what is pre experimental

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Pre-experimental Design: Definition, Types & Examples

• October 1, 2021

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Experimental research is conducted to analyze and understand the effect of a program or a treatment. There are three types of experimental research designs – pre-experimental designs, true experimental designs, and quasi-experimental designs . 

In this blog, we will be talking about pre-experimental designs. Let’s first explain pre-experimental research. 

What is Pre-experimental Research?

As the name suggests, pre- experimental research happens even before the true experiment starts. This is done to determine the researchers’ intervention on a group of people. This will help them tell if the investment of cost and time for conducting a true experiment is worth a while. Hence, pre-experimental research is a preliminary step to justify the presence of the researcher’s intervention. 

The pre-experimental approach helps give some sort of guarantee that the experiment can be a full-scale successful study. 

What is Pre-experimental Design?

The pre-experimental design includes one or more than one experimental groups to be observed against certain treatments. It is the simplest form of research design that follows the basic steps in experiments. 

The pre-experimental design does not have a comparison group. This means that while a researcher can claim that participants who received certain treatment have experienced a change, they cannot conclude that the change was caused by the treatment itself. 

The research design can still be useful for exploratory research to test the feasibility for further study. 

Let us understand how pre-experimental design is different from the true and quasi-experiments:

The above table tells us pretty much about the working of the pre-experimental designs. So we can say that it is actually to test treatment, and check whether it has the potential to cause a change or not. For the same reasons, it is advised to perform pre-experiments to define the potential of a true experiment.

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Types of Pre-experimental Designs

Assuming now you have a better understanding of what the whole pre-experimental design concept is, it is time to move forward and look at its types and their working:

One-shot case study design

• This design practices the treatment of a single group.
• It only takes a single measurement after the experiment.
• A one-shot case study design only analyses post-test results.

The one-shot case study compares the post-test results to the expected results. It makes clear what the result is and how the case would have looked if the treatment wasn’t done. 

A team leader wants to implement a new soft skills program in the firm. The employees can be measured at the end of the first month to see the improvement in their soft skills. The team leader will know the impact of the program on the employees.

One-group pretest-posttest design

• Like the previous one, this design also works on just one experimental group.
• But this one takes two measures into account. 
• A pre-test and a post-test are conducted. 

As the name suggests, it includes one group and conducts pre-test and post-test on it. The pre-test will tell how the group was before they were put under treatment. Whereas post-test determines the changes in the group after the treatment. 

This sounds like a true experiment , but being a pre-experiment design, it does not have any control group. 

Following the previous example, the team leader here will conduct two tests. One before the soft skill program implementation to know the level of employees before they were put through the training. And a post-test to know their status after the training.

Now that he has a frame of reference, he knows exactly how the program helped the employees. 

Static-group comparison

• This compares two experimental groups.
• One group is exposed to the treatment.
• The other group is not exposed to the treatment.
• The difference between the two groups is the result of the experiment.

As the name suggests, it has two groups, which means it involves a control group too. 

In static-group comparison design, the two groups are observed as one goes through the treatment while the other does not. They are then compared to each other to determine the outcome of the treatment.

The team lead decides one group of employees to get the soft skills training while the other group remains as a control group and is not exposed to any program. He then compares both the groups and finds out the treatment group has evolved in their soft skills more than the control group. 

Due to such working, static-group comparison design is generally perceived as a quasi-experimental design too. 

Characteristics of Pre-experimental Designs

In this section, let us point down the characteristics of pre-experimental design:

• Generally uses only one group for treatment which makes observation simple and easy.
• Validates the experiment in the preliminary phase itself. 
• Pre-experimental design tells the researchers how their intervention will affect the whole study. 
• As they are conducted in the beginning, pre-experimental designs give evidence for or against their intervention.
• It does not involve the randomization of the participants. 
• It generally does not involve the control group, but in some cases where there is a need for studying the control group against the treatment group, static-group comparison comes into the picture. 
• The pre-experimental design gives an idea about how the treatment is going to work in case of actual true experiments.  

Validity of results in Pre-experimental Designs

Validity means a level to which data or results reflect the accuracy of reality. And in the case of pre-experimental research design, it is a tough catch. The reason being testing a hypothesis or dissolving a problem can be quite a difficult task, let’s say close to impossible. This being said, researchers find it challenging to generalize the results they got from the pre-experimental design, over the actual experiment. 

As pre-experimental design generally does not have any comparison groups to compete for the results with, that makes it pretty obvious for the researchers to go through the trouble of believing its results. Without comparison, it is hard to tell how significant or valid the result is. Because there is a chance that the result occurred due to some uncalled changes in the treatment, maturation of the group, or is it just sheer chance. 

Let’s say all the above parameters work just in favor of your experiment, you even have a control group to compare it with, but that still leaves us with one problem. And that is what “kind” of groups we get for the true experiments. It is possible that the subjects in your pre-experimental design were a lot different from the subjects you have for the true experiment. If this is the case, even if your treatment is constant, there is still going to be a change in your results. 

Advantages of Pre-experimental Designs

• Cost-effective due to its easy process. 
• Very simple to conduct.
• Efficient to conduct in the natural environment. 
• It is also suitable for beginners. 
• Involves less human intervention. 
• Determines how your treatment is going to affect the true experiment. 

Disadvantages of Pre-experimental Designs

• It is a weak design to determine causal relationships between variables. 
• Does not have any control over the research. 
• Possess a high threat to internal validity. 
• Researchers find it tough to examine the results’ integrity. 
• The absence of a control group makes the results less reliable. 

This sums up the basics of pre-experimental design and how it differs from other experimental research designs. Curious to learn how you can use survey software to conduct your experimental research, book a meeting with us . 

Pre-experimental design is a research method that happens before the true experiment and determines how the researcher’s intervention will affect the experiment.

An example of a pre-experimental design would be a gym trainer implementing a new training schedule for a trainee.

Characteristics of pre-experimental design include its ability to determine the significance of treatment even before the true experiment is performed.

Researchers want to know how their intervention is going to affect the experiment. So even before the true experiment starts, they carry out a pre-experimental research design to determine the possible results of the true experiment.

The pre-experimental design deals with the treatment’s effect on the experiment and is carried out even before the true experiment takes place. While a true experiment is an actual experiment, it is important to conduct its pre-experiment first to see how the intervention is going to affect the experiment.

The true experimental design carries out the pre-test and post-test on both the treatment group as well as a control group. whereas in pre-experimental design, control group and pre-test are options. it does not always have the presence of those two and helps the researcher determine how the real experiment is going to happen.

The main difference between a pre-experimental design and a quasi-experimental design is that pre-experimental design does not use control groups and quasi-experimental design does. Quasi always makes use of the pre-test post-test model of result comparison while pre-experimental design mostly doesn’t.

Non-experimental research methods majorly fall into three categories namely: Cross-sectional research, correlational research and observational research.

