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NCJ Number: 221977 Find in a Library
Title: Analyzing Criminal Trajectory Profiles: Bridging Multilevel and Group-Based Approaches Using Growth Mixture Modeling
Journal: Journal of Quantitative Criminology  Volume:24  Issue:1  Dated:March 2008  Pages:1-31
Author(s): Frauke Kreuter; Bengt Muthen
Date Published: March 2008
Page Count: 31
Publisher: http://www.springer.com 
Type: Report (Study/Research)
Format: Article
Language: English
Country: United States of America
Annotation: Using the well-known Cambridge data and the Philadelphia cohort study, this article compares the two "classical models"--conventional growth curve model and group-based trajectory--for the statistical modeling of longitudinal data; two growth mixture models are introduced to bridge the gap between conventional growth models and group-based trajectory models.
Abstract: For the Cambridge data, substantive researchers can be encouraged by the essentially equivalent results of the models compared. For the Philadelphia cohort study, the results differ across the models and substantively different conclusions are likely to be drawn if only one of the modeling approaches is used. The authors identify three issues that should be considered in applying any of the models discussed. First, an important part of mixture models is the prediction of class membership probabilities from covariates. This gives the profiles of the individuals in the classes. If theories differentially relate auxiliary information in the form of covariates of class membership and growth factors, those should be included in the set of covariates in order to correctly specify the model, find the proper number of classes, and correctly estimate class proportions and class membership. Second, similar to the examination of covariates, the predictive power of different trajectory types for later outcomes should be considered in the modeling. Third, researchers should also be aware that a model comparison can lead to different results for different data, as well as differently scaled outcome variables. Modeling comparison for ordered categorical and continuous outcomes can be found in Muthen (2001a, 2001b, 2004). More research on binary outcomes and counts is required. This article provides guidance on how such modeling comparisons can be approached. 11 tables, 9 figures, 47 references, and appended Zero-inflated Poisson Model
Main Term(s): Criminology
Index Term(s): Juvenile to adult criminal careers; Longitudinal studies; Modeling techniques; Research methods; Statistical analysis
To cite this abstract, use the following link:
http://www.ncjrs.gov/App/publications/abstract.aspx?ID=243870

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