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experimental

Definition of experimental

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Examples of experimental in a Sentence

These examples are programmatically compiled from various online sources to illustrate current usage of the word 'experimental.' Any opinions expressed in the examples do not represent those of Merriam-Webster or its editors. Send us feedback about these examples.

Word History

Middle English, borrowed from Medieval Latin experīmentālis, from Latin experīmentum "testing, experience, proof" + -ālis -al entry 1 — more at experiment entry 1

15th century, in the meaning defined at sense 1

Phrases Containing experimental

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“Experimental.” Merriam-Webster.com Dictionary , Merriam-Webster, https://www.merriam-webster.com/dictionary/experimental. Accessed 15 Aug. 2024.

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What is the Background of a Study and How Should it be Written?

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The background of a study is one of the most important components of a research paper. The quality of the background determines whether the reader will be interested in the rest of the study. Thus, to ensure that the audience is invested in reading the entire research paper, it is important to write an appealing and effective background. So, what constitutes the background of a study, and how must it be written?

What is the background of a study?

The background of a study is the first section of the paper and establishes the context underlying the research. It contains the rationale, the key problem statement, and a brief overview of research questions that are addressed in the rest of the paper. The background forms the crux of the study because it introduces an unaware audience to the research and its importance in a clear and logical manner. At times, the background may even explore whether the study builds on or refutes findings from previous studies. Any relevant information that the readers need to know before delving into the paper should be made available to them in the background.

How is a background different from the introduction?

The introduction of your research paper is presented before the background. Let’s find out what factors differentiate the background from the introduction.

• The introduction only contains preliminary data about the research topic and does not state the purpose of the study. On the contrary, the background clarifies the importance of the study in detail.
• The introduction provides an overview of the research topic from a broader perspective, while the background provides a detailed understanding of the topic.
• The introduction should end with the mention of the research questions, aims, and objectives of the study. In contrast, the background follows no such format and only provides essential context to the study.

How should one write the background of a research paper?

The length and detail presented in the background varies for different research papers, depending on the complexity and novelty of the research topic. At times, a simple background suffices, even if the study is complex. Before writing and adding details in the background, take a note of these additional points:

• Start with a strong beginning: Begin the background by defining the research topic and then identify the target audience.
• Cover key components: Explain all theories, concepts, terms, and ideas that may feel unfamiliar to the target audience thoroughly.
• Take note of important prerequisites: Go through the relevant literature in detail. Take notes while reading and cite the sources.
• Maintain a balance: Make sure that the background is focused on important details, but also appeals to a broader audience.
• Include historical data: Current issues largely originate from historical events or findings. If the research borrows information from a historical context, add relevant data in the background.
• Explain novelty: If the research study or methodology is unique or novel, provide an explanation that helps to understand the research better.
• Increase engagement: To make the background engaging, build a story around the central theme of the research

Avoid these mistakes while writing the background:

• Ambiguity: Don’t be ambiguous. While writing, assume that the reader does not understand any intricate detail about your research.
• Unrelated themes: Steer clear from topics that are not related to the key aspects of your research topic.
• Poor organization: Do not place information without a structure. Make sure that the background reads in a chronological manner and organize the sub-sections so that it flows well.

Writing the background for a research paper should not be a daunting task. But directions to go about it can always help. At Elsevier Author Services we provide essential insights on how to write a high quality, appealing, and logically structured paper for publication, beginning with a robust background. For further queries, contact our experts now!

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• Guide to Experimental Design | Overview, Steps, & Examples

Guide to Experimental Design | Overview, 5 steps & Examples

Published on December 3, 2019 by Rebecca Bevans . Revised on June 21, 2023.

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 create 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 random assignment of participants to control and treatment groups is impossible, unethical, or highly difficult, consider an observational study instead. This minimizes several types of research bias, particularly sampling bias , survivorship bias , and attrition bias as time passes.

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, other interesting articles, frequently asked questions about experiments.

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.

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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 generalized 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 randomized design vs a randomized block design .
• A between-subjects design vs a within-subjects design .

Randomization

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

• In a completely randomized design , every subject is assigned to a treatment group at random.
• In a randomized 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 randomization 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 (randomizing 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 minimize research bias or error.

Some variables, like temperature, can be objectively measured with scientific instruments. Others may need to be operationalized 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.

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.

• Student’s  t -distribution
• Normal distribution
• Null and Alternative Hypotheses
• Chi square tests
• Confidence interval
• Cluster sampling
• Stratified sampling
• Data cleansing
• Reproducibility vs Replicability
• Peer review
• Likert scale

Research bias

• Implicit bias
• Framing effect
• Cognitive bias
• Placebo effect
• Hawthorne effect
• Hindsight bias
• Affect heuristic

Experimental design means planning a set of procedures to investigate a relationship between variables . To design a controlled experiment, you need:

• A testable hypothesis
• At least one independent variable that can be precisely manipulated
• At least one dependent variable that can be precisely measured

When designing the experiment, you decide:

• How you will manipulate the variable(s)
• How you will control for any potential confounding variables
• How many subjects or samples will be included in the study
• How subjects will be assigned to treatment levels

Experimental design is essential to the internal and external validity of your experiment.

The key difference between observational studies and experimental designs is that a well-done observational study does not influence the responses of participants, while experiments do have some sort of treatment condition applied to at least some participants by random assignment .

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.

An experimental group, also known as a treatment group, receives the treatment whose effect researchers wish to study, whereas a control group does not. They should be identical in all other ways.

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Home » Experimental Design – Types, Methods, Guide

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.

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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.

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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

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

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• 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.

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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.

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• What is experimental research: Definition, types & examples

Defne Çobanoğlu

Life and its secrets can only be proven right or wrong with experimentation. You can speculate and theorize all you wish, but as William Blake once said, “ The true method of knowledge is experiment. ”

It may be a long process and time-consuming, but it is rewarding like no other. And there are multiple ways and methods of experimentation that can help shed light on matters. In this article, we explained the definition, types of experimental research, and some experimental research examples . Let us get started with the definition!

• What is experimental research?

Experimental research is the process of carrying out a study conducted with a scientific approach using two or more variables. In other words, it is when you gather two or more variables and compare and test them in controlled environments. 

With experimental research, researchers can also collect detailed information about the participants by doing pre-tests and post-tests to learn even more information about the process. With the result of this type of study, the researcher can make conscious decisions. 

The more control the researcher has over the internal and extraneous variables, the better it is for the results. There may be different circumstances when a balanced experiment is not possible to conduct. That is why are are different research designs to accommodate the needs of researchers.

• 3 Types of experimental research designs

There is more than one dividing point in experimental research designs that differentiates them from one another. These differences are about whether or not there are pre-tests or post-tests done and how the participants are divided into groups. These differences decide which experimental research design is used.

Types of experimental research designs

1 - Pre-experimental design

This is the most basic method of experimental study. The researcher doing pre-experimental research evaluates a group of dependent variables after changing the independent variables . The results of this scientific method are not satisfactory, and future studies are planned accordingly. The pre-experimental research can be divided into three types:

A. One shot case study research design

Only one variable is considered in this one-shot case study design. This research method is conducted in the post-test part of a study, and the aim is to observe the changes in the effect of the independent variable.

B. One group pre-test post-test research design

In this type of research, a single group is given a pre-test before a study is conducted and a post-test after the study is conducted. The aim of this one-group pre-test post-test research design is to combine and compare the data collected during these tests. 

C. Static-group comparison

In a static group comparison, 2 or more groups are included in a study where only a group of participants is subjected to a new treatment and the other group of participants is held static. After the study is done, both groups do a post-test evaluation, and the changes are seen as results.

