In recent years, there has been a growing interest among researchers in the use of latent
class and growth mixture modeling techniques for applications in the social and …

Estimating models within the mixture model framework, like latent growth mixture modeling
(LGMM) or latent class growth analysis (LCGA), involves making various decisions …

Data collected in the social sciences are rarely normally distributed. The linear regression
methods that are usually employed to test mediation hypotheses consider moments no …

Growth mixture modeling (GMM) is a method for identifying multiple unobserved sub-
populations, describing longitudinal change within each unobserved sub-population, and …

This book provides a comprehensive introduction to latent variable growth curve modeling
(LGM) for analyzing repeated measures. It presents the statistical basis for LGM and its …

Sources of population heterogeneity may or may not be observed. If the sources of
heterogeneity are observed (eg, gender), the sample can be split into groups and the data …

Structural equation mixture modeling (SEMM) integrates continuous and discrete latent
variable models. Drawing on prior research on the relationships between continuous and …

9 Literaturverzeichnis Page 1 9 Literaturverzeichnis Aish, ΑΜ, & Jöreskog, ΚG(1990). A panel
model for political efficacy and responsiveness: An application of LISREL7 with weighted least …

Latent variable models exist with continuous, categorical, or both types of latent variables.
The role of latent variables is to account for systematic patterns in the observed responses …

Prior studies of nonsuicidal self-injury (NSSI) suggest the existence of multiple NSSI
typologies. Using data from 2,101 university students, this study employed latent class …