A researcher observes that children who attend a private preschool have higher reading scores in first grade than children who attend a public preschool. The researcher concludes that attending private preschool causes an increase in reading ability. Which statement provides the most accurate critique of this conclusion?

What is the primary reason that quasi-experimental research cannot definitively establish cause-and-effect relationships?

While quasi-experimental research manipulates an independent variable before measuring a dependent variable, it still cannot definitively establish cause-and-effect relationships. The absence of random assignment or counterbalancing means researchers cannot confidently rule out confounding variables—pre-existing differences between conditions—as the true cause of any observed outcomes.

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Quasi-experimental research is a methodological design that incorporates some, but not all, of the essential features of a true experiment. While researchers using this approach may introduce a treatment or comparison condition, they frequently fail to use random assignment to place participants into distinct groups or omit counterbalancing to control for potential order effects. Consequently, while it offers more control than purely correlational studies, quasi-experimental research generally possesses lower internal validity than true experimental research.

Quasi-Experimental Research

A confounding variable is an extraneous variable that differs on average across levels of the independent variable, meaning it varies systematically alongside it. Because this variable differs systematically across conditions just like the independent variable, it provides a plausible alternative explanation for any observed difference in the dependent variable, making it difficult to establish a clear cause-and-effect relationship. For instance, if an extraneous variable like intelligence is substantially lower in one group than another, it becomes a confounder that confuses the results.

Confounding Variable

Spielman, R. M., Jenkins, W. J., & Lovett, M. D. (2020, April 22). Psychology (2nd ed.). OpenStax. https://openstax.org/books/psychology-2e/pages/1-introduction

OpenStax Psychology (2nd ed.) Textbook

https://kpu.pressbooks.pub/psychmethods4e/

KPU Research Methods in Psychology - 4th American Edition

An example of a quasi-experimental study is investigating the effect of a person's sex on their spatial memory. In this case, sex is the independent variable, and spatial memory is the dependent variable. Researchers can observe and compare differences between males and females, but they cannot manipulate or randomly assign a participant's sex, which prevents them from making a causal claim.

Example of a Quasi-Experimental Study: Sex and Spatial Memory

A one-group posttest only design is a quasi-experimental approach where a researcher applies a treatment or manipulates an independent variable, and then measures the dependent variable just once after the intervention. It is considered the weakest quasi-experimental design because it lacks a control or comparison group. Without this comparison, there is no way to determine what the participants' outcomes would have been had they not completed the treatment.

One-Group Posttest Only Design

A one-group pretest-posttest design is a quasi-experimental research method where a single group of participants is measured on a dependent variable both before and after a treatment is administered. Unlike a within-subjects experiment, this design does not counterbalance the order of conditions because the treatment must inherently occur between the initial baseline measurement (pretest) and the subsequent measurement (posttest). While an improvement in posttest scores compared to pretest scores might intuitively suggest that the treatment was effective, such conclusions are difficult to make with certainty because the absence of a control group leaves the study highly vulnerable to alternative explanations and threats to internal validity.

One-Group Pretest-Posttest Design

A nonequivalent groups design is a specific type of between-subjects design and a quasi-experimental approach in which researchers do not use random assignment to allocate participants to their respective conditions. Due to this absence of random assignment, the resulting comparison groups are nonequivalent, meaning they are likely to have inherent, pre-existing differences. These initial dissimilarities introduce potential confounding variables that can significantly threaten the internal validity of the research.

Nonequivalent Groups Design

An example of a quasi-experimental study lacking random assignment involves evaluating an anti-bullying program across two similar schools. A researcher implements the program in one school (the treatment group) and compares the bullying incidents to the other school (the control group). Although this design uses a comparison group, the inability to randomly assign individual students to the schools means there may be a selection effect; preexisting differences between the students in the two schools could alternatively explain any observed differences in bullying.

