Psychology 209 – Longitudinal Data Analysis and Bayesian Extensions

Syllabus

Lecture Notes (warning: do not print these all at once–they will change throughout the semester)

Assignments:

• • Assignment 1 – Due: 9/12/17
    • Datafile
    • R syntax printed in assignment
  • Assignment 2 – Due: 9/21/17
    • Datafile
  • Assignment 3 – Due: 10/12/17
  • Assignment 4 – Due: 10/31/17
    • GCM datafile
    • GMM datafile
  • Assignment 5 – Due: 11/14/17
    • LTA datafile
  • Assignment 6 – Due: 11/28/17
    • OpenBUGS syntax and data
      • For some screenshots that might be helpful see this file.
  • Assignment 7 – Due:
    • Monte Carlo syntax example
    • Analysis model syntax example
    • Depaoli (2012) – see in list below
  • Term Paper     – Due: 12/11/17 by 10:00am (to me and your partner)

• R Script File: Note that additional R code is scattered throughout the lecture notes
  • 8/27
  • 9/16
• Mplus Files: Note that additional Mplus code is scattered throughout the lecture notes
  • 10/15 GCM
  • 10/15 GCM with predictors
  • 10/17 GMM
• WinBUGS Files:
  •  Example from class: Repeated measures ANOVA code and data

• Additional Resources
  • Collins, L. M., & Lanza, S. T. (2010). Latent Class and Latent Transition Analysis. Hoboken, NJ: John Wiley & Sons. Chapter 7.
  • Depaoli, S. (2013). 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. (2011). Two studies of specification error in models for categorical latent variables. Structural Equation Modeling, 18, 397-418.
  • 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.