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Pre Experimental Design

Pre-Experimental Design: Statistics is about collecting, observing, calculating, and interpreting numerical data. It involves lots of experiments and research. A statistical experiment is a planned procedure to test and verify a hypothesis. Before starting an experiment, clear questions need to be identified. To reduce variability in the results, the experiment must be well-designed. This careful planning is called experimental design or the design of experiments (DOE).

In this article, we will look at the overview, definition, importance, purpose, types, advantages, limitations, applications, characteristics and examples of pre-experimental designs.

Table of Content

Definition of Pre-Experimental Design

Importance and purpose of pre-experimental design, what is pre-experimental design, types of pre-experimental designs, advantages of pre-experimental designs, limitations of pre-experimental designs, applications of pre-experimental design, characterstics of pre-experimental design, examples of pre-experimental design in practice.

• Improving the Validity of Pre-Experimental Designs

The experimental design or design of experiments (DOE) refers to planning an experiment where variation is present or absent and is fully controlled by the researcher. This term is usually used for controlled experiments. These experiments aim to minimize the effects of variables to increase the reliability of the results. An experimental unit in this design can be a group of people, plants, animals, etc.

The importance and purpose of pre-experimental design lie in its ability to provide initial insights and feasibility testing for more careful experiments.

• Pre-experimental designs allow researchers to test the practicality and viability of their experimental setups before committing to more complex studies. This helps in refining methodologies and identifying potential issues early on.
• They provide preliminary data on how a treatment or intervention might affect the study variables. This preliminary insight informs researchers about the potential impact and feasibility of scaling up to larger, more controlled experiments.
• By being simpler and requiring fewer resources compared to true experiments, pre-experimental designs offer a cost-effective way to explore hypotheses and gather initial data.
• Findings from pre-experimental designs can guide the design of stronger experiments. They help researchers refine hypotheses, determine appropriate variables to measure, and plan for the inclusion of control groups if necessary.
• These designs are often used in educational settings to introduce students and new researchers to experimental methodologies. They provide hands-on experience in conducting experiments and analyzing data.

Pre-experimental design involves one or more experimental groups that are observed under certain treatments. It's the simplest type of research design and follows the basic steps of an experiment.

However, pre-experimental design lacks a comparison group. This means researchers can say that participants who received a treatment showed some change, but they can't be sure that the treatment caused the change.

Despite this limitation, pre-experimental design can still be useful for exploratory research to see if further study is feasible.

There are 3 types of Pre-Experimental Designs:

• One-shot case study design
• One-group pretest-posttest design
• Static-group comparison

OneShot Case Study Design

In this design, a single group is observed at one point in time after receiving some treatment. Researchers compare the outcomes to what they expect would have happened without the treatment and to other casually observed events. There is no control or comparison group.

For example, An organization implements a new customer service protocol across all branches. After a week, customer satisfaction scores are compared to previous records to assess the impact of the new protocol.

One-Group Pretest-Posttest Design

Here, a single group is observed twice—once before the treatment and once after. Any changes are assumed to be the result of the treatment. There is no control or comparison group.

For example, A school introduces a new tutoring program for struggling students. Before starting the program, students' academic performance is assessed through tests. After the program ends, their academic performance is tested again to measure improvement.

Static-Group Comparison

This design involves comparing a group that has received a treatment with one that has not. Differences between the two groups are assumed to be due to the treatment.

For example, A hospital introduces a new pain management technique for post-operative patients in one ward, while another ward continues with the standard pain management protocol. Pain levels and recovery times are compared between the two groups to evaluate the effectiveness of the new technique.

The list of advantages of Pre-Experimental Designs is as follows:

• It is cost-effective due to its simplicity.
• It is easy to conduct, and makes it accessible for beginners.
• It is efficient when it is conducted in natural environments.
• It requires less human intervention.
• It provides insights into how treatments may impact true experiments.

The list of limitations of Pre-Experimental Designs is as follows:

• It is weak in determining the causal relationships between variables.
• It lacks control over research conditions.
• It has high threat to internal validity.
• It challenges in assessing the integrity of results.
• The results are less reliable due to the absence of a control group.

Pre-experimental designs find practical applications in several fields where initial testing and feasibility assessment are crucial. Some common applications of Pre-Experimental Design include:

Educational Settings

• Testing new teaching methods or educational programs to see their immediate impact on student learning outcomes.
• Assessing the effectiveness of interventions such as tutoring programs or study skills workshops.

Healthcare and Medicine

• Introducing new medical treatments or therapies to evaluate their initial effects on patient health outcomes.
• Testing new healthcare protocols or procedures before implementing them broadly across medical practices.

Business and Organizational Development

• Implementing new training programs or workshops to improve employee skills or productivity.
• Evaluating the impact of organizational changes or new management strategies on employee satisfaction or performance.

Social Sciences and Psychology

• Studying the effects of interventions aimed at behavior change or social attitudes.
• Testing new counseling techniques or therapies to assess their effectiveness in improving mental health outcomes.

Engineering and Technology

• Introducing new technologies or engineering solutions to evaluate their feasibility and initial performance.
• Assessing the effectiveness of process improvements or innovations in manufacturing or production environments.

Environmental and Agricultural Studies

• Testing new agricultural techniques or crop treatments to improve yield or sustainability.
• Evaluating the impact of environmental interventions or conservation practices on ecological systems.

The characteristics of pre-experimental design are as follows:

• It involves only one group for treatment, simplifying observation.
• Validates the experiment in its preliminary phase.
• Provides insight into how an intervention will impact the entire study.
• Provides initial evidence supporting or refuting the intervention.
• Does not randomize participants.
• Often lacks a control group, though static-group comparison may be used when comparing treatment and control groups is necessary.
• Offers insight into how treatments might perform in true experiments.

Here are some of the examples of Pre-Experimental Design in Practice:

• A school introduces a new reading program for struggling students. At the end of the program, the reading abilities of the students are tested to see if there's been improvement. However, without a comparison group, it's challenging to determine if the improvement is solely due to the program or other factors.
• A company implements a new training program to improve employee productivity. Before starting the training, they assess productivity levels. After the training ends, they measure productivity again to see if there's been any change. This design helps evaluate the impact of the training, but it lacks a control group for comparison.
• A hospital introduces a new pain management technique in one department while using the standard technique in another department. They compare pain levels and recovery times between the two groups to assess the effectiveness of the new technique. This design allows for a comparison between groups but may still lack randomization.

Improving Validity of Pre-Experimental Designs

Improving the validity of pre-experimental designs, which is how accurately the results reflect reality, is a big challenge. Some ways to make sure the results are reliable include:

• Include Comparison Groups: Adding groups that don't get the treatment helps us see if any changes are because of the treatment or other reasons like natural changes or luck.
• Randomization: Randomly assigning people to treatment or control groups helps reduce bias. This makes sure the groups are similar, so we can trust the results more.
• Control External Factors: We should watch out for things outside the experiment that could affect the results, like people naturally getting better over time or unexpected changes in the environment.
• Match Participants: It's important that the people in our pre-experiment are like the ones we'll have in the real experiment. This way, even if we keep the treatment the same, we'll get more reliable results.
• Repeat Experiments: Doing the pre-experiment more than once can show if the results are consistent. This helps us trust that the effects we see are real.
• Use Good Tools: Using tools that are known to be accurate helps us measure changes in the things we're studying correctly. This makes our results more believable.

