A New Perspective on the Effects of Covariates in Mixture Models

Gabriela Stegmann, Kevin Grimm

Research output: Contribution to journalArticle

3 Citations (Scopus)

Abstract

In this simulation study, we explored the effect of introducing covariates to a growth mixture model when covariates were also generated by a mixture model. We varied the association between the latent classes underlying the growth trajectories and the covariates, the degree of separation between the latent classes underlying the covariates, the number of covariates included, and amount of missing data in the growth data. We found that adding covariates to the growth mixture model generally hurt class recovery except where the latent classes underlying the growth trajectories and the covariates were the same or very strongly associated, and there was a large degree of separation between the classes underlying the covariates. We found that when covariates were introduced, entropy might no longer be an accurate indicator of the distinctiveness of the growth trajectory classes.

Original languageEnglish (US)
Pages (from-to)1-12
Number of pages12
JournalStructural Equation Modeling
DOIs
StateAccepted/In press - May 17 2017

Fingerprint

Mixture Model
Covariates
Latent Class
Trajectories
Trajectory
Growth Model
entropy
Mixture model
Entropy
Missing Data
Recovery
simulation
Simulation Study

Keywords

  • covariates
  • entropy
  • finite mixture models
  • growth mixture model

ASJC Scopus subject areas

  • Decision Sciences(all)
  • Modeling and Simulation
  • Sociology and Political Science
  • Economics, Econometrics and Finance(all)

Cite this

A New Perspective on the Effects of Covariates in Mixture Models. / Stegmann, Gabriela; Grimm, Kevin.

In: Structural Equation Modeling, 17.05.2017, p. 1-12.

Research output: Contribution to journalArticle

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