2 - Quasi-experimental design

This research type is quite similar to the experimental design; however, it changes in a few aspects. Quasi-experimental research is done when experimentation is needed for accurate data, but it is not possible to do one because of some limitations. Because you can not deliberately deprive someone of medical treatment or give someone harm, some experiments are ethically impossible. In this experimentation method, the researcher can only manipulate some variables. There are three types of quasi-experimental design:

A. Nonequivalent group designs

A nonequivalent group design is used when participants can not be divided equally and randomly for ethical reasons. Because of this, different variables will be more than one, unlike true experimental research.

B. Regression discontinuity

In this type of research design, the researcher does not divide a group into two to make a study, instead, they make use of a natural threshold or pre-existing dividing point. Only participants below or above the threshold get the treatment, and as the divide is minimal, the difference would be minimal as well.

C. Natural Experiments

In natural experiments, random or irregular assignment of patients makes up control and study groups. And they exist in natural scenarios. Because of this reason, they do not qualify as true experiments as they are based on observation.

3 - True experimental design

In true experimental research, the variables, groups, and settings should be identical to the textbook definition. Grouping of the participant are divided randomly, and controlled variables are chosen carefully. Every aspect of a true experiment should be carefully designed and acted out. And only the results of a true experiment can really be fully accurate . A true experimental design can be divided into 3 parts:

A. Post-test only control group design

In this experimental design, the participants are divided into two groups randomly. They are called experimental and control groups. Only the experimental group gets the treatment, while the other one does not. After the experiment and observation, both groups are given a post-test, and a conclusion is drawn from the results.

B. Pre-test post-test control group

In this method, the participants are divided into two groups once again. Also, only the experimental group gets the treatment. And this time, they are given both pre-tests and post-tests with multiple research methods. Thanks to these multiple tests, the researchers can make sure the changes in the experimental group are directly related to the treatment.

C. Solomon four-group design

This is the most comprehensive method of experimentation. The participants are randomly divided into 4 groups. These four groups include all possible permutations by including both control and non-control groups and post-test or pre-test and post-test control groups. This method enhances the quality of the data.

• Advantages and disadvantages of experimental research

Just as with any other study, experimental research also has its positive and negative sides. It is up to the researchers to be mindful of these facts before starting their studies. Let us see some advantages and disadvantages of experimental research:

Advantages of experimental research:

• All the variables are in the researchers’ control, and that means the researcher can influence the experiment according to the research question’s requirements.
• As you can easily control the variables in the experiment, you can specify the results as much as possible.
• The results of the study identify a cause-and-effect relation .
• The results can be as specific as the researcher wants.
• The result of an experimental design opens the doors for future related studies.

Disadvantages of experimental research:

• Completing an experiment may take years and even decades, so the results will not be as immediate as some of the other research types.
• As it involves many steps, participants, and researchers, it may be too expensive for some groups.
• The possibility of researchers making mistakes and having a bias is high. It is important to stay impartial
• Human behavior and responses can be difficult to measure unless it is specifically experimental research in psychology.
• Examples of experimental research

When one does experimental research, that experiment can be about anything. As the variables and environments can be controlled by the researcher, it is possible to have experiments about pretty much any subject. It is especially crucial that it gives critical insight into the cause-and-effect relationships of various elements. Now let us see some important examples of experimental research:

An example of experimental research in science:

When scientists make new medicines or come up with a new type of treatment, they have to test those thoroughly to make sure the results will be unanimous and effective for every individual. In order to make sure of this, they can test the medicine on different people or creatures in different dosages and in different frequencies. They can double-check all the results and have crystal clear results.

An example of experimental research in marketing:

The ideal goal of a marketing product, advertisement, or campaign is to attract attention and create positive emotions in the target audience. Marketers can focus on different elements in different campaigns, change the packaging/outline, and have a different approach. Only then can they be sure about the effectiveness of their approaches. Some methods they can work with are A/B testing, online surveys , or focus groups .

• Frequently asked questions about experimental research

Is experimental research qualitative or quantitative?

Experimental research can be both qualitative and quantitative according to the nature of the study. Experimental research is quantitative when it provides numerical and provable data. The experiment is qualitative when it provides researchers with participants' experiences, attitudes, or the context in which the experiment is conducted.

What is the difference between quasi-experimental research and experimental research?

In true experimental research, the participants are divided into groups randomly and evenly so as to have an equal distinction. However, in quasi-experimental research, the participants can not be divided equally for ethical or practical reasons. They are chosen non-randomly or by using a pre-existing threshold.

• Wrapping it up

The experimentation process can be long and time-consuming but highly rewarding as it provides valuable as well as both qualitative and quantitative data. It is a valuable part of research methods and gives insight into the subjects to let people make conscious decisions.

In this article, we have gathered experimental research definition, experimental research types, examples, and pros & cons to work as a guide for your next study. You can also make a successful experiment using pre-test and post-test methods and analyze the findings. For further information on different research types and for all your research information, do not forget to visit our other articles!

Defne is a content writer at forms.app. She is also a translator specializing in literary translation. Defne loves reading, writing, and translating professionally and as a hobby. Her expertise lies in survey research, research methodologies, content writing, and translation.

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Experimental Research: Meaning And Examples Of Experimental Research

Ever wondered why scientists across the world are being lauded for discovering the Covid-19 vaccine so early? It’s because every…

Ever wondered why scientists across the world are being lauded for discovering the Covid-19 vaccine so early? It’s because every government knows that vaccines are a result of experimental research design and it takes years of collected data to make one. It takes a lot of time to compare formulas and combinations with an array of possibilities across different age groups, genders and physical conditions. With their efficiency and meticulousness, scientists redefined the meaning of experimental research when they discovered a vaccine in less than a year.

What Is Experimental Research?

Characteristics of experimental research design, types of experimental research design, advantages and disadvantages of experimental research, examples of experimental research.

Experimental research is a scientific method of conducting research using two variables: independent and dependent. Independent variables can be manipulated to apply to dependent variables and the effect is measured. This measurement usually happens over a significant period of time to establish conditions and conclusions about the relationship between these two variables.

Experimental research is widely implemented in education, psychology, social sciences and physical sciences. Experimental research is based on observation, calculation, comparison and logic. Researchers collect quantitative data and perform statistical analyses of two sets of variables. This method collects necessary data to focus on facts and support sound decisions. It’s a helpful approach when time is a factor in establishing cause-and-effect relationships or when an invariable behavior is seen between the two.  

Now that we know the meaning of experimental research, let’s look at its characteristics, types and advantages.

The hypothesis is at the core of an experimental research design. Researchers propose a tentative answer after defining the problem and then test the hypothesis to either confirm or disregard it. Here are a few characteristics of experimental research:

• Dependent variables are manipulated or treated while independent variables are exerted on dependent variables as an experimental treatment. Extraneous variables are variables generated from other factors that can affect the experiment and contribute to change. Researchers have to exercise control to reduce the influence of these variables by randomization, making homogeneous groups and applying statistical analysis techniques.
• Researchers deliberately operate independent variables on the subject of the experiment. This is known as manipulation.
• Once a variable is manipulated, researchers observe the effect an independent variable has on a dependent variable. This is key for interpreting results.
• A researcher may want multiple comparisons between different groups with equivalent subjects. They may replicate the process by conducting sub-experiments within the framework of the experimental design.

Experimental research is equally effective in non-laboratory settings as it is in labs. It helps in predicting events in an experimental setting. It generalizes variable relationships so that they can be implemented outside the experiment and applied to a wider interest group.