With the help of above discussion, we can conclude that pre-experimental design plays a crucial role in research by providing a foundational understanding of how treatments or interventions may impact study variables. By testing hypotheses and evaluating feasibility, these designs offer valuable initial insights before advancing to more complex experiments.

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FAQs on Pre Experimental Design

What is pre-experimental design.

Pre-experimental design is a research method that happens before the true experiment and determines how the researcher’s intervention will affect the experiment.

What is an example of pre-experimental design?

An example of a pre-experimental design would be a gym trainer implementing a new training schedule for a trainee.

What is the difference between the two types of experimental research design?

True experimental design carries out the pre-test and post-test on both the treatment group as well as a control group. whereas in pre-experimental design, control group and pre-test are options. it does not always have the presence of those two and helps the researcher determine how the real experiment is going to happen.

Which is better between the two types of experimental research?

Pre-experimental design deals with the treatment’s effect on the experiment and is carried out even before the true experiment takes place. While a true experiment is an actual experiment, it is important to conduct its pre-experiment first to see how the intervention is going to affect the experiment.

What are non-experimental methods?

Non-experimental research methods majorly fall into three categories namely: Cross-sectional research, correlational research and observational research.

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Pre-experimental designs.

Pre-experiments are the simplest form of research design. In a pre-experiment either a single group or multiple groups are observed subsequent to some agent or treatment presumed to cause change.

Types of Pre-Experimental Design

One-shot case study design, one-group pretest-posttest design, static-group comparison.

A single group is studied at a single point in time after some treatment that is presumed to have caused change. The carefully studied single instance is compared to general expectations of what the case would have looked like had the treatment not occurred and to other events casually observed. No control or comparison group is employed.

A single case is observed at two time points, one before the treatment and one after the treatment. Changes in the outcome of interest are presumed to be the result of the intervention or treatment. No control or comparison group is employed.

A group that has experienced some treatment is compared with one that has not. Observed differences between the two groups are assumed to be a result of the treatment.

Validity of Results

An important drawback of pre-experimental designs is that they are subject to numerous threats to their  validity . Consequently, it is often difficult or impossible to dismiss rival hypotheses or explanations. Therefore, researchers must exercise extreme caution in interpreting and generalizing the results from pre-experimental studies.

One reason that it is often difficult to assess the validity of studies that employ a pre-experimental design is that they often do not include any control or comparison group. Without something to compare it to, it is difficult to assess the significance of an observed change in the case. The change could be the result of historical changes unrelated to the treatment, the maturation of the subject, or an artifact of the testing.

Even when pre-experimental designs identify a comparison group, it is still difficult to dismiss rival hypotheses for the observed change. This is because there is no formal way to determine whether the two groups would have been the same if it had not been for the treatment. If the treatment group and the comparison group differ after the treatment, this might be a reflection of differences in the initial recruitment to the groups or differential mortality in the experiment.

Advantages and Disadvantages

As exploratory approaches, pre-experiments can be a cost-effective way to discern whether a potential explanation is worthy of further investigation.

Disadvantages

Pre-experiments offer few advantages since it is often difficult or impossible to rule out alternative explanations. The nearly insurmountable threats to their validity are clearly the most important disadvantage of pre-experimental research designs.

Pre-Experimental Design

Pre-experimental design refers to the simplest form of research design often used in the field of psychology, sociology, education, and other social sciences. These designs are called “pre-experimental” because they precede true experimental design in terms of complexity and rigor.

In pre-experimental designs, researchers observe or measure subjects without manipulating variables or controlling conditions. Often, these designs lack certain elements of a true experiment, such as random assignment, control groups, or pretest measurements, making it difficult to determine causality.

Three common types of pre-experimental designs include the one-shot case study, the one-group pretest-posttest design, and the static-group comparison. These designs offer a starting point for researchers but are typically seen as less reliable than more controlled experimental designs due to the lack of randomization and the potential for confounding variables.

Characteristics of Pre-Experimental Design

Pre-experimental designs are characterized by their simplicity and ease of execution. They are typically used when resources are limited, or when the research question does not require a high degree of control or precision. Key characteristics of these designs include the use of a single group, the lack of a control group, and the absence of random assignment.

Single Group

In a pre-experimental design, there is typically only one group of subjects, and this group is measured or observed both before and after an intervention or treatment.

Lack of Control Group

Pre-experimental designs often lack a control group for comparison. As a result, it’s difficult to determine whether observed changes are the result of the intervention or due to extraneous factors.

Absence of Random Assignment

Another characteristic of pre-experimental design is the absence of random assignment. Subjects are not randomly assigned to groups, which can lead to selection bias and limits the generalizability of the findings.

There are several types of pre-experimental designs, including the one-shot case study, the one-group pretest-posttest design, and the static-group comparison.

One-Shot Case Study

In a one-shot case study, a single group or case is studied at a single point in time after some intervention or treatment that is presumed to cause change.

One-Group Pretest-Posttest Design

In the one-group pretest-posttest design, a single group is observed at two time points, one before the treatment and one after the treatment.

Static-Group Comparison

In a static-group comparison, there are two groups that are not created through random assignment. One group receives the treatment and the other does not, and the outcomes are compared.

Limitations

While pre-experimental designs offer advantages in terms of simplicity and convenience, they also come with notable limitations. The lack of a control group and the absence of random assignment limits the ability to establish causality. There is also a risk of selection bias, and the findings may not be generalizable to other populations or settings.

Despite these limitations, pre-experimental designs can serve as valuable starting points in exploratory research, laying the groundwork for more rigorous experimental designs in the future.

In conclusion, pre-experimental design, while limited in its ability to provide strong evidence of causality, plays a crucial role in exploratory research. It presents a simplified and cost-effective approach to experimentation that is especially useful when resources are limited or when the goal is to explore a new area of study. However, the inherent limitations of pre-experimental designs necessitate caution in interpreting their results. Consequently, they are often used as stepping stones towards more rigorous research designs. As such, understanding pre-experimental designs is a fundamental part of the researcher’s toolkit, paving the way for more comprehensive and controlled investigations.

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14.4 Pre-experimental design

Learning objectives.

Learners will be able to…

• Describe pre-experimental designs in social work research
• Discuss how pre-experimental research differs from true and quasi-experimental research
• Demonstrate an understanding of the different types of pre-experimental designs
• Determine what kinds of research questions pre-experimental designs are suited for
• Discuss advantages and disadvantages of pre-experimental designs

The previous sections have laid out the basics of some rigorous approaches to establish that an intervention is associated with, or even responsible for, changes we observe in research participants when comparing them to those who have not received the intervention. This type of evidence is extremely important to build an evidence base for social work interventions, but it’s not the only type of evidence to consider. Pre-experimental design is also often a stepping stone for more rigorous experimental design in the future, as it can help test the feasibility of your research. In general, pre-experimental designs do not support causality nor address threats to internal validity. However, that’s not really their intention. Pre-experimental designs are useful when researchers are developing new interventions, testing out new measurement instruments, or want to build toward more rigorous experimental designs.

