Modeling Uncertainty in Latent Class Membership: A Case Study in Criminology
作者:
Kathryn Roeder,
KevinG. Lynch,
DanielS. Nagin,
期刊:
Journal of the American Statistical Association
(Taylor Available online 1999)
卷期:
Volume 94,
issue 447
页码: 766-776
ISSN:0162-1459
年代: 1999
DOI:10.1080/01621459.1999.10474179
出版商: Taylor & Francis Group
关键词: Classification error;Latent class analysis;Mixture models
数据来源: Taylor
摘要:
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.
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