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Pre-experimental Design: Definition, Types & Examples

• October 1, 2021

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

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

What is Pre-experimental Research?

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

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

What is Pre-experimental Design?

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

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

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

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

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

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Types of Pre-experimental Designs

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

One-shot case study design

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

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

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

One-group pretest-posttest design

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

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

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

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

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

Static-group comparison

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

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

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

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

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

Characteristics of Pre-experimental Designs

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

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

Validity of results in Pre-experimental Designs

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

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

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

Advantages of Pre-experimental Designs

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

Disadvantages of Pre-experimental Designs

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

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

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

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

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

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

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

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

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

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

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Pre-experimental designs.

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

Types of Pre-Experimental Design

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

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

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

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

Validity of Results

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

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

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

Advantages and Disadvantages

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

Disadvantages

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

Pre-Experimental Design

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

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

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

Characteristics of Pre-Experimental Design

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

Single Group

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

Lack of Control Group

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

Absence of Random Assignment

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

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

One-Shot Case Study

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

One-Group Pretest-Posttest Design

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

Static-Group Comparison

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

Limitations

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

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

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

• Descubre el diseño pre experimental: la clave para una investigación efectiva

El diseño pre experimental es una técnica utilizada en investigación para evaluar el efecto de una variable independiente sobre una variable dependiente en un grupo de individuos. En este tipo de diseño, el investigador no dispone de control sobre la asignación de los participantes a los grupos de estudio, ni tampoco se aplica una aleatorización en el proceso de selección. A pesar de estas limitaciones, el diseño pre experimental es una herramienta valiosa para la investigación científica, ya que permite obtener información útil que puede ser utilizada para el diseño de estudios futuros más rigurosos. En este artículo, se explorará en detalle qué es un diseño pre experimental, cómo se realiza y cuáles son sus ventajas y limitaciones.

Desventajas

¿cuál es la definición de diseño de investigación pre-experimental de acuerdo a sampieri, ¿qué clases de diseños experimentales existen, ¿qué significa diseño experimental y cuáles son algunos ejemplos, 1) diseño pre-experimental: definición y aplicaciones en la investigación social, 2) explorando el diseño pre-experimental: cómo se utiliza y cuándo es apropiado en la investigación científica.

• El diseño pre experimental ofrece una forma conveniente y efectiva de realizar investigaciones preliminares o exploratorias. Este tipo de diseño permite a los investigadores recopilar datos rápidamente y evaluar la viabilidad y el potencial de la hipótesis de investigación, lo que puede ahorrar tiempo y recursos en investigaciones posteriores más complejas.
• El diseño pre experimental es especialmente útil para investigaciones en situaciones difíciles de controlar o en las que no es posible utilizar un grupo de control adecuado. Al permitir a los investigadores comparar dos o más grupos, antes y después de la introducción del tratamiento experimental, este diseño puede proporcionar información valiosa, aunque limitada, sobre los efectos potenciales de la intervención o tratamiento.
• Falta de control sobre las variables: En un diseño pre experimental, no hay un control adecuado sobre las variables que pueden afectar el resultado de los experimentos. Por lo tanto, es difícil determinar si los resultados obtenidos son realmente causados ​​por la intervención o si existe alguna otra variable que pudo haber influido en los resultados.
• Limitaciones en la generalización de los resultados: Otro problema con el diseño pre experimental es que los resultados obtenidos pueden no ser generalizables a otras poblaciones o situaciones debido a la falta de control sobre las variables. Esto puede limitar la capacidad de los investigadores para sacar conclusiones y hacer predicciones sobre cómo cualquier tratamiento o intervención afectará a las personas en situaciones similares.

De acuerdo con Hernández Sampieri en su libro Metodología de la investigación, el diseño de investigación pre-experimental con un solo grupo, también conocido como diseño de preprueba - posprueba con un solo grupo, implica la aplicación de una prueba previa al tratamiento experimental, seguida de la administración del tratamiento y, finalmente, la aplicación de una prueba posterior al tratamiento. Este diseño se utiliza cuando no es posible contar con un grupo de control y, por lo tanto, no se puede establecer una comparación entre un grupo tratado y un grupo no tratado. Es importante tener en cuenta que este diseño tiene limitaciones y puede generar resultados no concluyentes.

El diseño de investigación pre-experimental con un solo grupo puede ser útil cuando no es posible contar con un grupo de control, pero es importante tener en cuenta sus limitaciones y la posibilidad de obtener resultados no concluyentes. Este diseño implica la aplicación de una prueba previa al tratamiento experimental, seguida de la administración del tratamiento y, finalmente, la aplicación de una prueba posterior al tratamiento.