A significant benefit of these types of designs is that they’re pretty easy to execute in a practice or agency setting. They don’t use comparison or control group, but they do examine outcomes for people who have gone through an intervention or been exposed to a condition.  Below, we will go into some detail about the different types of pre-experimental design.

One group pretest/posttest design

Also known as a before-after one-group design, this type of research design does not have a comparison group; everyone who participates in the research receives the intervention or is exposed to the experimental condition (Figure 14.8). This is a common type of design in program evaluation in the practice world. Controlling for extraneous variables is difficult or impossible in this design, but given that it is still possible to establish some measure of time order, it can begin to provide potential support for causality.

Imagine a researcher who is interested in the effectiveness of an anti-drug education program on elementary school students’ attitudes toward illegal drugs. The researcher could assess students’ attitudes about illegal drugs (O 1 ), implement the anti-drug program (X), and then immediately after the program ends, the researcher could once again measure students’ attitudes toward illegal drugs (O 2 ). You can see how this would be relatively simple to do in practice, and you may have been involved in this type of research design yourself, even if informally. But hopefully, you can also see that this design would not provide us with much evidence for causality because we have no way of controlling for the effect of extraneous variables. A lot of things could have affected any change in students’ attitudes—maybe something happened in the community while the program was underway that caused the change or maybe when the students took the pretest they were trying to impress the researchers, but once they got to know them, they felt more comfortable and were more honest about their attitudes.

All of that doesn’t mean these results aren’t useful, however. If we find that children’s attitudes didn’t change at all after the drug education program, then we need to think seriously about how to make it more effective or whether we should be using it at all. (This immediate, practical application of our results highlights a key difference between program evaluation and research, which we will discuss in Chapter 23.)

One group posttest-only design

As the name suggests, this type of pre-experimental design involves measurement only after an intervention. In fact, sometimes it is called the after-only design. As in other pre-experimental designs, there is no comparison or control group; everyone receives the intervention (Figure 14.9).

Because there is no pretest and no comparison group, this design is not useful for supporting causality since we can’t establish time order and we can’t control for extraneous variables. However, that doesn’t mean it’s not useful at all! Sometimes, agencies need to gather information about how their programs are functioning. A classic example of this design is satisfaction surveys—realistically, these can only be administered after a program or intervention. Questions regarding satisfaction, ease of use or engagement, or other questions that don’t involve comparisons are best suited for this type of design.

Pre-experimental research designs are easy to execute in practice, but we must be cautious about drawing causal conclusions from the results. A positive result may still suggest that we should continue using a particular intervention (and no result or a negative result should make us reconsider whether we should use that intervention at all). You will likely see pre-experimental research in your graduate research assistant (GRA) assignments or in the articles you read. Knowing the basics of how to structure such a project, will help you prepare for collaborative research in the future.

Key Takeaways

• Pre-experimental designs are useful for describing phenomena, but cannot demonstrate causality.
• After-only designs are often used in agency and practice settings because practitioners are often not able to set up pretest/posttest designs.
• Pre-experimental designs are useful for explanatory questions in program evaluation and are helpful for researchers when they are trying to develop a new assessment or scale.
• Pre-experimental designs are well-suited to qualitative methods.

TRACK 1 (IF YOU ARE CREATING A RESEARCH PROPOSAL FOR THIS CLASS):

• If you were to use a pre-experimental design for your research project, which would you choose? Why?
• Have you conducted pre-experimental research in your practice or professional life? Which type of pre-experimental design was it?

TRACK 2 (IF YOU AREN’T CREATING A RESEARCH PROPOSAL FOR THIS CLASS):

Imagine you are interested in studying child welfare practice. You are interested in learning more about community-based programs aimed to prevent child maltreatment and to prevent out-of-home placement for children.

• If you were to use a pre-experimental design for this research project, which would you choose? Why?

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Pre-Experimental Designs

Pre-experiments are the simplest form of research design. In a pre-experiment either a single group or multiple groups are observed subsequent to some agent or treatment presumed to cause change.

Types of Pre-Experimental Design

One-shot case study design, one-group pretest-posttest design, static-group comparison.

A single group is studied at a single point in time after some treatment that is presumed to have caused change. The carefully studied single instance is compared to general expectations of what the case would have looked like had the treatment not occurred and to other events casually observed. No control or comparison group is employed.

A single case is observed at two time points, one before the treatment and one after the treatment. Changes in the outcome of interest are presumed to be the result of the intervention or treatment. No control or comparison group is employed.

A group that has experienced some treatment is compared with one that has not. Observed differences between the two groups are assumed to be a result of the treatment.

Validity of Results

An important drawback of pre-experimental designs is that they are subject to numerous threats to their validity . Consequently, it is often difficult or impossible to dismiss rival hypotheses or explanations. Therefore, researchers must exercise extreme caution in interpreting and generalizing the results from pre-experimental studies.

One reason that it is often difficult to assess the validity of studies that employ a pre-experimental design is that they often do not include any control or comparison group. Without something to compare it to, it is difficult to assess the significance of an observed change in the case. The change could be the result of historical changes unrelated to the treatment, the maturation of the subject, or an artifact of the testing.

Even when pre-experimental designs identify a comparison group, it is still difficult to dismiss rival hypotheses for the observed change. This is because there is no formal way to determine whether the two groups would have been the same if it had not been for the treatment. If the treatment group and the comparison group differ after the treatment, this might be a reflection of differences in the initial recruitment to the groups or differential mortality in the experiment.

Advantages and Disadvantages

As exploratory approaches, pre-experiments can be a cost-effective way to discern whether a potential explanation is worthy of further investigation.

Disadvantages

Pre-experiments offer few advantages since it is often difficult or impossible to rule out alternative explanations. The nearly insurmountable threats to their validity are clearly the most important disadvantage of pre-experimental research designs.

The 3 Types Of Experimental Design

Dave Cornell (PhD)

Dr. Cornell has worked in education for more than 20 years. His work has involved designing teacher certification for Trinity College in London and in-service training for state governments in the United States. He has trained kindergarten teachers in 8 countries and helped businessmen and women open baby centers and kindergartens in 3 countries.

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Chris Drew (PhD)

This article was peer-reviewed and edited by Chris Drew (PhD). The review process on Helpful Professor involves having a PhD level expert fact check, edit, and contribute to articles. Reviewers ensure all content reflects expert academic consensus and is backed up with reference to academic studies. Dr. Drew has published over 20 academic articles in scholarly journals. He is the former editor of the Journal of Learning Development in Higher Education and holds a PhD in Education from ACU.

Experimental design refers to a research methodology that allows researchers to test a hypothesis regarding the effects of an independent variable on a dependent variable.

There are three types of experimental design: pre-experimental design, quasi-experimental design, and true experimental design.

