control group experiment purpose

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Control Group Definition and Examples

The control group is the set of subjects that does not receive the treatment in a study. In other words, it is the group where the independent variable is held constant. This is important because the control group is a baseline for measuring the effects of a treatment in an experiment or study. A controlled experiment is one which includes one or more control groups.

• The experimental group experiences a treatment or change in the independent variable. In contrast, the independent variable is constant in the control group.
• A control group is important because it allows meaningful comparison. The researcher compares the experimental group to it to assess whether or not there is a relationship between the independent and dependent variable and the magnitude of the effect.
• There are different types of control groups. A controlled experiment has one more control group.

Control Group vs Experimental Group

The only difference between the control group and experimental group is that subjects in the experimental group receive the treatment being studied, while participants in the control group do not. Otherwise, all other variables between the two groups are the same.

Control Group vs Control Variable

A control group is not the same thing as a control variable. A control variable or controlled variable is any factor that is held constant during an experiment. Examples of common control variables include temperature, duration, and sample size. The control variables are the same for both the control and experimental groups.

Types of Control Groups

There are different types of control groups:

• Placebo group : A placebo group receives a placebo , which is a fake treatment that resembles the treatment in every respect except for the active ingredient. Both the placebo and treatment may contain inactive ingredients that produce side effects. Without a placebo group, these effects might be attributed to the treatment.
• Positive control group : A positive control group has conditions that guarantee a positive test result. The positive control group demonstrates an experiment is capable of producing a positive result. Positive controls help researchers identify problems with an experiment.
• Negative control group : A negative control group consists of subjects that are not exposed to a treatment. For example, in an experiment looking at the effect of fertilizer on plant growth, the negative control group receives no fertilizer.
• Natural control group : A natural control group usually is a set of subjects who naturally differ from the experimental group. For example, if you compare the effects of a treatment on women who have had children, the natural control group includes women who have not had children. Non-smokers are a natural control group in comparison to smokers.
• Randomized control group : The subjects in a randomized control group are randomly selected from a larger pool of subjects. Often, subjects are randomly assigned to either the control or experimental group. Randomization reduces bias in an experiment. There are different methods of randomly assigning test subjects.

Control Group Examples

Here are some examples of different control groups in action:

Negative Control and Placebo Group

For example, consider a study of a new cancer drug. The experimental group receives the drug. The placebo group receives a placebo, which contains the same ingredients as the drug formulation, minus the active ingredient. The negative control group receives no treatment. The reason for including the negative group is because the placebo group experiences some level of placebo effect, which is a response to experiencing some form of false treatment.

Positive and Negative Controls

For example, consider an experiment looking at whether a new drug kills bacteria. The experimental group exposes bacterial cultures to the drug. If the group survives, the drug is ineffective. If the group dies, the drug is effective.

The positive control group has a culture of bacteria that carry a drug resistance gene. If the bacteria survive drug exposure (as intended), then it shows the growth medium and conditions allow bacterial growth. If the positive control group dies, it indicates a problem with the experimental conditions. A negative control group of bacteria lacking drug resistance should die. If the negative control group survives, something is wrong with the experimental conditions.

• Bailey, R. A. (2008).  Design of Comparative Experiments . Cambridge University Press. ISBN 978-0-521-68357-9.
• Chaplin, S. (2006). “The placebo response: an important part of treatment”.  Prescriber . 17 (5): 16–22. doi: 10.1002/psb.344
• Hinkelmann, Klaus; Kempthorne, Oscar (2008).  Design and Analysis of Experiments, Volume I: Introduction to Experimental Design  (2nd ed.). Wiley. ISBN 978-0-471-72756-9.
• Pithon, M.M. (2013). “Importance of the control group in scientific research.” Dental Press J Orthod . 18 (6):13-14. doi: 10.1590/s2176-94512013000600003
• Stigler, Stephen M. (1992). “A Historical View of Statistical Concepts in Psychology and Educational Research”. American Journal of Education . 101 (1): 60–70. doi: 10.1086/444032

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control group

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• Verywell Mind - What Is a Control Group?
• National Center for Biotechnology Information - PubMed Central - Control Group Design: Enhancing Rigor in Research of Mind-Body Therapies for Depression

control group , the standard to which comparisons are made in an experiment. Many experiments are designed to include a control group and one or more experimental groups; in fact, some scholars reserve the term experiment for study designs that include a control group. Ideally, the control group and the experimental groups are identical in every way except that the experimental groups are subjected to treatments or interventions believed to have an effect on the outcome of interest while the control group is not. Inclusion of a control group greatly strengthens researchers’ ability to draw conclusions from a study. Indeed, only in the presence of a control group can a researcher determine whether a treatment under investigation truly has a significant effect on an experimental group, and the possibility of making an erroneous conclusion is reduced. See also scientific method .

A typical use of a control group is in an experiment in which the effect of a treatment is unknown and comparisons between the control group and the experimental group are used to measure the effect of the treatment. For instance, in a pharmaceutical study to determine the effectiveness of a new drug on the treatment of migraines , the experimental group will be administered the new drug and the control group will be administered a placebo (a drug that is inert, or assumed to have no effect). Each group is then given the same questionnaire and asked to rate the effectiveness of the drug in relieving symptoms . If the new drug is effective, the experimental group is expected to have a significantly better response to it than the control group. Another possible design is to include several experimental groups, each of which is given a different dosage of the new drug, plus one control group. In this design, the analyst will compare results from each of the experimental groups to the control group. This type of experiment allows the researcher to determine not only if the drug is effective but also the effectiveness of different dosages. In the absence of a control group, the researcher’s ability to draw conclusions about the new drug is greatly weakened, due to the placebo effect and other threats to validity. Comparisons between the experimental groups with different dosages can be made without including a control group, but there is no way to know if any of the dosages of the new drug are more or less effective than the placebo.

It is important that every aspect of the experimental environment be as alike as possible for all subjects in the experiment. If conditions are different for the experimental and control groups, it is impossible to know whether differences between groups are actually due to the difference in treatments or to the difference in environment. For example, in the new migraine drug study, it would be a poor study design to administer the questionnaire to the experimental group in a hospital setting while asking the control group to complete it at home. Such a study could lead to a misleading conclusion, because differences in responses between the experimental and control groups could have been due to the effect of the drug or could have been due to the conditions under which the data were collected. For instance, perhaps the experimental group received better instructions or was more motivated by being in the hospital setting to give accurate responses than the control group.

In non-laboratory and nonclinical experiments, such as field experiments in ecology or economics , even well-designed experiments are subject to numerous and complex variables that cannot always be managed across the control group and experimental groups. Randomization, in which individuals or groups of individuals are randomly assigned to the treatment and control groups, is an important tool to eliminate selection bias and can aid in disentangling the effects of the experimental treatment from other confounding factors. Appropriate sample sizes are also important.

A control group study can be managed in two different ways. In a single-blind study, the researcher will know whether a particular subject is in the control group, but the subject will not know. In a double-blind study , neither the subject nor the researcher will know which treatment the subject is receiving. In many cases, a double-blind study is preferable to a single-blind study, since the researcher cannot inadvertently affect the results or their interpretation by treating a control subject differently from an experimental subject.

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What Is a Control Group?

Control Groups vs. Experimental Groups in Psychology Research

Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book."

Emily is a board-certified science editor who has worked with top digital publishing brands like Voices for Biodiversity, Study.com, GoodTherapy, Vox, and Verywell.

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Control Group vs. Experimental Group

Types of control groups.

In simple terms, the control group comprises participants who do not receive the experimental treatment. When conducting an experiment, these people are randomly assigned to this group. They also closely resemble the participants who are in the experimental group or the individuals who receive the treatment.

Experimenters utilize variables to make comparisons between an experimental group and a control group. A variable is something that researchers can manipulate, measure, and control in an experiment. The independent variable is the aspect of the experiment that the researchers manipulate (or the treatment). The dependent variable is what the researchers measure to see if the independent variable had an effect.

While they do not receive the treatment, the control group does play a vital role in the research process. Experimenters compare the experimental group to the control group to determine if the treatment had an effect.

By serving as a comparison group, researchers can isolate the independent variable and look at the impact it had.

The simplest way to determine the difference between a control group and an experimental group is to determine which group receives the treatment and which does not. To ensure that the results can then be compared accurately, the two groups should be otherwise identical.

Not exposed to the treatment (the independent variable)

Used to provide a baseline to compare results against

May receive a placebo treatment

Exposed to the treatment

Used to measure the effects of the independent variable

Identical to the control group aside from their exposure to the treatment

Why a Control Group Is Important

While the control group does not receive treatment, it does play a critical role in the experimental process. This group serves as a benchmark, allowing researchers to compare the experimental group to the control group to see what sort of impact changes to the independent variable produced.  

Because participants have been randomly assigned to either the control group or the experimental group, it can be assumed that the groups are comparable.

Any differences between the two groups are, therefore, the result of the manipulations of the independent variable. The experimenters carry out the exact same procedures with both groups with the exception of the manipulation of the independent variable in the experimental group.

There are a number of different types of control groups that might be utilized in psychology research. Some of these include:

• Positive control groups : In this case, researchers already know that a treatment is effective but want to learn more about the impact of variations of the treatment. In this case, the control group receives the treatment that is known to work, while the experimental group receives the variation so that researchers can learn more about how it performs and compares to the control.
• Negative control group : In this type of control group, the participants are not given a treatment. The experimental group can then be compared to the group that did not experience any change or results.
• Placebo control group : This type of control group receives a placebo treatment that they believe will have an effect. This control group allows researchers to examine the impact of the placebo effect and how the experimental treatment compared to the placebo treatment.
• Randomized control group : This type of control group involves using random selection to help ensure that the participants in the control group accurately reflect the demographics of the larger population.
• Natural control group : This type of control group is naturally selected, often by situational factors. For example, researchers might compare people who have experienced trauma due to war to people who have not experienced war. The people who have not experienced war-related trauma would be the control group.

