Statistical methods in public policy research

Andrew Heiss
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Andrew Heiss: Georgia State University

No cwymb_v1, SocArXiv from Center for Open Science

Abstract: This essay provides an overview of statistical methods in public policy, focused primarily on the United States. I trace the historical development of quantitative approaches in policy research, from early ad hoc applications through the 19th and early 20th centuries, to the full institutionalization of statistical analysis in federal, state, local, and nonprofit agencies by the late 20th century. I then outline three core methodological approaches to policy-centered statistical research across social science disciplines: description, explanation, and prediction, framing each in terms of the focus of the analysis. In descriptive work, researchers explore *what exists* and examine any variable of interest to understand their different distributions and relationships. In explanatory work, researchers ask *why does it exist* and *how can it be influenced*. The focus of the analysis is on explanatory variables (X) to either (1) accurately estimate their relationship with an outcome variable (Y), or (2) causally attribute the effect of specific explanatory variables on outcomes. In predictive work, researchers as *what will happen next* and focus on the outcome variable (Y) and on generating accurate forecasts, classifications, and predictions from new data. For each approach, I examine key techniques, their applications in policy contexts, and important methodological considerations. I then consider critical perspectives on quantitative policy analysis framed around issues related to a three-part "data imperative" where governments are driven to count, gather, and learn from data. Each of these imperatives entail substantial issues related to privacy, accountability, democratic participation, and epistemic inequalities—issues at odds with public sector values of transparency and openness. I conclude by identifying some emerging trends in public sector-focused data science, inclusive ethical guidelines, open research practices, and future directions for the field.

Date: 2025-03-12
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Persistent link: https://EconPapers.repec.org/RePEc:osf:socarx:cwymb_v1

DOI: 10.31219/osf.io/cwymb_v1

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