Experimental Design in a Nutshell

A typical and simple experiment will look like the following:

• The experiment consists of two groups: treatment and control.
• Participants are randomly assigned to be in one of the groups (‘conditions’).
• The treatment group participants are administered the independent variable (e.g. given a medication).
• The control group is not given the treatment.
• The researchers then measure a dependent variable (e.g improvement in health between the groups).

If the independent variable affects the dependent variable, then there should be noticeable differences on the dependent variable between the treatment and control conditions.

The experiment is a type of research methodology that involves the manipulation of at least one independent variable and the measurement of at least one dependent variable.

If the independent variable affects the dependent variable, then the researchers can use the term “causality.”

Types of Experimental Design

1. pre-experimental design.

A researcher may use pre-experimental design if they want to test the effects of the independent variable on a single participant or a small group of participants.

The purpose is exploratory in nature , to see if the independent variable has any effect at all.

The pre-experiment is the simplest form of an experiment that does not contain a control condition.

However, because there is no control condition for comparison, the researcher cannot conclude that the independent variable causes change in the dependent variable.

Examples include:

• Action Research in the Classroom: Action research in education involves a teacher conducting small-scale research in their classroom designed to address problems they and their students currently face.
• Case Study Research : Case studies are small-scale, often in-depth, studies that are notusually generalizable.
• A Pilot Study: Pilot studies are small-scale studies that take place before the main experiment to test the feasibility of the project.
• Ethnography: An ethnographic research study will involve thick research of a small cohort to generate descriptive rather than predictive results.

2. Quasi-Experimental Design

The quasi-experiment is a methodology to test the effects of an independent variable on a dependent variable. However, the participants are not randomly assigned to treatment or control conditions. Instead, the participants already exist in representative sample groups or categories, such as male/female or high/low SES class.

Because the participants cannot be randomly assigned to male/female or high/low SES, there are limitations on the use of the term “causality.”

Researchers must refrain from inferring that the independent variable caused changes in the dependent variable because the participants existed in already formed categories before the study began.

• Homogenous Representative Sampling: When the research participant group is homogenous (i.e. not diverse) then the generalizability of the study is diminished.
• Non-Probability Sampling: When researchers select participants through subjective means such as non-probability sampling, they are engaging in quasi-experimental design and cannot assign causality.

See more Examples of Quasi-Experimental Design

3. True Experimental Design

A true experiment involves a design in which participants are randomly assigned to conditions, there exists at least two conditions (treatment and control) and the researcher manipulates the level of the independent variable (independent variable).

When these three criteria are met, then the observed changes in the dependent variable (dependent variable) are most likely caused by the different levels of the independent variable.

The true experiment is the only research design that allows the inference of causality .

Of course, no study is perfect, so researchers must also take into account any threats to internal validity that may exist such as confounding variables or experimenter bias.

• Heterogenous Sample Groups: True experiments often contain heterogenous groups that represent a wide population.
• Clinical Trials: Clinical trials such as those required for approval of new medications are required to be true experiments that can assign causality.

See More Examples of Experimental Design

Experimental Design vs Observational Design

Experimental design is often contrasted to observational design. Defined succinctly, an experimental design is a method in which the researcher manipulates one or more variables to determine their effects on another variable, while observational design involves the observation and analysis of a subject without influencing their behavior or conditions.

Observational design primarily involves data collection without direct involvement from the researcher. Here, the variables aren’t manipulated as they would be in an experimental design.

An example of an observational study might be research examining the correlation between exercise frequency and academic performance using data from students’ gym and classroom records.

The key difference between these two designs is the degree of control exerted in the experiment . In experimental studies, the investigator controls conditions and their manipulation, while observational studies only allow the observation of conditions as independently determined (Althubaiti, 2016).

Observational designs cannot infer causality as well as experimental designs; but they are highly effective at generating descriptive statistics.

For more, read: Observational vs Experimental Studies

Generally speaking, there are three broad categories of experiments. Each one serves a specific purpose and has associated limitations . The pre-experiment is an exploratory study to gather preliminary data on the effectiveness of a treatment and determine if a larger study is warranted.

The quasi-experiment is used when studying preexisting groups, such as people living in various cities or falling into various demographic categories. Although very informative, the results are limited by the presence of possible extraneous variables that cannot be controlled.

The true experiment is the most scientifically rigorous type of study. The researcher can manipulate the level of the independent variable and observe changes, if any, on the dependent variable. The key to the experiment is randomly assigning participants to conditions. Random assignment eliminates a lot of confounds and extraneous variables, and allows the researchers to use the term “causality.”

For More, See: Examples of Random Assignment

Baumrind, D. (1991). Parenting styles and adolescent development. In R. M. Lerner, A. C. Peterson, & J. Brooks-Gunn (Eds.), Encyclopedia of Adolescence (pp. 746–758). New York: Garland Publishing, Inc.

Cook, T. D., & Campbell, D. T. (1979). Quasi-experimentation: Design & analysis issues in field settings . Boston, MA: Houghton Mifflin.

Loftus, E. F., & Palmer, J. C. (1974). Reconstruction of automobile destruction: An example of the interaction between language and memory. Journal of Verbal Learning and Verbal Behavior, 13 (5), 585–589.

Matthew L. Maciejewski (2020) Quasi-experimental design. Biostatistics & Epidemiology, 4 (1), 38-47. https://doi.org/10.1080/24709360.2018.1477468

Thyer, Bruce. (2012). Quasi-Experimental Research Designs . Oxford University Press. https://doi.org/10.1093/acprof:oso/9780195387384.001.0001

• Dave Cornell (PhD) https://helpfulprofessor.com/author/dave-cornell-phd/ 23 Achieved Status Examples
• Dave Cornell (PhD) https://helpfulprofessor.com/author/dave-cornell-phd/ 25 Defense Mechanisms Examples
• Dave Cornell (PhD) https://helpfulprofessor.com/author/dave-cornell-phd/ 15 Theory of Planned Behavior Examples
• Dave Cornell (PhD) https://helpfulprofessor.com/author/dave-cornell-phd/ 18 Adaptive Behavior Examples

• Chris Drew (PhD) https://helpfulprofessor.com/author/chris-drew-phd/ 23 Achieved Status Examples
• Chris Drew (PhD) https://helpfulprofessor.com/author/chris-drew-phd/ 15 Ableism Examples
• Chris Drew (PhD) https://helpfulprofessor.com/author/chris-drew-phd/ 25 Defense Mechanisms Examples
• Chris Drew (PhD) https://helpfulprofessor.com/author/chris-drew-phd/ 15 Theory of Planned Behavior Examples

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Entries a-z, subject index.

