In research design, quasi-experimental design (QED) offers a pragmatic approach when true experimental conditions are not feasible. By exploring cause-and-effect relationships in real-world settings, quasi-experimental designs bridge the gap between experimental rigour and practical application. In this post, we will look at quasi-experimental design, its methodology, and its applications.
What is a Quasi-Experiment
A quasi-experiment is a research design that omits random assignment, a key feature of true experiments. Researchers employ quasi-experimental designs to investigate causal relationships when random assignment is not feasible due to ethical or practical constraints.
For example, in a non-equivalent groups design, researchers compare outcomes between groups formed by pre-existing conditions rather than random allocation. This method allows for the study of interventions in real-world settings, which can be particularly valuable in psychology and social sciences. Another approach is the regression discontinuity design, which assigns treatments based on a predefined threshold, enabling robust analysis around the cut-off point.
By understanding and employing quasi-experimental designs, researchers can derive meaningful insights into cause-and-effect relationships even when ideal experimental conditions are unattainable.
Quasi-experimental research is a quantitative research method. It involves numerical data collection and statistical analysis.
Types of Quasi-Experimental Designs (QEDs)
Comparing individuals with blue eyes and those with brown eyes, for instance, would make eye colour the quasi-independent variable. It cannot be randomly assigned as it is an inherent difference between groups. Other examples could be individuals diagnosed with a cold versus those who do not have a cold.
There are various Quasi-experimental design methodologies, each tailored to different research contexts.
- Non-equivalent Groups Design: This method compares outcomes between a treatment group and a control group not formed by random assignment. Instead, pre-existing conditions determine group membership, offering insights when randomisation isn't feasible.
- Regression Discontinuity Design: Here, treatment assignment is based on a predefined threshold. By focusing on units around the cut-off point, researchers can analyse causal effects with enhanced accuracy, leveraging the comparability of subjects just above and below the threshold.
- Time-Series Design: This design involves multiple observations over time, both before and after an intervention. Through detailed trend analysis, it provides a robust framework for inferring causal relationships, capturing the temporal dynamics of change.
Natural and quasi-experiments both examine causal relationships without the random assignment found in true experiments. However, they differ primarily in the way they occur. Natural experiments occur when external factors or events create conditions that mimic random assignment, such as policy changes or natural disasters, allowing researchers to study the effects on affected groups as compared to unaffected ones. In contrast, quasi-experiments are intentionally designed by researchers to study pre-existing groups or conditions where random assignment is not possible. While both types of experiments offer valuable insights in real-world settings, quasi-experiments provide a more structured approach to examining specific hypotheses within controlled parameters.
Advantages and Disadvantages of Quasi-Experimental Design
Quasi-experimental designs are good for investigating causal relationships when randomisation is impractical or unethical. They excel in utilising retrospective data and often yield findings with robust external validity, thanks to their real-world context.
However, the absence of random assignment can compromise internal validity, making it difficult to control for confounding variables. This introduces potential biases and therefore you should be cautious of the findings. While quasi-experiments offer practical advantages, researchers must remain vigilant about these challenges, especially when delineating the effects of an intervention from other variables
Application Examples: Research
Quasi-experimental designs have been pivotal in studying educational interventions. For instance, a study assessing the impact of a new teaching method might compare student performance in schools that voluntarily adopt the method versus those that do not, using non-equivalent groups design. Another example is evaluating public health policies, such as smoking bans, by comparing health outcomes in regions with and without the bans, employing a natural experiment approach.
Critically Appraising a Quasi-Experimental Study
Begin by identifying the specific quasi-experimental design employed and evaluate its suitability for the research question. Scrutinise how the study manages confounding variables, as the absence of random assignment increases the risk of biases. Assess the robustness of data collection and analysis methods, noting whether they integrate both qualitative and quantitative data effectively.
Consider the study’s internal validity by evaluating the clarity with which causal relationships are delineated amidst potential confounders. External validity should also be examined, determining the generalisability of the findings to broader contexts. Ethical considerations are important; ensure the study adheres to ethical guidelines, especially in scenarios where random assignment might be infeasible or unethical.
Improve Your Critical Appraisal Skills Through Our Training
Would you like to learn more about different research types and how to make sense of them? CASP aims to spread critical appraisal skills training to as many people as possible. Offering both online training courses and virtual training workshops we look at the appraisal of different study types, working through a published paper, and looking at making sense of the statistics used in research.