Dentro de los diseños experimentales, el diseño pre-experimental es uno de los más utilizados. Este diseño se divide en tres tipos: Diseño de investigación de un grupo, Equiparación de dos grupos estáticos y Diseño de investigación de una muestra. El diseño pre-experimental se caracteriza por no tener un grupo de control y no poder establecer una relación causal entre las variables. A pesar de sus limitaciones, este diseño es útil para estudios preliminares o pequeñas investigaciones donde no se cuenta con muchos recursos.

Los diseños pre-experimentales son ampliamente utilizados en la investigación, y se dividen en tres tipos diferentes. Aunque tienen limitaciones, estas investigaciones son útiles en estudios preliminares o investigaciones pequeñas donde los recursos son limitados. Sin embargo, debido a la falta de un grupo de control, no se puede establecer una relación causal entre las variables.

El diseño experimental es una herramienta crucial en la investigación científica para validar o refutar hipótesis sobre las causas y efectos de dos variables. Es comúnmente utilizado en estudios clínicos, como el análisis de un medicamento en grupos de control, pero también puede aplicarse en diferentes campos, desde la psicología hasta la ingeniería. Los investigadores utilizan técnicas de estadística para analizar los datos recopilados y determinar la relación entre las variables estudiadas. El diseño experimental es esencial para la elaboración de teorías confiables y la toma de decisiones basadas en evidencia sólida.

El diseño experimental respalda la validez de las hipótesis sobre las causas y efectos de las variables en múltiples campos de investigación científica. La aplicación de técnicas estadísticas permite analizar los datos y establecer relaciones relevantes. Proporciona una herramienta esencial para la toma de decisiones basadas en teorías sólidas y evidencia confiable.

El diseño pre-experimental es una técnica de investigación social que se utiliza para medir la relación entre dos o más variables sin la aplicación de un control riguroso. En este tipo de diseño, no se elige aleatoriamente a los individuos o grupos de estudio, sino que se les selecciona según su disponibilidad o conveniencia. Por lo tanto, la información que se obtiene a través de este método es limitada y no puede ser considerada concluyente. Sin embargo, se utiliza con frecuencia en investigaciones preliminares que buscan establecer una base de datos para futuros estudios más rigurosos.

El diseño pre-experimental es una técnica de investigación social que no implica un control riguroso y no se realiza a través de una selección aleatoria. A pesar de su limitada eficacia y conclusividad, se utiliza comúnmente para crear una base de datos en investigaciones preliminares que pueden ser utilizadas en futuros estudios más rigurosos.

El diseño pre-experimental se refiere a aquellos estudios en los que el investigador no manipula directamente las variables independientes, sino que simplemente las observa en un entorno natural. Este enfoque puede ser útil en situaciones en las que no se tiene control sobre las condiciones experimentales o cuando se desea obtener información preliminar antes de embarcarse en un estudio más exhaustivo. Es importante tener en cuenta que los resultados obtenidos a partir de un diseño pre-experimental deben ser interpretados con precaución, ya que no existe un grupo de control y no se puede asegurar que cualquier cambio observado sea el resultado de la variable independiente en particular.

El diseño pre-experimental es útil para obtener información preliminar de variables independientes en un entorno natural, pero la falta de un grupo de control y la falta de control sobre las condiciones experimentales requieren una interpretación cuidadosa de los resultados.

En resumen, un diseño pre-experimental es un tipo de diseño de investigación que se utiliza en situaciones en las que no es posible o conveniente establecer un grupo control o asignar aleatoriamente los participantes a los grupos de tratamiento. Si bien puede proporcionar información valiosa, este tipo de diseño tiene algunas limitaciones, como la falta de control sobre las variables que podrían afectar los resultados y la dificultad para establecer relaciones causales. Por lo tanto, es esencial considerar cuidadosamente las ventajas y desventajas de un diseño pre-experimental antes de elegirlo para una investigación en particular. Además, se deben utilizar los métodos de análisis estadísticos adecuados para evitar la interpretación errónea de los resultados. En resumen, el diseño pre-experimental es un método útil para estudiar ciertos fenómenos, pero debe utilizarse con precaución y con una comprensión clara de sus limitaciones.

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58 12.2 Pre-experimental and quasi-experimental design

Learning objectives.

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

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

Quasi-experimental designs are similar to true experiments, but they lack random assignment to experimental and control groups. The most basic of these quasi-experimental designs is the nonequivalent comparison groups design (Rubin & Babbie, 2017).  [1] The nonequivalent comparison group design looks a lot like the classic experimental design, except it does not use random assignment. In many cases, these groups may already exist. For example, a researcher might conduct research at two different agency sites, one of which receives the intervention and the other does not. No one was assigned to treatment or comparison groups. Those groupings existed prior to the study. While this method is more convenient for real-world research, researchers cannot be sure that the groups are comparable. Perhaps the treatment group has a characteristic that is unique–for example, higher income or different diagnoses–that make the treatment more effective.

