An introduction to latent variable mixture modeling (Part 1): Overview and cross-sectional latent class and latent profile analyses

Kristoffer S. Berlin, Natalie A. Williams, Gilbert R. Parra

Research output: Contribution to journalReview articlepeer-review

235 Scopus citations

Abstract

Objective Pediatric psychologists are often interested in finding patterns in heterogeneous cross-sectional data. Latent variable mixture modeling is an emerging person-centered statistical approach that models heterogeneity by classifying individuals into unobserved groupings (latent classes) with similar (more homogenous) patterns. The purpose of this article is to offer a nontechnical introduction to cross-sectional mixture modeling. Method An overview of latent variable mixture modeling is provided and 2 cross-sectional examples are reviewed and distinguished. Results Step-by-step pediatric psychology examples of latent class and latent profile analyses are provided using the Early Childhood Longitudinal Study-Kindergarten Class of 1998-1999 data file. Conclusions Latent variable mixture modeling is a technique that is useful to pediatric psychologists who wish to find groupings of individuals who share similar data patterns to determine the extent to which these patterns may relate to variables of interest.

Original languageEnglish (US)
Pages (from-to)174-187
Number of pages14
JournalJournal of pediatric psychology
Volume39
Issue number2
DOIs
StatePublished - Mar 2014

Keywords

  • cross-sectional data analysis
  • latent class
  • latent profile
  • person-centered
  • statistical analysis
  • structural equation modeling

ASJC Scopus subject areas

  • Pediatrics, Perinatology, and Child Health
  • Developmental and Educational Psychology

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