the definition of pre experimental design

Child Care and Early Education Research Connections

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.

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2 Pre-Experimental Research Designs

• Published: February 2012
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The simplest of the group research designs involve the assessment of the functioning of a single group of persons who receive social work services. These methods are called pre-experimental designs. Tightly controlled studies done in laboratory or special treatment settings are known as efficacy studies, and are used to demonstrate if a given treatment can produce positive results under ideal conditions. Outcome studies done with more clinically representative clients and therapists, in real world agency settings, are known as effectiveness studies. Ideally the latter are conducted after the former, under conditions of increasing complexity, so as to determine treatments that work well in real-world contexts. Among the pre-experimental designs are the one group posttreatment-only study and the one group pretest-posttest design. Various ways in which these designs can be strengthened are presented, along with descriptions of published articles illustrating their use in social work and other human service settings. The limitations of these designs are also discussed, as is a review of the major threats to internal validity that can inhibit causal inferences.

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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 . 

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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 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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8.2 Quasi-experimental and pre-experimental designs

Learning objectives.

• Identify and describe the various types of quasi-experimental designs
• Distinguish true experimental designs from quasi-experimental and pre-experimental designs
• Identify and describe the various types of quasi-experimental and pre-experimental designs

As we discussed in the previous section, time, funding, and ethics may limit a researcher’s ability to conduct a true experiment. For researchers in the medical sciences and social work, conducting a true experiment could require denying needed treatment to clients, which is a clear ethical violation. Even those whose research may not involve the administration of needed medications or treatments may be limited in their ability to conduct a classic experiment. When true experiments are not possible, researchers often use quasi-experimental designs.

Quasi-experimental designs

Quasi-experimental designs are similar to true experiments, but they lack random assignment to experimental and control groups. Quasi-experimental designs have a comparison group that is similar to a control group except assignment to the comparison group is not determined by random assignment. The most basic of these quasi-experimental designs is the nonequivalent comparison groups design (Rubin & Babbie, 2017).  The nonequivalent comparison group design looks a lot like the classic experimental design, except it does not use random assignment. In many cases, these groups may already exist. For example, a researcher might conduct research at two different agency sites, one of which receives the intervention and the other does not. No one was assigned to treatment or comparison groups. Those groupings existed prior to the study. While this method is more convenient for real-world research, it is less likely that that the groups are comparable than if they had been determined by random assignment. Perhaps the treatment group has a characteristic that is unique–for example, higher income or different diagnoses–that make the treatment more effective.

Quasi-experiments are particularly useful in social welfare policy research. Social welfare policy researchers often look for what are termed natural experiments , or situations in which comparable groups are created by differences that already occur in the real world. Natural experiments are a feature of the social world that allows researchers to use the logic of experimental design to investigate the connection between variables. For example, Stratmann and Wille (2016) were interested in the effects of a state healthcare policy called Certificate of Need on the quality of hospitals. They clearly could not randomly assign states to adopt one set of policies or another. Instead, researchers used hospital referral regions, or the areas from which hospitals draw their patients, that spanned across state lines. Because the hospitals were in the same referral region, researchers could be pretty sure that the client characteristics were pretty similar. In this way, they could classify patients in experimental and comparison groups without dictating state policy or telling people where to live.

Matching is another approach in quasi-experimental design for assigning people to experimental and comparison groups. It begins with researchers thinking about what variables are important in their study, particularly demographic variables or attributes that might impact their dependent variable. Individual matching involves pairing participants with similar attributes. Then, the matched pair is split—with one participant going to the experimental group and the other to the comparison group. An ex post facto control group , in contrast, is when a researcher matches individuals after the intervention is administered to some participants. Finally, researchers may engage in aggregate matching , in which the comparison group is determined to be similar on important variables.

Time series design

There are many different quasi-experimental designs in addition to the nonequivalent comparison group design described earlier. Describing all of them is beyond the scope of this textbook, but one more design is worth mentioning. The time series design uses multiple observations before and after an intervention. In some cases, experimental and comparison groups are used. In other cases where that is not feasible, a single experimental group is used. By using multiple observations before and after the intervention, the researcher can better understand the true value of the dependent variable in each participant before the intervention starts. Additionally, multiple observations afterwards allow the researcher to see whether the intervention had lasting effects on participants. Time series designs are similar to single-subjects designs, which we will discuss in Chapter 15.

Pre-experimental design

When true experiments and quasi-experiments are not possible, researchers may turn to a pre-experimental design (Campbell & Stanley, 1963).  Pre-experimental designs are called such because they often happen as a pre-cursor to conducting a true experiment.  Researchers want to see if their interventions will have some effect on a small group of people before they seek funding and dedicate time to conduct a true experiment. Pre-experimental designs, thus, are usually conducted as a first step towards establishing the evidence for or against an intervention. However, this type of design comes with some unique disadvantages, which we’ll describe below.

A commonly used type of pre-experiment is the one-group pretest post-test design . In this design, pre- and posttests are both administered, but there is no comparison group to which to compare the experimental group. Researchers may be able to make the claim that participants receiving the treatment experienced a change in the dependent variable, but they cannot begin to claim that the change was the result of the treatment without a comparison group.   Imagine if the students in your research class completed a questionnaire about their level of stress at the beginning of the semester.  Then your professor taught you mindfulness techniques throughout the semester.  At the end of the semester, she administers the stress survey again.  What if levels of stress went up?  Could she conclude that the mindfulness techniques caused stress?  Not without a comparison group!  If there was a comparison group, she would be able to recognize that all students experienced higher stress at the end of the semester than the beginning of the semester, not just the students in her research class.

In cases where the administration of a pretest is cost prohibitive or otherwise not possible, a one- shot case study design might be used. In this instance, no pretest is administered, nor is a comparison group present. If we wished to measure the impact of a natural disaster, such as Hurricane Katrina for example, we might conduct a pre-experiment by identifying  a community that was hit by the hurricane and then measuring the levels of stress in the community.  Researchers using this design must be extremely cautious about making claims regarding the effect of the treatment or stimulus. They have no idea what the levels of stress in the community were before the hurricane hit nor can they compare the stress levels to a community that was not affected by the hurricane.  Nonetheless, this design can be useful for exploratory studies aimed at testing a measures or the feasibility of further study.

In our example of the study of the impact of Hurricane Katrina, a researcher might choose to examine the effects of the hurricane by identifying a group from a community that experienced the hurricane and a comparison group from a similar community that had not been hit by the hurricane. This study design, called a static group comparison , has the advantage of including a comparison group that did not experience the stimulus (in this case, the hurricane). Unfortunately, the design only uses for post-tests, so it is not possible to know if the groups were comparable before the stimulus or intervention.  As you might have guessed from our example, static group comparisons are useful in cases where a researcher cannot control or predict whether, when, or how the stimulus is administered, as in the case of natural disasters.

As implied by the preceding examples where we considered studying the impact of Hurricane Katrina, experiments, quasi-experiments, and pre-experiments do not necessarily need to take place in the controlled setting of a lab. In fact, many applied researchers rely on experiments to assess the impact and effectiveness of various programs and policies. You might recall our discussion of arresting perpetrators of domestic violence in Chapter 2, which is an excellent example of an applied experiment. Researchers did not subject participants to conditions in a lab setting; instead, they applied their stimulus (in this case, arrest) to some subjects in the field and they also had a control group in the field that did not receive the stimulus (and therefore were not arrested).