Examples of Control Groups

Control groups can be used in a variety of situations. For example, imagine a study in which researchers example how distractions during an exam influence test results. The control group would take an exam in a setting with no distractions, while the experimental groups would be exposed to different distractions. The results of the exam would then be compared to see the effects that distractions had on test scores.

Experiments that look at the effects of medications on certain conditions are also examples of how a control group can be used in research. For example, researchers looking at the effectiveness of a new antidepressant might use a control group that receives a placebo and an experimental group that receives the new medication. At the end of the study, researchers would compare measures of depression for both groups to determine what impact the new medication had.

After the experiment is complete, researchers can then look at the test results and start making comparisons between the control group and the experimental group.

Uses for Control Groups

Researchers utilize control groups to conduct research in a range of different fields. Some common uses include:

• Psychology : Researchers utilize control groups to learn more about mental health, behaviors, and treatments.
• Medicine : Control groups can be used to learn more about certain health conditions, assess how well medications work to treat these conditions, and assess potential side effects that may result.
• Education : Educational researchers utilize control groups to learn more about how different curriculums, programs, or instructional methods impact student outcomes.
• Marketing : Researchers utilize control groups to learn more about how consumers respond to advertising and marketing efforts.

Malay S, Chung KC. The choice of controls for providing validity and evidence in clinical research . Plast Reconstr Surg. 2012 Oct;130(4):959-965. doi:10.1097/PRS.0b013e318262f4c8

National Cancer Institute. Control group.

Pithon MM. Importance of the control group in scientific research . Dental Press J Orthod. 2013;18(6):13-14. doi:10.1590/s2176-94512013000600003

Karlsson P, Bergmark A. Compared with what? An analysis of control-group types in Cochrane and Campbell reviews of psychosocial treatment efficacy with substance use disorders . Addiction . 2015;110(3):420-8. doi:10.1111/add.12799

Myers A, Hansen C. Experimental Psychology . Belmont, CA: Cengage Learning; 2012.

By Kendra Cherry, MSEd Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book."

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Control Group in an Experiment

By Jim Frost 3 Comments

A control group in an experiment does not receive the treatment. Instead, it serves as a comparison group for the treatments. Researchers compare the results of a treatment group to the control group to determine the effect size, also known as the treatment effect.

Imagine that a treatment group receives a vaccine and it has an infection rate of 10%. By itself, you don’t know if that’s an improvement. However, if you also have an unvaccinated control group with an infection rate of 20%, you know the vaccine improved the outcome by 10 percentage points.

By serving as a basis for comparison, the control group reveals the treatment’s effect.

Related post : Effect Sizes in Statistics

Using Control Groups in Experiments

Most experiments include a control group and at least one treatment group. In an ideal experiment, the subjects in all groups start with the same overall characteristics except that those in the treatment groups receive a treatment. When the groups are otherwise equivalent before treatment begins, you can attribute differences after the experiment to the treatments.

Randomized controlled trials (RCTs) assign subjects to the treatment and control groups randomly. This process helps ensure the groups are comparable when treatment begins. Consequently, treatment effects are the most likely cause for differences between groups at the end of the study. Statisticians consider RCTs to be the gold standard. To learn more about this process, read my post, Random Assignment in Experiments .

Observational studies either can’t use randomized groups or don’t use them because they’re too costly or problematic. In these studies, the characteristics of the control group might be different from the treatment groups at the start of the study, making it difficult to estimate the treatment effect accurately at the end. Case-Control studies are a specific type of observational study that uses a control group.

For these types of studies, analytical methods and design choices, such as regression analysis and matching, can help statistically mitigate confounding variables. Matching involves selecting participants with similar characteristics. For each participant in the treatment group, the researchers find a subject with comparable traits to include in the control group. To learn more about this type of study and matching, read my post, Observational Studies Explained .

Control groups are key way to increase the internal validity of an experiment. To learn more, read my post about internal and external validity .

Randomized versus non-randomized control groups are just several of the different types you can have. We’ll look at more kinds later!

Related posts : When to Use Regression Analysis

Example of a Control Group

Suppose we want to determine whether regular vitamin consumption affects the risk of dying. Our experiment has the following two experimental groups:

• Control group : Does not consume vitamin supplements
• Treatment group : Regularly consumes vitamin supplements.

In this experiment, we randomly assign subjects to the two groups. Because we use random assignment, the two groups start with similar characteristics, including healthy habits, physical attributes, medical conditions, and other factors affecting the outcome. The intentional introduction of vitamin supplements in the treatment group is the only systematic difference between the groups.

After the experiment is complete, we compare the death risk between the treatment and control groups. Because the groups started roughly equal, we can reasonably attribute differences in death risk at the end of the study to vitamin consumption. By having the control group as the basis of comparison, the effect of vitamin consumption becomes clear!

Types of Control Groups

Researchers can use different types of control groups in their experiments. Earlier, you learned about the random versus non-random kinds, but there are other variations. You can use various types depending on your research goals, constraints, and ethical issues, among other things.

Negative Control Group

The group introduces a condition that the researchers expect won’t have an effect. This group typically receives no treatment. These experiments compare the effectiveness of the experimental treatment to no treatment. For example, in a vaccine study, a negative control group does not get the vaccine.

Positive Control Group

Positive control groups typically receive a standard treatment that science has already proven effective. These groups serve as a benchmark for the performance of a conventional treatment. In this vein, experiments with positive control groups compare the effectiveness of a new treatment to a standard one.

For example, an old blood pressure medicine can be the treatment in a positive control group, while the treatment group receives the new, experimental blood pressure medicine. The researchers want to determine whether the new treatment is better than the previous treatment.

In these studies, subjects can still take the standard medication for their condition, a potentially critical ethics issue.

Placebo Control Group

Placebo control groups introduce a treatment lookalike that will not affect the outcome. Standard examples of placebos are sugar pills and saline solution injections instead of genuine medicine. The key is that the placebo looks like the actual treatment. Researchers use this approach when the recipients’ belief that they’re receiving the treatment might influence their outcomes. By using placebos, the experiment controls for these psychological benefits. The researchers want to determine whether the treatment performs better than the placebo effect.

Learn more about the Placebo Effect .

Blinded Control Groups

If the subject’s awareness of their group assignment might affect their outcomes, the researchers can use a blinded experimental design that does not tell participants their group membership. Typically, blinded control groups will receive placebos, as described above. In a double-blinded control group, both subjects and researchers don’t know group assignments.

Waitlist Control Group

When there is a waitlist to receive a new treatment, those on the waitlist can serve as a control group until they receive treatment. This type of design avoids ethical concerns about withholding a better treatment until the study finishes. This design can be a variation of a positive control group because the subjects might be using conventional medicines while on the waitlist.

Historical Control Group

When historical data for a comparison group exists, it can serve as a control group for an experiment. The group doesn’t exist in the study, but the researchers compare the treatment group to the existing data. For example, the researchers might have infection rate data for unvaccinated individuals to compare to the infection rate among the vaccinated participants in their study. This approach allows everyone in the experiment to receive the new treatment. However, differences in place, time, and other circumstances can reduce the value of these comparisons. In other words, other factors might account for the apparent effects.

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Reader Interactions

December 19, 2021 at 9:17 am

Thank you very much Jim for your quick and comprehensive feedback. Extremely helpful!! Regards, Arthur

December 17, 2021 at 4:46 pm

Thank you very much Jim, very interesting article.

Can I select a control group at the end of intervention/experiment? Currently I am managing a project in rural Cambodia in five villages, however I did not select any comparison/control site at the beginning. Since I know there are other villages which have not been exposed to any type of intervention, can i select them as a control site during my end-line data collection or it will not be a legitimate control? Thank you very much, Arthur

December 18, 2021 at 1:51 am

You might be able to use that approach, but it’s not ideal. The ideal is to have control groups defined at the beginning of the study. You can use the untreated villages as a type of historical control groups that I talk about in this article. Or, if they’re awaiting to receive the intervention, it might be akin to a waitlist control group.

If you go that route, you’ll need to consider whether there was some systematic reason why these villages have not received any intervention. For example, are the villages in question more remote? And, if there is a systematic reason, would that affect your outcome variable? More generally, are they systematically different? How well do the untreated villages represent your target population?

If you had selected control villages at the beginning, you’d have been better able to ensure there weren’t any systematic differences between the villages receiving interventions and those that didn’t.

If the villages that didn’t receive any interventions are systematically different, you’ll need to incorporate that into your interpretation of the results. Are they different in ways that affect the outcomes you’re measuring? Can those differences account for the difference in outcomes between the treated and untreated villages? Hopefully, you’d be able to measure those differences between untreated/treated villages.

So, yes, you can use that approach. It’s not perfect and there will potentially be more things for you to consider and factor into your conclusions. Despite these drawbacks, it’s possible that using a pseudo control group like that is better than not doing that because at least you can make comparisons to something. Otherwise, you won’t know whether the outcomes in the intervention villages represent an improvement! Just be aware of the extra considerations!

Best of luck with your research!

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Understanding Control Groups for Research

Introduction

What are control groups in research, examples of control groups in research, control group vs. experimental group, types of control groups, control groups in non-experimental research.

A control group is typically thought of as the baseline in an experiment. In an experiment, clinical trial, or other sort of controlled study, there are at least two groups whose results are compared against each other.

The experimental group receives some sort of treatment, and their results are compared against those of the control group, which is not given the treatment. This is important to determine whether there is an identifiable causal relationship between the treatment and the resulting effects.

As intuitive as this may sound, there is an entire methodology that is useful to understanding the role of the control group in experimental research and as part of a broader concept in research. This article will examine the particulars of that methodology so you can design your research more rigorously .

Suppose that a friend or colleague of yours has a headache. You give them some over-the-counter medicine to relieve some of the pain. Shortly after they take the medicine, the pain is gone and they feel better. In casual settings, we can assume that it must be the medicine that was the cause of their headache going away.

In scientific research, however, we don't really know if the medicine made a difference or if the headache would have gone away on its own. Maybe in the time it took for the headache to go away, they ate or drank something that might have had an effect. Perhaps they had a quick nap that helped relieve the tension from the headache. Without rigorously exploring this phenomenon , any number of confounding factors exist that can make us question the actual efficacy of any particular treatment.