The way a researcher assigns subjects to different groups determines the types of experimental research design .

Pre-experimental Research Design

In a pre-experimental research design, researchers observe a group or various groups to see the effect an independent variable has on the dependent variable to cause change. There is no control group as it is a simple form of experimental research . It’s further divided into three categories:

• A one-shot case study research design is a study where one dependent variable is considered. It’s a posttest study as it’s carried out after treating what presumably caused the change.
• One-group pretest-posttest design is a study that combines both pretest and posttest studies by testing a single group before and after administering the treatment.
• Static-group comparison involves studying two groups by subjecting one to treatment while the other remains static. After post-testing all groups the differences are observed.

This design is practical but lacks in certain areas of true experimental criteria.

True Experimental Research Design

This design depends on statistical analysis to approve or disregard a hypothesis. It’s an accurate design that can be conducted with or without a pretest on a minimum of two dependent variables assigned randomly. It is further classified into three types:

• The posttest-only control group design involves randomly selecting and assigning subjects to two groups: experimental and control. Only the experimental group is treated, while both groups are observed and post-tested to draw a conclusion from the difference between the groups.
• In a pretest-posttest control group design, two groups are randomly assigned subjects. Both groups are presented, the experimental group is treated and both groups are post-tested to measure how much change happened in each group.
• Solomon four-group design is a combination of the previous two methods. Subjects are randomly selected and assigned to four groups. Two groups are tested using each of the previous methods.

True experimental research design should have a variable to manipulate, a control group and random distribution.

With experimental research, we can test ideas in a controlled environment before marketing. It acts as the best method to test a theory as it can help in making predictions about a subject and drawing conclusions. Let’s look at some of the advantages that make experimental research useful:

• It allows researchers to have a stronghold over variables and collect desired results.
• Results are usually specific.
• The effectiveness of the research isn’t affected by the subject.
• Findings from the results usually apply to similar situations and ideas.
• Cause and effect of a hypothesis can be identified, which can be further analyzed for in-depth ideas.
• It’s the ideal starting point to collect data and lay a foundation for conducting further research and building more ideas.
• Medical researchers can develop medicines and vaccines to treat diseases by collecting samples from patients and testing them under multiple conditions.
• It can be used to improve the standard of academics across institutions by testing student knowledge and teaching methods before analyzing the result to implement programs.
• Social scientists often use experimental research design to study and test behavior in humans and animals.
• Software development and testing heavily depend on experimental research to test programs by letting subjects use a beta version and analyzing their feedback.

Even though it’s a scientific method, it has a few drawbacks. Here are a few disadvantages of this research method:

• Human error is a concern because the method depends on controlling variables. Improper implementation nullifies the validity of the research and conclusion.
• Eliminating extraneous variables (real-life scenarios) produces inaccurate conclusions.
• The process is time-consuming and expensive
• In medical research, it can have ethical implications by affecting patients’ well-being.
• Results are not descriptive and subjects can contribute to response bias.

Experimental research design is a sophisticated method that investigates relationships or occurrences among people or phenomena under a controlled environment and identifies the conditions responsible for such relationships or occurrences

Experimental research can be used in any industry to anticipate responses, changes, causes and effects. Here are some examples of experimental research :

• This research method can be used to evaluate employees’ skills. Organizations ask candidates to take tests before filling a post. It is used to screen qualified candidates from a pool of applicants. This allows organizations to identify skills at the time of employment. After training employees on the job, organizations further evaluate them to test impact and improvement. This is a pretest-posttest control group research example where employees are ‘subjects’ and the training is ‘treatment’.
• Educational institutions follow the pre-experimental research design to administer exams and evaluate students at the end of a semester. Students are the dependent variables and lectures are independent. Since exams are conducted at the end and not the beginning of a semester, it’s easy to conclude that it’s a one-shot case study research.
• To evaluate the teaching methods of two teachers, they can be assigned two student groups. After teaching their respective groups on the same topic, a posttest can determine which group scored better and who is better at teaching. This method can have its drawbacks as certain human factors, such as attitudes of students and effectiveness to grasp a subject, may negatively influence results. 

Experimental research is considered a standard method that uses observations, simulations and surveys to collect data. One of its unique features is the ability to control extraneous variables and their effects. It’s a suitable method for those looking to examine the relationship between cause and effect in a field setting or in a laboratory. Although experimental research design is a scientific approach, research is not entirely a scientific process. As much as managers need to know what is experimental research , they have to apply the correct research method, depending on the aim of the study.

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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

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experimental

[ ik-sper- uh - men -tl ]

an experimental science.

The new program is still in an experimental stage.

an experimental airplane.

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• something that is experimental.

/ ɪkˌspɛrɪˈmɛntəl /

an experimental study

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• exˌperiˈmentally , adverb

Other Words From

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Word History and Origins

Origin of experimental 1

Example Sentences

Einstein was doubted for two decades, and cosmologists are still searching for experimental proofs of relativity.

Instead, Utah and Washington both had different counties adopt the system at different times, providing a shifting set of experimental and control conditions.

The team searched for experimental issues that could explain the result, but came up empty.

The country has offered experimental vaccines to officials at state enterprises in China.

If evolution was a scientist, then centenarians, and the rest of us, are two experimental groups in action.

The course Jackson taught at VMI, “Natural and Experimental Philosophy,” was brutally difficult.

I made experimental commercials in the experimental division of a production house, Film X, that made commercials for ad agencies.

All the Virgin Galactic test flying was done under a special experimental permit issued by the Federal Aviation Administration.

“Rather than the finalized models, we display the experimental work-in-progress ones,” Migarou says.

But owing to another experimental vaccine he received, its impossible to say whether the blood is what saved him.

Several Americans, we are informed, have gallantly offered themselves for experimental purposes.

Neither I nor my companion would have had the courage to have gone in her on her experimental trip.

The work of this department is experimental and investigative, with a view to the improvement of agriculture in all its branches.

He was the advocate of experimental rather than theoretical systems of medicine, and early adopted the practice of inoculation.

He appeared as cool and steady as if he were commanding on an experimental test instead of making his first rescue in the air.

Related Words

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educational research techniques

Research techniques and education.

Experimental Design: A Background

Experimental design is now considered a standard methodology in research. However, this now classic design has not always been a standard approach. In this post, we will the following

• The definition of experiment
• The history of experiments
• When to conduct experiments

The word experiment is derived from the word experience. When conducting an experiment, the researcher assigns people to have different experiences. He then determines if the experience he assigned people to had some effect on some sort of outcome. For example, if I want to know if the experience of sunlight affects the growth of plants I may develop two different experiences

• Experience 1: Some plants receive sunlight
• Experience 2: Some plants receive no sunlight

The outcome is the growth of the plants.By giving the plants different experiences of sunlight I can determine if sunlight influences the growth of plants.

History of Experiments

Since the 1980’s experiments have become much more complicated with the development of more advanced statistical software problems. Despite all of the new complexity, normally simple experimental designs are easier to understand

When to Conduct Experiments

Experiments are conducted to attempt to establish a cause and effect relationship between independent and dependent variables. You try to create a controlled environment in which you provide the experience or independent variable(s) and then measure how they affect the outcome or dependent variable.

Since the setting of the experiment is controlled. You can say withou a doubt that only the experience influence the outcome. Off course, in reality, it is difficult to control all the factors in a study. The real goal is to try and limit the effect that these other factors have on the outcomes of a study.

Despite their long history, experiments are relatively new in research. This design has grown and matured over the years to become a powerful method for determining cause and effect. Therefore, researchers should e aware of this approach for use in their studies.

