Webinar “Latent Class Analysis with Mplus”

Marja Aartsen, Research Professor

NOVA-Norwegian Social research at Oslo Metropolitan University

marja.aartsen@oslomet.no

**Date: March 31, 2022**

**Duration: **2 hours

**Time: **10:00 —12:00

**Platform**: ZOOM (registration is required)

# Scope

In many of the traditional variable-centred inferential statistics it is assumed that all individuals in a study sample are drawn from the same population for which an average set of parameters can be estimated. This assumption however does not hold if the population from which the study sample is drawn is not homogeneous, but in fact consist of various subpopulations who act or respond differently. With Latent Class Analyses (LCA), which is a person-centred approach, it is possible to identify these hidden or latent subgroups of homogeneous people in a heterogeneous population. It is similar to other classification techniques such as cluster analysis or k-means clustering but provides you with some formative statistical tests to decide on the number of clusters or classes and how well the model represents the data. LCA is a specific type of structural equation models (SEM), also referred to as mixture modelling.

During this webinar, you will get an introduction to LCA, learn the basics of LCA, how to decide on the number of classes, how to set up the syntax for a simple LCA model, how to add outcome and predictor variables and how to interpret the output. Note that the free demo version cannot be used for mixture modelling, such as LCA. For that you need the Base module and the Mixture add-on.

# You will receive:

Handouts and Mplus input file for LCA with Mplus

**References:**

Berlin, K. S., Williams, N. A., & Parra, G. R. (2014). An introduction to latent variable mixture modeling (part 1): Overview and cross-sectional latent class and latent profile analyses. *Journal of pediatric psychology*, *39*(2), 174-187.

Finch, W. H., & Bronk, K. C. (2011). Conducting confirmatory latent class analysis using M plus. *Structural Equation Modeling*, *18*(1), 132-151.

Ferguson, S. L., G. Moore, E. W., & Hull, D. M. (2020). Finding latent groups in observed data: A primer on latent profile analysis in Mplus for applied researchers. *International Journal of Behavioral Development*, *44*(5), 458-468.

Nylund-Gibson, K., & Choi, A. Y. (2018). Ten frequently asked questions about latent class analysis. *Translational Issues in Psychological Science*, *4*(4), 440-461.