Key Takeaways

• Quasi-experimental designs do not use random assignment.
• Comparison groups are used in quasi-experiments.
• Matching is a way of improving the comparability of experimental and comparison groups.
• Quasi-experimental designs and pre-experimental designs are often used when experimental designs are impractical.
• Quasi-experimental and pre-experimental designs may be easier to carry out, but they lack the rigor of true experiments.
• Aggregate matching – when the comparison group is determined to be similar to the experimental group along important variables
• Comparison group – a group in quasi-experimental design that does not receive the experimental treatment; it is similar to a control group except assignment to the comparison group is not determined by random assignment
• Ex post facto control group – a control group created when a researcher matches individuals after the intervention is administered
• Individual matching – pairing participants with similar attributes for the purpose of assignment to groups
• Natural experiments – situations in which comparable groups are created by differences that already occur in the real world
• Nonequivalent comparison group design – a quasi-experimental design similar to a classic experimental design but without random assignment
• One-group pretest post-test design – a pre-experimental design that applies an intervention to one group but also includes a pretest
• One-shot case study – a pre-experimental design that applies an intervention to only one group without a pretest
• Pre-experimental designs – a variation of experimental design that lacks the rigor of experiments and is often used before a true experiment is conducted
• Quasi-experimental design – designs lack random assignment to experimental and control groups
• Static group design – uses an experimental group and a comparison group, without random assignment and pretesting
• Time series design – a quasi-experimental design that uses multiple observations before and after an intervention

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cat and kitten   matching avocado costumes on the couch looking at the camera by Your Best Digs CC-BY-2.0

Foundations of Social Work Research Copyright © 2020 by Rebecca L. Mauldin is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

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7.4: Pre-Experimental Designs

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Learning Objectives

• Discuss when is the appropriate time to use a pre-experimental Design.
• Identify and describe the various types of pre-experimental designs.

What is it and When to Use it?

Time, other resources such as funding, and even one’s topic may limit a researcher’s ability to use a solid experimental design such a a between subject (which includes the classical experiment) or a within subject design. For researchers in the medical and health sciences, conducting one of these more solid designs could require denying needed treatment to patients, which is a clear ethical violation. Even those whose research may not involve the administration of needed medications or treatments may be limited in their ability to conduct a classic experiment. In social scientific experiments, for example, it might not be equitable or ethical to provide a large financial or other reward only to members of the experimental group. When random assignment of participants into experimental and control groups (using either randomization or matching) is not feasible, researchers may turn to a pre-experimental design (Campbell & Stanley, 1963).Campbell, D., & Stanley, J. (1963). Experimental and quasi-experimental designs for research . Chicago, IL: Rand McNally. However, this type of design comes with some unique disadvantages, which we’ll describe as we review the pre-experimental designs available.

If we wished to measure the impact of some natural disaster, for example, Hurricane Katrina, we might conduct a pre-experiment by identifying an experimental group from a community that experienced the hurricane and a control group from a similar community that had not been hit by the hurricane. This study design, called a static group comparison , has the advantage of including a comparison control group that did not experience the stimulus (in this case, the hurricane) but the disadvantage of containing experimental and control groups that were determined by a factor or factors other than random assignment. As you might have guessed from our example, static group comparisons are useful in cases where a researcher cannot control or predict whether, when, or how the stimulus is administered, as in the case of natural disasters.

In cases where the administration of the stimulus is quite costly or otherwise not possible, a one-shot case study design might be used. In this instance, no pretest is administered, nor is a control group present. In our example of the study of the impact of Hurricane Katrina, a researcher using this design would test the impact of Katrina only among a community that was hit by the hurricane and not seek out a comparison group from a community that did not experience the hurricane. Researchers using this design must be extremely cautious about making claims regarding the effect of the stimulus, though the design could be useful for exploratory studies aimed at testing one’s measures or the feasibility of further study.

Finally, if a researcher is unlikely to be able to identify a sample large enough to split into multiple groups, or if he or she simply doesn’t have access to a control group, the researcher might use a one-group pre-/posttest design. In this instance, pre- and posttests are both taken but, as stated, there is no control group to which to compare the experimental group. We might be able to study of the impact of Hurricane Katrina using this design if we’d been collecting data on the impacted communities prior to the hurricane. We could then collect similar data after the hurricane. Applying this design involves a bit of serendipity and chance. Without having collected data from impacted communities prior to the hurricane, we would be unable to employ a one-group pre-/posttest design to study Hurricane Katrina’s impact.

Table 7.2 summarizes each of the preceding examples of pre-experimental designs.

As implied by the preceding examples where we considered studying the impact of Hurricane Katrina, experiments do not necessarily need to take place in the controlled setting of a lab. In fact, many applied researchers rely on experiments to assess the impact and effectiveness of various programs and policies.

KEY TAKEAWAYS

• Pre-experimental designs are not ideal, but have to be done under certain circumstances.
• There are three major types of this design.

Pre-Experimental Designs

DOI link for Pre-Experimental Designs

Click here to navigate to parent product.

This chapter discusses the three designs are called pre-experimental designs because they use the elements of an experiment but lack the necessary ingredients to be a quasi-experiment or true experimental design, such as pretest and posttest, and control group. Changes from pretest to posttest in Design 4 may be attributable to history, maturation, instrumentation, testing, and statistical regression. Design 5 is called the one-shot case study. In it, one group is given a treatment (X) followed by a test (O). Design 6, the static-group comparison design, has two groups, but participants are not assigned to the groups at random. The dashed line between the two groups indicates they are intact groups. The obvious problem with Design 6 is the threat to internal validity called selection. Because of their weaknesses, these three pre-experimental designs are of very limited value in exploring cause-and-effect relationships.

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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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• Knowledge Base

Methodology

• 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.

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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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Experimental Research Design — 6 mistakes you should never make!

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

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

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

Table of Contents

What Is Experimental Research Design?

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

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

When Can a Researcher Conduct Experimental Research?

A researcher can conduct experimental research in the following situations —

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

Importance of Experimental Research Design

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

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

Types of Experimental Research Designs

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

1. Pre-experimental Research Design

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

Pre-experimental research is of three types —

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

2. True Experimental Research Design

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

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

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

3. Quasi-experimental Research Design

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

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

Advantages of Experimental Research

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

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

6 Mistakes to Avoid While Designing Your Research

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

1. Invalid Theoretical Framework

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

2. Inadequate Literature Study

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

3. Insufficient or Incorrect Statistical Analysis

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

4. Undefined Research Problem

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

5. Research Limitations

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

6. Ethical Implications

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

Experimental Research Design Example

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

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

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

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

Frequently Asked Questions

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

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

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

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

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

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

A Quick Guide to Experimental Design | 5 Steps & Examples

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

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

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

There are five key steps in designing an experiment:

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

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

Table of contents

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

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

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

Start by simply listing the independent and dependent variables .

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

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

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

Prevent plagiarism, run a free check.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Randomisation

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

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

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

Between-subjects vs within-subjects

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

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

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

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

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

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

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

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

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

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

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

To design a successful experiment, first identify:

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

When designing the experiment, first decide:

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

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

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

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

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

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

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

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

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The 3 Types Of Experimental Design

Dave Cornell (PhD)

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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

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.

• 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
• Dave Cornell (PhD) https://helpfulprofessor.com/author/dave-cornell-phd/ 15 Cooperative Play Examples

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.

