R Script File: Note that additional R code is scattered throughout the lecture notes
Mplus Files: Note that additional Mplus code is scattered throughout the lecture notes
10/15 GCM with predictors
Example from class: Repeated measures ANOVA code and data
Collins, L. M., & Lanza, S. T. (2010). Latent Class and Latent Transition Analysis. Hoboken, NJ: John Wiley & Sons. Chapter 7.
Depaoli, S. (accepted for publication). Mixture class recovery in GMM under varying degrees of class separation: Frequentist versus Bayesian estimation. Psychological Methods.
Depaoli, S. (2012). The ability for posterior predictive checking to identify model mis-specification in Bayesian growth mixture modeling. Structural Equation Modeling, 19, 534-560.
Grimm, K. A., & Ram, N. (2009). Nonlinear growth models in Mplus and SAS. Structural Equation Modeling, 16, 676–701.
Jasra, A., Holmes, C. C., & Stephens, D. A. (2005). Markov chain Monte Carlo methods and the label switching problem in Bayesian mixture modeling. Statistical Science, 20, 50–67.
Kaplan, D. (2008). An overview of Markov chain methods for the study of stage-sequential developmental processes. Developmental Psychology, 44, 457-467.
Kaplan, D., & Depaoli, S. (2012). Bayesian statistical methods. In T. Little (Ed.), Handbook of quantitative methods (pp. TBD). Oxford: Oxford University Press.
Kaplan, D. & Walpole, S. (2005). A Stage–Sequential Model of Reading Transitions: Evidence From the Early Childhood Longitudinal Study. Journal of Educational Psychology, 97, 551–563.
Muthen, B. O. (2001). Second-generation structural equation modeling with a combination of categorical and continuous latent variables: New opportunities for latent class/latent growth modeling. In L. M. Collins & A. G. Sayer (Eds.), New methods for the analysis of change (pp. 291–322). Washington DC: APA.
Muthen, B. O. (2004). Latent variable analysis: Growth mixture modeling and related techniques for longitudinal data. In D. Kaplan (Ed.), Handbook of quantitative methodology for the social sciences (pp. 345–368). Newbury Park, CA: Sage Publications.
Muthen, B. O., & Asparouhov, T. (2008). Growth mixture modeling: Analysis with non-Gaussian random effects. In G. Fitzmaurice, M. Davidian, G. Verbeke, & G. Molenberghs (Eds.), Longitudinal data analysis (pp. 143–165). Boca Raton: Chapman & Hall/CRC Press.
Nylund, K. (2007). Latent transition analysis: Modeling extensions and an application to peer victimization. Doctoral dissertation, University of California, Los Angeles.
Nylund, K., Asparouhov, T. & Muthen, B. (2007). Deciding on the number of classes in latent class analysis and growth mixture modeling: A Monte Carlo simulation study. Structural Equation Modeling, 14, 535–569.
Sinharay, S. (2004). Experiences with Markov chain Monte Carlo convergence assessment in two psychometric examples. Journal of Educational and Behavioral Statistics, 29, 461–488.
Tofighi, D., & Enders, C. K. (2008). Identifying the correct number of classes in growth mixture models. In G. R. Hancock & K. M. Samuelson (Eds.), Advances in latent variable mixture models (pp. 317–341). Charlotte, NC: Information age Publishing.