Quasi-experiments are particularly useful in social welfare policy research. Social welfare policy researchers like me often look for what are termed natural experiments , or situations in which comparable groups are created by differences that already occur in the real world. For example, Stratmann and Wille (2016)  [2] were interested in the effects of a state healthcare policy called Certificate of Need on the quality of hospitals. They clearly cannot assign states to adopt one set of policies or another. Instead, researchers used hospital referral regions, or the areas from which hospitals draw their patients, that spanned across state lines. Because the hospitals were in the same referral region, researchers could be pretty sure that the client characteristics were pretty similar. In this way, they could classify patients in experimental and comparison groups without affecting policy or telling people where to live.

There are important examples of policy experiments that use random assignment, including the Oregon Medicaid experiment. In the Oregon Medicaid experiment, the wait list for Oregon was so long, state officials conducted a lottery to see who from the wait list would receive Medicaid (Baicker et al., 2013). [3] Researchers used the lottery as a natural experiment that included random assignment. People selected to be a part of Medicaid were the experimental group and those on the wait list were in the control group. There are some practical complications with using people on a wait list as a control group—most obviously, what happens when people on the wait list are accepted into the program while you’re still collecting data? Natural experiments aren’t a specific kind of experiment like quasi- or pre-experimental designs. Instead, they are more like a feature of the social world that allows researchers to use the logic of experimental design to investigate the connection between variables.

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

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

When true experiments and quasi-experiments are not possible, researchers may turn to a pre-experimental design (Campbell & Stanley, 1963).  [4] Pre-experimental designs are called such because they often happen before a true experiment is conducted. Researchers want to see if their interventions will have some effect on a small group of people before they seek funding and dedicate time to conduct a true experiment. Pre-experimental designs, thus, are usually conducted as a first step towards establishing the evidence for or against an intervention. However, this type of design comes with some unique disadvantages, which we’ll describe as we review the pre-experimental designs available.

If we wished to measure the impact of a natural disaster, such as Hurricane Katrina for example, we might conduct a pre-experiment by identifying an experimental group from a community that experienced the hurricane and a control group from a similar community that had not been hit by the hurricane. This study design, called a static group comparison , has the advantage of including a comparison group that did not experience the stimulus (in this case, the hurricane). Unfortunately, it is difficult to know those groups are truly comparable because the experimental and control groups were determined by factors other than random assignment. Additionally, the design would only allow for posttests, unless one were lucky enough to be gathering the data already before Katrina. As you might have guessed from our example, static group comparisons are useful in cases where a researcher cannot control or predict whether, when, or how the stimulus is administered, as in the case of natural disasters.

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

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

As implied by the preceding examples where we considered studying the impact of Hurricane Katrina, experiments do not necessarily need to take place in the controlled setting of a lab. In fact, many applied researchers rely on experiments to assess the impact and effectiveness of various programs and policies. You might recall our discussion of arresting perpetrators of domestic violence in Chapter 6, which is an excellent example of an applied experiment. Researchers did not subject participants to conditions in a lab setting; instead, they applied their stimulus (in this case, arrest) to some subjects in the field and they also had a control group in the field that did not receive the stimulus (and therefore were not arrested).

Key Takeaways

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

Image attributions

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

• Rubin, C. & Babbie, S. (2017). Research methods for social work (9th edition) . Boston, MA: Cengage. ↵
• Stratmann, T. & Wille, D. (2016). Certificate-of-need laws and hospital quality . Mercatus Center at George Mason University, Arlington, VA. Retrieved from: https://www.mercatus.org/system/files/mercatus-stratmann-wille-con-hospital-quality-v1.pdf ↵
• Baicker, K., Taubman, S. L., Allen, H. L., Bernstein, M., Gruber, J. H., Newhouse, J. P., ... & Finkelstein, A. N. (2013). The Oregon experiment—effects of Medicaid on clinical outcomes.  New England Journal of Medicine ,  368 (18), 1713-1722. ↵
• Campbell, D., & Stanley, J. (1963). Experimental and quasi-experimental designs for research . Chicago, IL: Rand McNally. ↵

Scientific Inquiry in Social Work Copyright © 2018 by Matthew DeCarlo is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

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14.4 Pre-experimental design

Learning objectives.

Learners will be able to…

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

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

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

One group pretest/posttest design

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

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

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

One group posttest-only design

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

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

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

Key Takeaways

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

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

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

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

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

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

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