Linear logistic scoring equations for latent class and latent profile models: A simple method for classifying new cases
Vermunt,J.K. Magidson,J.
Vermunt,J.K.
Magidson,J.
Abstract
Researchers are often interested in using latent class or latent profile parameter estimates to obtain posterior class membership probabilities for observations other than those of the original sample. In this paper, we demonstrate that these probabilities typically take on the form of linear logistic equations with coefficients which are functions of the original model parameters. In other words, the posterior class membership probabilities can be computed with a prediction formula similar to that of a multinomial logistic regression model. We derive the scoring equations for nominal, ordinal, count, and continuous indicators, as well as investigate models with missing values on class indicators, local dependencies, covariates, or multiple latent variables. In addition to the mathematical derivations of the scoring equations, we describe how either exact or approximate scoring equations can be obtained by estimating a multinomial regression model using a weighted data set.
Description
Date
2024
Journal Title
Journal ISSN
Volume Title
Publisher
Research Projects
Organizational Units
Journal Issue
Keywords
Canonical link functions, Classification, Multinomial logistic regression, Out-of-sample prediction, Posterior class membership probabilities
Citation
Vermunt, J K & Magidson, J 2024, 'Linear logistic scoring equations for latent class and latent profile models : A simple method for classifying new cases', Structural Equation Modeling. https://doi.org/10.1080/10705511.2024.2375736
License
info:eu-repo/semantics/openAccess