• Chris Drew (PhD) https://helpfulprofessor.com/author/chris-drew-phd-2/ 25 Defense Mechanisms Examples
• Chris Drew (PhD) https://helpfulprofessor.com/author/chris-drew-phd-2/ 15 Theory of Planned Behavior Examples
• Chris Drew (PhD) https://helpfulprofessor.com/author/chris-drew-phd-2/ 18 Adaptive Behavior Examples
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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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• Study protocol
• Open access
• Published: 18 June 2024

The effect of an online acceptance and commitment intervention on the meaning-making process in cancer patients following hematopoietic cell transplantation: study protocol for a randomized controlled trial enhanced with single-case experimental design

• Aleksandra Kroemeke   ORCID: orcid.org/0000-0001-8707-742X 1 ,
• Joanna Dudek 2 ,
• Marta Kijowska 1 ,
• Ray Owen 3 &
• Małgorzata Sobczyk-Kruszelnicka 4  

Trials volume  25 , Article number:  392 ( 2024 ) Cite this article

Metrics details

Hematopoietic cell transplantation (HCT) is a highly invasive and life-threatening treatment for hematological neoplasms and some types of cancer that can challenge the patient’s meaning structures. Restoring meaning (i.e., building more flexible and significant explanations of the disease and treatment burden) can be aided by strengthening psychological flexibility by means of an Acceptance and Commitment Therapy (ACT) intervention. Thus, this trial aims to examine the effect of the ACT intervention on the meaning-making process and the underlying mechanisms of change in patients following HCT compared to a minimally enhanced usual care (mEUC) control group. The trial will be enhanced with a single-case experimental design (SCED), where ACT interventions will be compared between individuals with various pre-intervention intervals.

In total, 192 patients who qualify for the first autologous or allogeneic HCT will be recruited for a two-armed parallel randomized controlled trial comparing an online self-help 14-day ACT training to education sessions (recommendations following HCT). In both conditions, participants will receive once a day a short survey and intervention proposal (about 5–10 min a day) in the outpatient period. Double-blinded assessment will be conducted at baseline, during the intervention, immediately, 1 month, and 3 months after the intervention. In addition, 6–9 participants will be invited to SCED and randomly assigned to pre-intervention measurement length (1–3 weeks) before completing ACT intervention, followed by 7-day observations at the 2nd and 3rd post-intervention measure. The primary outcome is meaning-related distress. Secondary outcomes include psychological flexibility, meaning-making coping, meanings made, and well-being as well as global and situational meaning.

This trial represents the first study that integrates the ACT and meaning-making frameworks to reduce meaning-related distress, stimulate the meaning-making process, and enhance the well-being of HCT recipients. Testing of an intervention to address existential concerns unique to patients undergoing HCT will be reinforced by a statistically rigorous idiographic approach to see what works for whom and when. Since access to interventions in the HCT population is limited, the web-based ACT self-help program could potentially fill this gap.

Trial registration

ClinicalTrials.gov ID: NCT06266182. Registered on February 20, 2024.

Peer Review reports

Administrative information

Note: the numbers in curly brackets in this protocol refer to SPIRIT checklist item numbers. The order of the items has been modified to group similar items (see http://www.equator-network.org/reporting-guidelines/spirit-2013-statement-defining-standard-protocol-items-for-clinical-trials/ ).

Introduction

Background and rationale {6a}.

Hematologic neoplasms (e.g., lymphomas or acute leukemias) due to unique and sometimes increased challenges are highly stressful conditions. Treatment-related challenges can impede the realization of life goals and violate general beliefs and a sense of meaning as defined by the integrative meaning-making model [ 1 ]. A significant point on the trajectory of coping, challenging the patient meaning structures, may be hematopoietic cell transplantation (HCT). HCT is a highly invasive and life-threatening treatment for hematological neoplasms and some types of cancer (e.g., testicular cancer). In the acute phase, HCT involves the destruction of the patient hematopoietic system through radio and/or chemotherapy and then its restoration via autologous or allogeneic cell transplantation [ 2 , 3 ]. During in- and outpatient conditions, patients usually experience burdensome adverse effects and have to follow strong medical regimens [ 2 , 4 ]. Evidence suggests that HCT affects a patient physical (e.g., fatigue), psychological (e.g., anxiety and depression symptoms), social (e.g., financial concerns, employment disruptions), and spiritual (e.g., existential concerns) well-being [ 5 ]. HCT recipients may confront fear of death, loss of control, feelings of uncertainty and social isolation, increased dependence, or disabling physical symptoms in the short and long term after transplantation [ 6 , 7 ]. Some models of adaptation and adjustment argue that restoring meaning is central to adapting to these conditions [ 8 ].

Meaning-making process following HCT

The most commonly mentioned factors of meaning reconstruction are meaning-making coping and meanings made [ 1 , 9 ]. Meaning-making is related to the process of searching for meaning and explanation for adversity (i.e., seeking understanding of disease), whereas meanings made is the product of the meaning-making process (i.e., giving meaning to the disease, acceptance, finding benefits, or change of identity due to disease). According to the integrative meaning-making model [ 1 , 10 ], distress related to the discrepancy between global meaning (i.e., basic goals and beliefs, and fundamental assumptions about life) and situational meaning (i.e., the personal significance of a particular situation) initiates meaning-making coping, which impacts the meanings made and then well-being. However, a prolonged unsuccessful search for meaning can be maladaptive. Indeed, the adaptability of meaning-making coping depends on whether the meaning has been found or restored [ 10 ].

A review of the narratives shows that HCT recipients who were able to find meaning in their experience were better able to cope with physical symptoms and were less likely to report unfavorable psychological outcomes after transplant than those who had difficulty finding meaning [ 6 ]. Meanings made was also an essential link connecting meaning-making and well-being in HCT recipients in a daily diary study lasting 28 days after hospital discharge [ 11 ]. The direct effect of average meaning-making coping was unfavorable but positive when mediated by meanings made. In another study among HCT recipients in the late outpatient period with a 4-month follow-up interval, only changes in meaning-making coping were associated with changes in well-being, and these correlates were positive and negative [ 12 ]. The role of meanings made in these relationships was, however, not tested. Indeed, few studies tested the assumptions of the integrative meaning-making model in the context of HCT. More often, the focus is on the global meaning which turns out to be a dynamic construct. In a longitudinal study, sense of meaning decreased 1 month post-HCT and returned to pre-transplant levels by 6 months post-HCT. Moreover, a greater pre-HCT sense of meaning predicted more favorable psychological and physical outcomes during the 12 months following HCT [ 13 ].

Hence, an intervention targeting the ability to successfully search for meaning and find it holds promise in terms of facilitating recovery following HCT and adjustment. To date, no trials tested such interventions among patients undergoing HCT. To the best of our knowledge, two studies are currently underway in HCT recipients that include modules directed at searching for meaning i.e., identifying benefits and meaning. The first one examines the effect of one-on-one, in-person intervention promoting resilience in stress management [ 14 ], whereas the second is a phone-delivered positive psychology intervention [ 15 ]. Both, however, will not evaluate the outcomes from the perspective of the meaning-making model. A systematic review shows that various psychosocial interventions can promote meaning and purpose in the cancer population [ 16 ]. Nevertheless, these targeting meaning enhancements demonstrate a higher effect size. One of the promising approaches potentially fostering meaning-making in disease is Acceptance and Commitment Therapy [ 17 ].

