Zimbabwe. Crit. Afr. Stud. 6 , 211–222. https://doi.org/10.1080/21681392.2014.940077 (2014).

Fenton, A., Ahmed, W., Hardey, M., Boardman, R. & Kavanagh, E. Women’s football subculture of misogyny: the escalation to online gender-based violence. Eur. Sport Manag. Q. https://doi.org/10.1080/16184742.2023.2270566 (2023).

Pazhoohi, F., Garza, R. & Kingstone, A. The interacting effects of height and shoulder-to-hip ratio on perceptions of attractiveness, masculinity, and fighting ability: Experimental design and ecological validity considerations. Arch. Sex Behav. 52 , 301–314. https://doi.org/10.1007/s10508-022-02416-2 (2023).

Holzleitner, I. J. et al. Men’s facial masculinity: When (body) size matters. Perception 43 , 1191–1202. https://doi.org/10.1068/p7673 (2014).

Bulus, M. pwrss: Statistical Power and Sample Size Calculation Tools. R package. Available: https://CRAN.R-project.org/package=pwrss (2023).

Lee, J. W. & Guerrero, L. K. Types of touch in cross-sex relationships between coworkers: Perceptions of relational and emotional messages, inappropriateness, and sexual harassment. J. Appl. Commun. Res. 29 , 197–220. https://doi.org/10.1080/00909880128110 (2001).

Article   CAS   Google Scholar  

Reeves, B. & Nass, C. I. The Media Equation: How People Treat Computers, Television, and New Media Like Real People and Places (Cambridge University Press, 1996).

Nass, C. & Moon, Y. Machines and mindlessness: Social responses to computers. J. Soc. Issues 56 , 81–103. https://doi.org/10.1111/0022-4537.00153 (2000).

Li, J. J., Ju, W. & Reeves, B. Touching a mechanical body: Tactile contact with body parts of a humanoid robot is physiologically arousing. J. Hum. Robot Interact. 6 , 118 (2017).

Langton, R. Sexual solipsism: Philosophical essays on pornography and Objectification. Available: https://books.google.com/books?hl=en&lr=&id=N-ITDAAAQBAJ&oi=fnd&pg=PR5&dq=Sexual+Solipsism:%C2%A0Philosophical+Essays+on+Pornography+and+Objectification&ots=EomhcYc82F&sig=mZGBwiiOWUs2mv4-Wa8nxh7kkY4 (2009).

Shieh, G. Detection of interactions between a dichotomous moderator and a continuous predictor in moderated multiple regression with heterogeneous error variance. Behav. Res. Methods 41 , 61–74. https://doi.org/10.3758/BRM.41.1.61 (2009).

Greenwald, A. G., Poehlman, T. A., Uhlmann, E. L. & Banaji, M. R. Understanding and using the Implicit Association Test: III. Meta-analysis of predictive validity. J. Pers. Soc. Psychol. 97 , 17–41. https://doi.org/10.1037/a0015575 (2009).

Lim, S. & Reeves, B. Being in the game: Effects of avatar choice and point of view on psychophysiological responses during play. Media Psychol. 12 , 348–370. https://doi.org/10.1080/15213260903287242 (2009).

Peck, T. C., Seinfeld, S., Aglioti, S. M. & Slater, M. Putting yourself in the skin of a black avatar reduces implicit racial bias. Conscious Cogn. 22 , 779–787. https://doi.org/10.1016/j.concog.2013.04.016 (2013).

Maloney, D. Mitigating negative effects of immersive virtual avatars on racial bias. In Proceedings of the 2018 Annual Symposium on Computer-Human Interaction in Play Companion Extended Abstracts 39–43 (Association for Computing Machinery, 2018). https://doi.org/10.1145/3270316.3270599 .

Yaremych, H. E. & Persky, S. Tracing physical behavior in virtual reality: A narrative review of applications to social psychology. J. Exp. Soc. Psychol. 85 , 103845. https://doi.org/10.1016/j.jesp.2019.103845 (2019).

Article   PubMed   PubMed Central   Google Scholar  

Download references

Acknowledgements

We would like to thank the AT&T endowment to the Media & Information Department at MSU, which supports Dr. Ratan’s AT&T Endowed Chair position, as well as the following individuals who provided feedback or served as research assistants or developers on this project: Christine Alexander, Daniel Anderson, Connor Bird, Ian Crist, Aileen Dwyer, Gabriela Gendreau, William Johnston, Nathan Kellman, Skylar Yiming Lei, Andrea Stevenson Won, Hanna Wong, Whitney Zhou.

Author information

Authors and affiliations.

Michigan State University, East Lansing, USA

Rabindra Ratan, George McNeill, Ann Desrochers, Stefani Taskas, Dayeoun Jang & Taj Makki

University of South Florida, Tampa, USA

Josephine Boumis

You can also search for this author in PubMed   Google Scholar

Contributions

R.R.: Team leadership, study design, simulation design, writing, data analysis; J.B.: Team leadership, writing, data analysis; G.M.: Study design, simulation design, writing, data analysis; A.D.: Study design, simulation design, writing, data analysis; S.T.: Study design, simulation design, simulation development; D.J.: Writing, data analysis with the revision; T.M.: Writing.

