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A New Strategy for Reducing Selection Bias in Nonexperimental Evaluations, and the Case of How Public Assistance Receipt Affects Charitable Giving
Author(s) -
Peck Laura R.,
D'Attoma Ida,
Camillo Furio,
Guo Chao
Publication year - 2012
Publication title -
policy studies journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.773
H-Index - 69
eISSN - 1541-0072
pISSN - 0190-292X
DOI - 10.1111/j.1541-0072.2012.00466.x
Subject(s) - receipt , selection bias , selection (genetic algorithm) , population , multivariate analysis , multivariate statistics , public assistance , public economics , psychology , political science , business , economics , sociology , medicine , demography , statistics , computer science , accounting , pathology , artificial intelligence , law , welfare , mathematics
Prior research considers the extent to which public assistance recipients' charitable activity differs from the habits of the general population. Although receiving public assistance is negatively associated with donating money, the relationship to volunteering is unclear. In response to challenges overcoming selection bias, we conducted a multivariate cluster‐based subgroup analysis to reduce bias in our claims about the ways in which public assistance receipt affects charitable activity. This innovative approach to dealing with the problem of selection bias has implications and applications across the social sciences.