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Kathryn Roeder - Kevin G. Lynch - Daniel S. Nagin
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 data set 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.
Keywords: Classification error, Latent class analysis, Mixture models