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Using Propensity Score Matching to Improve Validity in Public Administration Research

Using Propensity Score Matching to Improve Validity in Public Administration Research

Michael Howell-Moroney
Copyright: © 2022 |Pages: 16
ISBN13: 9781799882435|ISBN10: 1799882438|ISBN13 Softcover: 9781799882442|EISBN13: 9781799882459
DOI: 10.4018/978-1-7998-8243-5.ch003
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MLA

Howell-Moroney, Michael. "Using Propensity Score Matching to Improve Validity in Public Administration Research." Public Affairs Education and Training in the 21st Century, edited by Onur Kulaç, et al., IGI Global, 2022, pp. 45-60. https://doi.org/10.4018/978-1-7998-8243-5.ch003

APA

Howell-Moroney, M. (2022). Using Propensity Score Matching to Improve Validity in Public Administration Research. In O. Kulaç, C. Babaoğlu, & E. Akman (Eds.), Public Affairs Education and Training in the 21st Century (pp. 45-60). IGI Global. https://doi.org/10.4018/978-1-7998-8243-5.ch003

Chicago

Howell-Moroney, Michael. "Using Propensity Score Matching to Improve Validity in Public Administration Research." In Public Affairs Education and Training in the 21st Century, edited by Onur Kulaç, Cenay Babaoğlu, and Elvettin Akman, 45-60. Hershey, PA: IGI Global, 2022. https://doi.org/10.4018/978-1-7998-8243-5.ch003

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Abstract

Randomized clinical trials have a longstanding status as the gold standard in detecting causal effects. In the social sciences, randomized clinical trials are rare because of their attendant logistical and cost burdens. Most social science research makes use of observational data. The empirical challenge posed by observational data is that treatment assignment is no longer random. This challenge continues to spur innovation across many disciplines toward more sophisticated techniques for estimating causal relationships. Scholars have developed a common theoretical framework for estimating causal effects, often called the potential outcomes or counterfactual framework. This chapter demonstrates the propensity score matching methodology as a way to estimate causal effects using observational data. Throughout, an example from public administration research, the effect of government employment on volunteerism, is used to illustrate the concepts. Empirical estimates of the treatment effects show that there may be a causal effect of government employment on volunteerism.

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