Quasi-Experimental Designs in Nursing Research: An Overview

Quasi-experimental designs represent a valuable research methodology that bridges the gap between the rigor of true experimental methods and the flexibility of observational studies (Maciejewski, 2020). This approach is particularly useful in nursing research and other fields such as social sciences, education, and public health, where true experiments may not be feasible or ethical. Quasi-experimental studies are often utilized when the investigator cannot implement a control group or randomize study groups. If it is not feasible to randomize an intervention or establish a control group, additional factors can be included in the design to strengthen internal validity (Gallin, 2018).

Understanding Quasi-Experimental Designs

The quasi-experimental design is a research methodology that lies between the rigor of a true experimental method (true experimental design includes random assignment to at least one control and one experimental/interventional group) (Hulley, 2013) and the flexibility of observational studies (Maciejewski, 2020). Quasi-experimental design strategies are those that, while not incorporating every component of a true experiment, can be developed to make some inferences by managing potential risks to internal validity. Internal validity represents the level of confidence that a cause-and-effect relationship observed in a study is not influenced by other variables (Patino & Ferreira, 2018).

Feasibility and Ethical Considerations

The quasi-experimental method is often used when classic experimental designs are not feasible or ethical, thus bridging the gap between observational studies and true experiments. Quasi-experimental studies are often used in real-world settings and can leverage events, such as examining health outcomes following a hurricane or other natural disasters (de Vocht et al., 2021; Gallin, 2018). Suppose an investigator is investigating health outcomes after a natural disaster. In that case, the study will likely be an observational quasi-experimental design wherein no intervention is given to the study participants. For example, if an investigator wants to assess stress levels associated with surviving a hurricane, an investigator could not randomize participants in this situation and may consider using a quasi-experimental design and the hurricane’s geographical location as an eligibility criterion. Quasi-experimental designs are also employed for studying behavioral interventions using natural settings (i.e., a walking initiative developed in a local city). When conducting these studies, the investigator must consider potential sources of bias and threats to validity and select the most appropriate research design (Gallin, 2018).

Key Designs in Quasi-Experimental Research

Quasi- experimental designs include the posttest-only design with a control group, one-group pretest-posttest design, and pretest-posttest with a control group (Gray, 2023; Harris et al., 2006).

Types of Quasi-Experimental Designs

Posttest-Only Design with a Control Group

In the posttest-only design with a control group, there are two groups: an experimental group that receives an intervention and a control (comparison) group that does not (Gray, 2023). Both groups are measured after the intervention.

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For example, a posttest-only design with a control group might be employed to assess the impact of a new hand hygiene intervention among hospital staff to reduce rates of healthcare- associated infections. For this study, two hospitals will be selected with similar size and level of patient acuity. One would implement the new hand hygiene for its hospital staff, while the other would not. Infection rates would be taken from both groups after three months of following or not following the new hand hygiene intervention.

It is essential to acknowledge that this design does not allow for definitive conclusions regarding causality or efficacy due to various threats to validity (Gray, 2023). Moreover, there is a potential risk of selection bias affecting both groups and including a control group could create a false sense of certainty regarding the study results. Additionally, the absence of a pretest, which would involve measuring infection rates before the implementation of the new hand hygiene intervention, hinders our ability to determine whether any observed disparities in infection rates between the two facilities can be attributed to the intervention itself or are a result of pre-existing discrepancies, such as staffing differences. Krishnan P. (2019) offers a review of the non-equivalent control group post-test-only design.

One-Group Pretest-Posttest Design

The one-group pretest-posttest structure is a frequently used quasi-experimental design. The participants are selected based on convenience and suitability to the study (Gray, 2023). In this design, participants are measured before (pretest) and after (posttest) the intervention. The effect of the intervention is inferred from the difference in pretest and posttest results.

Even with the inclusion of a pretest, this design has significant limitations that may render the results incomprehensible (Gray, 2023). Pretest results cannot effectively act as a control group. Certain events that influence the posttest responses might occur between the pretest and posttest. Scores on the posttest could be affected by factors such as historical events and maturation (Gray, 2023).

A historical event is considered a threat to internal validity when an external event unrelated to the study takes place and influences the outcome of the dependent variable. For example, in continuing with the high-intensity training for weight loss, a historical event for this study could be a new dietary supplement promoted on social media and available over the counter for weight loss. Participants could take the new supplement while completing high-intensity training. Maturation, for this study, poses a risk to internal validity when normal changes stemming from the progression of the impact of time on the outcome of the dependent variable.