Acceptance and Commitment Therapy (ACT) intervention

Acceptance and Commitment Therapy (ACT) is a transdiagnostic therapeutic approach rooted in the contextual behavioral science that aims to improve the psychological functioning and well-being of individual by increasing psychological flexibility (i.e., the ability to engage in values-based actions even in the presence of unpleasant or difficult experiences) [ 17 ]. To achieve this goal ACT targets six core processes: ( 1 ) contact with the present moment—paying attention to different aspects of the internal and external environment; ( 2 ) self-as-context—the ability to look at one’s internal experiences from a broader perspective; ( 3 ) acceptance—making room for thoughts, feelings, and sensations, even those that are unpleasant; ( 4 ) defusion—noticing thoughts instead of being controlled by them; ( 5 ) values—knowing what really matters; and ( 6 ) committed action—taking values-congruent actions even in the presence of difficulties. During the therapy, the individual learns to assess the workability of strategies used to cope with difficult, unwanted private experiences and to use mindfulness and acceptance skills when necessary. Those skills allow the individual to recognize moments when they have an opportunity to engage in behaviors consistent with their values and fully immerse themselves in those activities, even in the presence of painful thoughts, feelings, or sensations. Individuals are not asked to accept painful private experiences (e.g., physical pain) if there is an effective way to get rid of the pain; acceptance means embracing painful private experiences only when there is no effective way of escaping painful experiences on a long-term basis or when the means of escape comes at too high a cost in terms of valued living. Techniques used in ACT to obtain the aforementioned changes include using metaphors, experiential exercises, and functional analysis [ 17 ].

Besides the typical use of ACT as an individual face-to-face therapy, ACT was also tested in a group format (e.g., for anxiety and depression [ 18 ] or chronic pain [ 19 ]), as a self-help form [ 20 ] as well as technology-supported intervention (using online materials, web or phone applications, telephone) with or without therapeutic guidance [ 21 ].

ACT has been proven to be an effective intervention for various conditions [ 22 ], with the growing number of randomized controlled trials [ 23 ] and mediational studies showing that psychological flexibility is a mediator of the intervention [ 24 ]. Several systematic reviews and metanalyses provide evidence for ACT effectiveness in improving the quality of life and decreasing psychological distress among cancer patients [ 25 , 26 , 27 , 28 , 29 ]. Other systematic reviews support ACT efficacy in improving quality of life and symptoms for long-term chronic conditions [ 30 , 31 ], also including the technology-supported delivery of ACT [ 21 ]. Finally, ACT is considered to be an effective treatment for chronic pain, being recognized by the American Psychological Association as an evidence-based treatment with “strong research support” [ 32 ].

The links between ACT and the meaning-making process

ACT and meaning-making frameworks share common philosophical roots, including constructivism and existentialism [ 9 ]. The ACT model promotes acceptance of what is difficult to change or is not subject to change (such as chronic disease or burden of toxic treatment), taking responsibility for one’s own experiences and actions and creating a meaningful life by engaging in activities that match one’s values [ 33 ]. While meaning-making is not an explicit goal of ACT, creating psychological flexibility should foster meaning-making in disease or following HCT by building more flexible and workable meaning-making explanations of disease [ 34 ]. ACT emphasizes increased awareness of what matters most to the individual and a stepping back from automatic patterns of thought and behavior. Both of these abilities should facilitate meaning-making, i.e., changing global meaning or a reappraisal of situational meaning to achieve congruence, thus alleviating the distress of the event such as HCT. Achieving congruence should end meaning-making coping and be associated with meanings made and improved well-being.

Objectives {7}

This trial aims to examine the effect of an online self-help ACT intervention on the meaning-making process and the underlying mechanisms of change in patients following HCT compared to a minimally enhanced usual care (mEUC) control group. The trial will be enhanced with a single-case experimental design (SCED), where ACT interventions will be compared between individuals with various pre-intervention intervals. As the change process is characterized by complexity, traditional examination of intervention efficacy will be enriched with a temporal perspective (i.e., examination of trajectories of change in primary and secondary outcomes over time) and a systems perspective (i.e., network analysis depicting the pattern of connections between components of the system). The latter assumes that an intervention transforms the connectivity of the networks of intervention goals, the outcome of the intervention, and the connections between the two networks [ 35 , 36 ].

It is hypothesized that the ACT intervention group would show increased psychological flexibility and decreased meaning-related distress compared with the control group (hypothesis 1). Additionally, an increase in meanings made and well-being is anticipated (hypothesis 2). In more exploratory terms, the moderating effect of individual resources (i.e., global and situational meaning, baseline well-being) and demographic and clinical factors on the effect of the intervention will also be examined. Moreover, it is hypothesized that psychological flexibility and meaning-making coping would mediate the ACT intervention effects on meaning-related distress, meanings made, and well-being in HCT recipients (hypothesis 3). Finally, following the network theory, it is hypothesized that the ACT intervention group will display more robust positive connections within the psychological flexibility and meaning-making coping network (hypothesis 4), weaker connections within the distress network (hypothesis 5), more negative connections of distress with psychological flexibility and meaning-making coping (hypothesis 6), and more positive connections between psychological flexibility, meaning-making coping, meanings made, and well-being as compared to control conditions (hypothesis 7).

Trial design {8}

A two-armed parallel randomized controlled trial (RCT) will be conducted to determine the effects of an online Acceptance and Commitment Therapy ACT intervention on the meaning-making process in patients following HCT. Participants will be randomly assigned in a double-blinded manner to ACT intervention and education conditions at a ratio of 1:1. RCT will be enhanced with a randomized multiple-baseline single-case experimental design (SCED). SCED will proceed according to the AB + post-intervention design, where A is the pre-intervention phase and B is the intervention phase, followed by the post-intervention phase. Participants will be randomly assigned to one of three pre-intervention measurement lengths (7 days, 14 days, 21 days) followed by 7-day observations at the 2nd and 3rd post-intervention measure.

Methods: participants, interventions and outcomes

Study setting {9}.

Recruitment will take place in the Department of Bone Marrow Transplantation and Oncohematology of the Maria Sklodowska-Curie National Research Institute of Oncology (MSCNRIO) Gliwice Branch. MSCNRIO branch in Gliwice is the leading facility in Poland that performs HCT. Approximately 150 primary transplants are performed there annually (approx. 200 HCT in total).

Eligibility criteria {10}

The participation criteria will include ( a ) qualification for the first autologous or allogeneic HCT due to hematologic malignancies or solid tumors, ( b ) age ≥ 18 years, ( c ) signed written informed consent, ( d ) ability to read and write in Polish, and ( e ) daily access to the Internet by computer and/or mobile device. The exclusion criteria will be as follows: ( a ) major psychiatric or cognitive disorder that would impede providing informed consent and study participation, ( b ) inability to cooperate and give informed consent, ( c ) hearing, seeing, or movement impairment that precludes participation, ( d ) current participation in any form of psychotherapy, ( e ) no access to the Internet and computer and/or mobile device, and ( f ) inability to use a computer and/or mobile device and the Internet.

Who will take informed consent? {26a}

Written informed consent to participate in the study will be obtained by the recruiter (member of the research team), in direct contact with the participant and after an extensive briefing.

Additional consent provisions for collection and use of participant data and biological specimens {26b}

N/A. Biological specimens will not be collected.

Interventions

Explanation for the choice of comparators {6b}.

In RCT, the ACT intervention will be compared with minimally enhanced usual care (mEUC). Standard psychological care following HCT does not include a standard psychological care protocol. Psychological care for HCT recipients is provided if needed according to the physician’s recommendation in the event of the patient’s functioning deteriorating. Thus, to maintain the same conditions in both trials, participants in the control condition will receive cognitively neutral tasks (education) from which no effects are expected for the meaning-making process. In SCED, comparisons between participants with different pre-intervention measurement lengths will be conducted.

Intervention description {11a}

ACT intervention “The Path to Health” will start on the second day after hospital discharge for individuals in RCT or after 7–21-day pre-intervention measurement in individuals in SCED. It will take 14 days (+ day 0 with organizational information). Each day, participants will receive a web-based intervention consisting of the theoretical introduction (including examples of patients’ experiences and metaphors) and practical ACT activity (e.g., reflective questions, experiential exercise, values card sorting test). Most of the activities are followed by a debrief that includes the patient’s reactions to this particular exercise and practical tips. On some days, participants will also receive additional exercise (optional).

