Vol.:(0123456789) Behaviormetrika https://doi.org/10.1007/s41237-019-00084-6 1 3 ORIGINAL PAPER Categorical latent variable modeling utilizing fuzzy clustering generalized structured component analysis as an alternative to latent class analysis Ji Hoon Ryoo 1  · Seohee Park 2  · Seongeun Kim 3 Received: 20 November 2018 / Accepted: 29 April 2019 © The Behaviormetric Society 2019 Abstract Latent class analysis is becoming popular in many areas of education, psychology, social and behavioral sciences, public health, and medicine. However, it often suf- fers from identifcation issues due to the large number of parameters involved when using maximum likelihood (ML) estimation. Increasing the sample size, reducing sparseness, and strengthening the relationship between the observed variables and the latent variables all improve the information and thus reduce the identifcation issues, but the identifcation issue still afects the validity of parameter estimates in ML estimation and the defnition of identifcation is not sufcient to guarantee the existence of an ML solution. In this paper, generalized structured component analysis (GSCA), which is a component-based approach that utilizes optimal scaling and fuzzy clustering, is applied to avoid these identifcation issues and develop more stable solutions for the heterogeneity of a population based on a set of categorical responses. Testing our proposed new approach, component-based (CB) latent class analysis (LCA), on real world substance use data from Add Health produced not only the same features as those yielded by conventional ML LCA but also stable estimation without identifcation issues. Comparing the results obtained from ML LCA using Mplus and poLCA in R, with those from our proposed CB LCA using GSCA in R revealed a similar number of latent classes and posterior probabilities and only minor discrepancies in individual latent class classifcations when the pos- terior probabilities of membership are not distinct. Keywords Fuzzy clustering · Generalized structured component analysis · Latent class analysis · Optimal scaling Communicated by Heungsun Hwang * Ji Hoon Ryoo jryoo@usc.edu; jryoo@chla.usc.edu Extended author information available on the last page of the article