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Video transcript

Educational resources and simple solutions for your research journey

What is a Theoretical Framework? How to Write It (with Examples) 

Theoretical framework 1,2 is the structure that supports and describes a theory. A theory is a set of interrelated concepts and definitions that present a systematic view of phenomena by describing the relationship among the variables for explaining these phenomena. A theory is developed after a long research process and explains the existence of a research problem in a study. A theoretical framework guides the research process like a roadmap for the research study and helps researchers clearly interpret their findings by providing a structure for organizing data and developing conclusions.   

A theoretical framework in research is an important part of a manuscript and should be presented in the first section. It shows an understanding of the theories and concepts relevant to the research and helps limit the scope of the research.  

Table of Contents

What is a theoretical framework ?  

A theoretical framework in research can be defined as a set of concepts, theories, ideas, and assumptions that help you understand a specific phenomenon or problem. It can be considered a blueprint that is borrowed by researchers to develop their own research inquiry. A theoretical framework in research helps researchers design and conduct their research and analyze and interpret their findings. It explains the relationship between variables, identifies gaps in existing knowledge, and guides the development of research questions, hypotheses, and methodologies to address that gap.  

Now that you know the answer to ‘ What is a theoretical framework? ’, check the following table that lists the different types of theoretical frameworks in research: 3

Developing a theoretical framework in research can help in the following situations: 4

• When conducting research on complex phenomena because a theoretical framework helps organize the research questions, hypotheses, and findings  
• When the research problem requires a deeper understanding of the underlying concepts  
• When conducting research that seeks to address a specific gap in knowledge  
• When conducting research that involves the analysis of existing theories  

Summarizing existing literature for theoretical frameworks is easy. Get our Research Ideation pack  

Importance of a theoretical framework  

The purpose of theoretical framework s is to support you in the following ways during the research process: 2  

• Provide a structure for the complete research process  
• Assist researchers in incorporating formal theories into their study as a guide  
• Provide a broad guideline to maintain the research focus  
• Guide the selection of research methods, data collection, and data analysis  
• Help understand the relationships between different concepts and develop hypotheses and research questions  
• Address gaps in existing literature  
• Analyze the data collected and draw meaningful conclusions and make the findings more generalizable  

Theoretical vs. Conceptual framework  

While a theoretical framework covers the theoretical aspect of your study, that is, the various theories that can guide your research, a conceptual framework defines the variables for your study and presents how they relate to each other. The conceptual framework is developed before collecting the data. However, both frameworks help in understanding the research problem and guide the development, collection, and analysis of the research.  

The following table lists some differences between conceptual and theoretical frameworks . 5

How to write a theoretical framework  

The following general steps can help those wondering how to write a theoretical framework: 2

• Identify and define the key concepts clearly and organize them into a suitable structure.  
• Use appropriate terminology and define all key terms to ensure consistency.  
• Identify the relationships between concepts and provide a logical and coherent structure.  
• Develop hypotheses that can be tested through data collection and analysis.  
• Keep it concise and focused with clear and specific aims.  

Write a theoretical framework 2x faster. Get our Manuscript Writing pack  

Examples of a theoretical framework  

Here are two examples of a theoretical framework. 6,7

Example 1 .   

An insurance company is facing a challenge cross-selling its products. The sales department indicates that most customers have just one policy, although the company offers over 10 unique policies. The company would want its customers to purchase more than one policy since most customers are purchasing policies from other companies.  

Objective : To sell more insurance products to existing customers.  

Problem : Many customers are purchasing additional policies from other companies.  

Research question : How can customer product awareness be improved to increase cross-selling of insurance products?  

Sub-questions: What is the relationship between product awareness and sales? Which factors determine product awareness?  

Since “product awareness” is the main focus in this study, the theoretical framework should analyze this concept and study previous literature on this subject and propose theories that discuss the relationship between product awareness and its improvement in sales of other products.  

Example 2 .

A company is facing a continued decline in its sales and profitability. The main reason for the decline in the profitability is poor services, which have resulted in a high level of dissatisfaction among customers and consequently a decline in customer loyalty. The management is planning to concentrate on clients’ satisfaction and customer loyalty.  

Objective: To provide better service to customers and increase customer loyalty and satisfaction.  

Problem: Continued decrease in sales and profitability.  

Research question: How can customer satisfaction help in increasing sales and profitability?  

Sub-questions: What is the relationship between customer loyalty and sales? Which factors influence the level of satisfaction gained by customers?  

Since customer satisfaction, loyalty, profitability, and sales are the important topics in this example, the theoretical framework should focus on these concepts.  

Benefits of a theoretical framework  

There are several benefits of a theoretical framework in research: 2  

• Provides a structured approach allowing researchers to organize their thoughts in a coherent way.  
• Helps to identify gaps in knowledge highlighting areas where further research is needed.  
• Increases research efficiency by providing a clear direction for research and focusing efforts on relevant data.  
• Improves the quality of research by providing a rigorous and systematic approach to research, which can increase the likelihood of producing valid and reliable results.  
• Provides a basis for comparison by providing a common language and conceptual framework for researchers to compare their findings with other research in the field, facilitating the exchange of ideas and the development of new knowledge.  

Frequently Asked Questions 

Q1. How do I develop a theoretical framework ? 7

A1. The following steps can be used for developing a theoretical framework :  

• Identify the research problem and research questions by clearly defining the problem that the research aims to address and identifying the specific questions that the research aims to answer.
• Review the existing literature to identify the key concepts that have been studied previously. These concepts should be clearly defined and organized into a structure.
• Develop propositions that describe the relationships between the concepts. These propositions should be based on the existing literature and should be testable.
• Develop hypotheses that can be tested through data collection and analysis.
• Test the theoretical framework through data collection and analysis to determine whether the framework is valid and reliable.

Q2. How do I know if I have developed a good theoretical framework or not? 8

A2. The following checklist could help you answer this question:  

• Is my theoretical framework clearly seen as emerging from my literature review?  
• Is it the result of my analysis of the main theories previously studied in my same research field?  
• Does it represent or is it relevant to the most current state of theoretical knowledge on my topic?  
• Does the theoretical framework in research present a logical, coherent, and analytical structure that will support my data analysis?  
• Do the different parts of the theory help analyze the relationships among the variables in my research?  
• Does the theoretical framework target how I will answer my research questions or test the hypotheses?  
• Have I documented every source I have used in developing this theoretical framework ?  
• Is my theoretical framework a model, a table, a figure, or a description?  
• Have I explained why this is the appropriate theoretical framework for my data analysis?  

Q3. Can I use multiple theoretical frameworks in a single study?  

A3. Using multiple theoretical frameworks in a single study is acceptable as long as each theory is clearly defined and related to the study. Each theory should also be discussed individually. This approach may, however, be tedious and effort intensive. Therefore, multiple theoretical frameworks should be used only if absolutely necessary for the study.  

Q4. Is it necessary to include a theoretical framework in every research study?  

A4. The theoretical framework connects researchers to existing knowledge. So, including a theoretical framework would help researchers get a clear idea about the research process and help structure their study effectively by clearly defining an objective, a research problem, and a research question.  

Q5. Can a theoretical framework be developed for qualitative research?  

A5. Yes, a theoretical framework can be developed for qualitative research. However, qualitative research methods may or may not involve a theory developed beforehand. In these studies, a theoretical framework can guide the study and help develop a theory during the data analysis phase. This resulting framework uses inductive reasoning. The outcome of this inductive approach can be referred to as an emergent theoretical framework . This method helps researchers develop a theory inductively, which explains a phenomenon without a guiding framework at the outset.  

