This Monte Carlo simulation study assessed the degree of classification success associated with resubstitution methods in latent class analysis (LCA) and compared those results to those of the leaveone- out (L-O-O) method for computing classification success. Specifically, this study considered a latent class model with two classes, dichotomous manifest variables, restricted conditional probabilities for each latent class and relatively small sample sizes. The performance of resubstitution and L-O-O methods on the lambda classification index was assessed by examining the degree of bias.
Kroopnick, Marc H.; Chen, Jinsong; Choi, Jaehwa; and Dayton, C. Mitchell
"Assessing Classification Bias in Latent Class Analysis: Comparing Resubstitution and Leave-One-Out Methods,"
Journal of Modern Applied Statistical Methods:
1, Article 7.
Available at: http://digitalcommons.wayne.edu/jmasm/vol9/iss1/7