Latent class trees with the three-step approach

Mattis Van den Bergh*, Jeroen K. Vermunt

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

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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.

Original languageEnglish
Pages (from-to)481-492
JournalStructural Equation Modeling
Volume26
Issue number3
DOIs
Publication statusPublished - 2019

Keywords

  • Latent classes
  • three-step approach
  • latent class trees
  • mixture models
  • CLASS MEMBERSHIP
  • VARIABLES
  • MODELS

Cite this

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title = "Latent class trees with the three-step approach",
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.",
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Latent class trees with the three-step approach. / Van den Bergh, Mattis; Vermunt, Jeroen K.

In: Structural Equation Modeling, Vol. 26, No. 3, 2019, p. 481-492.

Research output: Contribution to journalArticleScientificpeer-review

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