Prediction from Latent Classes: A Demonstration of Different Approaches to Include Distal Outcomes in Mixture Models

By Karen Nylund-Gibson, Ryan P. Grimm, Katherine E. Masyn. Structural Equation Modeling: A Multidisciplinary Journal, 2019

391 citations recorded by the source corpus.

Including auxiliary variables such as antecedent and consequent variables in mixture models provides valuable insight in understanding the population heterogeneity embodied by a latent class variable. The model building process regarding how to include predictors/correlates and outcomes of the latent class variables into mixture models is an area of active research. As such, new methods of including these variables continue to emerge and best practices for the application of these methods in real data settings (including simple guidelines for choosing amongst them) are still not well established. This paper focuses on one type of auxiliary variable—distal outcomes—providing an overview of the methods currently available for estimating the effects of latent class membership on subsequent distal outcomes. We illustrate the recommended methods in the software packages Mplus and Latent Gold using a latent class model to capture population heterogeneity in students’ mathematics attitudes, linking latent class membership to two distal outcomes.

Subfields: Artificial Intelligence, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/226a44a5c84a6d38158039dc72385754dd49a9c5

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

In Statistics and Probability

Citations in the corpus

Finding latent groups in observed data: A primer on latent profile analysis in Mplus for applied researchers (2020, methodology): “For further reading on this method, readers are pointed to Asparouhov and Muthén (2014b) and Nylund-Gibson, Grimm, and Masyn (2019).”
All better than being disengaged: Student engagement patterns and their relations to academic self-concept and achievement (2020, background): “In this analysis, profile membership is considered as a latent variable with an associated measurement error instead of a manifest observed variable (Nylund-Gibson et al. 2019).”