Mastery Assessment with Latent Class and Quasi-Independence Models Representing Homogeneous Item Domains

This article discusses mastery classification involving the use of latent class and quasi-independence models. Extensions of mastery classification techniques developed by Macready and Dayton are presented. These extensions provide decision rules for assigning individuals to latent classes in complex models involving more than two latent categories. Procedures for identifying the minimally acceptable proportion of misclassified individuals in complex latent class models are also detailed.

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Bayesian nonparametric latent class model for longitudinal data

Statistical Methods in Medical Research ◽

10.1177/0962280220928384 ◽

2020 ◽

Vol 29 (11) ◽

pp. 3381-3395

Author(s):

Wonmo Koo ◽

Heeyoung Kim

Keyword(s):

Longitudinal Data ◽

Latent Class ◽

Latent Class Model ◽

Latent Class Models ◽

Model Parameters ◽

Latent Classes ◽

Bayesian Nonparametric ◽

Class Model ◽

Proposed Model ◽

Class Models

Latent class models have been widely used in longitudinal studies to uncover unobserved heterogeneity in a population and find the characteristics of the latent classes simultaneously using the class allocation probabilities dependent on predictors. However, previous latent class models for longitudinal data suffer from uncertainty in the choice of the number of latent classes. In this study, we propose a Bayesian nonparametric latent class model for longitudinal data, which allows the number of latent classes to be inferred from the data. The proposed model is an infinite mixture model with predictor-dependent class allocation probabilities; an individual longitudinal trajectory is described by the class-specific linear mixed effects model. The model parameters are estimated using Markov chain Monte Carlo methods. The proposed model is validated using a simulated example and a real-data example for characterizing latent classes of estradiol trajectories over the menopausal transition using data from the Study of Women’s Health Across the Nation.

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Model Selection Information Criteria for Non-Nested Latent Class Models

Journal of Educational and Behavioral Statistics ◽

10.3102/10769986022003249 ◽

1997 ◽

Vol 22 (3) ◽

pp. 249-264 ◽

Cited By ~ 127

Author(s):

Ting Hsiang Lin ◽

C. Mitchell Dayton

Keyword(s):

Model Selection ◽

Latent Class ◽

Error Rates ◽

Latent Class Models ◽

Information Criteria ◽

Behavioral Analysis ◽

Nested Models ◽

Complex Models ◽

Class Models ◽

Simple Models

Latent class models have been developed for assessment of hierarchic relations in scaling and behavioral analysis. This article investigated the use of three model selection information criteria—Akaike AIC, Schwarz SIC, and Bozdogan CAIC—for non-nested models. In general, SIC and CAIC were superior to AIC for relatively simple models, whereas AIC was superior for more complex models, although accuracy was often quite low for such models. In addition, some effects were detected for error rates in the models.

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Latent Class Models Reveal Poor Agreement between Discrete-Choice and Time Tradeoff Preferences

Medical Decision Making ◽

10.1177/0272989x19841592 ◽

2019 ◽

Vol 39 (4) ◽

pp. 421-436 ◽

Cited By ~ 1

Author(s):

Eleanor M. Pullenayegum ◽

A. Simon Pickard ◽

Feng Xie

Keyword(s):

Random Effects ◽

Discrete Choice ◽

Latent Class ◽

Latent Class Models ◽

Latent Classes ◽

Poor Agreement ◽

Health States ◽

Time Tradeoff ◽

Anxiety Depression ◽

Class Models

Background. In health economics, there has been interest in using discrete-choice experiments (DCEs) to derive preferences for health states in lieu of previously established approaches like time tradeoff (TTO). We examined whether preferences elicited through DCEs are associated and agree with preferences elicited through TTO tasks. Methods. We used data from 1073 respondents to the Canadian EQ-5D-5L valuation study. Multivariate mixed-effects models specified a common likelihood for the TTO and discrete-choice data, with separate but correlated random effects for the TTO and DCE data, for each of the 5 EQ-5D-5L dimensions. Multivariate latent class models allowed separate but associated latent classes for the DCE and TTO data. Results. Correlation between the random effects for the 2 tasks ranged from −0.12 to 0.75, with only pain/discomfort and anxiety/depression having at least a 50% posterior probability of strong (>0.6) correlation. Latent classes for the TTO and DCE data both featured 1 latent class capturing participants attaching large disutilities to pain/discomfort, another capturing participants attaching large disutility to anxiety/depression, and the third class capturing the remainder. Agreement in class membership was poor (κ coefficient: 0.081; 95% credible interval, 0.033–0.13). Fewer respondents expressed strong disutilities for problems with anxiety/depression or pain/discomfort in the TTO than the DCE data (17% v. 55%, respectively). Conclusions. Stated preferences using TTO and DCEs show association across dimensions but poor agreement at the level of individual health states within respondents. Joint models that assume agreement between DCE and TTO have been used to develop national value sets for the EQ-5D-5L. This work indicates that when combining data from both techniques, methods requiring association but not agreement are needed.

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