Corresponding author

Correspondence to Rabindra Ratan .

Ethics declarations

Competing interests.

Dr. Ratan’s lab received unrestricted (“gift”) funding in 2022 from Meta, Inc. (Facebook, Inc., at the time) without their involvement in any design, data collection, analysis, writing or other research activities. Dr. Makki is employed by Google as a UX researcher. Ms. Boumis, Mr. McNeill, Ms. Desrochers, Ms. Taskas, and Ms. Jang and have no financial or non-financial competing interests to declare.

Additional information

Publisher's note.

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

Rights and permissions

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

Reprints and permissions

About this article

Cite this article.

Ratan, R., Boumis, J., McNeill, G. et al. Examining the Proteus effect on misogynistic behavior induced by a sports mascot avatar in virtual reality. Sci Rep 14 , 19659 (2024). https://doi.org/10.1038/s41598-024-70450-2

Download citation

Received : 24 August 2023

Accepted : 16 August 2024

Published : 23 August 2024

DOI : https://doi.org/10.1038/s41598-024-70450-2

Share this article

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

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

Provided by the Springer Nature SharedIt content-sharing initiative

• Proteus effect

By submitting a comment you agree to abide by our Terms and Community Guidelines . If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.

Quick links

• Explore articles by subject
• Guide to authors
• Editorial policies

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

COLUMBIA UNIVERSITY IN THE CITY OF NEW YORK

Technician A

• Columbia University Medical Center
• Opening on: Aug 24 2024
• Job Type: Support Staff - Non-Union
• Bargaining Unit:
• Regular/Temporary: Temporary
• End Date if Temporary: 12/1/2024
• Hours Per Week: 15
• Standard Work Schedule:
• Salary Range: $24.71 - $24.71

Position Summary

We are seeking a part-time Technician A to complete experiments furthering the investigation of a monogenic human immunodeficiency. The successful applicant will work remotely and on-site to complete experiments and analysis of previously acquired experimental data towards the goal of submitting a manuscript. This position will be funded for a period of one year.

At CUIMC, we are leaders in teaching, research, and patient care and are proud of the service and support we provide to our community. We apply the same rigor in our commitment to fostering an inclusive, thriving community and caring for our employees and their loved ones. 

Responsibilities

• Review and interpret data obtained from previous experiments, ensuring accuracy and consistency.
• Develop and plan experimental protocols, ensuring they align with research objectives and adhere to established scientific methodologies.
• Compile and organize experimental data into coherent narratives for manuscript preparation, ensuring clarity and logical flow.
• Submit manuscripts to peer-reviewed journals and address reviewer comments and suggestions for revisions.
• Gather and consolidate patient data from various sources, including medical records, lab results, and clinical trials.
• Performs related duties & responsibilities as assigned/requested.

Minimum Qualifications

• Two years of college or a completed A.A program with an emphasis in chemistry, biology or physics or a completed animal husbandry program.
• One year of related experience, or equivalent in education, training and/ or experience.

Preferred Qualifications

• Experience with the analysis of flow cytometry data using FlowJo.
• Experience with scientific writing and figure preparation.

Other Requirements

• This position will be funded for a period of one year.

Equal Opportunity Employer / Disability / Veteran

Columbia University is committed to the hiring of qualified local residents.

Commitment to Diversity 

Columbia university is dedicated to increasing diversity in its workforce, its student body, and its educational programs. achieving continued academic excellence and creating a vibrant university community require nothing less. in fulfilling its mission to advance diversity at the university, columbia seeks to hire, retain, and promote exceptionally talented individuals from diverse backgrounds.  , share this job.

Thank you - we'll send an email shortly.

Other Recently Posted Jobs

Project Coordinator

Clinical research coord i, coordinator.

Refer someone to this job

• ©2022 Columbia University
• Accessibility
• Administrator Log in

Wait! Before you go, are you interested in a career at Columbia University? Sign up here! 

Thank you, for sharing your information. A member of our team will reach out to you soon!

This website uses cookies as well as similar tools and technologies to understand visitors' experiences. By continuing to use this website, you consent to Columbia University's usage of cookies and similar technologies, in accordance with the Columbia University Website Cookie Notice .

CERN Accelerating science

Students from Estonia, Japan and the USA win the 11th edition of Beamline for Schools

Three teams of secondary school pupils from Estonia, Japan and the United States have been selected to carry out their own experiments using accelerator beams at CERN and DESY

25 June, 2024

Winners of the 2024 CERN Beamline for Schools competition: Sakura Particles” from Japan (left), “Mavericks” from Estonia (top right) and “SPEEDers” from the USA (bottom right) “(Images: Sakura Particles, Mavericks, SPEEDers)

Geneva and Hamburg, 25 June 2024.  Beamline for Schools (BL4S)  is a physics competition run by CERN , the European laboratory for particle physics, open to secondary school pupils from all around the world. Participants are invited to prepare a proposal for a physics experiment that can be undertaken at the beamline of a particle accelerator, either at CERN or at DESY (Deutsches Elektronen-Synchrotron in Hamburg, Germany). In 2024, three winning teams have been chosen, based on the scientific merit of their proposal and the communication merit of their video.