Q6. What is the main difference between a literature review and a theoretical framework ?  

A6. A literature review explores already existing studies about a specific topic in order to highlight a gap, which becomes the focus of the current research study. A theoretical framework can be considered the next step in the process, in which the researcher plans a specific conceptual and analytical approach to address the identified gap in the research.  

Theoretical frameworks are thus important components of the research process and researchers should therefore devote ample amount of time to develop a solid theoretical framework so that it can effectively guide their research in a suitable direction. We hope this article has provided a good insight into the concept of theoretical frameworks in research and their benefits.  

References  

• Organizing academic research papers: Theoretical framework. Sacred Heart University library. Accessed August 4, 2023. https://library.sacredheart.edu/c.php?g=29803&p=185919#:~:text=The%20theoretical%20framework%20is%20the,research%20problem%20under%20study%20exists .  
• Salomao A. Understanding what is theoretical framework. Mind the Graph website. Accessed August 5, 2023. https://mindthegraph.com/blog/what-is-theoretical-framework/  
• Theoretical framework—Types, examples, and writing guide. Research Method website. Accessed August 6, 2023. https://researchmethod.net/theoretical-framework/  
• Grant C., Osanloo A. Understanding, selecting, and integrating a theoretical framework in dissertation research: Creating the blueprint for your “house.” Administrative Issues Journal : Connecting Education, Practice, and Research; 4(2):12-26. 2014. Accessed August 7, 2023. https://files.eric.ed.gov/fulltext/EJ1058505.pdf  
• Difference between conceptual framework and theoretical framework. MIM Learnovate website. Accessed August 7, 2023. https://mimlearnovate.com/difference-between-conceptual-framework-and-theoretical-framework/  
• Example of a theoretical framework—Thesis & dissertation. BacherlorPrint website. Accessed August 6, 2023. https://www.bachelorprint.com/dissertation/example-of-a-theoretical-framework/  
• Sample theoretical framework in dissertation and thesis—Overview and example. Students assignment help website. Accessed August 6, 2023. https://www.studentsassignmenthelp.co.uk/blogs/sample-dissertation-theoretical-framework/#Example_of_the_theoretical_framework  
• Kivunja C. Distinguishing between theory, theoretical framework, and conceptual framework: A systematic review of lessons from the field. Accessed August 8, 2023. https://files.eric.ed.gov/fulltext/EJ1198682.pdf  

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Experimental Method In Psychology

Saul McLeod, PhD

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Saul McLeod, PhD., is a qualified psychology teacher with over 18 years of experience in further and higher education. He has been published in peer-reviewed journals, including the Journal of Clinical Psychology.

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Olivia Guy-Evans, MSc

Associate Editor for Simply Psychology

BSc (Hons) Psychology, MSc Psychology of Education

Olivia Guy-Evans is a writer and associate editor for Simply Psychology. She has previously worked in healthcare and educational sectors.

On This Page:

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

What is an Experiment?

An experiment is an investigation in which a hypothesis is scientifically tested. An independent variable (the cause) is manipulated in an experiment, and the dependent variable (the effect) is measured; any extraneous variables are controlled.

An advantage is that experiments should be objective. The researcher’s views and opinions should not affect a study’s results. This is good as it makes the data more valid  and less biased.

There are three types of experiments you need to know:

1. Lab Experiment

A laboratory experiment in psychology is a research method in which the experimenter manipulates one or more independent variables and measures the effects on the dependent variable under controlled conditions.

A laboratory experiment is conducted under highly controlled conditions (not necessarily a laboratory) where accurate measurements are possible.

The researcher uses a standardized procedure to determine where the experiment will take place, at what time, with which participants, and in what circumstances.

Participants are randomly allocated to each independent variable group.

Examples are Milgram’s experiment on obedience and  Loftus and Palmer’s car crash study .

• Strength : It is easier to replicate (i.e., copy) a laboratory experiment. This is because a standardized procedure is used.
• Strength : They allow for precise control of extraneous and independent variables. This allows a cause-and-effect relationship to be established.
• Limitation : The artificiality of the setting may produce unnatural behavior that does not reflect real life, i.e., low ecological validity. This means it would not be possible to generalize the findings to a real-life setting.
• Limitation : Demand characteristics or experimenter effects may bias the results and become confounding variables .

2. Field Experiment

A field experiment is a research method in psychology that takes place in a natural, real-world setting. It is similar to a laboratory experiment in that the experimenter manipulates one or more independent variables and measures the effects on the dependent variable.

However, in a field experiment, the participants are unaware they are being studied, and the experimenter has less control over the extraneous variables .

Field experiments are often used to study social phenomena, such as altruism, obedience, and persuasion. They are also used to test the effectiveness of interventions in real-world settings, such as educational programs and public health campaigns.

An example is Holfing’s hospital study on obedience .

• Strength : behavior in a field experiment is more likely to reflect real life because of its natural setting, i.e., higher ecological validity than a lab experiment.
• Strength : Demand characteristics are less likely to affect the results, as participants may not know they are being studied. This occurs when the study is covert.
• Limitation : There is less control over extraneous variables that might bias the results. This makes it difficult for another researcher to replicate the study in exactly the same way.

3. Natural Experiment

A natural experiment in psychology is a research method in which the experimenter observes the effects of a naturally occurring event or situation on the dependent variable without manipulating any variables.

Natural experiments are conducted in the day (i.e., real life) environment of the participants, but here, the experimenter has no control over the independent variable as it occurs naturally in real life.

Natural experiments are often used to study psychological phenomena that would be difficult or unethical to study in a laboratory setting, such as the effects of natural disasters, policy changes, or social movements.

For example, Hodges and Tizard’s attachment research (1989) compared the long-term development of children who have been adopted, fostered, or returned to their mothers with a control group of children who had spent all their lives in their biological families.

Here is a fictional example of a natural experiment in psychology:

Researchers might compare academic achievement rates among students born before and after a major policy change that increased funding for education.

In this case, the independent variable is the timing of the policy change, and the dependent variable is academic achievement. The researchers would not be able to manipulate the independent variable, but they could observe its effects on the dependent variable.

• Strength : behavior in a natural experiment is more likely to reflect real life because of its natural setting, i.e., very high ecological validity.
• Strength : Demand characteristics are less likely to affect the results, as participants may not know they are being studied.
• Strength : It can be used in situations in which it would be ethically unacceptable to manipulate the independent variable, e.g., researching stress .
• Limitation : They may be more expensive and time-consuming than lab experiments.
• Limitation : There is no control over extraneous variables that might bias the results. This makes it difficult for another researcher to replicate the study in exactly the same way.

Key Terminology

Ecological validity.

The degree to which an investigation represents real-life experiences.

Experimenter effects

These are the ways that the experimenter can accidentally influence the participant through their appearance or behavior.

Demand characteristics

The clues in an experiment lead the participants to think they know what the researcher is looking for (e.g., the experimenter’s body language).

Independent variable (IV)

The variable the experimenter manipulates (i.e., changes) is assumed to have a direct effect on the dependent variable.

Dependent variable (DV)

Variable the experimenter measures. This is the outcome (i.e., the result) of a study.

Extraneous variables (EV)

All variables which are not independent variables but could affect the results (DV) of the experiment. EVs should be controlled where possible.

Confounding variables

Variable(s) that have affected the results (DV), apart from the IV. A confounding variable could be an extraneous variable that has not been controlled.

Random Allocation

Randomly allocating participants to independent variable conditions means that all participants should have an equal chance of participating in each condition.