• Pre-experimental Designs
• By: Maria Jimenez-Buedo
• In: The SAGE Encyclopedia of Educational Research, Measurement, and Evaluation
• Chapter DOI: https:// doi. org/10.4135/9781506326139.n536
• Subject: Education
• Show page numbers Hide page numbers

Pre-experimental designs are research schemes in which a subject or a group is observed after a treatment has been applied, in order to test whether the treatment has the potential to cause change. The prefix pre- conveys two different senses in which this type of design differs from experiments: (1) pre-experiments are a more rudimentary form of design relative to experiments, devised in order to anticipate any problems that experiments may later encounter vis-à-vis causal inference, and (2) pre-experiments are often preparative forms of exploration prior to engaging in experimental endeavors, providing cues or indications that an experiment is worth pursuing. Because pre-experiments typically tend to overstate rather than understate the presence of causal relations between variables, it is sometimes useful to run a pre-experiment (or more commonly, to observe the results of an existing one) in order to decide whether an experiment should be undertaken.

Experimental evidence is defined in contrast to observational evidence: Although the former involves some form of intervention, the latter is limited to recordings of events as they naturally occur, without controlling the behavior of the object being studied. An experiment is normally used to create a controlled environment to aid in establishing valid inferences about the behavior of the object being studied; typically, the control involved in the experiment is used to infer causality among variables. In the case of a pre-experiment, although there is some intervention in the object, the level of intervention does not provide the control required for valid inferences [Page 1290] regarding the causal processes involved. Thus, pre-experiments differ from observational data because they are based on some form of intervention. At the same time, they are different from experiments because not enough control is achieved to ensure valid causal inferences.

Types of Pre-Experimental Designs

The category of pre-experimental designs is necessarily an open one because it is defined negatively, in opposition to true experimental designs. Yet, three types of designs are normally considered standard pre-experiments and are used routinely by researchers: the one-shot case study , the single-group before and after , and the static group comparison .

One-Shot Case Study

Also referred to as a single-group posttest design , this type of research involves a single group of subjects being studied at a single point in time after some treatments have taken effect, or more broadly, after some relevant intervention that is supposed to cause change has taken place. In order to make inferences about the treatment, the measurements taken in the one-shot case study are compared to the general expectations about what the case would have looked like if the treatment had not been put in place because there is no control or comparison group involved.

In the standard representational language of experimental research design, a one-shot case study is represented as follows:

X → O ,

where the X represents the treatment or intervention and the O represents the observation by researchers of the variable of interest.

Single-Group Before and After

Also known as a one-group pretest–posttest design , this method involves a single case observed at two different points in time—before and after an intervention or treatment. Whatever changes happen in the outcome of interest are presumed to be the result of the intervention. Again, there is no control or comparison group involved in this type of study design.

In the standard representational language of experimental research design, a single-group before-and-after design is represented as follows:

O → X → O .

Static Group Comparison

Also referred to as a cross-sectional or transversal study , this type of design involves two groups: one on which a treatment intervention has been carried out (O 1 ) and another group on which no intervention has been performed (O 2 ). The difference in the outcome of interest between the two groups is assumed to be caused by the intervention.

In the standard representational language of experimental research design, a static group comparison is represented as follows:

X → O 1 → O 2 .

Validity and Relevant Comparisons

The main advantage of pre-experimental designs is their cost: The majority of pre-experiments lack a comparison group, which makes them less expensive to run than true experiments. Therefore, they may be the better option if resources are limited. Because of this lack of comparison group, however, they are vulnerable to a number of validity threats. The one-shot case study is vulnerable to the following biases: history, maturation, selection, mortality, and selection treatment. In turn, the single-group, before-and-after design is often affected by biases such as history, maturation, testing, regression, selection maturation, and selection treatment. Finally, the static group comparison often displays problems such as selection mortality, selection maturation, maturation, and selection treatment.

The limitations of pre-experimental design all highlight the importance of comparison groups; this in turn helps to underline the absolute centrality of comparison in making causal inferences. In fact, the kind of inferences that pre-experiments allow, and the inferential difficulties they present, [Page 1291] resembles those of observational studies with small sample sizes (small- N studies). It is thus no surprise that static group comparisons are on practical grounds analytically indistinguishable from observational cross-sectional studies and that one-shot case studies and single-group, before-and-after designs share many of the features of observational case studies, which are typical of qualitative social sciences research. Thus, for the interpretation of pre-experimental evidence, the analytical strategies that have been made available to researchers dealing with small- N studies can often be of use. These are works that develop the comparative method in the tradition initiated by John Stuart Mill (1806–1873) in order to determine which logical conclusions can be supported by a data set composing just a few cases.

In this way, pre-experiments, together with quasi-experiments, demonstrate that there is actually a continuum between observational and experimental studies in terms of the type of inferences they allow. The thread of the continuum is provided by the central notion of comparison : Meaningful comparisons are needed in order to establish valid inferences. In the case of pre-experimental research, the absence of relevant comparison groups can be partially circumvented by the background knowledge of the researcher, together with a large dose of caution in the drawing of causal conclusions. In any event, it is always advisable, in the presence of pre-experimental evidence suggesting a causal effect, to run subsequent studies that can rule out validity threats.

See also Experimental Designs ; External Validity ; Internal Validity ; Qualitative Data Analysis ; Quasi-Experimental Designs ; Threats to Research Validity

