Visualizing Latent Class Models with Analysis-of-distance BIPLOTS

The authors propose using categorical analysis-of-distance biplots to visualize the posterior classifications arising from a latent class (LC) model. Using this multivariate plot, it is possible to visualize in two (or three) dimensions the profile of multiple LCs, specifically both the within- and between-class variation, and the overlap or separation of the classes together with the class weights. Furthermore, visualization of the relative density of each of the data patterns associated with a class is possible. The authors illustrate this approach with real data examples of LC models with three and more classes.

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Bayesian Multilevel Latent Class Models for the Multiple Imputation of Nested Categorical Data

Journal of Educational and Behavioral Statistics ◽

10.3102/1076998618769871 ◽

2018 ◽

Vol 43 (5) ◽

pp. 511-539 ◽

Cited By ~ 1

Author(s):

Davide Vidotto ◽

Jeroen K. Vermunt ◽

Katrijn van Deun

Keyword(s):

Multiple Imputation ◽

Mixture Model ◽

Categorical Data ◽

Latent Class ◽

Real Data ◽

Latent Class Models ◽

Parameter Estimates ◽

Complex Interactions ◽

Analysis Models ◽

Class Models

With this article, we propose using a Bayesian multilevel latent class (BMLC; or mixture) model for the multiple imputation of nested categorical data. Unlike recently developed methods that can only pick up associations between pairs of variables, the multilevel mixture model we propose is flexible enough to automatically deal with complex interactions in the joint distribution of the variables to be estimated. After formally introducing the model and showing how it can be implemented, we carry out a simulation study and a real-data study in order to assess its performance and compare it with the commonly used listwise deletion and an available R-routine. Results indicate that the BMLC model is able to recover unbiased parameter estimates of the analysis models considered in our studies, as well as to correctly reflect the uncertainty due to missing data, outperforming the competing methods.

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Classical latent variable models for medical research

Statistical Methods in Medical Research ◽

10.1177/0962280207081236 ◽

2008 ◽

Vol 17 (1) ◽

pp. 5-32 ◽

Cited By ~ 83

Author(s):

Sophia Rabe-Hesketh ◽

Anders Skrondal

Keyword(s):

Medical Research ◽

Latent Variable ◽

Structural Equation ◽

Latent Class ◽

Structural Equation Models ◽

Real Data ◽

Latent Variable Models ◽

Latent Class Models ◽

Forced Expiratory Flow ◽

Class Models

Latent variable models are commonly used in medical statistics, although often not referred to under this name. In this paper we describe classical latent variable models such as factor analysis, item response theory, latent class models and structural equation models. Their usefulness in medical research is demonstrated using real data. Examples include measurement of forced expiratory flow, measurement of physical disability, diagnosis of myocardial infarction and modelling the determinants of clients' satisfaction with counsellors' interviews.

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