Using the metaphor of life as a journey, participants will learn to recognize where they are headed (values), when there is a moment of choice between actions that lead towards values or away from them, and how to use attention flexibly to free themselves from the power of thoughts, to open up and accept emotions so that they can effectively take action in line with their values (Table  1 ). Each introduction and each activity will be available in written form and audio. The ACT intervention is built from standard ACT activities [ 37 , 38 , 39 , 40 ] and tailored to the context of the disease and treatment. Participants will be advised to do one activity a day, but they will be able to come back to the chosen activities or practice them a couple of times if necessary.

During the same period, participants allocated to the education in RCT will receive an online guide to post-HCT recommendations. Each day, participants will receive information about post-transplant prescriptions along with exercises. Participants will receive guidelines in several areas: diet, physical activity, hygiene, rest, social interactions, and sexual health. During the first 3 days, nutrition will be discussed, including the principles of healthy diet after HCT. On the fourth day, participants will learn the rules of personal hygiene. The fifth day is devoted to presenting the rules aimed at preventing infection. On the sixth day, the issue of body fatigue will be discussed. For the next 3 days, the main topic will be the resumption of activity, mostly physical activity. The tenth day is devoted to safe social contacts. On the eleventh day, participants will work on their sleep. On the twelfth day, sexual health will be discussed. Day 13 is devoted to discussing the issue of rest. And the last day will be a summary of all the guidelines. The exercises serve as an extension of the topic (e.g., watching a video presenting the principles of nutrition) or the emphasis is on practice to support the implementation (e.g., preparing a sequence of exercises and performing them several times a day). The content is prepared based on available guides for HCT recipients. It was also verified by a hemato-oncologist.

Criteria for discontinuing or modifying allocated interventions {11b}

Modification of assigned interventions is not provided for. Disease recurrence will be the criteria for discontinuation of the intervention. The participant can also discontinue the intervention at any time without any negative consequences.

Strategies to improve adherence to interventions {11c}

To improve adherence to the intervention, participants will receive daily reminders about the intervention. Also, direct technical support will be available 24/7. If participants drop out or stop using the intervention, they will be asked for the reason(s) why they decided to quit the intervention and/or study.

Relevant concomitant care permitted or prohibited during the trial {11d}

Individuals participating in any form of psychotherapy will not be eligible for the study. Participation in forms of psychological support will be monitored on an ongoing basis.

Provisions for post-trial care {30}

Upon completion of the study, all participants will have access to the self-help ACT intervention booklet with written and recorded exercises.

Outcomes {12}

The primary and secondary outcomes will be assessed at baseline (before HCT), during the intervention, immediately, 1 month, and 3 months after the intervention (Table  2 ). In SCED, 1 month and 3 months post-intervention assessments will be preceded by 7-day daily diaries. A summary of the outcome measures that will be used in this study is available in Table  3 .

Primary outcomes

The primary outcome will be the changes compared to the baseline in meaning-related distress as assessed by the Global Meaning Violation Scale (GMVS) [ 41 ].

Secondary outcomes

The secondary outcomes will be changes from baseline in global meaning, situational meaning, meanings made, and well-being. Global meaning will be measured by cognitive and emotional representations of illness and global presence of meaning using the Brief-Illness Perception Questionnaire (B-IBP) [ 42 ] and Meaning in Life Questionnaire (MLQ) [ 43 ], respectively. Coping self-efficacy, an indicator of situational meaning, will be assessed with the Perceived Coping Self-Efficacy (CSE) Scale [ 44 ]. Meanings made will be assessed using the “current standing” Post-Traumatic Growth Inventory-Short Form (C-PTGI-SF) [ 45 , 46 ] and 3-item scale based on the Meaning of Loss Codebook (MLC) [ 47 ]. Depressive and anxiety symptoms will be assessed with the Patient Health Questionnaire (PHQ-4) [ 48 ], while loneliness, as recommended by the British Office for National Statistics [ 49 ], will be evaluated with the enhanced R-UCLA 3-item Loneliness Scale [ 50 ] and direct question from the Community Life Survey [ 51 ].

Mediators and moderators

To assess putative mechanisms of change and change moderators, meaning-making coping and psychological flexibility will be measured longitudinally. In this scheme, deliberate and automatic meaning-making coping will be assessed with the Core Beliefs Inventory (CBI) [ 52 ] and the intrusive ruminations subscale from the Event-Related Rumination Inventory (ERRI) [ 53 ], respectively. Psychological flexibility will be measured using the Comprehensive Assessment of Acceptance and Commitment Therapy Processes (CompACT-9) [ 54 ]. In addition, fluctuations in meaning-making coping, meanings made, psychological flexibility, and well-being (i.e., subjective health and positive and negative affect) will be measured in an intensive longitudinal manner (i.e., daily) throughout the intervention in RCT and pre- to post-intervention in SCED. Daily meaning-making coping (deliberate and automatic) will be measured with an abbreviated and tailored to the daily measurement and context of the study 4-item version of the ERRI questionnaire. Daily meanings made will be evaluated using a contextualized 3-item scale based on the Meaning of Loss Codebook (MLC). Daily psychological flexibility will be measured using a shortened to 4-item version of the CompACT questionnaire. Daily subjective health will be assessed by a single-item statement “Generally, I can say my health today was…” on a 5-point scale ranging from 1 (bad) to 5 (excellent). Daily positive and negative affect will be assessed with two positive (happy, cheerful) and two negative adjectives (sad, gloomy) based on the Circumplex Model of Emotion [ 55 ].

Feasibility will be examined via attrition and adherence rates as well as questions about intervention engagement. Acceptability will be measured by intervention satisfaction and evaluation (attractiveness and easiness). Adherence to the intervention will be estimated based on the dropout rate (i.e., the percentage of participants who do not log in to the intervention on a given day) and self-reported questions about engagement in the intervention: ( 1 ) the number of days on which the proposed exercises were done seriously, ( 2 ) the number of minutes spent on average in training, and ( 3 ) the use of various training components. Satisfaction with the intervention will be measured using 4 questions (no. 3, 4, 7, and 8) from the Client Satisfaction Questionnaire (CSQ-8) [ 56 ] modified to the intervention context and online form. Evaluation of the intervention will be assessed using questions of the author’s own measuring the ease and attractiveness of the training.

The cost-effectiveness of the intervention will be examined by estimating health-related quality of life as measured by the Quality of Life Questionnaire of the European Organization for Research and Treatment of Cancer (EORTC QLQ-C30) [ 57 ].

Other measures

At the baseline, demographic data (e.g., age, sex, education, marital status, employment) will also be collected and partially measured using the Diversity Minimal Item Set (DiMIS) [ 58 ]. Clinical data (e.g., diagnosis, time since diagnosis, conditioning, concomitant diseases) will be obtained from the medical records.

Participant timeline {13}

Figure  1 describes the project timeline.