Additionally, suppose participants have high pretest scores (i.e., lower body weight - not overweight or obese). In that case, the tool may not be adequately sensitive to detect progress, or scores may gravitate toward the mean (Gray, 2023). In clinical research, gravitating toward the mean or regression to the mean refers to the statistical event where extreme initial measurements, unusually high or low, tend to move closer to the average in subsequent measurements (Barnett, van der Pols, & Dobson, 2005). Regression to the mean can cause misleading interpretations in research results, primarily if not adequately accounted for during the study design and analysis (Gray, 2023) (Barnett et al., 2005).

Pretest-Posttest Design with a Control Group

The pretest and posttest design with a control group is a widely used quasi- experimental design. In this design, the researcher selects a group to receive the treatment and another with similar characteristics to serve as the control group (Alessandri, Zuffianò, & Perinelli, 2017; Gray, 2023). Both groups complete a pretest, after which the treatment group receives the intervention, and finally, both groups complete a posttest. It is ideal if the groups’ mean scores on the pretest are similar (p-value > .05). Additionally, the investigator compares demographic characteristics and other variables influencing posttest scores, such as disease status or time since diagnosis.

To illustrate this design, investigators recruited older adults from two senior centers: Senior Center A and Senior Center B. The objective was to assess the impact of an app-based game on the memory of healthy, ambulatory older adults aged 75 and older from the same city. Participants from Senior Center A were provided with the app- based game. They were asked to attend the senior center five days a week for a month, dedicating 30 minutes to playing the game while at the center and participating in the usual activities. Meanwhile, participants from Senior Center B engaged in their usual activities, such as crafting, dancing, chair yoga, and board games. Also, they attended the senior center five days a week for a month.

The above example study, using the pretest and posttest design with a control group, has limitations regarding the design impacting internal validity. One of the main weaknesses is that participants are not randomized into the treatment and control groups (Naci, 2017). Therefore, any differences observed in the posttest scores of the treatment group may be attributed to an unmeasured confounding variable (a situation where a third variable affects the independent and dependent variables, leading to a distorted association (Capili, 2021). Additionally, external events or unrelated changes between the pretest and posttest may positively affect the dependent variable of the control group or reduce positive changes in the dependent variable of the treatment group (Gray, 2023; Harris et al., 2006).

Enhancing the Rigor of Quasi-Experimental Designs

If it is not feasible to randomize an intervention or establish a control group, additional factors can be included in the design to strengthen internal validity (Gallin, 2018).

Real-World Applications and Examples

Quasi-experimental studies are often used in real-world settings and can leverage events, such as examining health outcomes following a hurricane or other natural disasters (de Vocht et al., 2021; Gallin, 2018). An example of quasi-experimental research would be to ask "What is the effect of hand-washing posters in school bathrooms?" If researchers put posters in the same place in all of the bathrooms of a single high school and measured how often students washed their hands. An example of an experimental design would be randomly selecting all of the schools participating in the hand washing poster campaign. The schools would then randomly be assigned to either the poster-group or the control group, which would receive no posters in their bathroom. The only way to tell what kind of experimental design is in an article you're reading is to read the Methodologies section of the article.

Quasi-experimental designs are also employed for studying behavioral interventions using natural settings (i.e., a walking initiative developed in a local city). Suppose an investigator is investigating health outcomes after a natural disaster. In that case, the study will likely be an observational quasi-experimental design wherein no intervention is given to the study participants. For example, if an investigator wants to assess stress levels associated with surviving a hurricane. An investigator could not randomize participants in this situation and may consider using a quasi-experimental design and the hurricane’s geographical location as an eligibility criterion.

Threats to Validity

When conducting these studies, the investigator must consider potential sources of bias and threats to validity and select the most appropriate research design (Gallin, 2018).

Internal Validity

Internal validity represents the level of confidence that a cause-and-effect relationship observed in a study is not influenced by other variables (Patino & Ferreira, 2018).

External Validity

When choosing a research design, investigators should consider the internal and external validity (generalizability of the results beyond the study) trade-offs.

Reporting Guidelines

For additional information about the quasi-experimental design, the Transparent Reporting of Evaluations with Nonrandomized Designs (TREND) (Des Jarlais, Lyles, & Crepaz, 2004) is a 22-item checklist investigators can review when using a quasi- experimental design. The TREND guideline was developed to improve the reporting quality of nonrandomized behavioral and public health intervention studies.

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