On Inclusion of Covariates for Class Enumeration of Growth Mixture Models

Libo Li · Yih‐Ing Hser

84 citations0/0 files storedMultivariate Behavioral Research

No readable copy stored yet — this record is metadata only.

No open-access location is known for this record yet.

Literature note Cite in a project Citation map

Psychometric Methodologies and Testing Statistical Methods and Bayesian Inference Bayesian Methods and Mixture Models

Abstract

In this article, we directly questioned the common practice in growth mixture model (GMM) applications that exclusively rely on the fitting model without covariates for GMM class enumeration. We provided theoretical and simulation evidence to demonstrate that exclusion of covariates from GMM class enumeration could be problematic in many cases. Based on our findings, we provided recommendations for examining the class enumeration by the fitting model without covariates and discussed the potential of covariate inclusion as a remedy for the weakness of GMM class enumeration without including covariates. A real example on the development of children's cumulative exposure to risk factors for adolescent substance use was provided to illustrate our methodological developments.

Paper brief

Loading…

Cite this work

Citation unavailable for this work.

BibTeX RIS CSL-JSON

No public copy is stored for this record, so only the metadata is cited.

Citations in this library

Citation map →

Stored only when both papers are in the corpus — 84 citations exist upstream.

References

2007Deciding on the Number of Classes in Latent Class Analysis and Growth Mixture Modeling: A Monte Carlo Simulation Study 2001A Scaled Difference Chi-Square Test Statistic for Moment Structure Analysis 1987Factor Analysis and AIC 2000Integrating Person-Centered and Variable-Centered Analyses: Growth Mixture Modeling With Latent Trajectory Classes 2009Ensuring Positiveness of the Scaled Difference Chi-square Test Statistic 2004Latent Variable Analysis: Growth Mixture Modeling and Related Techniques for Longitudinal Data 2001Analyzing developmental trajectories of distinct but related behaviors: A group-based method.2003Distributional Assumptions of Growth Mixture Models: Implications for Overextraction of Latent Trajectory Classes.

Cited by

2015Multiple-Group Analysis of Similarity in Latent Profile Solutions 2016Covariates and Mixture Modeling: Results of a Simulation Study Exploring the Impact of Misspecified Effects on Class Enumeration 2016Modeling Predictors of Latent Classes in Regression Mixture Models 2013Different pathways towards dropout: the role of engagement in early school leaving

Explore the map →

Papers that cite the same references — the relational fallback while embeddings are still backfilling.

Imported from OpenAlex (CC0) — nothing here is generated.