Analysing Longitudinal Data: Using Latent Variable Models to Assess Change
Professor Gary Adamson
12/09/2019 - 13/09/2019
View in Google Maps (BT52 1SA)
Synopsis of the course
In the context of longitudinal data, latent variable modelling facilitates robust estimation of direct and indirect effects, together with controlling for, and assessing the impact of, moderating and mediating variables. This session will introduce some of the recent developments in the area. Furthermore, applications of the Cross-lagged panel model will be explored and extended to include mixture distributions.
Growth mixture models (GMMs) will be introduced. These models enable the researcher to explore longitudinal data for the presence of unobserved or latent subgroups. In GMMs the assumption of a single homogenous population with a single growth trajectory is relaxed. Instead, a latent categorical variable is introduced with the intention of capturing latent subpopulations in the longitudinal data. These subpopulations are not directly observed, but are inferred from the patterns of responses in the data. In sum, the GMM facilitates the exploration of longitudinal data for unobserved subgroups and estimates latent growth parameters for each of the subgroups.
This workshop will use the Mplus programme. It is expected that participants will have some knowledge and understanding of Structural Equation Modelling. This two day course will be held on Coleraine Campus, Ulster University.
Intermediate (some prior knowledge)
Fees (includes lunch and refreshments)
Website and registration
Quantitative Data Handling and Data Analysis
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