“Mavericks”, a team from the Secondary School of Sciences in Tallinn and the Hugo Treffner Gymnasium in Tartu, Estonia, and the team “Sakura Particles”, which brings together pupils from Kawawa Senior High School in Kanagawa, Joshigakuin Senior High School and Junten High School in Tokyo, Kawagoe Girls High School in Saitama and Kitano High School in Osaka, Japan, will travel to CERN in September 2024 to perform the experiments that they proposed. The team “SPEEDers” from Andover High School in Andover, USA, will carry out their experiment at a DESY beamline.

A beamline is a facility that provides high-energy fluxes of subatomic particles that can be used to conduct experiments in different fields, including fundamental physics, material science and medicine. 

BL4S started in 2014 in the context of CERN’s 60th anniversary. Over the past 10 years, more than 20 000 pupils from all over the world have taken part in the competition, and 25 teams have been selected as winners. The participation rate has been rising consistently over the years, with a record 461 teams from 78 countries submitting an experiment proposal in 2024. 

“Preparing a proposal for a particle physics experiment is a very challenging task. The success of Beamline for Schools shows that, when provided with the right support, high-school students can design feasible, interesting and imaginative experiments,” says Charlotte Warakaulle, CERN Director for International Relations. “We are continuously impressed by the quality of the proposals, and this year is no exception. The candidates demonstrated impressive creativity and great rigour, two essential qualities for students who might decide to take up scientific careers.”

The fruitful collaboration between CERN and DESY  started in 2019 during a long shutdown period of the CERN accelerators. This is the sixth year that the German laboratory has hosted competition winners. 

“Every year I am very impressed by the creativity and determination of the team members,” says Beate Heinemann, Director in charge of Particle Physics at DESY. “I am already looking forward to hosting the team from the USA this year. This programme is so important to me as it advances not only science but also the cultural exchange between young people from different nations.”

“Our experiment will focus on detector development for high-altitude ballooning applications,” says Saskia Põldmaa, one of the “Mavericks” members, from Estonia. “This is by far the biggest opportunity we have had so far in our lifetime so we will hold onto it dearly. We can’t wait to calibrate our homemade muon detector!”

“Our team focuses on detector development for muon tomography applications. We will test and optimise our homemade two-dimensional position-sensitive detector,” says Chiori Matsushita from the Japanese “Sakura Particles” team. “CERN has always been a dream for us. Finally getting to go there, not as a tourist but to do experiments, is amazing!”

“We focus on beam diagnostics: our aim is to measure and analyse the Smith-Purcell (SP) radiation emitted by different diffraction gratings when DESY’s electron or positron beams pass by,” says Niranjan Nair from the US “SPEEDers” team. “We are thrilled to have the opportunity to not just watch scientific advancement passively, but actively contribute to it at DESY: the ultimate goal of our experiment is to research SP radiation as a tool for beam diagnostics.”

The winning proposals were selected by a committee of CERN and DESY scientists from a shortlist of 49 particularly promising experiments. In addition, three teams will be recognised for the most creative video proposals and another 13 teams for the quality of physics outreach activities they are organising in their local communities, taking advantage of the knowledge gained by participating in BL4S.

Beamline for Schools is an education and outreach project funded by the  CERN & Society Foundation ’s donors.   This 11th edition is supported notably by ROLEX through its Perpetual Planet Initiative and by the Wilhelm and Else Heraeus Foundation.

Further information:

• BL4S website:  https://beamlineforschools.cern/
• 2024 edition:  https://beamline-for-schools.web.cern.ch/2024-edition
• Shortlisted teams and special prizes in 2024:  https://beamline-for-schools.web.cern.ch/sites/default/files/BL4S_all-winners_2024_final.pdf  
• Previous winners:  https://beamlineforschools.cern/resources/winners
• Countries represented among the shortlisted teams: Bahrain, Bangladesh, Belgium, Brazil, Canada, Chile, Czechia, Denmark, Estonia, France, Germany, Greece, Hong Kong SAR China, India, Indonesia, Italy, Japan, Kazakhstan, Pakistan, Poland, Romania, Singapore, Spain, Thailand, Türkiye, United Arab Emirates, United Kingdom, United States. 
• The prizes awarded for the best outreach project have been kindly provided by the Belgian project  “Stars Shine for Everyone” .

Also On At CERN

Join cern’s 70th birthday celebrations on 17 ..., how can i use cern ideasquare, cern science gateway featured in time magazin..., bike to work 2024: cern achieves the best par..., cern and pro helvetia announce the artists se..., coming together to celebrate cern70, how can i use cern’s open science office, building 60 renovations: one year on, news from the june 2024 cern council session.