title: An introduction to latent variable mixture modeling (part 1): overview and cross-sectional latent class and latent profile analyses.
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.
  
  
  METHODS
  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.
authors.affiliation: PhD, Department of Psychology, The University of Memphis, 202 Psychology Building, Memphis, TN 38152, USA. kristoffer.berlin@gmail.com.
authors.forename: Kristoffer S
authors.initials: KS
authors.lastname: Berlin
authors.forename: Natalie A
authors.initials: NA
authors.lastname: Williams
authors.forename: Gilbert R
authors.initials: GR
authors.lastname: Parra
mesh_terms: D002648:Child; D003430:Cross-Sectional Studies; D006801:Humans; D008960:Models, Psychological; D015233:Models, Statistical; D002666:Psychology, Child; D012107:Research Design
keywords: cross-sectional data analysis; latent class; latent profile; person-centered; statistical analysis; structural equation modeling