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Handbook of Quantitative Criminology pp 69–100Cite as

General Growth Mixture Analysis with Antecedents and Consequences of Change

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Abstract

Many studies of youth, adolescents, and adults related to delinquent, antisocial, and criminal offending, have utilized a language of trajectory typologies to describe individual differences in the behavioral course manifest in their longitudinal data. The two most common statistical methods currently in use are the semiparametric group-based modeling, also known as latent class growth analysis and general growth mixture analysis, with the latter method being the focus of this chapter. In concert with the growing popularity of these data-driven, group-based methods for studying developmental and life-course behavior trajectories have come active and spirited ontological discussions about the nature of the emergent trajectory groups resulting from the analyses. In this chapter, we presuppose that there are analytic, empirical, and substantive advantages inherent in using discrete components to (partially) describe population heterogeneity in longitudinal processes. Conceptually as well as empirically, we will discuss the use of auxiliary information in terms of antecedents and consequences of trajectory group membership. The inclusion of auxiliary information in growth mixture analysis is a necessary step in understanding as well as evaluating the fidelity and utility of the resultant trajectory profiles from a given study, regardless of one’s beliefs about the veracity of the method itself.

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Notes

  1. 1.

    Although we chose to use the Mplus modeling software, there are other software packages that can be used to estimated some (or all) of the models presented herein. Among the most prominent are: HLM (Raudenbush, Bryk, Cheong, & Congdon 2000); SAS Proc TRAJ (Jones, Nagin, & Roeder 2001); GLAMM (Rabe-Hesketh, Skrondal, & Pickles 2004); MLwiN (Rasbash, Steele, Browne, & Prosser 2004); Latent Gold (Vermunt & Magidson 2005); SuperMix (Hedecker & Gibbons 2008); and LISREL (Jöreskog & Sörbom 1996).

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Acknowledgement

We like to thank Alex Piquero, David Weisburd, Nicholas Ialongo and Bengt Muthén for their helpful comments on a prior draft of this manuscript.

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Authors and Affiliations

  1. Department of Criminology and Criminal Justice, University of Maryland, College Park, MD, USA

    Hanno Petras

  2. Department of Human and Community Development, University of California Davis, Davis, CA, USA

    Katherine Masyn

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  1. Hanno Petras
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Editors and Affiliations

  1. College of Criminology, Florida State University, West Call Street 643, Tallahassee, 32306, U.S.A.

    Alex R. Piquero

  2. Inst. Criminology, Hebrew University of Jerusalem, Jerusalem, 91905, Israel

    David Weisburd

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Petras, H., Masyn, K. (2010). General Growth Mixture Analysis with Antecedents and Consequences of Change. In: Piquero, A., Weisburd, D. (eds) Handbook of Quantitative Criminology. Springer, New York, NY. https://doi.org/10.1007/978-0-387-77650-7_5

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