Growth Curve Episode 8: Multivariate Growth скачать в хорошем качестве

Growth Curve Episode 8: Multivariate Growth

In this episode, Patrick continues his discussion of growth models with a focus on the simultaneous estimation of growth in two or more constructs. ... These models are sometimes called multivariate growth models and offer a variety of options for studying how two constructs jointly unfold over time. Patrick begins with a brief review of the time-varying covariate, or TVC, growth model. He then extends this to allow for the estimation of a growth process for the TVCs themselves. Next he explores options for structuring the relations among the growth factors, as well as incorporating time-invariant covariates and distal outcomes. He concludes with a summary of advanced multivariate models including the latent change score model, the auto-regressive latent trajectory model, and the latent curve model with structured residuals. A few sample citations relating to these model extensions are provided below. Bollen, K.A., & Curran, P.J. (2004). Autoregressive latent trajectory (ALT) models: A synthesis of two traditions. Sociological Methods and Research, 32, 336-383. Curran, P.J., Howard, A.L., Bainter, S.A., Lane, S.T., & McGinley J.S. (2013). The separation of between-person and within-person components of individual change over time: A latent curve model with structured residuals. Journal of Consulting and Clinical Psychology, 82, 879-894. Grimm, K. J., An, Y., McArdle, J. J., Zonderman, A. B., & Resnick, S. M. (2012). Recent changes leading to subsequent changes: Extensions of multivariate latent difference score models. Structural equation modeling: a multidisciplinary journal, 19, 268-292. Grimm, K. J., Ram, N., & Estabrook, R. (2017). Growth modeling: Structural equation and multilevel modeling approaches. New York, NY: Guilford.