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Assessing person‐centered outcomes in practice research: a latent transition profile framework
Author(s) -
Thompson Aaron M.,
Macy Rebecca J.,
Fraser Mark W.
Publication year - 2011
Publication title -
journal of community psychology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.585
H-Index - 86
eISSN - 1520-6629
pISSN - 0090-4392
DOI - 10.1002/jcop.20485
Subject(s) - psychological intervention , perspective (graphical) , cohort , latent variable , psychology , computer science , data science , applied psychology , statistics , machine learning , artificial intelligence , mathematics , psychiatry
Advances in statistics provide new methods for analyzing practice data. These advances include person‐centered methods (PCMs) that identify subgroups of research participants with similar characteristics. PCMs derive from a frame of reference that is similar to the risk factor perspective in practice. In practice, the delivery of services is often contingent on identifying at‐risk populations and then providing interventions to groups based on shared risk profiles. PCMs use this perspective. Moreover, PCMs provide a means for identifying high‐risk groups with a precision rarely afforded by routine variable‐centered methods. This article describes a latent profile transition analysis (LPTA), one of several PCMs. To demonstrate LPTA, we estimate risk profiles and treatment effects using data from a cohort study of a school‐based social skills training program. We define four steps in PCMs analysis, describe key statistical tests, and conclude with a discussion of the strengths and limitations of PCMs for practice research. © 2011 Wiley Periodicals, Inc.