Advanced Statistics for Educational Researchers: Analyzing Structural Equation Models and Latent Growth Curves with MPlus

Tutors: Njål Foldnes (UiS), Ulrich Dettweiler (UiS), Knud Knudsen (UiS), Thormod Idsøe (NUBU), Lars-Erik Malmberg (Oxford)

Course coordinator: Ulrich Dettweiler (UIS)

Credits: 5 ECTS

Course dates:

Meeting 1:  11 January,

Meeting 2:  14 January,

Meeting 3:  27 January,

Meeting 4:  3 February,

Meeting 5:  17 February,

Meeting 6:  2 March.

Please send your completed application form to the PhD coordinator Application form PhD-courses UiS

 

 

Advanced Statistics

1 January

We will resume fundamental statistical concepts such as confidence intervals, hypothesis testing and p-values. We will introduce into the software package R with practical illustrations of those concepts. 

Tutors: Njål Foldnes (UiS), Ulrich Dettweiler (UiS), Knud Knudsen (UiS), Thormod Idsøe (NUBU)

14 January

This session will present multi-level linear models to prepare for a gentle philosophical and technical introduction in Bayesian approaches to statistics. The latter are especially important to understand the more complex hierarchical and dynamic (time-lagged) structural equation models later in the course.

Tutors: Ulrich Dettweiler (UiS), Njål Foldnes, Knud Knudsen (UiS), Thormod Idsøe (NUBU)

27 January

This session will summarize core concepts of multiple regression and factor analysis, and give a brief overview of the  Mplus logic. We will then introduce multivariate approaches into the structural equation modelling (SEM) framework in Mplus.

Tutors: Knud Knudsen (UiS), Thormod Idsøe (NUBU), Njål Foldnes (UiS), Ulrich Dettweiler (UiS)

3 February

In this session, we will go deeper into SEM and see how Latent Growth Curve modelling can be understood as an extension of SEM with intercepts and/or slopes being modelled as latent variables, first as an unconditional latent curve model.

Tutors: Knud Knudsen (UiS), Thormod Idsøe (NUBU), Njål Foldnes (UiS), Ulrich Dettweiler (UiS)

17 February

We will then look at conditional Latent Growth Curve Models (including mediation models, cross-lagged models, hierarchical/multilevel models), and comparison of (latent) groups with different approaches of testing measurement invariance.

Tutors: Thormod Idsøe (NUBU), Knud Knudsen (UiS), Njål Foldnes (UiS), Ulrich Dettweiler (UiS)

2 March - ONLINE –

The next step is to look at Dynamic Structural Equation Models with autoregressive slopes (DSEM), i.e. accounting for the influence of the respective previous time points on the outcome variable (time-lagging). Those models are important to analyze intensive longitudinal data, where many observations are nested in individuals. In contrast to latent growth modelling, Borrowing logic from time-series analyses DSEM is interested in the dynamics over time, in terms of autoregressive associations between the same variable at Times T (concurrent time-point) and T-1 (the previous time-point), and cross-lagged associations between variables between T and T-1. Such models are possible to implement using Maximum Likelihood with user-specified lagged variables. More complex models (e.g., multiple random slopes) are possible to implement using the Bayesian estimator in Mplus.

Tutor: Lars-Erik Malmberg (Oxford), Ulrich Dettweiler (UiS) 

 

Tags: Educational Science, Research Methodology
Published Apr. 30, 2020 1:59 PM - Last modified Nov. 19, 2020 12:41 PM