Introduction
Quasi-experimental research is used when real experiments are impossible or unethical. This research method gives the researcher limited control over the independent variable. The researcher manipulates an independent variable and observes its effect on a dependent variable. This is because the independent variable is either influenced by a random occurrence or not at all. Quasi-experimental research can reveal causal chains despite this limitation.
The nonequivalent group design is a popular quasi-experimental strategy. This design compares dependent variables across groups (Salkind, 2017). Groups are not randomly assigned. Instead, the researcher selects volunteers from pre-existing groups using criteria. A researcher might compare students’ academic performance at two schools, one of which may have started a new program. The two schools’ students are pre-selected cohorts.
The nonequivalent group design can be used in educational and healthcare research to compare teaching methodologies and treatment outcomes. Researchers must consider bias when using this design. For instance, the contrasted groups may differ significantly, affecting the findings. ANCOVA or propensity score matching can be used to account for these disparities.
Quasi-experimental pretest-posttest designs are also used before and after manipulating the independent variable, and the dependent variable is assessed (Salkind, 2017). This experiment tests if the independent variable changes the dependent variable. A researcher might compare a group’s anxiety before and after mindfulness meditation training. To assess if the intervention reduced pressure, compare the pre-and post-test scores.
Program assessment often uses a pre-and post-test methodology to assess program efficacy. However, history and maturation may threaten the internal validity of this design. The benefits of the practice of mindfulness programme may be obscured if study participants encounter an actual occurrence in their lives, such as the death of a loved one, during the research. To lessen the threat to the investigation’s validity within the study, investigators can employ techniques like analysis of covariance (ANCOVA) and include a control group that does not participate in the intervention.
The third quasi-experimental design is a time series. This design takes several dependent variable measurements before and after manipulating the independent variable. This experiment tests if using the independent variable affects the dependent variable. A researcher might count auto accidents in a region before and after a new traffic safety program. The researcher can determine if the software reduced car accidents by comparing data from multiple time points.
In public health, time-series research is often used to evaluate treatments or policies that reduce health disparities or improve health outcomes (Salkind, 2017). However, design problems may compromise the study’s internal validity. These issues include regression to the mean and the historical effect. If the region had an exceptionally high number of car accidents before the traffic safety program was installed, casualties might naturally fall even without the program. Researchers can use interrupted time series analysis or a control group without the intervention to account for these concerns to the study’s internal validity.
Regression-discontinuity is a fourth quasi-experimental design. This design assigns participants to treatment or control groups based on criterion variable thresholds. This threshold determines therapy. For instance, a researcher may enrol students in a tutoring program if their pre-test scores are lower than a predetermined cutoff but not if they score better. The researcher can assess the tutoring program’s success by comparing both groups’ post-test scores.
The regression-discontinuity design is often used in education research to evaluate academic performance-boosting interventions. However, the history effect and regressing to the mean may impair the study’s internal validity. For instance, if kids with test scores below the minimum necessary for the tutoring program improve academically over time, this may disguise the program’s effects. Researchers can use statistical methods like propensity score matching or adding a control group for internal validity.
The final quasi-experimental design is the nonequivalent dependent variable. This design evaluates the same group of participants on several independent factors without random assignment (Krishnan, 2022). Instead, the researcher selects volunteers from pre-existing groups using criteria. A researcher may compare two schools’ students’ academic performance, self-esteem, and motivation. The two schools’ students are pre-selected cohorts.
The nonequivalent dependent variable design can be used in many fields, including educational research, to assess how different instructional practices affect different outcomes. Researchers must consider bias when using this design (Salkind, 2017). For instance, the groups being compared may differ significantly, affecting the results. ANCOVA or propensity score matching can be used to account for these disparities.
Depending on the research question and resources, researchers may use different quasi-experimental designs in addition to these five. In the interrupted time series design, the dependent variable is measured before and after an intervention, but there is a gap in the time series. The multiple baseline design measures the dependent variable many times across numerous settings or participants and manipulates the independent variable various times for each environment or participant. Cross-sectional time-series designs measure the dependent variable at multiple time points for distinct groups or populations and compare changes across time.
Conclusion
Quasi-experimental investigations are functional when standard experiments are unfeasible or unethical. These designs involve manipulating an independent variable and evaluating its effect on a dependent variable, but the researcher needs complete control. The pretest-posttest, time-series, regression-discontinuity, and nonequivalent dependent variable quasi-experimental designs are popular. However, researchers must consider bias and threats to the study’s internal validity when adopting these designs and use statistical tools or other methods to correct them.
References
Krishnan, P. (2022). A review of the nonequivalent control group post-test-only design. Retrieved from journals.rcni.com website: https://journals.rcni.com/nurse-researcher/evidence-and-practice/a-review-of-the-nonequivalent-control-group-posttestonly-design-nr.2018.e1582/abs
Salkind, N. J. (2017). Exploring Research. In Google Books. Pearson Educación. Retrieved from https://books.google.co.ke/books?hl=en&lr=&id=3uIW0vVD63wC&oi=fnd&pg=PR19&dq=Exploring+Research&ots=aJNAdd6UcM&sig=moCxQPaD-g9EqwLbTDCchoffFA4&redir_esc=y#v=onepage&q=Exploring%20Research&f=false
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