Experimental research relies on observing differences between the two groups by "controlling" the independent variable , or in the case of our example above, the medicine that is given or not given depending on the group. The dependent variable in this case is the change in how the person suffering the headache feels, and the difference between taking and not taking the medicine is evidence (or lack thereof) that the treatment is effective.

The catch is that, between the control group and other groups (typically called experimental groups), it's important to ensure that all other factors are the same or at least as similar as possible. Things such as age, fitness level, and even occupation can affect the likelihood someone has a headache and whether a certain medication is effective.

Faced with this dynamic, researchers try to make sure that participants in their control group and experimental group are as similar as possible to each other, with the only difference being the treatment they receive.

Experimental research is often associated with scientists in lab coats holding beakers containing liquids with funny colors. Clinical trials that deal with medical treatments rely primarily, if not exclusively, on experimental research designs involving comparisons between control and experimental groups.

However, many studies in the social sciences also employ some sort of experimental design which calls for the use of control groups. This type of research is useful when researchers are trying to confirm or challenge an existing notion or measure the difference in effects.

Workplace efficiency research

How might a company know if an employee training program is effective? They may decide to pilot the program to a small group of their employees before they implement the training to their entire workforce.

If they adopt an experimental design, they could compare results between an experimental group of workers who participate in the training program against a control group who continues as per usual without any additional training.

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Mental health research

Music certainly has profound effects on psychology, but what kind of music would be most effective for concentration? Here, a researcher might be interested in having participants in a control group perform a series of tasks in an environment with no background music, and participants in multiple experimental groups perform those same tasks with background music of different genres. The subsequent analysis could determine how well people perform with classical music, jazz music, or no music at all in the background.

Educational research

Suppose that you want to improve reading ability among elementary school students, and there is research on a particular teaching method that is associated with facilitating reading comprehension. How do you measure the effects of that teaching method?

A study could be conducted on two groups of otherwise equally proficient students to measure the difference in test scores. The teacher delivers the same instruction to the control group as they have to previous students, but they teach the experimental group using the new technique. A reading test after a certain amount of instruction could determine the extent of effectiveness of the new teaching method.

As you can see from the three examples above, experimental groups are the counterbalance to control groups. A control group offers an essential point of comparison. For an experimental study to be considered credible, it must establish a baseline against which novel research is conducted.

Researchers can determine the makeup of their experimental and control groups from their literature review . Remember that the objective of a review is to establish what is known about the object of inquiry and what is not known. Where experimental groups explore the unknown aspects of scientific knowledge, a control group is a sort of simulation of what would happen if the treatment or intervention was not administered. As a result, it will benefit researchers to have a foundational knowledge of the existing research to create a credible control group against which experimental results are compared, especially in terms of remaining sensitive to relevant participant characteristics that could confound the effects of your treatment or intervention so that you can appropriately distribute participants between the experimental and control groups.

There are multiple control groups to consider depending on the study you are looking to conduct. All of them are variations of the basic control group used to establish a baseline for experimental conditions.

No-treatment control group

This kind of control group is common when trying to establish the effects of an experimental treatment against the absence of treatment. This is arguably the most straightforward approach to an experimental design as it aims to directly demonstrate how a certain change in conditions produces an effect.

Placebo control group

In this case, the control group receives some sort of treatment under the exact same procedures as those in the experimental group. The only difference in this case is that the treatment in the placebo control group has already been judged to be ineffective, except that the research participants don't know that it is ineffective.

Placebo control groups (or negative control groups) are useful for allowing researchers to account for any psychological or affective factors that might impact the outcomes. The negative control group exists to explicitly eliminate factors other than changes in the independent variable conditions as causes of the effects experienced in the experimental group.

Positive control group

Contrasted with a no-treatment control group, a positive control group employs a treatment against which the treatment in the experimental group is compared. However, unlike in a placebo group, participants in a positive control group receive treatment that is known to have an effect.

If we were to use our first example of headache medicine, a researcher could compare results between medication that is commonly known as effective against the newer medication that the researcher thinks is more effective. Positive control groups are useful for validating experimental results when compared against familiar results.

Historical control group

Rather than study participants in control group conditions, researchers may employ existing data to create historical control groups. This form of control group is useful for examining changing conditions over time, particularly when incorporating past conditions that can't be replicated in the analysis.

Qualitative research more often relies on non-experimental research such as observations and interviews to examine phenomena in their natural environments. This sort of research is more suited for inductive and exploratory inquiries, not confirmatory studies meant to test or measure a phenomenon.

That said, the broader concept of a control group is still present in observational and interview research in the form of a comparison group. Comparison groups are used in qualitative research designs to show differences between phenomena, with the exception being that there is no baseline against which data is analyzed.

Comparison groups are useful when an experimental environment cannot produce results that would be applicable to real-world conditions. Research inquiries examining the social world face challenges of having too many variables to control, making observations and interviews across comparable groups more appropriate for data collection than clinical or sterile environments.

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Control Group: The Key Elements In Experimental Research

Understand the design and interpretation of control group in research experiments for powerful conclusions

The control group constitutes a baseline for comparison, enabling researchers to assess the true effects of independent variables. Researchers can effectively assess the impact of independent variables and discern causation from correlation, by comparing the results of experimental groups to those of control groups. This article will highlight the significance and implementation of control groups in research experiments, and explain their role in ensuring scientific methodology and reliable findings. We will explore the fundamental principles of control groups, examine their types, and discuss their importance in minimizing biases and confounding factors.

What Is A Control Group?

A control group is a fundamental component of scientific experiments designed to compare and evaluate the effects of an intervention or treatment. It serves as a baseline against which the experimental group is measured. The control group consists of individuals or subjects who do not receive the experimental treatment but are otherwise subjected to the same conditions and procedures as the experimental group. Working with a control group, researchers can assess the specific impact of the intervention by comparing the outcomes between the experimental and control groups.

Related article: The Role Of Experimental Groups In Research

The Role Of A Control Group In Scientific Experiments

A control group plays a crucial role in scientific experiments as it enables researchers to establish a valid cause-and-effect relationship between the experimental treatment and the observed outcomes. By comparing the experimental group’s results with those of the control group, researchers can determine whether any observed effects are due to the treatment or other factors. The control group serves as a standard for comparison, helping to isolate the specific influence of the intervention being tested. It provides a baseline against which experimental group outcomes can be evaluated and allows researchers to draw accurate conclusions about the treatment’s efficacy or the impact of other variables being studied.

Why Is A Control Group Necessary?

Including a control group in scientific experiments is essential for ensuring the reliability and validity of the findings. Without a control group, it becomes challenging to determine whether any observed changes or effects are truly attributable to the intervention or simply a result of chance or other factors. The control group allows researchers to differentiate between the effects of the experimental treatment and background noise or confounding variables because it provides a reference point. A well-designed control group is crucial for generating reliable and meaningful results, intensifying the scientific rigor of the study, and supporting evidence-based decision-making in various fields of research.

Types Of Control Groups

In scientific experiments, different types of control groups are used to ensure accurate and meaningful results. These control groups help researchers compare the effects of an intervention or treatment against a reference point. Four common types of control groups are negative controls, positive controls, placebo controls, and randomized control groups.

Negative Controls

Negative controls are an integral part of scientific experiments, serving as a reference to establish the absence of a specific effect. In these control groups, no treatment is administered, allowing researchers to compare the outcomes with the experimental group. Researchers can identify and account for confounding variables and background effects that may influence the results when they include negative control groups. This ensures the specificity of the treatment and enhances the validity of the study. Negative controls can take various forms, such as placebos or control groups receiving no treatment, depending on the research question.

Positive controls

Positive controls are references to validate the reliability and sensitivity of the experimental setup. In these control groups, a known treatment or condition is applied to generate an expected response or outcome. By including positive controls, researchers can assess whether the experimental conditions and methodology are capable of detecting the desired effect. Positive controls act as a benchmark, providing evidence that the experimental system is functioning properly and capable of producing the anticipated results. This helps researchers ensure the validity and accuracy of their findings by confirming that the experimental conditions are conducive to detecting the intended response.

Placebo controls

Placebo controls play a significant role in medical and clinical research by providing a baseline for comparison and evaluating the effectiveness of a new treatment or intervention. In a placebo control group, participants receive an inactive substance or sham procedure that is indistinguishable from the active treatment being tested. The purpose of the placebo control is to assess the specific effects of the treatment by comparing it to the effects observed in the placebo group. By administering a placebo, researchers can account for the psychological and physiological responses that may occur simply due to the participants’ belief in receiving treatment. This helps determine the true efficacy of the active treatment, as any observed improvements in the treatment group can be attributed to the treatment itself, beyond the placebo effect. Placebo controls are essential in clinical trials and other studies to minimize bias, establish the true therapeutic benefits of treatment, and ensure the reliability of the results.

Randomized Control Group

Randomized control groups are an essential component of research studies as they introduce unpredictability to control factors. By randomly assigning participants to either the control or treatment group, researchers ensure that the variables not specifically tested are evenly distributed. This randomization helps eliminate bias and allows for accurate analysis of the independent variable. By using randomized control groups, researchers can draw reliable conclusions about the impact of the variables being studied. 

Quasi-Experimental Designs And Their Role In Social Policy Studies

Quasi-experimental designs in social policy studies often utilize control groups to assess the impact of interventions or policies on a target population. While these designs do not involve random assignment of participants to groups, they still incorporate a control group to establish a baseline for comparison. The control group consists of individuals who do not receive the intervention or policy being studied, allowing researchers to evaluate the effects of the intervention by comparing outcomes between the treatment group and the control group. This helps control for confounding variables and provides insights into a causal relationship between the intervention and the observed outcomes. 

Implementing Control Groups In Experimental Design And Analysis

Control groups serve as a reference point against which the effects of experimental interventions can be measured. They provide a baseline to compare with the treatment group, allowing researchers to determine the true impact of the variables under investigation. This approach helps establish causal relationships and increases the internal validity of the research. 