The principle of random allocation is to avoid bias in how the experiment is carried out and limit the effects of participant variables.

Order effects

Changes in participants’ performance due to their repeating the same or similar test more than once. Examples of order effects include:

(i) practice effect: an improvement in performance on a task due to repetition, for example, because of familiarity with the task;

(ii) fatigue effect: a decrease in performance of a task due to repetition, for example, because of boredom or tiredness.

Experimental Psychology: History, Method and Characteristics

The Experimental psychology Is a stream that studies the psychological phenomena using an experimental methodology based on observation.

It guarantees a scientific practice and involves the observation, manipulation and registration of the variables that affect a subject under study.

Experimental psychologists are interested in studying human behavior by manipulating variables in controllable situations and in unnatural environments that affect and influence behavior.

Gustav Theodor Fechner Was one of the pioneers in the use of the experimental when trying to prove the relation between physical and sensorial magnitudes, in 1860.

However, it was in 1879 when Wilhelm Wundt , Considered one of the founders of this current, created the first laboratory of experimental psychology.

Definition of experimental psychology

This current of psychology defends the experimental method as the most suitable form for the study of human behavior.

Experimental psychology considers that psychological phenomena can be analyzed by experimental methods consisting in the observation, manipulation and recording of dependent, independent and extraneous variables that influence the object of study.

Many psychologists have used this method when carrying out their work to address multiple issues such as memory , Learning, sensation, perception, motivation and development processes, among others.

Professionals who adopt this method want to know the behavior of a subject by manipulating variables in controlled environments. The contexts in which they are carried out are the laboratories and instruments are used that guarantee a control and an exhaustive precision in their investigations.

The experiments can be performed in humans but mostly animals are used, because many times for ethical reasons people can not be used to perform such tests. In addition, animals provide greater availability and control to researchers.

The most scientific part of psychology is unified with experimental psychology, because the use of its methodology guarantees a scientific practice through observation and experimentation, removing the laws of behavior and mental processes.

With its emergence in the nineteenth century, psychology begins to focus and become interested in the study of observable phenomena, thus giving rise to an empirical science, that is, based on observation and experience of events.

Later, experimental psychology would use rigorous methods and instruments to carry out the measurements in its investigations.

Experimental psychology emerges in Germany as a modern discipline with Wundt, who created the first experimental laboratory in 1879 and introduced a mathematical and experimental approach to research.

Earlier in 1860 Gustav Theodor Fechner, a German psychologist, attempted to test and reason the link between physical and sensory magnitudes through experimental data in his work Elements of psychophysics .

Other authors who contributed to this growing science were Charles Bell , A British physiologist who investigated nerves; Ernst Heinrich Weber , A German physician and considered one of its founders and Oswald Külpe , The principal founder of the Würzburg School in Germany, among others.

The appearance of different schools was due to this tendency to the experimentation of the time, whose purpose was to try to observe the degree of relationship between the biological and the psychological.

Among these schools is the Russian who was interested in neurophysiology and was initiated by Pavlov Y Bechterev . Functionalism, which seeks to demonstrate the biological laws that delimit the behavior and behaviorism of Watson .

In the twentieth century behaviorism was the predominant school within psychology in general and especially in the United States. It is the branch of psychology that set aside mental phenomena within experimental psychology.

In Europe, however, this was not the case, since psychology was influenced by such authors as Craik, Hick and Broadbent who focused on subjects such as attention, thought and memory, thus laying the foundations of cognitive psychology.

In the last half of the century, psychologists used multiple methods, not only focused and limited to a strictly experimental approach.

In addition, the experimental method is used in many different fields within psychology such as social psychology and developmental psychology.

Experimental method

Experimental psychology considers that psychological phenomena can be studied through this method, thus constituting one of the bases of psychology as a science.

It involves the observation, manipulation and recording of dependent, independent and extraneous variables that are the object of study, in order to be able to describe and explain them in terms of their relation to human behavior.

This method aims to identify the causes and evaluate the consequences, the researcher tries to find a causality between different variables.

On the one hand, there is the medium variable that would act as an independent variable. The dependent would be that which is related to the behavior of the subject. Finally, all external factors influencing this would be weird variables.

The experiment is carried out in a controlled environment such as a laboratory, where the experimenter can manipulate variables and control those that can affect the others. In addition, it can thus form specific experimental groups of subjects according to their study interests.

The researcher is the one who creates the necessary conditions to be able to carry out the study and to apply the independent variable when he sees fit. In addition to this method can be repeated conditions to check the results as well as alter them to see the differences of behavior to study between different situations.

In this approach, the experimenter manipulates circumstances to control their increase or decrease as well as their effect on the observed behaviors, to be able to describe why that situation or change occurs.

Many times before carrying out an investigation one resorts to pilot experiments that are tests of the experiment to study some aspects of him. In addition the experiments have another positive part because being carried out in these controlled contexts can be replicated by other researchers in future situations.

Characteristics of experimental research

Some of the characteristics of experimental research are as follows:

• Subjects are randomly arranged into equivalent groups, giving rise to statistical equivalence so that the differences between the results are not due to initial differences between groups of subjects.
• Existence of two or more groups or conditions to be able to carry out the comparison between them. Experiments can not be performed with a single group or condition to be compared.
• Management of an independent variable, in the form of different values ​​or circumstances. This direct manipulation is done to be able to observe the changes that it produces in the dependent variables. In addition, the assignment of values ​​and conditions must be done by the researcher, because if this were not so, it would not be considered a real experiment.
• Measure each dependent variable by assigning numerical values ​​so that the result can be evaluated and thus speak of an experimental investigation.
• Have a design with which you can control to a greater extent the influence of the foreign variables and to avoid that the results are affected by them.
• Use inferential statistics to make generalizations of research to the population.

Phases of an experiment

1- approaching a knowledge problem.

Choosing the problem to be investigated depends on the experimenter and what you want to study, the research questions have to be able to be solved through an experimental process.

Depending on the problem, the methodological approach to be followed will be delimited.

2- Hypothesis Formulation

The hypotheses are statements that are formulated and that anticipate the results that could be obtained from the research, relate at least two variables and must be described in empirical terms, being able to be observed and measurable.

3- Making an appropriate design

With the design, the procedure or work plan of the researcher is plotted, indicating what is going to be done and how the study will be carried out, from the variables involved to the assignment of the subjects to the groups.

4- Collection and analysis of data

For the collection of data there are multiple instruments that are valid and reliable, and techniques that will be better or worse adapted and that will present advantages and disadvantages.

The analysis of the data is carried out by organizing the information so that it can be described, analyzed and explained.

5- Conclusions

In the conclusions, it is developed the fulfillment or not of the hypotheses raised, the limitations of the research work, the methodology that has been followed, implications for the practice, generalization at the population level, as well as future lines of research.

Objective and conditions of the experimental method

Its objective is to investigate the causal relationships between variables, that is, to analyze the changes that occurred in the dependent variable (behavior) as a consequence of the different values ​​presented by the independent variable (external factor).

The conditions to be able to conclude that there is a relationship between variables are:

• Temporal contingency between variables. The variable cause that would be the independent, has to precede the variable consequence, that would be the dependent one.
• Covariation between variables. In order for there to be a relationship between the two, a change in the values ​​of one would imply a proportional change in the values ​​of the second.
• The correlation between variables should not be attributable to the effect of foreign variables.

In short, the researcher must manipulate the independent variable, establish a temporal order among variables and have to eliminate the effect that is exerted as a consequence of extraneous variables.