Timeline for RCT and SCED study

Sample size {14}

In RCT, the sample size was calculated based on an analysis of variance with two groups (ACT versus mEUC) and four repeated measures of variance (ANOVA) with within-between interaction (group x time) using the G*Power calculator [ 59 ] and simulation study of the time course with dichotomous between-person level predictor [ 60 ]. Given the large effects of ACT on psychological well-being, including hope (Hedge’s g  = 0.88–2.17) and medium effects on psychological flexibility among cancer patients (Hedge’s g  = 0.58) [ 29 ], the stronger effects in the population of women with breast cancer compared to patients with other types of cancer (large versus medium effect sizes) [ 31 ], and medium effect sizes of technology-supported ACT interventions (Hedges’ g  = 0.44–0.48) [ 21 ], moderate differences between conditions were expected. Assuming a medium effect size of f  = 0.25, a power of 0.80, and an alpha level of 0.05 in repeated measures of ANOVA, a total sample size of N  = 178 is required. In turn, on the basis of a simulation study, a total sample size of N  = 136 is required for multilevel modeling. Therefore, the minimum sample size was assumed of N  = 160 (80 per condition). Allowing for the potential attrition rate of 20%, this leads to a sample size of N  = 192 participants, including 96 in each arm. In SCED, 6–9 participants will be investigated, a minimum of 2 per condition. According to the simulation study [ 61 ], sufficient power (0.80) can be reached in SCED with six to eight participants, depending on the assumed effect size (large versus medium, respectively).

Recruitment {15}

Recruitment will take place at a single center, after elective admission to the bone marrow transplantation and oncohematology unit due to HCT before the start of conditioning treatment. Recruitment will take place on average on the 2nd day after admission. Every 2 days, the transplant coordinator, PI, and physician (members of the research team) will review the lists of patients enrolled for HCT. Those who meet the inclusion criteria will be initially informed of the purpose of the study and invited for an extensive briefing by a recruiter (member of the research team). Patients will also be allowed to ask any remaining questions about the aim of the study and the study procedures. After receiving an extensive briefing, all patients who give written informed consent will proceed with baseline. Recruitment will be carried out until the desired sample size is achieved. The flowchart of the study is depicted in Fig.  2 .

Participant flowchart in RCT and SCED study. ACT, Acceptance and Commitment Therapy; mEUC, minimally enhanced usual care

Assignment of interventions: allocation

Sequence generation {16a}.

The allocation sequence will be generated using the method of minimization. Minimization can be classified as dynamic allocation or covariate adaptive methods because the allocation depends on the characteristics of the patients and is performed continuously [ 62 ]. Randomization will be stratified by type of transplant (autologous versus allogeneic) to ensure a balanced representation between the study conditions because autologous and allogeneic HCT recipients experience different recovery trajectories and HCT impact on well-being [ 63 , 64 ].

Concealment mechanism {16b}

The mechanism of implementing the allocation sequence will be central randomization. It means generating an allocation sequence after the patient is enrolled [ 65 ]. This way, randomization will not affect the recruitment process.

Implementation {16c}

The trial coordinator (member of the research team) will enroll participants, generate the allocation sequence, and assign participants to interventions. Other members of the team will be blind to the allocation of the participants to the conditions.

Assignment of interventions: blinding

Who will be blinded {17a}.

In RCT, trial participants, care providers, outcome assessors, and data analysts will be blinded after assignment to interventions. Blinding will be performed using two separate databases: one containing participant allocation information (blinded) and the other containing the remaining information (unblinded). Only the trial coordinator will have access to the blinded database.

Procedure for unblinding if needed {17b}

Disclosure of the participant allocation will take place after the completion of the study and analysis of the first results examining the efficacy of the online ACT intervention.

Data collection and management

Plans for assessment and collection of outcomes {18a}.

Data will be collected via self-reported online questionnaires at the baseline (before HCT), post-intervention, and 1 and 3-month follow-ups (Table  2 ). In addition, to assess momentary changes and mechanisms of change, participants will complete daily diaries throughout the intervention. SCED participants will complete 7-day daily diaries repeatedly, i.e., before 1 and 3-month follow-ups. The detailed characteristics of the study instruments are presented in Table  3 .

We intend to collect clinical data (e.g., diagnosis, time from diagnosis, type of transplant and conditioning treatment, comorbidities) from the patient’s medical records. The participants will give their additional consent for the data to be collected from their medical history by a physician (team member). If the participant does not approve of access to the data from medical records, they will be requested to provide information themselves.

Plans to promote participant retention and complete follow-up {18b}

To improve participant retention and complete follow-up, participants will receive email and phone reminders about the survey and subsequent measurements. If participants fail to complete study assessments, motivational reminders will be sent repeatedly by email. In daily diary measurements, participants who give written consent will receive SMS reminders. Since the daily diaries will not be filled retrospectively, a single reminder with the mailing of the survey will be used.

During the study, direct technical support will be available 24/7, and a research team member will contact the participant by phone to resolve any issues and answer questions. If participants drop out of the study, they will be asked for the reason(s). Any other attritions (e.g., disqualification from HCT, death) along with the reasons will be recorded.

Data management {19}

Questionnaire data collection will be done electronically (using the SurveyMonkey platform, which encrypts and secures data during transit and the data stored; the accounts are password-protected with available complexity controls). Medical data will be collected electronically directly from the medical records registry by the physician (member of the research team). Only informed consents will be paper documents, collected and entered by recruiter (member of the research team). The PI will be responsible for the secure delivery of the documents to the trial office. The PI and trial coordinator will oversee the quality of the data. Data and metadata storage will take place in the university’s central resources according to the 3–2-1 rule. The detailed data management plan is available at OSF .

Confidentiality {27}

Personal data such as phone numbers and email addresses of the participants will be encrypted (using individual trial identification number) and stored only during the data collection period. Written informed consent and the data identifying the participants will be stored separately under lock and key and will be kept strictly confidential. The data will be accessed by the PI of the project and selected team members who will be contacting the participants (trained in the General Data Protection Regulation). Access to the data will be monitored and possible only after obtaining the access rights that the PI of the project will grant. Once data collection is completed, the data will be anonymized and in this form will be analyzed statistically.

Plans for collection, laboratory evaluation and storage of biological specimens for genetic or molecular analysis in this trial/future use {33}

N/a. Biological specimens will not be collected.

Statistical methods

Statistical methods for primary and secondary outcomes {20a}.

Analyses will be conducted using the latest Mplus statistical package [ 66 ], R [ 67 ], and IBM SPSS (IBM Corp.; Armonk, NY). We will use the standard α  = 0.05 or 95% confidence interval for the determination of value probability. All data analysis will be performed according to the intention-to-treat principle, where all randomized participants are included in the analysis assuming missing data at random. The collected data will be first analyzed in terms of sample characteristics and comparisons (frequency, descriptive statistics; ANOVA, t -test or their nonparametric counterparts; χ 2 ; Pearson’s or Kendall’s correlation), missing data (frequency, multilevel modeling), and sample attrition (logistic regression analysis). Multilevel confirmatory factor analysis (MCFA) will be performed to establish the respective measurement models and calculate the indicator reliabilities (omega coefficient) at the within- and between-person levels [ 60 , 68 ]. To examine hypotheses 1–3, latent curve growth modeling (LCGM) [ 69 ] and multilevel (MSEM) and dynamic structural equation modeling (DSEM) will be applied [ 60 , 70 ]. All methods allow for the examination of the time course. In addition, MSEM and DSEM allow for the calculation of simple between- and within-person associations and more advanced associations such as mediations and moderations. Hypotheses 4–7 will be verified using a multilevel vector autoregressive (mlVAR) model [ 71 ]. mlVAR allows for the examination of a temporal network (i.e., lagged predictive relations between each node in the network and each node in the network at the next measurement occasion), a contemporaneous network (i.e., partial correlations within the same measurement occasion), and a between-person network (i.e., associations between nodes that are averaged across measurement occasion).

Interim analyses {21b}

Due to a known minimal risk, i.e., testing interventions with known positive effects, an interim analysis plan was not created. The principal investigator (PI) will make the final decision to terminate the study once the optimal number of study participants has been obtained.

