Propensity Score Matching, abbreviated as PSM, is a quasi-experimental technique endorsed by the U.S. Department of Education to control for covariates such as self-selection bias, non-equal groups, and non-random assignment. PSM is a statistical matching technique designed to estimate the effect of a treatment, policy, or other intervention by accounting for the covariates that are also related to the treatment. PSM can control for non-equated group differences and then apply the results to the matched groups.
Because SPSS has built in PSM formulas that allow for calculation and comparison of covariates, it is a useful tool for generating PSM data. SPSS requires all relevant covariates be included in the study - gender, age, and socio-economic status. PSM is practical in educational, medical, and economic research using non-random samples. However, PSM requires large sample sizes because many participants may be excluded due to poor matching.
The presentation below provides step-by-step directions on how SPSS can be used to generate PSM data.
Presentation on Summary of Propensity Score Matching in Education
Script for Summary of Propensity Score Matching in Education
There are two main traditions in research: Quantitative and Qualitative. Both are different in terms of goals, applications, sampling procedures, data analysis, etc. However, although they are different in many ways, they can also be complementary of one another.
The presentation below provides an in-depth comparison of the two different traditions with hyperlinks and resources sprinkled throughout. We also provide information on writing a good literature review, computer assisted qualitative data analysis (QDA), and bracketing:
Quantitative & Qualitative Research Traditions: Different but Complementary
Please note: superscripts are also hyperlinks so please click on them! Warning: you will need to be logged into the university in order to have access to some of the resources as some require subscriptions.
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