title: A Monte Carlo investigation of factors influencing latent class analysis: an application to eating disorder research.
abstract: OBJECTIVE
  Latent class analysis (LCA) has frequently been used to identify qualitatively distinct phenotypes of disordered eating. However, little consideration has been given to methodological factors that may influence the accuracy of these results.
  
  
  METHODS
  Monte Carlo simulations were used to evaluate methodological factors that may influence the accuracy of LCA under scenarios similar to those seen in previous eating disorder research.
  
  
  RESULTS
  Under these scenarios, the aBIC provided the best overall performance as an information criterion, requiring sample sizes of 300 in both balanced and unbalanced structures to achieve accuracy proportions of at least 80%. The BIC and cAIC required larger samples to achieve comparable performance, while the AIC performed poorly universally in comparison. Accuracy generally was lower with unbalanced classes, fewer indicators, greater or nonrandom missing data, conditional independence assumption violations, and lower base rates of indicator endorsement.
  
  
  CONCLUSIONS
  These results provide critical information for interpreting previous LCA research and designing future classification studies.
authors: Harvard School of Public Health, Department of Epidemiology, Boston, MA 02115, USA. sswanson@hsph.harvard.edu
authors: Sonja A
authors: SA
authors: Swanson
authors: Katajun
authors: K
authors: Lindenberg
authors: Stephanie
authors: S
authors: Bauer
authors: Ross D
authors: RD
authors: Crosby
mesh_terms: D035843:Biomedical Research; D039721:Diagnostic and Statistical Manual of Mental Disorders; D001068:Feeding and Eating Disorders / Q000145:classification* / Q000175:diagnosis; D006801:Humans; D009010:Monte Carlo Method; D010641:Phenotype