Methods for additional analyses (e.g., subgroup analyses) {20b}

All analyses will be supplemented by sensitivity analyses. In all models, possible confounders (i.e., demographics, clinical factors, and other confounders) will be considered after preliminary selection.

Methods in analysis to handle protocol non-adherence and any statistical methods to handle missing data {20c}

The statistical methods used (i.e., MSEM, DSEM) will allow the most recent flexible approach to the missing data (the full information maximum likelihood) [ 72 , 73 ]. In less sophisticated analyses, missing data will be multiple imputed in advance.

Plans to give access to the full protocol, participant-level data and statistical code {31c}

The full protocol, dataset, statistical codes, and outputs will be made available at the Open Science Framework (OSF). Participant-level datasets will be publicly available, however without demographics and clinical data due to privacy or ethical restrictions (the possibility of identification of participants).

Oversight and monitoring

Composition of the coordinating center and trial steering committee {5d}.

The study’s coordinating center is SWPS University. The study’s steering committee will consist of a health psychologist, a certified cognitive behavioral therapist (CBT) and ACT therapist, and a doctoral student (master’s degree in psychology). The committee’s responsibilities will be to develop the intervention and then implement it and monitor implementation. The committee will meet 2–4 times a month.

Composition of the data monitoring committee, its role and reporting structure {21a}

Due to known minimal risks, a formal committee of data monitoring is not needed.

Adverse event reporting and harms {22}

In this study, an adverse event will be defined as any deterioration in mood that requires specialized treatment, collected after the individual has received the intervention, and reported to the local institutional review board (IRB).

Frequency and plans for auditing trial conduct {23}

No audit procedures are planned. An independent audit may be conducted by the local IRB and the sponsor.

Plans for communicating important protocol amendments to relevant parties (e.g., trial participants, ethical committees) {25}

Communication of significant protocol modifications and study outcomes will be done to the funder, the ethics committee, and the public through ClinicalTrials.gov.

Dissemination plans {31a}

The results will be published in peer-reviewed journals and presented at thematic international scientific conferences. Also, during the debriefing, participants will be informed of the web address of the project website, where a lay summary of the study updated with the results (when available) will be posted.

Effective treatment of patients undergoing HCT likely requires a focus also on those mechanisms that support the reconstruction of meaning damaged by medical treatment and the disease itself. An intervention based on ACT, an empirically validated theoretical model [ 17 ], appears to be a promising psychological therapy to support the reconstruction of meanings [ 33 , 34 ]. This trial represents the first study that aims to integrate the ACT and meaning-making frameworks to reduce meaning-related distress, stimulate the meaning-making process, and enhance the well-being of HCT recipients. It builds on previous successful ACT interventions that strengthened cancer patient well-being albeit outside the context of meaning reconstruction [ 25 , 26 , 27 , 28 , 29 ]. Moreover, testing a specific theory-based intervention to address existential concerns unique to patients undergoing HCT will be reinforced by a statistically rigorous idiographic approach. SCED will allow us to go beyond aggregate group effects and see how a specific person responds to an ACT intervention, thereby providing clinical input into what works for whom and when . Beyond this, since access to interventions in the HCT population is limited, the web-based ACT self-help program we designed has the potential to fill that gap. Self-directed ACT interventions are considered cost-effective, flexible, and accessible for cancer patients [ 21 ]. They allow patients to self-determine what (content), when (time), where (location), and how (read or listen) to use ACT intervention booklets.

Despite these strengths, we expect several challenges and limitations. First, recruiting the HCT recipients will be challenging. Therefore, we allow for the possibility of recruiting at a second oncohematology center with identical credentials. Retaining participants in the study can be also a challenge, hence the contact maintenance and participation reminder activities we have planned. In addition, we plan to compensate participants for their participation at a rate of PLN 150 (approx. 34.5 Euros) in RCT and PLN 300 (approx. 69 Euros) in SCED. Another limitation is the targeting of the trial to all willing HCT recipients, regardless of the level of distress or the stage of the meaning reconstruction process. However, we are guided by pragmatic (restrictive inclusion/exclusion criteria would prolong the already long data collection time) and cognitive considerations (to our knowledge, this is the first study that will test the relationship of ACT interventions to meaning reconstruction processes) hoping that this will result in further research in this area.

Trial status

ClinicalTrials.gov, NCT06266182. Registered 20 February 2024, https://clinicaltrials.gov/study/NCT06266182 . Version 3.0 dated May 13, 2024. Patient recruitment began on March 6, 2024. Recruitment is expected to be completed in December 2025.

Availability of data and materials {29}

Data that will be collected during the current study (without demographics and clinical data due to the possibility of identification of participants), full protocol, statistical codes, and outputs will be made available at the Open Science Framework (OSF).

Abbreviations

• Acceptance and Commitment Therapy

Analysis of variance

Brief-Illness Perception Questionnaire

Core Beliefs Inventory

Cognitive behavioral therapy

Comprehensive Assessment of Acceptance and Commitment Therapy Processes

The “current standing” Post-Traumatic Growth Inventory-Short Form

Coping Self-Efficacy Scale

Client Satisfaction Questionnaire

Diversity Minimal Item Set

Dynamic structural equation modeling

Quality of Life Questionnaire of the European Organization for Research and Treatment of Cancer

Event-Related Rumination Inventory

Global Meaning Violation Scale

• Hematopoietic cell transplantation

Institutional review board

Latent curve growth modeling

Multilevel confirmatory factor analysis

Minimally enhanced usual care

Meaning of Loss Codebook

Meaning in Life Questionnaire

Multilevel vector autoregressive

Maria Sklodowska-Curie National Research Institute of Oncology

Multilevel structural equation modeling

Open Science Framework

Patient Health Questionnaire

Principal investigator

• Randomized controlled trial

Revised UCLA Loneliness Scale

• Single-case experimental design

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Acknowledgements

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The work is supported by the National Science Centre, Poland [grant number 2020/39/B/HS6/01927 awarded to AK]. The funders had no role in the study design, data collection, analysis, and interpretation, decision to publish, or preparation of the manuscript.

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AK is a principal investigator, who led the proposal and protocol development. JD developed the ACT intervention and contributed to the study design. MK developed cognitively neutral tasks (education) for the control group and assisted in the development of the ACT intervention. RO offered review and advice on ACT intervention component. MSK reviewed the manuscript. AK, MK, and MSK will be involved in the recruitment of participants and data collection. AK, JD, and MK drafted the manuscript. All authors have approved the manuscript.

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Kroemeke, A., Dudek, J., Kijowska, M. et al. The effect of an online acceptance and commitment intervention on the meaning-making process in cancer patients following hematopoietic cell transplantation: study protocol for a randomized controlled trial enhanced with single-case experimental design. Trials 25 , 392 (2024). https://doi.org/10.1186/s13063-024-08235-1

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Empirical evidence: A definition

Empirical evidence is information that is acquired by observation or experimentation.

The scientific method

Types of empirical research, identifying empirical evidence, empirical law vs. scientific law, empirical, anecdotal and logical evidence, additional resources and reading, bibliography.

Empirical evidence is information acquired by observation or experimentation. Scientists record and analyze this data. The process is a central part of the scientific method , leading to the proving or disproving of a hypothesis and our better understanding of the world as a result.

Empirical evidence might be obtained through experiments that seek to provide a measurable or observable reaction, trials that repeat an experiment to test its efficacy (such as a drug trial, for instance) or other forms of data gathering against which a hypothesis can be tested and reliably measured. 