Randomized Controlled Experiments (RCTs) For Public Policy Studies

Randomized controlled experiments are widely used in public policy studies. RCTs involve randomly assigning participants to either a treatment group or a control group. The treatment group receives the intervention or policy being tested, while the control group does not. RCTs help ensure that any observed differences between the groups are not due to pre-existing factors, increasing the reliability of the study’s findings. RCTs are particularly valuable in evaluating the impact of public policies and interventions on a large scale.

Non-Experimental Research Vs. Actual Experimentation

When determining the baseline for comparison in research, researchers must consider whether to use non-experimental research or actual experimentation. Non-experimental research involves observing and analyzing existing data without manipulating any variables. This approach is helpful in situations where it is not feasible or ethical to conduct an experiment. On the other hand, actual experimentation involves actively manipulating variables and comparing groups with and without the intervention. While actual experimentation provides stronger causal evidence, non-experimental research can still provide valuable insights when experiments are not possible.

Identifying Confounding Variables And Factors

Confounding variables and factors are extraneous variables that can influence the relationship between the independent and dependent variables in a study. Identifying and controlling for confounding variables is crucial to ensure accurate and valid results. Researchers employ various techniques to address confounding variables, such as random assignment of participants to groups, matching participants based on relevant characteristics, or statistical techniques like regression analysis. By accounting for confounding variables, researchers can strengthen the internal validity of their studies and draw more accurate conclusions about the relationship between variables.

The Vital Role Of The Control Group In Scientific Methodology And Analysis

In experimental studies, the control group serves as a standard against which the effects of a particular intervention or treatment are measured. By keeping all variables constant except for the one being studied, researchers can isolate the true impact of the intervention. This helps to establish causality and determine whether the observed effects are indeed due to the intervention or simply a result of other factors.

In addition to experimental studies, control groups are also essential in observational and epidemiological research. They help researchers account for potential biases and confounding factors when analyzing the relationship between variables. By comparing a group exposed to a certain risk factor or condition with a similar group that is not exposed, researchers can better understand the true impact of the risk factor or condition on the outcome of interest.

Overall, the control group serves as a guide in scientific methodology and analysis. It allows researchers to draw valid and reliable conclusions, enhance the internal validity of their studies, and provide more robust evidence for decision-making in various fields, including medicine, psychology, biology, and social sciences.

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Control Group

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A control group is one of multiple groups in an experimental treatment study, used as a baseline for the estimation of the effect of interest in the other groups.

Introduction

Experimental treatment studies are designed to estimate the effect of a particular treatment on one or more variables. Typically, the variables of interest are observed before and after treatment to detect changes that occurred in between. The two observations of the variables are called pretest and posttest to indicate their temporal position before and after the treatment. However, any differences between pre- and posttest need not be caused by the treatment. Therefore, experimental treatment studies use at least two groups: the experimental group receives the treatment while the control group does not. The effect of the treatment can be estimated by comparing the change observed in the treatment group with the change observed in the control group.

Treatment Groups as Independent Variables in an...

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Department of Psychology, Psychological Methods and Assessment, Münich, Germany

Sven Hilbert

Faculty of Psychology, Educational Science, and Sport Science, University of Regensburg, Regensburg, Germany

Psychological Methods and Assessment, LMU Munich, Munich, Germany

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Hilbert, S. (2020). Control Group. In: Zeigler-Hill, V., Shackelford, T.K. (eds) Encyclopedia of Personality and Individual Differences. Springer, Cham. https://doi.org/10.1007/978-3-319-24612-3_1290

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Control Group: Definition, Examples and Types

Design of Experiments > Control Group

What is a Control Group?

An experiment is split into two groups: the experimental group and the control group. The experimental group is given the experimental treatment and the control group is given either a standard treatment or nothing. For example, let’s say you wanted to know if Gatorade increased athletic performance. Your experimental group would be given the Gatorade and your control group would be given regular water.

The conditions must be exactly the same for all members in the experiment. The only difference between members must be the item or thing you are conducting the experiment to look at. Let’s say you wanted to know if a new fertilizer makes plants grow taller. You must ensure that the lighting, water supply, size of container and other important factors are held constant for every member in every group. The only thing that differs in this case is the type of fertilizer given to the plants.

Types of Control Groups in Medical Experiments

Control groups can be subdivided into the following types (see: FDA ):

• Placebo concurrent control : one group is given the treatment, the other a placebo (“sugar pill”).
• Dose-comparison concurrent control : two different doses are administered, a different one to each group.
• No treatment concurrent control : one group is given the treatment, the other group is given nothing.
• Active treatment concurrent control : one group is given the treatment, the other group is given an existing therapy that is known to be effective.
• Historical control: only one physical group exists experimentally (the experimental group). the control group is compiled from historical data.

Which type of control group you use depends largely on what type of patients you are administering a treatment too. In many cases, it would be unethical to withhold treatment from a control group or provide a placebo.

Next : The Placebo Effect.

Beyer, W. H. CRC Standard Mathematical Tables, 31st ed. Boca Raton, FL: CRC Press, pp. 536 and 571, 2002. Agresti A. (1990) Categorical Data Analysis. John Wiley and Sons, New York. Dodge, Y. (2008). The Concise Encyclopedia of Statistics . Springer. Gonick, L. (1993). The Cartoon Guide to Statistics . HarperPerennial.

Controlled Experiment

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This is when a hypothesis is scientifically tested.

In a controlled experiment, an independent variable (the cause) is systematically manipulated, and the dependent variable (the effect) is measured; any extraneous variables are controlled.

The researcher can operationalize (i.e., define) the studied variables so they can be objectively measured. The quantitative data can be analyzed to see if there is a difference between the experimental and control groups.

What is the control group?

In experiments scientists compare a control group and an experimental group that are identical in all respects, except for one difference – experimental manipulation.

Unlike the experimental group, the control group is not exposed to the independent variable under investigation and so provides a baseline against which any changes in the experimental group can be compared.

Since experimental manipulation is the only difference between the experimental and control groups, we can be sure that any differences between the two are due to experimental manipulation rather than chance.

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

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

What are extraneous variables?

The researcher wants to ensure that the manipulation of the independent variable has changed the changes in the dependent variable.

Hence, all the other variables that could affect the dependent variable to change must be controlled. These other variables are called extraneous or confounding variables.

Extraneous variables should be controlled were possible, as they might be important enough to provide alternative explanations for the effects.

In practice, it would be difficult to control all the variables in a child’s educational achievement. For example, it would be difficult to control variables that have happened in the past.

A researcher can only control the current environment of participants, such as time of day and noise levels.

Why conduct controlled experiments?

Scientists use controlled experiments because they allow for precise control of extraneous and independent variables. This allows a cause-and-effect relationship to be established.

Controlled experiments also follow a standardized step-by-step procedure. This makes it easy for another researcher to replicate the study.

Key Terminology

Experimental group.

The group being treated or otherwise manipulated for the sake of the experiment.

Control Group

They receive no treatment and are used as a comparison group.

Ecological validity

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

Experimenter effects

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

Demand characteristics

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

Independent variable (IV)

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

Dependent variable (DV)

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

Extraneous variables (EV)

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

Confounding variables

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

Random Allocation

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

Order effects

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

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

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

What is the control in an experiment?

In an experiment , the control is a standard or baseline group not exposed to the experimental treatment or manipulation. It serves as a comparison group to the experimental group, which does receive the treatment or manipulation.

The control group helps to account for other variables that might influence the outcome, allowing researchers to attribute differences in results more confidently to the experimental treatment.

Establishing a cause-and-effect relationship between the manipulated variable (independent variable) and the outcome (dependent variable) is critical in establishing a cause-and-effect relationship between the manipulated variable.

What is the purpose of controlling the environment when testing a hypothesis?

Controlling the environment when testing a hypothesis aims to eliminate or minimize the influence of extraneous variables. These variables other than the independent variable might affect the dependent variable, potentially confounding the results.

By controlling the environment, researchers can ensure that any observed changes in the dependent variable are likely due to the manipulation of the independent variable, not other factors.

This enhances the experiment’s validity, allowing for more accurate conclusions about cause-and-effect relationships.

It also improves the experiment’s replicability, meaning other researchers can repeat the experiment under the same conditions to verify the results.

Why are hypotheses important to controlled experiments?

Hypotheses are crucial to controlled experiments because they provide a clear focus and direction for the research. A hypothesis is a testable prediction about the relationship between variables.

It guides the design of the experiment, including what variables to manipulate (independent variables) and what outcomes to measure (dependent variables).

The experiment is then conducted to test the validity of the hypothesis. If the results align with the hypothesis, they provide evidence supporting it.

The hypothesis may be revised or rejected if the results do not align. Thus, hypotheses are central to the scientific method, driving the iterative inquiry, experimentation, and knowledge advancement process.

What is the experimental method?

The experimental method is a systematic approach in scientific research where an independent variable is manipulated to observe its effect on a dependent variable, under controlled conditions.

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Control Group

What is a control group in an experiment.

A control group is a set of subjects in an experiment who are not exposed to the independent variable. The purpose of a control group is to serve as a baseline for comparison. By having a group that is not exposed to the treatment, researchers can compare the results of the experimental group and determine whether the independent variable had an impact.

In some cases, there may be more than one control group. This is often done when there are multiple treatments or when researchers want to compare different groups of subjects. Having multiple control groups allows researchers to isolate the effect of each treatment and better understand how each one works.

Control groups are an important part of any experiment, as they help ensure that the results are accurate and reliable. Without a control group, it would be difficult to determine whether the results of an experiment are due to the independent variable or other factors.

When designing an experiment, it is important to carefully consider what kind of control group you will need. There are many different ways to set up a control group, and the best approach will depend on the specific goals of your research.

Control Group vs. Experimental Group

A control group is a group in an experiment that does not receive the experimental treatment. The purpose of a control group is to provide a baseline against which to compare the experimental group results.