Example of a Quasi-Experimental Study: Anti-Bullying Program

A selection effect is a type of confounding variable that occurs when the participants in a treatment group differ from those in a control group in some systematic, pre-existing way. This issue frequently arises in quasi-experimental designs where researchers cannot use random assignment to place participants into conditions. When a selection effect is present, it provides an alternative explanation for the results, making it difficult to confidently determine whether the observed differences in the dependent variable were caused by the experimental treatment or by the inherent differences between the groups.

Selection Effect

Experimental, quasi-experimental, and non-experimental (correlational) research vary systematically along a spectrum of internal validity. True experimental research generally possesses the highest internal validity because it utilizes the manipulation of an independent variable and strict control over extraneous variables to confidently rule out alternative explanations. In contrast, non-experimental research ranks lowest in internal validity because it fundamentally lacks variable manipulation and control. Quasi-experimental research occupies the middle ground; it incorporates some structural features of an experiment but lacks others, such as the random assignment of participants.

Comparison of Internal Validity Across Research Designs

The Tuskegee syphilis study, conducted by the US Public Health Service from 1932 to 1972, is a tragic historical example of extreme injustice in scientific research. The study involved poor African American men in Alabama who were misled into believing they were receiving treatment for 'bad blood' while actually being observed for the untreated progression of syphilis. The researchers deliberately denied the men treatment even after penicillin became the standard cure, clearly violating the ethical mandate to treat participants fairly and highlighting the urgent need to protect marginalized groups from disproportionate research risks. The profound betrayal of these men was formally acknowledged in 1997 when President Bill Clinton issued a public apology on behalf of the US government, emphasizing how the participants had been exploited without their knowledge or consent.

Tuskegee Syphilis Study

In quasi-experimental research, researchers manipulate an independent variable prior to measuring the dependent variable. This chronological sequence effectively resolves the directionality problem—a common issue in purely non-experimental studies—because the established timeline clarifies which variable is the potential cause and which is the effect.

Elimination of Directionality Problem in Quasi-Experiments

Quasi-experiments are frequently employed to assess the effectiveness of specific treatments, such as educational interventions or psychotherapeutic approaches. They are particularly valuable and common in real-world field settings where practical, logistical, or ethical constraints make it challenging or impossible to randomly assign participants to different conditions.

Applications of Quasi-Experimental Research

Causal Limitations of Quasi-Experimental Research

Which of the following is a key characteristic that distinguishes quasi-experimental research from a true experiment?

There are two general approaches to removing confounds: 
• Randomize participants between conditions of the experiment 
• Experimentally control for potential confounds

Two approaches to removing confounds

A classic example of a spurious correlation is the observed relationship between ice cream sales and crime rates. While data shows both increase concurrently, one does not cause the other. The association is more likely explained by a third, confounding variable: temperature. During warm weather, more people are outside, leading to increased social interactions and potential for crime. Simultaneously, warm weather drives up the demand for cool treats like ice cream. Therefore, the temperature independently influences both variables, creating a statistical correlation without a direct causal link.

Example of Spurious Correlation: Ice Cream Sales and Crime Rates

Based on the research finding described in the case study, identify a likely third variable that is influencing both the number of firefighters and the amount of property damage. Explain how this variable creates the misleading impression of a direct relationship between the two.

Analyzing a Research Finding

Based on the study described, identify a likely third variable that could be influencing both the number of books in the home and the child's grades, and explain how this variable could account for the observed correlation.

Interpreting Correlational Research

A between-subjects experiment is an experimental design approach wherein each participant is exposed to only a single level of the independent variable. This method differs fundamentally from within-subjects designs, as individuals are tested in just one specific condition rather than across all experimental variations.

Between-Subjects Experiment

In a hypothetical study evaluating the effect of mood on memory, participants in a positive mood condition might score higher on a memory task than those in a negative mood condition. However, if participants in the positive mood condition also possess substantially higher intelligence quotients (IQs) on average than those in the negative mood group, IQ acts as a confounding variable. This systematic difference provides an alternative explanation for the results, making it unclear whether the positive mood or the higher intelligence caused the superior memory performance.

Example of a Confounding Variable: Mood and Memory

What type of variable is an extraneous factor that differs on average across levels of the independent variable, providing a plausible alternative explanation for any observed difference?

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