341 – SPEED: Classification and Data Science
Assessing Divide-and-Conquer Latent Class Analysis
Qiao Ma
NORC at the University of Chicago
Meimeizi Zhu
NORC at the University of Chicago
Edward Mulrow
NORC at the University of Chicago
Latent class analysis (LCA) is generally used to construct unobserved classes from observed indicator variables. Fitting an LCA to a large dataset is challenging. Abarda et al. (2017) proposed a Divide-and-Conquer approach for LCA when dealing with big data. Divide-and-Conquer partitions a data set into multiple subsets with equal size, and fits a model to each subset. We assess LCA results in such a setting by fitting LCA models with and without the Divide-and-Conquer approach, and measure the relationships between the Bayesian Information Criteria (BIC) for the whole dataset and its subsets.