An experimental group is a group in an experiment that receives the experimental treatment. The purpose of an experimental group is to test whether or not the experimental treatment has an effect.

The differences between control and experimental groups are important to consider when designing an experiment. The most important difference is that the control group provides a comparison for the results of the experimental group. This comparison is essential in order to determine whether or not the experimental treatment had an effect. Without a control group, it would be impossible to know if the results of the experiment are due to the treatment or not.

Another important difference between a control group and an experimental group is that the experimental group is the only group that receives the experimental treatment. This is necessary in order to ensure that any results seen in the experimental group can be attributed to the treatment and not to other factors.

Control groups and experimental groups are both essential parts of experiments. Without a control group, it would be impossible to know if the results of an experiment are due to the treatment or not. Without an experimental group, it would be impossible to test whether or not a treatment has an effect.

What Is the Purpose of a Control Group

The purpose of a control group is to serve as a baseline for comparison. By having a group that is not exposed to the treatment, researchers can compare the results of the experimental group and determine whether the independent variable had an impact.

Why Is a Control Group Important in an Experiment

A control group is an essential part of any experiment. It is a group of subjects who are not exposed to the independent variable being tested. The purpose of a control group is to provide a baseline against which the results from the treatment group can be compared.

Without a control group, it would be impossible to determine whether the results of an experiment are due to the treatment or some other factor. For example, imagine you are testing the effects of a new drug on patients with high blood pressure. If you did not have a control group, you would not know if the decrease in blood pressure was due to the drug or something else, such as the placebo effect.

A control group must be carefully designed to match the treatment group in all important respects, except for the one factor that is being tested. This ensures that any differences in the results can be attributed to the independent variable and not to other factors.

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Control Group

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Control Group Definition

In scientific experiments, the control group is the group of subject that receive no treatment or a standardized treatment. Without the control group, there would be nothing to compare the treatment group to. When statistics refer to something being “X times more likely to happen” they are referring to the difference in the measurement between the treatment and control group. The control group provides a baseline in the experiment. The variable that is being studied in the experiment is not changed or is limited to zero in the control group. This insures that the effects of the variable are being studied. Most experiments try to add the variable back in increments to different treatment groups, to really begin to discern the effects of the variable in the system.

Ideally, the control group is subject to the same exact conditions as the treatment groups. This insures that only the effects produced by the variable are being measured. In a study of plants, for instance, all the plants would ideally be in the same room, with the same light and air conditions. In biological studies, it is also important that the organisms in the treatment and control groups come from the same population. Ideally, the organisms would all be clones of each other, to reduce genetic differences. This is the case in many artificially selected lab species, which have been selected to be very similar to each other. This ensures that the results obtained are valid.

Examples of Control Group

Testing enzyme strength.

In a simple biological lab experiment, students can test the effects of different concentrations of enzyme. The student can prepare a stock solution of enzyme by spitting into a beaker. Human spit contains the enzyme amylase, which breaks down starches. The concentration of enzyme can be varied by dividing the stock solution and adding in various amounts of water. Once various solutions of different strength enzyme have been produced, the experiment can begin.

In several treatment beakers are placed the following ingredients: starch, iodine, and the different solutions of enzyme. In the control group, a beaker is filled with starch and iodine, but no enzyme. When iodine is in the presence of starch, it turns black. As the enzyme depletes the starch in each beaker, the solution clears up and is a lighter yellow or brown color. In this way, the student can tell how long the enzymes in each beaker take to completely process the same amount of substrate. The control group is important because it will tell the student if the starch breaks down without the enzyme, which it will, given enough time.

Testing Drugs and the Placebo Effect

When drugs are tested on humans, control groups are also used. Although control groups were just considered good science, they have found an interesting phenomena in drug trials. Oftentimes, control groups in drug trials consist of people who also have the disease or ailment, but who don’t receive the medicine being tested. Instead, to keep the control group the same as the treatment groups, the patients in the control group are also given a pill. This is a sugar pill usually and contains no medicine. This practice of having a control group is important for drug trial, because it validates the results obtained. However, the control groups have also demonstrated an interesting effect, known as the placebo effect

In some drug trials, where the control group is given a fake medicine, patients start to see results. Scientists call this the placebo effect, and as of yet it is mostly unexplained. Some scientists have suggested that people get better simply because they believed they were going to get better, but this theory remains untested. Other scientists claim that unknown variables in the experiment caused the patients to get better. This theory remains unproven, as well.

Related Biology Terms

• Treatment Group – The group that receives the variable, or altered amounts of the variable.
• Variable – The part of the experiment being studied which is changed, or altered, throughout the experiment.
• Scientific Method – The steps scientist follow to ensure their results are valid and reproducible.
• Placebo Effect – A phenomenon when patients in the control group experience the same effects as those in the treatment group, though no treatment was given.

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Methodology

• What Is a Controlled Experiment? | Definitions & Examples

What Is a Controlled Experiment? | Definitions & Examples

Published on April 19, 2021 by Pritha Bhandari . Revised on June 22, 2023.

In experiments , researchers manipulate independent variables to test their effects on dependent variables. In a controlled experiment , all variables other than the independent variable are controlled or held constant so they don’t influence the dependent variable.

Controlling variables can involve:

• holding variables at a constant or restricted level (e.g., keeping room temperature fixed).
• measuring variables to statistically control for them in your analyses.
• balancing variables across your experiment through randomization (e.g., using a random order of tasks).

Table of contents

Why does control matter in experiments, methods of control, problems with controlled experiments, other interesting articles, frequently asked questions about controlled experiments.

Control in experiments is critical for internal validity , which allows you to establish a cause-and-effect relationship between variables. Strong validity also helps you avoid research biases , particularly ones related to issues with generalizability (like sampling bias and selection bias .)

• Your independent variable is the color used in advertising.
• Your dependent variable is the price that participants are willing to pay for a standard fast food meal.

Extraneous variables are factors that you’re not interested in studying, but that can still influence the dependent variable. For strong internal validity, you need to remove their effects from your experiment.

• Design and description of the meal,
• Study environment (e.g., temperature or lighting),
• Participant’s frequency of buying fast food,
• Participant’s familiarity with the specific fast food brand,
• Participant’s socioeconomic status.

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You can control some variables by standardizing your data collection procedures. All participants should be tested in the same environment with identical materials. Only the independent variable (e.g., ad color) should be systematically changed between groups.

Other extraneous variables can be controlled through your sampling procedures . Ideally, you’ll select a sample that’s representative of your target population by using relevant inclusion and exclusion criteria (e.g., including participants from a specific income bracket, and not including participants with color blindness).

By measuring extraneous participant variables (e.g., age or gender) that may affect your experimental results, you can also include them in later analyses.

After gathering your participants, you’ll need to place them into groups to test different independent variable treatments. The types of groups and method of assigning participants to groups will help you implement control in your experiment.

Control groups

Controlled experiments require control groups . Control groups allow you to test a comparable treatment, no treatment, or a fake treatment (e.g., a placebo to control for a placebo effect ), and compare the outcome with your experimental treatment.

You can assess whether it’s your treatment specifically that caused the outcomes, or whether time or any other treatment might have resulted in the same effects.

To test the effect of colors in advertising, each participant is placed in one of two groups:

• A control group that’s presented with red advertisements for a fast food meal.
• An experimental group that’s presented with green advertisements for the same fast food meal.

Random assignment

To avoid systematic differences and selection bias between the participants in your control and treatment groups, you should use random assignment .

This helps ensure that any extraneous participant variables are evenly distributed, allowing for a valid comparison between groups .

Random assignment is a hallmark of a “true experiment”—it differentiates true experiments from quasi-experiments .

Masking (blinding)

Masking in experiments means hiding condition assignment from participants or researchers—or, in a double-blind study , from both. It’s often used in clinical studies that test new treatments or drugs and is critical for avoiding several types of research bias .

Sometimes, researchers may unintentionally encourage participants to behave in ways that support their hypotheses , leading to observer bias . In other cases, cues in the study environment may signal the goal of the experiment to participants and influence their responses. These are called demand characteristics . If participants behave a particular way due to awareness of being observed (called a Hawthorne effect ), your results could be invalidated.

Using masking means that participants don’t know whether they’re in the control group or the experimental group. This helps you control biases from participants or researchers that could influence your study results.

You use an online survey form to present the advertisements to participants, and you leave the room while each participant completes the survey on the computer so that you can’t tell which condition each participant was in.

Although controlled experiments are the strongest way to test causal relationships, they also involve some challenges.

Difficult to control all variables

Especially in research with human participants, it’s impossible to hold all extraneous variables constant, because every individual has different experiences that may influence their perception, attitudes, or behaviors.

But measuring or restricting extraneous variables allows you to limit their influence or statistically control for them in your study.

Risk of low external validity

Controlled experiments have disadvantages when it comes to external validity —the extent to which your results can be generalized to broad populations and settings.

The more controlled your experiment is, the less it resembles real world contexts. That makes it harder to apply your findings outside of a controlled setting.

There’s always a tradeoff between internal and external validity . It’s important to consider your research aims when deciding whether to prioritize control or generalizability in your experiment.

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
• Quartiles & Quantiles
• Cluster sampling
• Stratified sampling
• Data cleansing
• Reproducibility vs Replicability
• Peer review
• Prospective cohort study

Research bias

• Implicit bias
• Cognitive bias
• Placebo effect
• Hawthorne effect
• Hindsight bias
• Affect heuristic
• Social desirability bias

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In a controlled experiment , all extraneous variables are held constant so that they can’t influence the results. Controlled experiments require:

• A control group that receives a standard treatment, a fake treatment, or no treatment.
• Random assignment of participants to ensure the groups are equivalent.

Depending on your study topic, there are various other methods of controlling variables .

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.

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.

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What Is a Controlled Experiment?

Definition and Example

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A controlled experiment is one in which everything is held constant except for one variable . Usually, a set of data is taken to be a control group , which is commonly the normal or usual state, and one or more other groups are examined where all conditions are identical to the control group and to each other except for one variable.

