Analyzing Criminal Trajectory Profiles: Bridging Multilevel and Group-based Approaches Using Growth Mixture Modeling

Kreuter, Frauke; Muthén, Bengt

doi:10.1007/s10940-007-9036-0

Cited by 141 publications

(135 citation statements)

References 37 publications

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“…This suggests that the GCM is able to parsimoniously and accurately capture the individual variation across time better than the GTM. This result differs from that found by Kreuter and Muthén (2008). 25 …”

Section: Model Comparisonscontrasting

confidence: 98%

“…Examples with GTM include Broidy et al (2003), and Nagin and Tremblay (2005). Examples with GCM include Lauritsen (1998), and Raudenbush and Chan (1993), and in a comparison framework, Kreuter and Muthén (2008), which applies both GCM and GTM.…”

Section: How Are Methods For Trajectories Applied?mentioning

confidence: 99%

“…4 The goal of this paper is to use direct estimates of the individual trajectories as a way of evaluating the relative strengths of either GTM or GCM regarding their ability to describe the underlying distribution of offending trajectories in the population. We, however, do not focus on differences in overall model fit (Kreuter and Muthén 2008) or on the underlying distribution of trajectory coefficients. Instead, we use Bayesian techniques to estimate a unique trajectory for each individual in the sample using GCM and GTM.…”

Section: Introductionmentioning

confidence: 99%

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Measuring Long Term Individual Trajectories of Offending Using Multiple Methods

2009

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Criminal career researchers and developmental criminologists have identified describing individual trajectories of offending over time as a key research question. In response, recently various statistical methods have been developed and used to describe individual offending patterns over the life-course. Two approaches that are prominent in the current literature are standard growth curve modeling (GCM) and group-based trajectory models (GTM). The goal of this paper is to explore ways in which these different models with different sets of assumptions, do in fact lead to different outcomes on individual trajectories. Using a particularly rich dataset, the criminal career and life-course study, we estimate a unique trajectory for each individual in the sample using the GCM and GTM. We also estimate separate trajectories for each individual directly using the long time series. We then compare these three separate trajectories for each individual. We find that the average trajectories obtained from the different approaches match each other. However, for any given individual, these approaches tell very different stories. For example, each method identifies a rather different set of individuals as desistors. This comparison highlights the strengths and weaknesses of each approach, and more broadly, it reveals the uncertainty involved with measuring long term patterns of change in latent propensity to commit crimes.

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Section: Model Comparisonscontrasting

confidence: 98%

Section: How Are Methods For Trajectories Applied?mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Measuring Long Term Individual Trajectories of Offending Using Multiple Methods

2009

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“…For instance, reviews of such pairs of mixture models have included latent class versus latent profile models (e.g., McDonald, 1962;Wolfe, 1970), latent class versus latent transition models (e.g., , and groups-based trajectory versus growth mixture models (e.g., Kreuter & Muthén, 2008;Muthén, 2004;Pickles & Croudace, 2010). The first goal of this article is to provide more inclusive coverage of prototypic mixture models, employing a cumulative model-building approach incrementing from the simplest univariate mixture model, to some of the most complex recent extensions, under unified notation.…”

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confidence: 99%

Understanding Linkages Among Mixture Models

Sterba

2013

Multivariate Behavioral Research

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The methodological literature on mixture modeling has rapidly expanded in the past 15 years, and mixture models are increasingly applied in practice. Nonetheless, this literature has historically been diffuse, with different notations, motivations, and parameterizations making mixture models appear disconnected. This pedagogical review facilitates an integrative understanding of mixture models. First, 5 prototypic mixture models are presented in a unified format with incremental complexity while highlighting their mutual reliance on familiar probability laws, common assumptions, and shared aspects of interpretation. Second, 2 recent extensionshybrid mixtures and parallel-process mixtures-are discussed. Both relax a key assumption of classic mixture models but do so in different ways. Similarities in construction and interpretation among hybrid mixtures and among parallel-process mixtures are emphasized. Third, the combination of both extensions is motivated and illustrated by means of an example on oppositional defiant and depressive symptoms. By clarifying how existing mixture models relate and can be combined, this article bridges past and current developments and provides a foundation for understanding new developments.

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“…This class is well represented by a straight line. A model in which the intercept and slopes of this class are held equal to zero does detection of unobserved heterogeneity with growth mixture models 25 not show any improvement in model fit, supporting the assumption that sporadic offences might occur even within this group of subjects (the specification of a so called zero-class has been proposed by Kreuter and Muthén, 2008). The second class represents the so called low increasers, with 197 subjects (13%).…”

Section: Applications With Panel Datamentioning

confidence: 58%