Divisive latent class modeling as a density estimation method for categorical data
van der Palm,D.W. van der Ark,L.A. Vermunt,J.K.
van der Palm,D.W.
van der Ark,L.A.
Vermunt,J.K.
Abstract
Traditionally latent class (LC) analysis is used by applied researchers as a tool for identifying substantively meaningful clusters. More recently, LC models have also been used as a density estimation tool for categorical variables. We introduce a divisive LC (DLC) model as a density estimation tool that may offer several advantages in comparison to a standard LC model. When using an LC model for density estimation, a considerable number of increasingly large LC models may have to be estimated before sufficient model-fit is achieved. A DLC model consists of a sequence of small LC models. Therefore, a DLC model can be estimated much faster and can easily utilize multiple processor cores, meaning that this model is more widely applicable and practical. In this study we describe the algorithm of fitting a DLC model, and discuss the various settings that indirectly influence the precision of a DLC model as a density estimation tool. These settings are illustrated using a synthetic data example, and the best performing algorithm is applied to a real-data example. The generated data example showed that, using specific decision rules, a DLC model is able to correctly model complex associations amongst categorical variables.
Description
Date
2016
Journal Title
Journal ISSN
Volume Title
Publisher
Research Projects
Organizational Units
Journal Issue
Keywords
Latent class analysis, Categorical data, Mixture model, Density estimation, Divisive latent class model, Missing data, Multiple imputation, TEST-SCORE RELIABILITY, MULTIPLE IMPUTATION, COMPONENTS, INFERENCE, MIXTURES, NUMBER
Citation
van der Palm, D W, van der Ark, L A & Vermunt, J K 2016, 'Divisive latent class modeling as a density estimation method for categorical data', Journal of Classification, vol. 33, no. 1, pp. 52-72. https://doi.org/10.1007/s00357-016-9195-5