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External Validity in Policy Evaluations That Choose Sites Purposively
Author(s) -
Olsen Robert B.,
Orr Larry L.,
Bell Stephen H.,
Stuart Elizabeth A.
Publication year - 2012
Publication title -
journal of policy analysis and management
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.898
H-Index - 84
eISSN - 1520-6688
pISSN - 0276-8739
DOI - 10.1002/pam.21660
Subject(s) - nonprobability sampling , sample (material) , proposition , selection bias , population , selection (genetic algorithm) , sampling (signal processing) , external validity , simple random sample , process (computing) , econometrics , public economics , psychology , social psychology , statistics , economics , computer science , sociology , mathematics , demography , philosophy , chemistry , epistemology , chromatography , filter (signal processing) , artificial intelligence , computer vision , operating system
Evaluations of the impact of social programs are often carried out in multiple sites, such as school districts, housing authorities, local TANF offices, or One‐Stop Career Centers. Most evaluations select sites purposively following a process that is nonrandom. Unfortunately, purposive site selection can produce a sample of sites that is not representative of the population of interest for the program. In this paper, we propose a conceptual model of purposive site selection. We begin with the proposition that a purposive sample of sites can usefully be conceptualized as a random sample of sites from some well‐defined population, for which the sampling probabilities are unknown and vary across sites. This proposition allows us to derive a formal, yet intuitive, mathematical expression for the bias in the pooled impact estimate when sites are selected purposively. This formula helps us to better understand the consequences of selecting sites purposively, and the factors that contribute to the bias. Additional research is needed to obtain evidence on how large the bias tends to be in actual studies that select sites purposively, and to develop methods to increase the external validity of these studies. © 2012 by the Association for Public Policy Analysis and Management.