Sometimes it's necessary to change more than one variable, but all of the other experimental conditions will be controlled so that only the variables being examined change. And what is measured is the variables' amount or the way in which they change.

Controlled Experiment

• A controlled experiment is simply an experiment in which all factors are held constant except for one: the independent variable.
• A common type of controlled experiment compares a control group against an experimental group. All variables are identical between the two groups except for the factor being tested.
• The advantage of a controlled experiment is that it is easier to eliminate uncertainty about the significance of the results.

Example of a Controlled Experiment

Let's say you want to know if the type of soil affects how long it takes a seed to germinate, and you decide to set up a controlled experiment to answer the question. You might take five identical pots, fill each with a different type of soil, plant identical bean seeds in each pot, place the pots in a sunny window, water them equally, and measure how long it takes for the seeds in each pot to sprout.

This is a controlled experiment because your goal is to keep every variable constant except the type of soil you use. You control these features.

Why Controlled Experiments Are Important

The big advantage of a controlled experiment is that you can eliminate much of the uncertainty about your results. If you couldn't control each variable, you might end up with a confusing outcome.

For example, if you planted different types of seeds in each of the pots, trying to determine if soil type affected germination, you might find some types of seeds germinate faster than others. You wouldn't be able to say, with any degree of certainty, that the rate of germination was due to the type of soil. It might as well have been due to the type of seeds.

Or, if you had placed some pots in a sunny window and some in the shade or watered some pots more than others, you could get mixed results. The value of a controlled experiment is that it yields a high degree of confidence in the outcome. You know which variable caused or did not cause a change.

Are All Experiments Controlled?

No, they are not. It's still possible to obtain useful data from uncontrolled experiments, but it's harder to draw conclusions based on the data.

An example of an area where controlled experiments are difficult is human testing. Say you want to know if a new diet pill helps with weight loss. You can collect a sample of people, give each of them the pill, and measure their weight. You can try to control as many variables as possible, such as how much exercise they get or how many calories they eat.

However, you will have several uncontrolled variables, which may include age, gender, genetic predisposition toward a high or low metabolism, how overweight they were before starting the test, whether they inadvertently eat something that interacts with the drug, etc.

Scientists try to record as much data as possible when conducting uncontrolled experiments, so they can see additional factors that may be affecting their results. Although it is harder to draw conclusions from uncontrolled experiments, new patterns often emerge that would not have been observable in a controlled experiment.

For example, you may notice the diet drug seems to work for female subjects, but not for male subjects, and this may lead to further experimentation and a possible breakthrough. If you had only been able to perform a controlled experiment, perhaps on male clones alone, you would have missed this connection.

• Box, George E. P., et al.  Statistics for Experimenters: Design, Innovation, and Discovery . Wiley-Interscience, a John Wiley & Soncs, Inc., Publication, 2005. 
• Creswell, John W.  Educational Research: Planning, Conducting, and Evaluating Quantitative and Qualitative Research . Pearson/Merrill Prentice Hall, 2008.
• Pronzato, L. "Optimal experimental design and some related control problems". Automatica . 2008.
• Robbins, H. "Some Aspects of the Sequential Design of Experiments". Bulletin of the American Mathematical Society . 1952.
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• Published: 24 June 2024

Evaluation of a knowledge-attitude-practice model based narrative life education program for community-dwelling older adults: a mixed-methods feasibility study

• Xifeng Xie 1   na1 ,
• Li Zhou 2   na1 ,
• Xiaoling Zhang 1 ,
• Huina Zou 1 ,
• Yuanfeng Lu 1 &
• Huimin Xiao 1 , 3  

BMC Geriatrics volume  24 , Article number:  547 ( 2024 ) Cite this article

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The global aging population presents challenges that are particularly acute in China. Older Chinese adults’ attitudes towards death significantly impact their quality of life. Death education is crucial for promoting positive perspectives on life and death. Narrative education offers a promising approach to facilitating death education. Integrating the Knowledge-Attitude-Practice (KAP) model into death education will enhance the feasibility and acceptability of death education programs.

This mixed-methods feasibility study included a quasi-experimental trial and semi-structured interviews. Older adults in the intervention group ( N  = 27) received a 6-week KAP-based narrative life education program in addition to standard community health education; participants in the control group ( N  = 20) received only the normal community health education. In both groups, attitudes toward death and the meaning of life were assessed at baseline and immediately after the intervention. A post-intervention semi-structured interview and satisfaction survey were also conducted for the intervention group.

Forty out of 47 older adults completed the program for an 85.1% retention rate. All of the older adults in the experiment were very satisfied and satisfied with the life education program, and no adverse events were reported. Compared to the control group, participants in the intervention group had a significant decrease in the fear of death ( P  =  0 .028), and substantial improvement in their value of life ( P  =  0 .031), goal of life ( P  =  0 .035), freedom of life ( P  =  0 .003), and the total score for purpose in life ( P  =  0 .017). The qualitative results yielded four themes: profound recognition of life and death, contradiction between thoughts and action, conflict between one’s acceptance and others’ avoidance, and evaluation of the life education program.

Conclusions

The KAP-based narrative life education program is feasible and acceptable for older Chinese community-dwelling adults. It is also potentially effective in improving attitudes toward death attitudes and the meaning of life in this cohort.

Trial registration

This study was retrospectively registered at China Clinical Trial Registry as ChiCTR2300069551 on 2023-03-20. URL of registration: https://www.chictr.org.cn/showproj.html?proj=183176 .

Peer Review reports

The elderly population is poised to significantly increase around the world. By 2050, adults over 65 are projected to account for 16% of the global population, with the proportion of individuals aged 60 and above in China likely reaching 35% [ 1 , 2 ]. Death is inevitable, but it is a sensitive topic, especially in China. Older adults are prone to being increasingly aware of death due to their decline in physical function, the threat of chronic disease, and an increased witnessing of their peers’ deaths [ 3 ].

Attitudes towards death are not only related to the physical and mental health of older adults, but also affect their preparation for death and quality of life beforehand [ 4 ]. Ignorance of death preparation increases fear and anxiety about death [ 5 ]. However, a deep-rooted traditional culture that, emphasizes life and neglects death has made death a taboo subject in China. It is not easy for most Chinese people to communicate about death-related issues [ 6 ]. Although older adults can accept death as a part of life, most of them still feel fear and avoid talking about death-related topics [ 7 ]. Compared to nursing home residents, community-dwelling older adults are more afraid of facing death and feel it is more difficult to deal with life-and death-related issues [ 8 ]. Thus, further exploration is needed regarding how to help Chinese older adults establish a reasonable understanding of and attitudes toward life and death.

The essence of life education for older adults is orientation regarding the subjects of life and death, with death education comprising the core content [ 9 ]. The program teaches individuals how to recognize and face death [ 10 ]. The goal is to facilitate acceptance of end of life, process of death, and experience of bereavement in terms of the individual’s knowledge, attitudes, and skills [ 11 , 12 ]. Previous studies have shown that death education promotes positive changes in death-related attitudes, enhances the sense of meaning in life, and improves the quality of life [ 13 , 14 , 15 ]. However, previous programs have mainly focused on the stages of life and meaning of death and failed to address cultural conflicts in the process of death education, which may result in participants’ psychological discomfort. Thus, developing death education programs have been proposed that operate from the perspective of life’s course in order to reduce negative emotions and the fear of death [ 16 ]. Given the sensitivity of the topic, the method of delivering such education must be carefully considered.

Narrative education is an approach to achieving educational and research purposes by narrating, explaining, and reconstructing the experiences of educators and participants [ 17 ]. When addressing a sensitive topic, narratives generate less resistance because of the storytelling model [ 18 ]. Narratives may also facilitate older adults establishing reasonable cognition, knowledge, and behaviors related to death through introspection regarding their experiences and creation of meaning in their lives [ 19 ]. Therefore, the method can be used in death education in older adults [ 20 , 21 ].

A theoretical model is critical for framing the program, guiding data collection, and interpreting findings [ 22 ]. Various death education models have been developed such as lecture teaching and experience-sharing models [ 16 , 23 ]. In fact, compared to non-narrative messages, messages in narrative education have a stronger persuasive impact on one’s attitudes, intentions, and behaviors, both immediately and over time [ 24 ]. Therefore, a model that comprehensively attaches information acceptance, attitude modification, and behavior transition should be employed. The theory of Knowledge-Attitude-Practice (KAP) was first proposed by Cust and Mayo to explain the progressive relationship of moving from knowledge acquisition to behavior modification in individuals [ 25 ]. With the goal of helping individuals establish positive attitudes and beliefs and shifting towards correct behavior based on the reception and mastery of relevant knowledge, the theory has been widely applied in predicting health-related behaviors and implementing practice-improvement programs [ 26 ]. However, few studies have integrated the KAP model into death education for older adults, though it has the potential to communicate essential information, achieve reasonable life and death cognition, facilitate the maintenance of a positive attitude, and encourage the development of death-coping strategies [ 27 ]. Therefore, this study aimed to develop a KAP-based narrative life education program and explore its feasibility and effects on attitudes toward death and sense of meaning of life in older community-dwelling adults.

Study design

This mixed-methods feasibility study involved a quasi-experimental trial and semi-structured interviews. The goal was to determine the feasibility, acceptability, and primary efficacy of a narrative death education program for community-dwelling older adults. This study was reported following the Mixed Methods Reporting in Rehabilitation & Health Sciences (MMR-RHS) and was performed in accordance with the Declarations of Helsinki [ 28 ].

Setting and sample

From September to November 2022, older adults were recruited from a community located in Fuzhou City, China, from September to November 2022. It home to approximately 4,500 individuals aged 60 and above, constituting more than 19.90% of the total residential population. The inclusion criteria were: (a) aged 60 years and above and (b) able to understand and communicate in Chinese. The exclusion criteria were: (a) with cognitive impairment or (b) with severe visual, auditory, or mental disorders. A sample size between 24 and 50 participants is recommended for feasibility studies [ 29 ]. A total of 47 community-dwelling older adults were recruited for this study. Details of recruitment are in Additional file 1 .

