title: Latent variable mixture modeling: a flexible statistical approach for identifying and classifying heterogeneity.
abstract: BACKGROUND
  Latent variable mixture modeling is becoming increasingly popular in nursing research, in part due to the sophistication of the method in identifying relationships, patterns, and clusters in the data.
  
  
  OBJECTIVE
  The aim of this study was to provide an overview of mixture modeling techniques, specifically as applied to nursing research, and to present examples from two studies to illustrate how these techniques may be used cross-sectionally and longitudinally.
  
  
  METHODS
  The first data example demonstrates the use of latent profile analysis as applied to the St. George respiratory symptoms questionnaire in 2,232 smokers from the Lovelace Smokers Cohort. The second data example demonstrates growth mixture modeling as applied to condom use trajectories among 728 at-risk adolescents on probation.
  
  
  RESULTS
  Three classes of symptoms emerged among the smokers cohort: those who were high on all symptoms, those who were low on all symptoms, and those who were high on cough and phlegm only. These classes were then distinguishable by participant gender and wood smoke exposure. In the second data example, four classes of condom use emerged. Only 59% of the sample indicated the previously reported decline in condom use over time; condom use remained stable or significantly increased for the remaining 41%.
  
  
  CONCLUSIONS
  Both sets of results provide additional substantive information about patterns in the data that were not apparent from previously applied traditional methodological techniques. Considerations for the use of latent variable mixture modeling in nursing research are discussed.
authors.affiliation: Department of Biostatistics and Informatics, Colorado School of Public Health, University of Colorado-Denver, Aurora, CO 80045, USA. Sarah.Schmiege@ucdenver.edu
authors.forename: Sarah J
authors.initials: SJ
authors.lastname: Schmiege
authors.forename: Paula
authors.initials: P
authors.lastname: Meek
authors.forename: Angela D
authors.initials: AD
authors.lastname: Bryan
authors.forename: Hans
authors.initials: H
authors.lastname: Petersen
mesh_terms: D000293:Adolescent; D000328:Adult; D000368:Aged; D017280:Condoms / Q000706:statistics & numerical data; D003430:Cross-Sectional Studies / Q000706:statistics & numerical data; D005260:Female; D006801:Humans; D007604:Juvenile Delinquency; D008137:Longitudinal Studies / Q000706:statistics & numerical data; D008297:Male; D008875:Middle Aged; D015233:Models, Statistical; D015399:Nursing Research / Q000706:statistics & numerical data*; D012140:Respiratory Tract Diseases / Q000209:etiology; D012907:Smoking / Q000009:adverse effects; D014481:United States