Latent Trait and Latent Class Analysis for Multiple Groups Using Mplus
Duration: 2 days (9.30am — 4:30pm)
Course fee: £350 (£250 for those from educational institutions)
CCSR offers 5 free places to research staff and students within the Faculty of Humanities at the University of Manchester and the North West Doctoral Training Centre.
Course Leader: Dr Jouni Kuha and Dr Sally Stares
Course requirements: Participants should be familiar with logistic regression modelling. Familiarity with factor analysis and structural equation modelling would be an advantage, but is not essential. No prior knowledge of Mplus or R is required.
Latent variable models are a broad family of models that can be used to capture abstract concepts by means of multiple indicators. Social scientists know them best in the form of factor analysis and structural equation models, in which continuous latent variables are captured by means of continuous observed variables. However, social surveys and many other applications often yield observed variables that are categorical instead of continuous. In this case, the appropriate latent variable models are latent trait (or item response theory) models for continuous latent variables and latent class models for categorical latent variables.
These methods can also be used to compare the distributions of latent variables between different groups. A common example is comparison of countries using data from cross-national surveys. Before doing so, we should also assess the extent to which we have measured the same concept in the same way across groups. Ignoring this question means that we cannot be confident about making valid comparisons of like with like. This question of “measurement equivalence” or “differential item functioning” can also be examined within the models.
This two day course aims to introduce participants to latent trait and latent class models (Day 1), and to multiple group latent trait and latent class analyses (Day 2). It provides training in the use of the Mplus programme to carry out the analyses. The course incorporates the use of marginal residual statistics to examine model fit. This offers a new way of assessing multiple group analyses, and draws on bespoke functions written by the course leader in R software.
Day 1: Introduction to latent trait and latent class models
- Introduction to latent variable models; using Mplus to run basic latent trait models for single groups, and R to calculate fit statistics to inform model selection.
- Latent class models for single groups; introducing parameter constraints into model specification
Day 2: Multiple group latent trait and latent class models
- Latent trait models with a group covariate; introduction to differential item functioning in latent trait models; models with direct effects and interaction effects between observed items, latent variable(s) and covariate.
- Alternative model specification for multiple group analysis in Mplus; latent class models with a group covariate; differential item functioning in latent class models.