• Experimental psychology. Recovered from ecured.cu.
• Experimental psychology. Retrieved from wikipedia.org.
• Definition of experimental psychology. Recovered from definicion.de.
• Definition, characteristics and objective of the experimental method. Retrieved from psikipedia.com.

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Meaning of experiment in English

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• Don't forget to try out the equipment before setting up the experiment.
• In the second experiment they obtained a very clear result .
• For the experiment to be valid , it is essential to record the data accurately .
• The experiments were conducted by scientists in New York.
• Our experiment worked better than we could have expected , and soon the baby was happy to sleep in her own bed .
• as an experiment
• background check
• experimental
• experimentally
• experimentation
• experimenter
• put someone/something through their/its paces idiom
• put something to the test idiom
• reinspection
• try something out
• uncheckable
• welfare check
• The young film-makers were given free rein to experiment with new themes and techniques .
• I like to experiment with different light filters on my camera .
• For a while the poet experimented with the idea of chanting his poems to music .
• The artist experimented with different pigments and mediums , often with disastrous results .
• I'd be very nervous about letting a trainee hairdresser experiment with my hair .

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Why Brandon Logan Is Poised to Be a Star at Notre Dame

Nathan erbach | aug 13, 2024.

It happens every year. Fans are upset or discouraged about a few of the recruiting misses in the class, and the Irish coaching staff finds themselves a "diamond in the rough" to replace a four or five star recruit. It is not the sexiest add to a class, but in many cases that player becomes a big-time contributor for Notre Dame. Is it the senior breakout or senior film? Is it that the recruit has focused on multiple sports and finally is concentrating more on football? Is it luck? The answer lies somewhere in all these questions, but it is an area of recruiting where Notre Dame has flourished in year's past. I would bet on Brandon Logan as that next guy.

Source: Safety Julian Love and the Seahawks reached agreement on a three/year extension worth up to $36 million. The deal was negotiated by Drew Rosenhaus, Jason Rosenhaus and Ryan Matha. pic.twitter.com/3RWfilS98X — Adam Schefter (@AdamSchefter) July 24, 2024

Recent Success Stories

I went back to the 2016 class to prove this point, and it turns out that is a good place to start. The 2016 class saw Notre Dame bring in 5 three star recruits (according to 247sports.com's composite rankings) that reached the NFL: S Jalen Elliott, CB Julian Love, QB Ian Book, DE Ade Ogundeji and DE Jamir Jones. Book and Ogundeji specifically were flips from G5 programs. 2017 is no different with several three-stars or late additions that turned into building blocks for the Irish in LB Jeremiah Owusu-Koramoah, DT Myron Tagovailoa-Amosa, DT Kurt Hinish and LB Drew White. 2018 brought in guys like CB TaRiq Bracy, S DJ Brown, TE Tommy Tremble and DE Justin Ademilola. 2019 and 2020: LB Marist Liufau, CB Cam Hart and CB Clarence Lewis. 2021 and 2022: RB Logan Diggs, TE Mitchell Evans, DT Jason Onye and C Ashton Craig. I won't dive into the 2023 and 2024 classes just yet because they are still so new, but there are examples from those classes that look like good bets to be hits as well. I can think of many more examples of players that were late adds or undervalued by the recruiting ranks and other college programs but took off as seniors and ended up as four-stars. Think Joe Alt and Jack Kiser. Brandon Logan could eventually fit into that category as well.

What This Means

The main point? While history backs the importance of recruiting rankings, some programs do a better job than others at evaluating and finding the underrated talent. Some programs don't even have to dip their toes into that pool of players - lucky them. However, recent history proves that the backend of Notre Dame's classes have high hit rates, all things considered. Keep that in mind the next time the Irish coaching staff adds a player you don't care for because their offer list is small or you watched five minutes of hudl highlights on him. Chances are, that player has a good chance at proving you wrong. Notre Dame football lands 2025 safety Brandon Logan: What Does It Mean? Notre Dame Football: Irish Join the 2024 College Football Preseason Elite

NATHAN ERBACH

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Fact-Checking Claims About Tim Walz’s Record

Republicans have leveled inaccurate or misleading attacks on Mr. Walz’s response to protests in the summer of 2020, his positions on immigration and his role in the redesign of Minnesota’s flag.

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By Linda Qiu

Since Gov. Tim Walz of Minnesota was announced as the Democratic nominee for vice president, the Trump campaign and its allies have gone on the attack.

Mr. Walz, a former teacher and football coach from Nebraska who served in the National Guard, was elected to the U.S. House of Representatives in 2006 and then as Minnesota’s governor in 2018. His branding of former President Donald J. Trump as “weird” this year caught on among Democrats and helped catapult him into the national spotlight and to the top of Vice President Kamala Harris’s list of potential running mates.

The Republican accusations, which include questions over his military service , seem intended at undercutting a re-energized campaign after President Biden stepped aside and Ms. Harris emerged as his replacement at the top of the ticket. Mr. Trump and his allies have criticized, sometimes inaccurately, Mr. Walz’s handling of protests in his state, his immigration policies, his comments about a ladder factory and the redesign of his state’s flag.

Here’s a fact check of some claims.

What Was Said

“Because if we remember the rioting in the summer of 2020, Tim Walz was the guy who let rioters burn down Minneapolis.” — Senator JD Vance of Ohio, the Republican nominee for vice president, during a rally on Wednesday in Philadelphia

This is exaggerated. Mr. Walz has faced criticism for not quickly activating the National Guard to quell civil unrest in Minneapolis in the summer of 2020 after the murder of George Floyd by a police officer. But claims that he did not respond at all, or that the city burned down, are hyperbolic.

Mr. Floyd was murdered on May 25, 2020, and demonstrators took to the streets the next day . The protests intensified, with some vandalizing vehicles and setting fires. More than 700 state troopers and officers with the Minnesota Department of Natural Resources’ mobile response team were deployed on May 26 to help the city’s police officers, according to a 2022 independent assessment by the state’s Department of Public Safety of the response to the unrest.

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Tim Walz couch joke explained; what he said about JD Vance

Minnesota governor and Democratic VP pick Tim Walz waded into internet meme territory Tuesday with a snappy one-liner about JD Vance.

During the Harris-Walz rally in Philadelphia , Walz delivered a couch-related zinger to Republican VP nominee Vance .

Here's what Tim Walz said about JD Vance and the much-discussed couch .

Tim Walz tells JD Vance to 'get off the couch' and agree to debate

During his speech at the Harris-Walz rally at Temple University in Philadelphia on Tuesday, Walz set up the zinger by telling the crowd that JD Vance doesn't represent the midwesterners Walz grew up with.

"JD studied at Yale, had his career funded by Silicon Valley millionaires, and then wrote a bestseller trashing that community," Walz said. "C'mon; that's not what middle America is."

Then came the zinger.

"And I gotta tell ya; I can't wait to debate this guy," Walz continued, before pausing for dramatic effect. "That's if he's willing to get off the couch and show up.

"See what I did there?"

The joke appeared to be off-script, given the facial reaction of running mate Vice President Kamala Harris, which sent the partisan crowd at the Liacouras Center into a frenzy.

@meidastouch Gov. Tim Walz obliterates JD Vance: I can't wait to debate the guy — that is if he's willing to get off the couch and show up. #timwalz #jdvance #couch #meidastouch ♬ original sound - MeidasTouch

JD Vance couch joke explained

Tim Walz' jab at JD Vance has many levels, and grew out of the false allegations that JD Vance had sex with his couch .

That many respected news outlets ran with it as fact only fueled the joke, which lead to the creation of several JD Vance couch sex-related memes and spawned dozens of related social media accounts.

