Non-experimental research is research that lacks the manipulation of an independent variable, random assignment of participants to conditions or orders of conditions, or both - characteristics pertinent to experimental designs (O'Dwyer & Bernauer, 2013). While non-experimental research can be both qualitative and quantitative, here we will focus on quantitative research. Experimental research can provide strong evidence that change in independent variable causes change in the dependent variable. Non-experimental research is used in cases when the research question or hypothesis can be about one variable rather than about the relationships between variables (e.g. How satisfied are participants with the meeting?), the research can be about a causal relationship, but the independent variable cannot be manipulated or participants cannot be randomly assigned to conditions or orders of conditions (e.g., Does length of TV-watching affect student achievement in ELA?), or it can try to identify predictive relationship between variables (e.g. Does parent education have predictive relationship with students’ intention to continue education?).
While there is no unanimity in labelling designs in research literature (e.g. predictive correlational design is also called multiple regression design) in this forum we will differentiate between the following designs that use different types of analyses:
- Descriptive Design
- Causal-comparative/Ex Post Facto Design
- Correlational Design
- Non Experimental Webinar Slides
- Non Experimental Webinar Recording Password fMebxfp4
Please post your questions and comments regarding Non-Experimental Research to this discussion thread
Sources to Consider for Non-Experimental Research
Allison, P.D. (1998). Multiple regression: A primer. Thousand Oaks, CA: Pine Forge Press.
Campbell, D. T., & Stanley, J. C. (1966). Experimental and quasi-experimental designs for research. Boston, MA: Houghton Mifflin.
Cohen, J., Cohen, P., West, S.G., & Aiken, L.S. (2002). Applied multiple regression/correlation analysis for the behavioral sciences. (3rd ed.). New York: Routledge.
Cooper, D. R., & Schindler, P. S. (2002). Business research methods (8th ed.). Boston, MA: McGraw-Hill: Irwin.
Field, A. (2016). An Adventure in Statistics: The Reality Enigma Retrieved from
Field, A. (2013). Discovering statistics using IBM SPSS statistics. (4th ed.) Thousand Oaks, CA: Sage Publications.
Grimm, L. G., & Yarnold, P. R. (Eds.). (1995). Reading and understanding multivariate statistics. Washington, DC: American Psychological Association.
Keith, T. Z. (2015). Multiple Regression and Beyond: An Introduction to Multiple Regression and Structural Equation Modeling, Kindle Edition
O'Dwyer, L. M., & Bernard, J. A. (2013). Quantitative research for the qualitative researcher. Sage Publications.
Stephens, L. J. (2004). Advanced statistics demystified. New York: McGraw-Hill.
Technik, B.G., & Fidela, L.S. (2012). Using multivariate statistics. (6th ed.) New York: Pearson.
Warner, R. M. (2012). Applied statistics: From bivariate through multivariate techniques. Thousand Oaks, CA: Sage Publications.