Recruitment

Participants were recruited at the community health service center via two approaches. For on-site recruitment, a recruitment poster was posted at the center. Potential participants who were interested in the study could directly contact the research assistant (RA). The RA then introduced this study to them through a face-to-face interview. For tele-recruitment, the RA interviewed potential participants via telephone, based on a list of older adults provided by the center. Written informed consent was obtained from each participant. After baseline data collection, individuals were invited to voluntarily join either the intervention or control group according to their preferences. Given the sensitivity of the topic of death among Chinese older adults, we did not employ randomization.

Intervention program

Both groups received the usual health education provided by the community health center. The intervention group also received the KAP-based Narrative Life Education (KAPNLE) program.

Intervention group

The KAPNLE program was initially drafted after reviewing KAP theory and multimedia material on life education developed by the research team and engaging in internal brainstorming. Details of the program were revised according to two rounds of comments from a six-expert panel whose research areas involved geriatric nursing, life education, community nursing, psychology, and social work. We also conducted interviews with five community-dwelling older adults and further refined the program based on their feedback.

The final version was composed of four modules: Understanding Life and Death (Knowledge), Viewing Life and Death (Attitude), Preparing for Death (Practice), and Transcending Life and Death (Practice). These four modules covered a total of six sessions, including Life Course , Growing Old Peacefully , Passing Away in Pain , Saying “Goodbye” Well , Expressing “Love” , and Living a Wonderful Life . Each session was conducted according to a four-step narrative process by a researcher who served as the facilitator. Initially, the facilitator presented material on life and death issues and created a context within which participants could easily discuss topics of life and death. Participants were then invited to redescribe the topics in their own way and share their impressions. Next, they were guided to further reflect on their own experiences and discuss views on issues related to life and death. In the final step, activities on related themes were conducted in a relaxed environment to deepen participants’ knowledge and experience of life and death. The program was conducted once a week over six weeks and lasted 30 to 60 min per session. It was held offline in the visiting room of the community healthcare center and attended by groups of six or seven older adults. The details of the program are shown in Additional file 2 .

Control group

The control group met twice a month and only received the usual community health education, which includes topics related to chronic diseases, medication safety, and lifestyle management, their course did not involve life education.

Measurements

Basic information questionnaire.

Demographics and information regarding life and death issues were collected by a self-reported questionnaire designed by our research team (Additional file 3 ). The demographic information included age, gender, religion, education level, marital status, living status, and number of children. The issues related to life and death included life-threatening illness experiences, self-perceived physical health, most profound encounters with death, and communication about death topics.

Death Attitude Profile-Revised (DAP-R)

Death attitudes were assessed using the Chinese version of Death Attitude Profile Revised (DAP-R) [ 30 ]. It consists of 32 items and five dimensions: fear of death, death avoidance, neutral acceptance, approach acceptance, and escape acceptance. Each item is rated on a five-point Likert scale ranging from 1 (strongly disagree) to 5 (strongly agree). Death attitudes are judged by the scores for each dimension. The higher the score, the more inclined the respondent is to this dimension’s attitude. In Chinese older adults, the Cronbach’s α values for the five dimensions were 0.796, 0.670, 0.621, 0.842 and 0.771.

Purpose in Life Test (PIL)

Meaning of life was measured using the Chinese version of Purpose in Life Test (PIL) [ 31 ]. It contains 20 items across four dimensions: quality of life, value of life, goal of life, and freedom of life. Scores are assigned using a five-point Likert scale, with each dimension including positive and negative questions. Scores selected for negative questions are reversed. Higher total scores indicate a greater sense of meaning of life. The Chinese version of the PIL has been validated, with a Cronbach’s α of 0.878.

Satisfaction of the program questionnaire

Respondents’ satisfaction was assessed using a self-designed questionnaire with six items: education theme, education content, education form, education schedule, benefits and practicability, and overall satisfaction. Each item is rated from “strongly satisfied” to “strongly dissatisfied” (Additional file 4 ).

Semi-structured interview

To assess the feasibility of the program for Chinese older adults, we conducted semi-structured interviews with the intervention participants. The interview outline was developed by the research team and began with a primary open-ended question: “What are your perceptions of the KAPNLE?” This question allowed participants to freely express their feelings and feedback about the program. Probing questions were then asked to facilitate in-depth exploration. The interview guideline is shown in Additional file 5 .

Data collection

The quantitative data were collected by another trained RA, who was blind to the group assignments. All participants’ death attitudes and ideas regarding the meaning of life were assessed at baseline and immediately after the program. In addition, the experimental participants were invited to describe their satisfaction with the program.

Participants in the intervention group were interviewed about their perceptions and experiences immediately after the program. A one-on-one semi-structured interview of each was conducted by the RA in the visiting room of the community healthcare center. Each interview lasted about 30 to 45 min, and the content was recorded with the participants’ informed consent.

Data analysis

Quantitative data.

The quantitative data were analyzed using IBM SPSS 27.0. The data were normality tested before being analyzed. Mean and standard deviation, median (P 25 , P 75 ), number, and percentage were determined to describe the older adults’ characteristics. A Chi-square test, t-test, Fisher’s Exact Test, Wilcoxon rank sum test, and multiple regression were used in this study. The Wilcoxon rank sum test was employed to test differences in the attitudes toward death and meaning of life between the groups for data with abnormal distributions. Multiple regression analysis was used to adjust for baseline imbalances in attitudes toward death and meaning of life between the two groups. Line graphs were used to describe any changes.

Qualitative data

The interviews were transcribed verbatim by a researcher within 24 h of their being conducted. Qualitative content analysis was used to analyze the qualitative data [ 32 ]. The steps applied were as follows: (a) identify and segment meaningful sentences within each interview text to generate “meaning units”, (b) condense semantic units into “condensed meaning units”, (c) abstract condensed semantic units to generate “codes”, (d) compare codes for commonalities, categorize codes into “categories”, (e) discuss and consensus on categories and formulate “themes” (Table  1 ). The interview data were coded by two researchers working independently. To ensure the credibility of the results, we used peer debriefing, member checks and held regular meetings to discuss the data analysis process and inconsistent opinions.

Feasibility of the program

A total of 47 older adults were recruited, including 27 in the intervention group and 20 in the control group. Seven in the intervention group withdrew from the study. Therefore, a total of 40 individuals completed the follow-up measurement and were included in analysis (Additional file 3 ). The total retention rate was 85.1%. Sixteen participants in the intervention group finished four to six sessions of the program, while four participants missed three out of six sessions. Their reasons included physical illness, family members’ illness or death, schedule conflicts, and self-isolation due to COVID-19 infection. The completion rate was 80.0% (16/20). All 20 intervention participants were very satisfied or satisfied with the education program, including its modules, sessions, implementation theme, and overall participation experience. No adverse events were reported during the study.

Participant characteristics

The mean age of the participants was 73.33 (6.16) years, with ages ranging from 63 to 88 years. Most of them were women (62.5%), not religious (75.0%), married (75.0%), had a high school education or above (75.0%), had one child (62.5%), lived with their children or spouse (85.0%), perceived themselves as in general or poor physical health (65.0%),were moved by their parents’ death (72.5%), and never communicated about death (62.5%). A small percentage had suffered (22.5%) or had family member who suffered (15.0%) from a life-threatening disease. There were no significant differences in demographic characteristics between the two groups (Additional file 6 ).

Preliminary efficacy of the program

Death attitudes.

Compared to the control group, a significant decrease was observed in fear of death in the intervention group ( P  = 0.028), no significant differences were detected in the other dimensions or total score of the DAP-R ( P  > 0.05). However, upward trends were observed in the DAP-R’s natural acceptance and approach acceptance. For the dimensions of death avoidance and escape acceptance, slight changes could also be found. After the intervention, there was no significant difference in total score of the DAP-R between the two groups, but the score for the control group fluctuated greatly during the follow-up.

Meaning of life

Compared to the baseline measurement, there were greater increases in value of life, goal of life, freedom of life, and total score of the PIL for the intervention group ( P <  0.05), and a slight upward trend was observed for freedom of life. However, no significant difference in the PIL’s quality of life was found between the two groups ( P  = 0.141). As shown in the line chart, differences were observed in the trends in the total scores for the PIL between the two groups, after the intervention, the scores for the intervention group increased markedly compared to the control group after the intervention (Additional file 7 and Additional file 8 ).

Perceptions of the program

According to the post-intervention interview, four themes and ten sub-themes were identified: (a) profound recognition of life, (b) contradiction between thoughts and action, (c) conflict between self-acceptance and others’ avoidance, and (d) evaluation of the life education program.

Profound recognition of life

This theme relates to older adults’ cognition of life and death, and contains three sub-themes:

Vague concept of life and death at the early stage

Some respondents stated that they had a vague understanding of life and death at the beginning of the program and had difficulties in describing or explaining them. They also expressed that they paid no attention to life and death-related issues in their daily lives over in years past.

“When you asked me about life and death, I really didn’t know how to answer. I never thought about it before. Life means I’m still alive; and death means I am away from the world. Is it right?” (Participant 2) .

Gradually clarifying life and death issues

After the first two sessions of the program. participants expressed that they had a figurative understanding of life and death. They realized the logical relationship between the two, and further accepted their unique lives.

“I didn’t know what life was like before, but now I do. My life is like a sunflower gone to seed. If I pass away like a flower withers, I still have something left in this world.” (Participant 1) .

“I feel my that life is a line with ups and downs, starting from zero, maybe ending at 100. Each number represents a stage of my life, and contains many important things.” (Participant 10) .

Discussing life and death with an open mind at the final stage

At the end of the program, participants expressed that they could easily discuss topics related to life and death and felt comfortable in the process. They also said that the program reduced their negative feelings about death and encouraged them to pursue meaning in their lives.

“Now I have a new perspective on life, and the fears about death seem to have vanished. Dying at the age of 20 or 100 are both lifetimes and being dead or alive cannot be decided by oneself. So, I will cherish my life when I am alive and enjoy life every day.” (Participant 11) .

