Going beyond parametric regression in public management research

Peter A. Jones, Vincent Reitano, J. S. Butler, Robert Greer

Research output: Contribution to journalArticlepeer-review

Abstract

Purpose: Public management researchers commonly model dichotomous dependent variables with parametric methods despite their relatively strong assumptions about the data generating process. Without testing for those assumptions and consideration of semiparametric alternatives, such as maximum score, estimates might be biased, or predictions might not be as accurate as possible. Design/methodology/approach: To guide researchers, this paper provides an evaluative framework for comparing parametric estimators with semiparametric and nonparametric estimators for dichotomous dependent variables. To illustrate the framework, the article estimates the factors associated with the passage of school district bond referenda in all Texas school districts from 1998 to 2015. Findings: Estimates show that the correct prediction of a bond passing increases from 77.2 to 78%, with maximum score estimation relative to a commonly used parametric alternative. While this is a small increase, it is meaningful in comparison to the random prediction base model. Originality/value: Future research modeling any dichotomous dependent variable can use the framework to identify the most appropriate estimator and relevant statistical programs.

Original languageEnglish
Pages (from-to)630-650
Number of pages21
JournalInternational Journal of Public Sector Management
Volume34
Issue number6
DOIs
StatePublished - Oct 26 2021

Bibliographical note

Publisher Copyright:
© 2021, Emerald Publishing Limited.

Keywords

  • Nonparametric
  • Regression
  • Research methodology
  • Semiparametric

ASJC Scopus subject areas

  • Geography, Planning and Development
  • Public Administration
  • Political Science and International Relations
  • Management, Monitoring, Policy and Law

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