Overview
Motivated by the limited available literature on the treatment of longitudinal binary and ordinal outcomes in a growth modeling framework, the goal of this entry is to provide an accessible and practical introduction of this topic for a criminological audience. The parameterization of categorical latent growth models is explained by integrating aspects of the more familiar conventional latent growth models and generalized linear models. Emphasis is placed on the process of model building, evaluation, and interpretation. The entry contains an elaboration of how to include predictors of developmental change in the model for covariate-related hypothesis tests along with remarks regarding of the importance of auxiliary information for assessing model validity and utility. Finally, several model extensions including nonlinear change, generalized growth mixture modeling, and longitudinal latent class analysis are discussed.
Introduction
Criminologists typically encounter data on...
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Acknowledgment
Dr. Weiwei Liu received support through a training grant from the National Institute of Mental Health while working on this entry (T32 MH18834). Correspondence concerning this article should be addressed to Hanno Petras, Ph.D., at JBS International, Inc., 5515 Security Lane, Suite 800, North Bethesda, MD 20852-5007, USA. Phone: (240) 645-4921. Email: hpetras@jbsinternational.com.
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Masyn, K.E., Petras, H., Liu, W. (2014). Growth Curve Models with Categorical Outcomes. In: Bruinsma, G., Weisburd, D. (eds) Encyclopedia of Criminology and Criminal Justice. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-5690-2_404
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