Modeling Uncertainty in Latent Class Membership: A Case Study in Criminology
本文提出一种高效便捷的方法，将潜在特质（如犯罪倾向）与可测量的个体协变量（如不良教养）联系起来，并应用于剑桥犯罪发展纵向数据，检验犯罪发展理论中关于两类犯罪人群的假设。
Abstract Social scientists are commonly interested in relating a latent trait (e.g., criminal tendency) to measurable individual covariates (e.g., poor parenting) to understand what defines or perhaps causes the latent trait. In this article we develop an efficient and convenient method for answering such questions. The basic model presumes that two types of variables have been measured: Response variables (possibly longitudinal) that partially determine the latent class membership, and covariates or risk factors that we wish to relate to these latent class variables. The model assumes that these observable variables are conditionally independent, given the latent class variable. We use a mixture model for the joint distribution of the observables. We apply this model to a longitudinal dataset assembled as part of the Cambridge Study of Delinquent Development to test a fundamental theory of criminal development. This theory holds that crime is committed by two distinct groups within the population: Adolescent-limited offenders and life-course-persistent offenders. As these labels suggest, the two groups are distinguished by the longevity of their offending careers. The theory also predicts that life-course-persistent offenders are disproportionately comprised of individuals born with neurological deficits and reared by caregivers without the skills and resources to effectively socialize a difficult child.