"If a statement is about something that is itself observable, then the empirical testing can be direct. We just have a look to see if it is true. For example, the statement, 'The litmus paper is pink', is subject to direct empirical testing," wrote Peter Kosso in " A Summary of Scientific Method " (Springer, 2011).

"Science is most interesting and most useful to us when it is describing the unobservable things like atoms , germs , black holes , gravity , the process of evolution as it happened in the past, and so on," wrote Kosso. Scientific theories , meaning theories about nature that are unobservable, cannot be proven by direct empirical testing, but they can be tested indirectly, according to Kosso. "The nature of this indirect evidence, and the logical relation between evidence and theory, are the crux of scientific method," wrote Kosso.

The scientific method begins with scientists forming questions, or hypotheses , and then acquiring the knowledge through observations and experiments to either support or disprove a specific theory. "Empirical" means "based on observation or experience," according to the Merriam-Webster Dictionary . Empirical research is the process of finding empirical evidence. Empirical data is the information that comes from the research.

Before any pieces of empirical data are collected, scientists carefully design their research methods to ensure the accuracy, quality and integrity of the data. If there are flaws in the way that empirical data is collected, the research will not be considered valid.

The scientific method often involves lab experiments that are repeated over and over, and these experiments result in quantitative data in the form of numbers and statistics. However, that is not the only process used for gathering information to support or refute a theory. 

This methodology mostly applies to the natural sciences. "The role of empirical experimentation and observation is negligible in mathematics compared to natural sciences such as psychology, biology or physics," wrote Mark Chang, an adjunct professor at Boston University, in " Principles of Scientific Methods " (Chapman and Hall, 2017).

"Empirical evidence includes measurements or data collected through direct observation or experimentation," said Jaime Tanner, a professor of biology at Marlboro College in Vermont. There are two research methods used to gather empirical measurements and data: qualitative and quantitative.

Qualitative research, often used in the social sciences, examines the reasons behind human behavior, according to the National Center for Biotechnology Information (NCBI) . It involves data that can be found using the human senses . This type of research is often done in the beginning of an experiment. "When combined with quantitative measures, qualitative study can give a better understanding of health related issues," wrote Dr. Sanjay Kalra for NCBI.

Quantitative research involves methods that are used to collect numerical data and analyze it using statistical methods, ."Quantitative research methods emphasize objective measurements and the statistical, mathematical, or numerical analysis of data collected through polls, questionnaires, and surveys, or by manipulating pre-existing statistical data using computational techniques," according to the LeTourneau University . This type of research is often used at the end of an experiment to refine and test the previous research.

Identifying empirical evidence in another researcher's experiments can sometimes be difficult. According to the Pennsylvania State University Libraries , there are some things one can look for when determining if evidence is empirical:

• Can the experiment be recreated and tested?
• Does the experiment have a statement about the methodology, tools and controls used?
• Is there a definition of the group or phenomena being studied?

The objective of science is that all empirical data that has been gathered through observation, experience and experimentation is without bias. The strength of any scientific research depends on the ability to gather and analyze empirical data in the most unbiased and controlled fashion possible. 

However, in the 1960s, scientific historian and philosopher Thomas Kuhn promoted the idea that scientists can be influenced by prior beliefs and experiences, according to the Center for the Study of Language and Information . 

— Amazing Black scientists

— Marie Curie: Facts and biography

— What is multiverse theory?

"Missing observations or incomplete data can also cause bias in data analysis, especially when the missing mechanism is not random," wrote Chang.

Because scientists are human and prone to error, empirical data is often gathered by multiple scientists who independently replicate experiments. This also guards against scientists who unconsciously, or in rare cases consciously, veer from the prescribed research parameters, which could skew the results.

The recording of empirical data is also crucial to the scientific method, as science can only be advanced if data is shared and analyzed. Peer review of empirical data is essential to protect against bad science, according to the University of California .

Empirical laws and scientific laws are often the same thing. "Laws are descriptions — often mathematical descriptions — of natural phenomenon," Peter Coppinger, associate professor of biology and biomedical engineering at the Rose-Hulman Institute of Technology, told Live Science. 

Empirical laws are scientific laws that can be proven or disproved using observations or experiments, according to the Merriam-Webster Dictionary . So, as long as a scientific law can be tested using experiments or observations, it is considered an empirical law.

Empirical, anecdotal and logical evidence should not be confused. They are separate types of evidence that can be used to try to prove or disprove and idea or claim.

Logical evidence is used proven or disprove an idea using logic. Deductive reasoning may be used to come to a conclusion to provide logical evidence. For example, "All men are mortal. Harold is a man. Therefore, Harold is mortal."

Anecdotal evidence consists of stories that have been experienced by a person that are told to prove or disprove a point. For example, many people have told stories about their alien abductions to prove that aliens exist. Often, a person's anecdotal evidence cannot be proven or disproven. 

There are some things in nature that science is still working to build evidence for, such as the hunt to explain consciousness .

Meanwhile, in other scientific fields, efforts are still being made to improve research methods, such as the plan by some psychologists to fix the science of psychology .

" A Summary of Scientific Method " by Peter Kosso (Springer, 2011)

"Empirical" Merriam-Webster Dictionary

" Principles of Scientific Methods " by Mark Chang (Chapman and Hall, 2017)

"Qualitative research" by Dr. Sanjay Kalra National Center for Biotechnology Information (NCBI)

"Quantitative Research and Analysis: Quantitative Methods Overview" LeTourneau University

"Empirical Research in the Social Sciences and Education" Pennsylvania State University Libraries

"Thomas Kuhn" Center for the Study of Language and Information

"Misconceptions about science" University of California

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Analysis and testing of pre-cut sugarcane seed stalk sawing performance parameters.

1. Introduction

2. materials and methods, 2.1. test materials, 2.2. definition of test parameters, 2.3. sugarcane stalk sawing experiment rig, 2.3.1. sawing system, 2.3.2. transmission system, 2.3.3. data acquisition system, 2.4. evaluation indexes, 3. design of the experiment, 3.1. single-factor test, 3.2. multi-factor test, 4.1. single-factor test results, 4.2. multi-factor test results, 5. discussion, 5.1. single-factor test analysis, 5.1.1. feeding speed, 5.1.2. sawing speed, 5.1.3. stalk diameter, 5.2. multi-factor test analysis, 5.2.1. analysis of variance (anova), 5.2.2. test residual analysis, 5.2.3. response surface analysis.

• Interactive effects of different factors on peak sawing force.

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Yan, B.; Liu, H.; He, F.; Deng, G.; Zheng, S.; Cui, Z.; Zhou, S.; Dai, Y.; Wang, X.; Qin, S.; et al. Analysis and Testing of Pre-Cut Sugarcane Seed Stalk Sawing Performance Parameters. Agriculture 2024 , 14 , 953. https://doi.org/10.3390/agriculture14060953

Yan B, Liu H, He F, Deng G, Zheng S, Cui Z, Zhou S, Dai Y, Wang X, Qin S, et al. Analysis and Testing of Pre-Cut Sugarcane Seed Stalk Sawing Performance Parameters. Agriculture . 2024; 14(6):953. https://doi.org/10.3390/agriculture14060953

Yan, Bin, Haitao Liu, Fengguang He, Ganran Deng, Shuang Zheng, Zhende Cui, Sili Zhou, Ye Dai, Xilin Wang, Shuangmei Qin, and et al. 2024. "Analysis and Testing of Pre-Cut Sugarcane Seed Stalk Sawing Performance Parameters" Agriculture 14, no. 6: 953. https://doi.org/10.3390/agriculture14060953

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