Question
What is Quasi-experimental design?
Answer
Quasi-experimental design is a research methodology used to estimate the causal impact of an intervention without the use of random assignment. It is particularly useful in situations where randomized controlled trials (RCTs) are impractical or unethical.
Key Characteristics
Lack of Randomization: Unlike RCTs, quasi-experimental designs do not involve random allocation of participants to treatment or control groups, which can make statistical analysis more challenging (Miller, Smith and Pugatch, 2020; Shuttleworth, 2022).
Causal Inference: These designs aim to determine causal relationships by using various methods to control for confounding variables, such as difference-in-differences, regression discontinuity, and interrupted time series (Goldfarb, Tucker and Wang, 2022; Carter et al., 2024; Andrade, 2021).
Common Methods
Pre-Post Designs: These involve measurements before and after an intervention, often with a non-equivalent control group (Miller, Smith and Pugatch, 2020; Nianogo, Benmarhnia and O’Neill, 2023).
Interrupted Time Series (ITS): This method analyses data at multiple time points before and after an intervention to detect changes in trends (Miller, Smith and Pugatch, 2020; Nianogo, Benmarhnia and O’Neill, 2023).
Difference-in-Differences (DID): This approach compares changes over time between a treatment group and a control group (Goldfarb, Tucker and Wang, 2022; Tenekedjiev et al., 2025).
Applications
Social Sciences and Education: Widely used to evaluate the effects of policies and interventions where randomization is not feasible (Gopalan, Rosinger and Ahn, 2020).
Public Health and Medicine: Useful for assessing the impact of health policies and interventions, especially when ethical concerns prevent randomization (Carter et al., 2024; De Vocht et al., 2021).
Limitations
Potential Bias: Without randomization, there is a risk of bias due to confounding variables that may not be fully accounted for (Andrade, 2021).
Assumptions: Many quasi-experimental methods rely on assumptions that can be difficult to verify, such as the parallel trends assumption in DID (Tenekedjiev et al., 2025).
Quasi-experimental designs are valuable tools for causal inference in settings where RCTs are not possible. They offer flexibility and applicability across various fields, though they require careful consideration of potential biases and assumptions to ensure valid results.
References
Miller, C., Smith, S., & Pugatch, M., 2020. Experimental and quasi-experimental designs in implementation research. Psychiatry Research, 283. https://doi.org/10.1016/j.psychres.2019.06.027
Shuttleworth, M., 2022. Quasi-Experimental Design. The SAGE Encyclopedia of Research Design. https://doi.org/10.4135/9781452229300.n1559
Gopalan, M., Rosinger, K., & Ahn, J., 2020. Use of Quasi-Experimental Research Designs in Education Research: Growth, Promise, and Challenges. Review of Research in Education, 44, pp. 218 – 243. https://doi.org/10.3102/0091732X20903302
Goldfarb, A., Tucker, C., & Wang, Y., 2022. Conducting Research in Marketing with Quasi-Experiments. Journal of Marketing, 86, pp. 1 – 20. https://doi.org/10.1177/00222429221082977
Carter, A., Jayawardana, S., Costa‐Font, J., Nasir, K., Krumholz, H., & Mossialos, E., 2024. How to Use Quasi-Experimental Methods in Cardiovascular Research: A Review of Current Practice. Circulation: Cardiovascular Quality and Outcomes, 17, pp. e010078. https://doi.org/10.1161/CIRCOUTCOMES.123.010078
Nianogo, R., Benmarhnia, T., & O’Neill, S., 2023. A comparison of quasi-experimental methods with data before and after an intervention: an introduction for epidemiologists and a simulation study.. International journal of epidemiology. https://doi.org/10.1093/ije/dyad032
De Vocht, F., Katikireddi, S., McQuire, C., Tilling, K., Hickman, M., & Craig, P., 2021. Conceptualising natural and quasi experiments in public health. BMC Medical Research Methodology. https://doi.org/10.1186/s12874-021-01224-x
Andrade, C., 2021. The Limitations of Quasi-Experimental Studies, and Methods for Data Analysis When a Quasi-Experimental Research Design Is Unavoidable. Indian Journal of Psychological Medicine, 43, pp. 451 – 452. https://doi.org/10.1177/02537176211034707
Tenekedjiev, K., Panayotova, D., Daboos, M., Ivanova, S., Symes, M., Panayotov, P., & Nikolova, N., 2025. Quasi-Experimental Design for Medical Studies with the Method of the Fuzzy Pseudo-Control Group. Applied Sciences. https://doi.org/10.3390/app15031370
