Benefits & Limitations of Quasi-Experimental Research

Benefits & Limitations of Quasi-Experimental Research

The benefits and limitations of quasi-experimental research are discussed in this module.

Learning Objectives

  • Describe the advantages and benefits of using quasi-experimental design in research
  • Describe the disadvantages and limitations of using quasi-experimental design in research


Before considering the benefits and limitations of experimental research, it is helpful to review quasi-experimental research and the terms associated with it, as well as be introduced to a discussion of the most discussed advantages and disadvantages. 


Many of the benefits and limitations to quasi-experimental research have been alluded to in previous modules in this series. Following is a summary regarding both the advantages and the limitations or disadvantages of using quasi-experimental research approaches.

Benefits and Advantages

  • Quasi-experimental research may be more feasible because it often does not have the time and logistical constraints associated with many true experimental designs.
  • True experimental designs are sometimes impractical or impossible because the research can only effectively be carried out in natural settings. Experimental research can create artificial situations that do not always represent real-life situations. This is largely due to fact that all other variables are tightly controlled which may not create a fully realistic situation.  For this reason, external validity is increased quasi-experimental research.
  • Reactions of test subjects are more likely to be genuine because it not an artificial research environment.
  • It can be very useful in identifying general trends from the results, especially in social science disciplines.
  • It reduces the difficulty and ethical concerns that may surround the pre-selection and random assignment of test subjects. For example, if examining the effects of cigarette smoking by pregnant women on the fetus, it would be unethical to randomly assign pregnant women to groups.
  • Matching procedures may be used to help create a reasonable control group, making generalization more feasible. For example, if one group of migraine suffers received a new treatment and a second group did not receive the treatment, the difference in the pain levels may be attributed to the treatment if the control group is an appropriate comparison group.
  • The results generated can often be used to reinforce the findings of case studies by conducting research that may lend itself to statistical analysis.
  • Quasi-experimental approaches may reduce the time and resources required because extensive pre-screening and randomization is not required or utilized.
  • Threats to validity can be identified and addressed in the research design to minimize their impact.
  • There are many variations of experimental research, and the researcher can tailor the experiment while still maintaining the validity of the design.

Limitations and Disadvantages

  • The lack of random assignment into test groups leads to non-equivalent test groups which can limit the generalizability of the results to a larger population. Beside of the lack of randomization and the reduced internal validity, conclusions about causality are less definitive in quasi-experimental designs.
  • Statistical analyses may not be meaningful due to the lack of randomization and the threats to internal validity.
  • Pre-existing factors and other influences are not considered because variables are less controlled in quasi-experimental research. For example, when examining the impact of smoking by pregnant mothers, there may be other factors such as diet, education, overall health, and access to health care in general that may be playing a role in the outcome.  If other variables are controlled, the researcher can be assured that the treatment was the sole factor causing the outcome.
  • Human error also plays a key role in the validity of any project as discussed in previous modules.
  • The research must adhere to ethical standards in order to be valid. These will be discussed in the next module of this series.


Suggested Readings

Bernard, H. R., & Bernard, H. R. (2012). Social research methods: Qualitative and quantitative approaches. Sage.
Bordens, K. S., & Abbott, B. B. (2002). Research design and methods: A process approach. McGraw-Hill.
Brown, L. (2010). Quasi-experimental research. Doing Early Childhood Research: International perspectives on theory and practice, 345.
Campbell, D. T., & Stanley, J. C. (2015). Experimental and quasi-experimental designs for research. Ravenio Books.
Creswell, J. W. (2013). Research design: Qualitative, quantitative, and mixed methods approaches. Sage publications.
Isaac, S., & Michael, W. B. (1971). Handbook in research and evaluation.
Lipsey, M. W. (1990). Design sensitivity: Statistical power for experimental research (Vol. 19). Sage.
Punch, K. F. (2013). Introduction to social research: Quantitative and qualitative approaches. Sage.
Rosenthal, R., & Rosnow, R. L. (1991). Essentials of behavioral research: Methods and data analysis. McGraw-Hill Humanities Social.
William R. Shadish, Cook, T. D., & Campbell, D. T. (2002). Experimental and quasi-experimental designs for generalized causal inference. Wadsworth Cengage learning.

---------- Grouped Links ---------

numOfValidGroupedLinks: 9

Quasi-Experimental Research:

Overview of Quasi-Experimental Research:

Quasi-Experimental Research Designs:

Constructing a Comparison Group:

Validity in Quasi-Experimental Research:

Data Analysis for Quasi-Experimental Research:

Benefits & Limitations in Quasi-Experimental Research:

Ethics & Quasi-Experimental Research:

Extra Quasi-Experimental Research Links:


-------------- Links -------------

numOfValidLinks: 0


this.updated: True

links.count: 0

obj.hasPermission(enums.PermissionVerb.Edit): False

numOfValidLinks: 0

linksJSON.groups.count: 1

numOfValidGroupedLinks: 9

numOfValidGroupedLinks -> numOfLinksToDisplay: 9

numOfLinksToDisplay = 9

this.layout = 2

view = 2

numColumns = 1

lineBetween = 1

arrowStyle = 3

barStyle = 1

barColor = #470a68

results = 10

Viewed 134,458 times