“I realized that talking about death is not as difficult as I imagined. It actually could be very easy, just like this program.” (Participant 6) .

Contradictions between thoughts and actions

This theme is related to inconsistencies between positive thoughts about life and death and passive behaviors regarding death preparation, it includes the following two sub-themes:

Hold positive thoughts on to embrace life and deaths

Some participants expressed that they held rational and open attitudes about life and death after the program. They realized the inevitability of death, and calmly accept it as a normal phenomenon.

“I think that everyone will die in the end, and nobody can avoid death. I must go on my last journey well. As long as I have done all things, I can go without any regret.” (Participant 12) .

“I never thought about it (death) until I participate in this program. It reminded me that I would pass away one day. Then, I started to think about death issues in advance. If I had not participated in it, I wouldn’t have come to this step.” (Participant 5) .

Hesitating to make life and death plan

Some of the older adults emphasized living in the present, and were unwilling to make death preparation in advance.

“I am not thinking about what I should do about death at present. I just want live in the moment, do what I need to do at present, and stay happy.” (Participant 7) .

“I will think about these things, such as the cemetery or family arrangements, when I am more than 70 or 80 years old. But now, I live in the present and enjoy life.” (Participant 8) .

Conflict between one’s acceptance and others’ avoidance

This theme is related to the acceptability of life education, two sub-themes comprise this category.

Self-acceptance and openly discussion about life and death

Some participants noted that the program changed their attitudes about life and death. They not only felt comfortable talking about death-related topics, but also recommended the program to others.

“I think this program should be recommended to more older adults, especially to those who are sensitive and concerned about death. It can teach them how to deal with life and death, and overcome fear of death.” (Participant 10) .

Others’ opposition to life education due to stereotypes

Some participants mentioned that their family members or friends opposed their participation in the life education program due to the sensitivity of death-related topics.

“Most older adults around me resist talking about death. They tried to persuade me not to participate in life education because death is a taboo.” (Participant 4) .

“My family didn’t want me to attend such activity, for it will bring bad fortune. Therefore, I attended this class without telling my family.” (Participant 13) .

Evaluation of the life education program

This theme is related to the participants’ perspectives on the program and includes two sub-themes:

Affirmation of the program

Some of the older adults reflected that conversations about life and death were sensitive but acceptable. They further expressed that they could benefit from life education.

“It is not easy to talk about death-related topics, but I think life education is very important. Because it can help older adults do enough preparation and pass away without any regrets. I support this program.” (Participant 3) .

Some participants believed that the program was feasible because narrative life education enabled them to pick up death topic more easily. Moreover, the program was conducted in groups, which established a supportive environment.

“The most impressive thing about this program is telling stories. I have received various stories from others. Then, I felt pleased to share my thoughts and discuss with others about these stories.” (Participant 15) .

“It was easier for me to talk about life and death in groups. When someone started to talk about that, then we thought we could talk about that as well. You know, such an environment is important.” (Participant 17) .

Comments and suggestions

Some of the respondents mentioned that the group-based education might ignore individuals’ specific needs and suggested combining group education with individual counseling in the future.

“There was often someone absent in the group. Therefore, I think the program could add some individual education content, which would help the absentee to catch up with the progress.” (Participant 10) .

Regarding the resistance of their family or close friends to this program, some of the older adults hoped that the program could expand to include outside participants, such as by allowing them to invite people around them to participate.

“In fact, I still hope to get support and understanding from my family or friends, so maybe you can try to invite them to participate in this life education together.” (Participant 4) .

To the best of our knowledge, this is the first study to develop and evaluate the feasibility and preliminary effects of the KAPNLE. Our quantitative findings demonstrate that the program is effective at promoting a positive transition in death attitude and improving the meaning of life for community-dwelling older adults. The qualitative results indicate that the program is both acceptable and feasible. It also supports the potential of using the KAPNLE to change people’s attitudes toward life and death.

Our study indicates an acceptable feasibility among older adults. Seven participants withdrew from the study (a dropout rate of 14.8%), which was higher than a previous study [ 33 ]. One possible reason for the dropout rate may be that the sensitivity of the topic may have negatively affected these Chinese older adults’ willingness to participate [ 34 ]. Additionally, the study was conducted during the period of the COVID-19 pandemic, when older adults were more concerned about their physical health and less likely to engage in social activities [ 35 ]. There were no reported adverse events during the study for the intervention group, and all participants were either very satisfied or satisfied with the KAPNLE, indicating that the program is acceptable and safe. The KAP theory focuses on shared goals, transparency, accountability, and respect, and these factors are essential for effective collaboration in patient engagement [ 36 ]. Based on group-formatted discussions, the contents of the KAPNLE programs matched knowledge to attitudes, and then to action, in sequence rather than in a fragmented fashion. Participants’ feeling of freedom was encouraged at every step to promote their acceptance of death education. The older adults participating in our study stated that narration helped them address topics of life and death more easily and discuss related issues with less emotional resistance. In fact, the narrative approach emphasized finding psychosocial strengths and highlighting their own personal meaning of life [ 37 ]. Compared to didactic messages, those in narrative form tend to be more acceptable due to their natural format and the emotional engagement, and positive thoughts they inspire [ 38 ]. The narrative method can provide rich insights into the meanings associated with phenomena due to its deep subjectivity and inherent explanations of information [ 39 ]. Based on the multimedia cases we provided, the KAPNLE program adopted a four-step group-based narrative process that created a relaxed and supportive environment by focusing on storytelling, thus promoting acceptance of issues related to life and death [ 40 ]. This approach is useful in helping older adults understand and reconsider events in their lives, encouraging them to share information and transform their attitudes about death in an acceptable way [ 41 ].

Our quantitative results demonstrate that it is feasible to use the KAPNLE to promote a positive transition in attitudes toward death, especially in terms of reducing the fear of death, and these results are consistent with previous studies [ 33 , 42 ]. The post-program qualitative interviews indicated similar findings. The program provided relevant information about the process of life, hospice care, living wills, and death preparations. Correlating with KAP theory, the provision of comprehensive information about death can promote a more positive attitude, which can then help bridge the knowledge-intention gap in discussions about and preparations for death [ 43 ]. Moreover, participants also disclosed that the program had a promising effect on their understanding of the meaning of life and improved their acceptance of death. The KAPNLE guided these older adults to realize the inevitability of death through free discussion in a non-didactic group format and relaxing circumstance. Once their cognition of death was modified, they may experience greater openness to the possibilities available to them throughout the rest of their lives [ 44 ]. In line with previous studies, our results suggest that the KAPNLE improves the sense of meaning of life in older adults [ 45 , 46 ]. Participants were guided to be more aware of their achievements and self-value by reviewing their lives, and resist the consciousness of death by perceiving and maintaining a positive sense of self-meaning [ 47 ]. In line with concepts of KAP theory, modification of the cognition of and attitudes about death plays a crucial role in clarifying the meaning of life, finding purpose in life, and identifying suitable coping strategies [ 48 ].

Although the program encouraged participants to set goals and make plans for the rest of their lives, no direct changes in participants’ behavior were observed. The behaviors related to death preparation among older adults are often affected by family circumstances and subjective norms, and it is challenging to establish advanced death preparation due to the Chinese culture [ 49 , 50 ]. In addition, health status is associated with death preparation in older adults. Most participants in our study were not facing life-threatening illnesses, which may have reduced their initiative to engage with this program [ 51 ].

This study has some limitations, because of the cultural influences and stereotypes of death education from older adults’ family members, the dropout rate was relatively high. Moreover, although most participants had accepted the life process and found the meaning of life, they remained negative about making pre-death plans. This indicates that some of them may not have been operationally prepared for death and thus could not achieve the behavioral transition envisioned by the KAP model. Some participants also suggested that their family members or acquaintances should be involved in this program. In fact, due to the sensitivity of discussing about death within the traditional views of Chinese older adults, we allowed participants to choose group assignment by themselves to prevent ethical conflicts. This probably introduced selection bias for participants who chose to receive the intervention may have already been more open-minded about discussing death. In addition, this study did not employ a randomized controlled trial because our purpose was to explore the feasibility and acceptability of the program. Further research with rigorous design is needed to enhance participants’ adherence to narrative death education and to optimize intervention strategies.

This study constructed a KAP-based narrative life education program for community-dwelling older adults. We found that the life education program is acceptable and feasible among this cohort and could potentially improve attitudes toward death and the meaning of life. Future research with a rigorous design is necessary to test the effectiveness of narrative death education in older adults.

Data availability

The datasets used and/or analyzed during the current study are available from the corresponding author upon reasonable request. The data are not publicly available due to privacy and ethical restrictions.

Abbreviations

Knowledge-Attitude-Practice

KAP-based Narrative Life Education

Research assistant

Death Attitude Profile-Revised

Purpose in Life Test

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The authors gratefully thank all participants for their support and cooperation.

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Xifeng Xie and Li Zhou contributed equally to this work.

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School of Nursing, Fujian Medical University, Fuzhou City, Fujian Province, China

Xifeng Xie, Xiaoling Zhang, Huina Zou, Yuanfeng Lu & Huimin Xiao

Nanjie Community Health Service Center, Fuzhou City, Fujian Province, China

Research Center for Nursing Humanity, Fujian Medical University, Fuzhou City, Fujian Province, China

Huimin Xiao

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X.X.: Methodology, Investigation, Data Curation, Data Analysis, Visualization, Writing - Original Draft; L.Z.: Methodology, Investigation, Data Curation, Data Analysis; X.Z.: Data Curation, Data Analysis; H.Z.: Data Curation, Data Analysis; Y.L.: Writing-Review & Editing, Revision; H.X.: Conceptualization, Methodology, Resources, Supervision, Writing-Review & Editing. All authors reviewed the manuscript.

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Xie, X., Zhou, L., Zhang, X. et al. Evaluation of a knowledge-attitude-practice model based narrative life education program for community-dwelling older adults: a mixed-methods feasibility study. BMC Geriatr 24 , 547 (2024). https://doi.org/10.1186/s12877-024-05153-4

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