Analysing longitudinal data using latent variable models
14/09/2023 - 15/09/2023
Professor Gary Adamson and Professor Mark Shevlin
Advanced (specialised prior knowledge)
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Ulster University, Cromore Road, Coleraine
The analysis of change is central to much psychological and social research. Latent Growth Models (LGM) are an important class of models for the assessment of change. In essence these describe individuals’ behaviour in terms of an initial starting point (intercept) and their subsequent developmental trajectories (slope). The technique also allows for the introduction of predictors (covariates) of change. These predictors can be both time-invariant and time-varying and the model can be extended to incorporate other advantages of latent variable framework, e.g., the ability to handle missing data, to introduce both direct and indirect effects and correction for measurement error.
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.
Student: £220; Educational/charitable sector: £330; Government/commercial sector: £400
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Data Collection, Quantitative Data Handling and Data Analysis
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