JD Vance couch story

Social media then amplified the JD Vance couch sex rumor even further by tying it to Vance's book, " Hillbilly Elegy ," a best-selling memoir written by Vance in 2016 and made into a film in 2020 .

Many JD couch-meme creators have identified themselves and seem to enjoy that these memes generated so much traction that well-known publications had to issue corrections and retract stories that stated it as fact that JD Vance had intercourse with his couch.

Damon C. Williams is a Philadelphia-based journalist reporting on trending topics across the Mid-Atlantic Region.

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Kamala harris is again facing attacks on her racial identity. here’s more about her background..

• Race and Ethnicity

Vice President Kamala Harris campaigns for President as the presumptive Democratic candidate during an event July 23, 2024, in West Allis, Wis. (AP)

Vice President Kamala Harris, the Democrats’ presumptive presidential nominee, is facing a reprisal of attacks from some conservatives and their surrogates on her racial and cultural heritage, ethnic background and gender. A list recently shared across social media titled " Kamala Harris Facts ," contains wrong or missing-context claims about her background, including that she is "Not African American — Indian & Jamaican." These attacks — similar to ones raised when she was named President Joe Biden’s running mate in 2020 — misrepresent Harris’ heritage.

On television and social media, some people have also falsely and repeatedly claimed Harris isn’t eligible for the presidency because of her family’s immigrant background. Harris was born in 1964 in Oakland, California, and as someone born in the U.S., is constitutionally eligible to be president. 

All of this reflects a poor understanding of history and the fluid nature and various interpretations of racial identity in the United States, race and politics experts say.

"The approach to Harris in this instance, the attempt to ‘other’ her, is a common practice in American politics," said Keneshia Grant, a political science professor at Howard University. "These tactics will continue because they work. People have to prepare themselves to check their own biases and fears and use logic and facts to guide their decision-making when these kinds of attacks occur." According to a October 2022 report by the Center for Democracy & Technology, a nonprofit that seeks to advance civil rights and liberties in technology, women of color candidates in the 2020 general elections "were twice as likely as other candidates to be targeted with or be the subject of mis- and disinformation, and more likely to receive sexist and racist abuse than any other group."

We consulted experts in Caribbean and Africana studies, political science professors and anthropologists to learn more about how Harris’ gender, multicultural and multiracial background offers a unique glimpse into how American politics grapples with issues of race and identity.

Harris grew up in a Black middle-class neighborhood in Berkeley, California, where her mother, Shyamala Gopalan Harris and father, Donald Harris, would often join civil rights protests. Donald Harris was born in Jamaica and immigrated to the U.S. after he got into the University of California, Berkeley, Kamala Harris wrote in her autobiography, "The Truths We Hold: An American Journey." Shyamala Gopalan Harris was born in Chennai, India, and moved to California after graduating from the University of Delhi to pursue a doctorate in nutrition and endocrinology at Berkeley. The couple separated around the time Harris was 5 and divorced a few years later, Harris wrote in her book. Kamala Harris lived in California until she was in middle school, when she moved to Montreal after her mother was offered a teaching position at McGill University. Kamala Harris attended college at Howard University, an historically Black university, in Washington, D.C., and earned her law degree at the University of California, Hastings in 1989.

Several experts told us that the implication that Jamaicans aren't African or connected to Africa is wrong on its face. According to a 2011 census , 92.1% of Jamaicans are Black, with genetic studies showing that the vast majority are descendants of people from sub-Saharan Africa. "Jamaica is a country where more than 90% of the population is of African ancestry," Judith Byfield, a Cornell University professor who teaches Caribbean and African history, previously told PolitiFact. "So the idea that because her dad is Jamaican she has no African ancestry is completely false." Byfield said people scrutinizing Harris’ ethnic background often conflate several different categories. "Jamaican is a national identity at the same time that it’s also a cultural identity and you can say the same for her Indian heritage," she said. "Those are her parents, but she's born here, and I think for first-generation people, there's always a bit of tension between the extent that they are American, and by the extent they've been shaped and framed by their parents’ cultural affiliations." The African diaspora refers to the many communities of people of African descent dispersed throughout the world as a result of historic movements, both voluntary and involuntary. During the more-than-400-years-long trans-Atlantic slave trade , an estimated 15 million African men, women and children were kidnapped from their homelands, forced into ships, and forced to endure a weekslong journey in crowded. filthy conditions before being sold into enslavement. The slave trade took millions of people to different regions throughout the Americas and the Caribbean. In a 2020 op-ed , New York Times columnist Jamelle Bouie described how Jamaica was home to a brutal and violent plantation system and was a pivotal point in the slave trade. "Many Jamaicans trace their origins directly to slavery and the mass importation of African captives," Bouie wrote. "Based on a genealogical account by her father , there is a strong chance Kamala Harris is one of them. What’s more, many descendants of enslaved people in the Americas have European ancestry on account of the pervasive sexual violence whites perpetuated wherever slavery took root." Some anthropologists and ethnographers consider the African American identifier more broadly to encompass Black people who come from a wide range of countries, while others see it as being limited to Americans descended from people who were enslaved in America. Grant said that claims that say Jamaican people are not African are unaware of slavery’s global reach. "Slavery impacted many people from Africa, and we went to many places," Grant said. "Harris’ father’s people got dropped off in Jamaica. Mine got dropped off in Haiti. The African diaspora is huge, and it is worldwide, so to suggest that a Jamaican is not African, or connected to Africa is not acknowledging the vestiges of slavery."

Harris told The Washington Post in 2019 that she identifies simply as "an American," and that she’s been comfortable with her identity from an early age, something she credits to her Hindu immigrant single mother, who adopted Black culture and immersed her daughters in it. Harris said that she grew up embracing her Indian culture while proudly living as a Black girl. She said the same in her book, "The Truths We Hold."

She told the Post that she hasn’t spent much time dwelling on how to categorize herself, but being forced to define herself was more of a struggle when she first ran for office. We have plenty of ways to categorize people, but racialized categorization has structural implications, Tracie Canada, a sociocultural anthropologist and an assistant professor of cultural anthropology and gender, sexuality, and feminist studies at Duke University, previously told us when she taught at Notre Dame. "Anti-Black racism, anti-Black violence, those are the things that actually matter," Canada said. "Those are systemic problems and structural issues, so no matter how she identifies or how we identify her, is she going to be implicated in that systemic problem?" Dianne Pinderhughes, a professor of Africana studies and political science at the University of Notre Dame, had told PolitiFact that the subject of racial identity is complex, especially for Harris, because she was immersed in Black culture and community since she was very young. "You have a person who was socialized from her earliest years to be socially, culturally African American and also was supported and immersed in African American organizations," Pinderhughes said. "I think the way race is played out in the United States, it’s just been the case for centuries, that people who have some color are usually assimilated in an African American community of some sort, and that community recognizes people who are willing to look in the mirror and recognize them as well." Another aspect of racial identity has a lot to do with where a person grew up or now lives. People’s local community tends to weigh heavily on how they identify themselves, experts said. Byfield, who is of Jamaican descent and grew up in Queens, New York, said her life experience involved a blended community of Black individuals who came from countries all over the world. But they banded together in their identity and shared experiences. That may have been Harris’ experience, too. "She has chosen to define herself in terms of the American landscape, and I think those of us who have had a multinational, cultural lifestyle, we've all had to figure out individually how to come to terms with it," Byfield said. "You have all these different groups from different African countries, as well as Caribbean countries. African American community in the U.S. is not from one place, everyone is from everywhere." RELATED : Fact-checking ‘Kamala Harris Facts’

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