Further Readings

• Predictive Validity
• Premack Principle
• Standards for Educational and Psychological Testing
• Accessibility of Assessment
• Accommodations
• African Americans and Testing
• Asian Americans and Testing
• Ethical Issues in Testing
• Gender and Testing
• High-Stakes Tests
• Latinos and Testing
• Minority Issues in Testing
• Second Language Learners, Assessment of
• Test Security
• Testwiseness
• Ability Tests
• Achievement Tests
• Adaptive Behavior Assessments
• Admissions Tests
• Alternate Assessments
• Aptitude Tests
• Attenuation, Correction for
• Attitude Scaling
• Basal Level and Ceiling Level
• Buros Mental Measurements Yearbook
• Classification
• Cognitive Diagnosis
• Computer-Based Testing
• Computerized Adaptive Testing
• Confidence Interval
• Curriculum-Based Assessment
• Diagnostic Tests
• Difficulty Index
• Discrimination Index
• English Language Proficiency Assessment
• Formative Assessment
• Intelligence Tests
• Interquartile Range
• Minimum Competency Testing
• Personality Assessment
• Power Tests
• Progress Monitoring
• Projective Tests
• Psychometrics
• Reading Comprehension Assessments
• Screening Tests
• Self-Report Inventories
• Sociometric Assessment
• Speeded Tests
• Standards-Based Assessment
• Summative Assessment
• Technology-Enhanced Items
• Test Battery
• Testing, History of
• Value-Added Models
• Written Language Assessment
• Authentic Assessment
• Backward Design
• Bloom’s Taxonomy
• Classroom Assessment
• Constructed-Response Items
• Curriculum-Based Measurement
• Essay Items
• Fill-in-the-Blank Items
• Game-Based Assessment
• Matching Items
• Multiple-Choice Items
• Paper-and-Pencil Assessment
• Performance-Based Assessment
• Portfolio Assessment
• Selection Items
• Student Self-Assessment
• Supply Items
• Technology in Classroom Assessment
• True-False Items
• Universal Design of Assessment
• a Parameter
• b Parameter
• c Parameter
• Conditional Standard Error of Measurement
• Differential Item Functioning
• Item Information Function
• Item Response Theory
• Multidimensional Item Response Theory
• Rasch Model
• Test Information Function
• Testlet Response Theory
• Coefficient Alpha
• Decision Consistency
• Inter-Rater Reliability
• Internal Consistency
• Kappa Coefficient of Agreement
• Phi Coefficient (in Generalizability Theory)
• Reliability
• Spearman-Brown Prophecy Formula
• Split-Half Reliability
• Test–Retest Reliability
• Age Equivalent Scores
• Analytic Scoring
• Automated Essay Evaluation
• Criterion-Referenced Interpretation
• Grade-Equivalent Scores
• Guttman Scaling
• Holistic Scoring
• Intelligence Quotient
• Interval-Level Measurement
• Ipsative Scales
• Levels of Measurement
• Likert Scaling
• Multidimensional Scaling
• Nominal-Level Measurement
• Norm-Referenced Interpretation
• Normal Curve Equivalent Score
• Ordinal-Level Measurement
• Percentile Rank
• Primary Trait Scoring
• Propensity Scores
• Rating Scales
• Reverse Scoring
• Score Reporting
• Semantic Differential Scaling
• Standardized Scores
• Thurstone Scaling
• Visual Analog Scales
• W Difference Scores
• Bayley Scales of Infant and Toddler Development
• Beck Depression Inventory
• Dynamic Indicators of Basic Early Literacy Skills
• Educational Testing Service
• Iowa Test of Basic Skills
• Kaufman-ABC Intelligence Test
• Minnesota Multiphasic Personality Inventory
• National Assessment of Educational Progress
• Partnership for Assessment of Readiness for College and Careers
• Peabody Picture Vocabulary Test
• Programme for International Student Assessment
• Progress in International Reading Literacy Study
• Raven’s Progressive Matrices
• Smarter Balanced Assessment Consortium
• Standardized Tests
• Stanford-Binet Intelligence Scales
• Torrance Tests of Creative Thinking
• Trends in International Mathematics and Science Study
• Wechsler Intelligence Scales
• Woodcock-Johnson Tests of Achievement
• Woodcock-Johnson Tests of Cognitive Ability
• Woodcock-Johnson Tests of Oral Language
• Concurrent Validity
• Consequential Validity Evidence
• Construct Irrelevance
• Construct Underrepresentation
• Content-Related Validity Evidence
• Criterion-Based Validity Evidence
• Measurement Invariance
• Multicultural Validity
• Multitrait–Multimethod Matrix
• Sensitivity
• Social Desirability
• Specificity
• Unitary View of Validity
• Validity Coefficients
• Validity Generalization
• Validity, History of
• Critical Thinking
• Learned Helplessness
• Locus of Control
• Long-Term Memory
• Metacognition
• Problem Solving
• Self-Efficacy
• Self-Regulation
• Short-Term Memory
• Working Memory
• Data Visualization Methods
• Graphical Modeling
• Scatterplots
• Asperger’s Syndrome
• Attention-Deficit/Hyperactivity Disorder
• Autism Spectrum Disorder
• Bipolar Disorder
• Developmental Disabilities
• Intellectual Disability and Postsecondary Education
• Learning Disabilities
• F Distribution
• Areas Under the Normal Curve
• Bernoulli Distribution
• Distributions
• Moments of a Distribution
• Normal Distribution
• Poisson Distribution
• Posterior Distribution
• Prior Distribution
• Brown v. Board of Education
• Adequate Yearly Progress
• Americans with Disabilities Act
• Coleman Report
• Common Core State Standards
• Corporal Punishment
• Every Student Succeeds Act
• Family Educational Rights and Privacy Act
• Great Society Programs
• Health Insurance Portability and Accountability Act
• Individualized Education Program
• Individuals With Disabilities Education Act
• Least Restrictive Environment
• No Child Left Behind Act
• Policy Research
• Race to the Top
• School Vouchers
• Special Education Identification
• Special Education Law
• State Standards
• Advocacy in Evaluation
• Collaboration, Evaluation of
• Conceptual Framework
• Evaluation Versus Research
• Evaluation, History of
• Feasibility
• Goals and Objectives
• Logic Models
• Program Theory of Change
• Stakeholders
• Triangulation
• Appreciative Inquiry
• CIPP Evaluation Model
• Collaborative Evaluation
• Consumer-Oriented Evaluation Approach
• Cost–Benefit Analysis
• Culturally Responsive Evaluation
• Democratic Evaluation
• Developmental Evaluation
• Empowerment Evaluation
• Evaluation Capacity Building
• Evidence-Centered Design
• External Evaluation
• Feminist Evaluation
• Formative Evaluation
• Four-Level Evaluation Model
• Goal-Free Evaluation
• Internal Evaluation
• Needs Assessment
• Participatory Evaluation
• Personnel Evaluation
• Policy Evaluation
• Process Evaluation
• Program Evaluation
• Responsive Evaluation
• Success Case Method
• Summative Evaluation
• Utilization-Focused Evaluation
• Adolescence
• Cognitive Development, Theory of
• Erikson’s Stages of Psychosocial Development
• Kohlberg’s Stages of Moral Development
• Parenting Styles
• Accreditation
• Angoff Method
• Body of Work Method
• Bookmark Method
• Construct-Related Validity Evidence
• Content Analysis
• Content Standard
• Content Validity Ratio
• Curriculum Mapping
• Ebel Method
• Instructional Sensitivity
• Item Analysis
• Item Banking
• Item Development
• Learning Maps
• Modified Angoff Method
• Proficiency Levels in Language
• Readability
• Score Linking
• Standard Setting
• Table of Specifications
• Vertical Scaling
• American Educational Research Association
• American Evaluation Association
• American Psychological Association
• Institute of Education Sciences
• Interstate School Leaders Licensure Consortium Standards
• Joint Committee on Standards for Educational Evaluation
