Latent Class Trees with the Three-Step Approach. Issue 3 (4th May 2019)

• Record Type: Journal Article
• Authors: Van Den Bergh, Mattis; Vermunt, Jeroen K.
• Is Part Of: Structural equation modeling. Volume 26:Issue 3(2019)
• Page Start: 481
• Page End: 492
• Publication Date: 2019-05-04
• Subjects: Latent classes -- three-step approach -- latent class trees -- mixture models; Multivariate analysis -- Periodicals; Social sciences -- Statistical methods -- Periodicals; 519.535
• DOI: 10.1080/10705511.2018.1550364
• Languages: English
• ISSNs: 1070-5511

Abstract

Abstract : Latent class (LC) analysis is widely used in the social and behavioral sciences to find meaningful clusters based on a set of categorical variables. To deal with the common problem that a standard LC analysis may yield a large number classes and thus a solution that is difficult to interpret, recently an alternative approach has been proposed, called Latent Class Tree (LCT) analysis. It involves starting with a solution with a small number of "basic" classes, which may subsequently be split into subclasses at the next stages of an analysis. However, in most LC analysis applications, we not only wish to identify the relevant classes, but also want to see how they relate to external variables (covariates or distal outcomes). For this purpose, researchers nowadays prefer using the bias-adjusted three-step method. Here, we show how this bias-adjusted three-step procedure can be applied in the context of LCT modeling. More specifically, an R-package is presented that performs a three-step LCT analysis: it builds a LCT and allows checking how splits are related to the relevant external variables. The new tool is illustrated using a cross-sectional application with multiple indicators on social capital and demographics as external variables and with a longitudinal application with a mood variable measured multiple times during the day and personality traits as external variables.