• National Council on Measurement in Education
• National Science Foundation
• Office of Elementary and Secondary Education
• Organisation for Economic Co-operation and Development
• Teachers’ Associations
• U.S. Department of Education
• World Education Research Association
• Diagnostic and Statistical Manual of Mental Disorders
• Guiding Principles for Evaluators
• Accountability
• Certification
• Classroom Observations
• Confidentiality
• Conflict of Interest
• Data-Driven Decision Making
• Educational Researchers, Training of
• Ethical Issues in Educational Research
• Ethical Issues in Evaluation
• Evaluation Consultants
• Federally Sponsored Research and Programs
• Framework for Teaching
• Professional Development of Teachers
• Professional Learning Communities
• School Psychology
• Teacher Evaluation
• Demographics
• Dissertations
• Journal Articles
• Literature Review
• Methods Section
• Research Proposals
• Results Section
• Delphi Technique
• Discourse Analysis
• Document Analysis
• Ethnography
• Field Notes
• Focus Groups
• Grounded Theory
• Historical Research
• Interviewer Bias
• Market Research
• Member Check
• Narrative Research
• Naturalistic Inquiry
• Participant Observation
• Qualitative Data Analysis
• Qualitative Research Methods
• Transcription
• Trustworthiness
• Applied Research
• Aptitude-Treatment Interaction
• Causal Inference
• Ecological Validity
• External Validity
• File Drawer Problem
• Fraudulent and Misleading Data
• Generalizability
• Hypothesis Testing
• Impartiality
• Interaction
• Internal Validity
• Objectivity
• Order Effects
• Representativeness
• Response Rate
• Scientific Method
• Type III Error
• ABA Designs
• Action Research
• Case Study Method
• Causal-Comparative Research
• Cross-Cultural Research
• Crossover Design
• Design-Based Research
• Double-Blind Design
• Experimental Designs
• Gain Scores, Analysis of
• Latin Square Design
• Meta-Analysis
• Mixed Methods Research
• Monte Carlo Simulation Studies
• Nonexperimental Designs
• Pilot Studies
• Posttest-Only Control Group Design
• Pretest–Posttest Designs
• Quasi-Experimental Designs
• Regression Discontinuity Analysis
• Repeated Measures Designs
• Single-Case Research
• Solomon Four-Group Design
• Split-Plot Design
• Static Group Design
• Time Series Analysis
• Triple-Blind Studies
• Twin Studies
• Zelen’s Randomized Consent Design
• Cluster Sampling
• Control Variables
• Convenience Sampling
• Expert Sampling
• Judgment Sampling
• Markov Chain Monte Carlo Methods
• Quantitative Research Methods
• Quota Sampling
• Random Assignment
• Random Selection
• Replication
• Simple Random Sampling
• Snowball Sampling
• Stratified Random Sampling
• Survey Methods
• Systematic Sampling
• Bubble Drawing
• C Programming Languages
• Collage Technique
• Computer Programming in Quantitative Analysis
• Concept Mapping
• HyperResearch
• Johari Window
• 45 CFR Part 46
• Belmont Report
• Cultural Competence
• Deception in Human Subjects Research
• Declaration of Helsinki
• Falsified Data in Large-Scale Surveys
• Flynn Effect
• Human Subjects Protections
• Human Subjects Research, Definition of
• Informed Consent
• Institutional Review Boards
• Nuremberg Code
• Service-Learning
• Social Justice
• STEM Education
• Matrices (in Social Network Analysis)
• Network Centrality
• Network Cohesion
• Network Density
• Social Network Analysis
• Social Network Analysis Using R
• Bayes’s Theorem
• Bayesian Statistics
• Marginal Maximum Likelihood Estimation
• Maximum Likelihood Estimation
• Analysis of Covariance
• Analysis of Variance
• Binomial Test
• Canonical Correlation
• Chi-Square Test
• Cluster Analysis
• Cochran Q Test
• Confirmatory Factor Analysis
• Cramér’s V Coefficient
• Descriptive Statistics
• Discriminant Function Analysis
• Exploratory Factor Analysis
• Fisher Exact Test
• Friedman Test
• Goodness-of-Fit Tests
• Hierarchical Regression
• Inferential Statistics
• Kolmogorov-Smirnov Test
• Kruskal-Wallis Test
• Levene’s Homogeneity of Variance Test
• Logistic Regression
• Mann-Whitney Test
• Mantel-Haenszel Test
• McNemar Change Test
• Measures of Central Tendency
• Measures of Variability
• Median Test
• Mixed Model Analysis of Variance
• Multiple Linear Regression
• Multivariate Analysis of Variance
• Part Correlations
• Partial Correlations
• Path Analysis
• Pearson Correlation Coefficient
• Phi Correlation Coefficient
• Repeated Measures Analysis of Variance
• Simple Linear Regression
• Spearman Correlation Coefficient
• Standard Error of Measurement
• Stepwise Regression
• Structural Equation Modeling
• Survival Analysis
• Two-Way Analysis of Variance
• Two-Way Chi-Square
• Wilcoxon Signed Ranks Test
• Alpha Level
• Autocorrelation
• Bonferroni Procedure
• Bootstrapping
• Categorical Data Analysis
• Central Limit Theorem
• Conditional Independence
• Convergence
• Correlation
• Data Mining
• Dummy Variables
• Effect Size
• Estimation Bias
• Eta Squared
• Gauss-Markov Theorem
• Holm’s Sequential Bonferroni Procedure
• Latent Class Analysis
• Local Independence
• Longitudinal Data Analysis
• Matrix Algebra
• Mediation Analysis
• Missing Data Analysis
• Multicollinearity
• Parameter Invariance
• Parameter Mean Squared Error
• Parameter Random Error
• Post Hoc Analysis
• Power Analysis
• Probit Transformation
• Robust Statistics
• Sample Size
• Significance
• Simpson’s Paradox
• Standard Deviation
• Type I Error
• Type II Error
• Winsorizing
• Cross-Classified Models
• Diagnostic Classification Models
• Dyadic Data Analysis
• Generalized Linear Mixed Models
• Growth Curve Modeling
• Hierarchical Linear Modeling
• Model–Data Fit
• Active Learning
• Bilingual Education, Research on
• College Success
• Constructivist Approach
• Cooperative Learning
• Distance Learning
• Evidence-Based Interventions
• Homeschooling
• Instructional Objectives
• Instructional Rounds
• Kindergarten
• Kinesthetic Learning
• Learning Progressions
• Learning Styles
• Learning Theories
• Mastery Learning
• Montessori Schools
• Out-of-School Activities
• Pygmalion Effect
• Quantitative Literacy
• Reading Comprehension
• Scaffolding
• School Leadership
• Self-Directed Learning
• Social Learning
• Socio-Emotional Learning
• Waldorf Schools
• g Theory of Intelligence
• Ability–Achievement Discrepancy
• Applied Behavior Analysis
• Attribution Theory
• Behaviorism
• Cattell–Horn–Carroll Theory of Intelligence
• Classical Conditioning
• Classical Test Theory
• Cognitive Neuroscience
• Educational Psychology
• Educational Research, History of
• Emotional Intelligence
• Epistemologies, Teacher and Student
• Experimental Phonetics
• Feedback Intervention Theory
• Generalizability Theory
• Improvement Science Research
• Information Processing Theory
• Instructional Theory
• Multiple Intelligences, Theory of
• Operant Conditioning
• Paradigm Shift
• Phenomenology
• Postpositivism
• Pragmatic Paradigm
• Reinforcement
• Response to Intervention
• School-Wide Positive Behavior Support
• Social Cognitive Theory
• Speech-Language Pathology
• Terman Study of the Gifted
• Transformative Paradigm
• Triarchic Theory of Intelligence
• Universal Design in Education
• Wicked Problems
• Zone of Proximal Development
• Hawthorne Effect
• Instrumentation
• John Henry Effect
• Nonresponse Bias
• Observer Effect
• Placebo Effect
• Reactive Arrangements
• Regression Toward the Mean
• Restriction of Range
• Selection Bias
• Threats to Research Validity
• Treatment Integrity

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