scholarly journals Regularized Latent Class Analysis for Polytomous Item Responses: An Application to SPM-LS Data

2020 ◽  
Vol 8 (3) ◽  
pp. 30 ◽  
Author(s):  
Alexander Robitzsch
Keyword(s):  
Latent Class ◽  
Latent Class Model ◽  
Simulated Data ◽  
Latent Class Models ◽  
Latent Classes ◽  
Polytomous Item ◽  
Item Functioning ◽  
Item Responses ◽  
Polytomous Item Responses ◽  
Raven's Standard Progressive Matrices

The last series of Raven’s standard progressive matrices (SPM-LS) test was studied with respect to its psychometric properties in a series of recent papers. In this paper, the SPM-LS dataset is analyzed with regularized latent class models (RLCMs). For dichotomous item response data, an alternative estimation approach based on fused regularization for RLCMs is proposed. For polytomous item responses, different alternative fused regularization penalties are presented. The usefulness of the proposed methods is demonstrated in a simulated data illustration and for the SPM-LS dataset. For the SPM-LS dataset, it turned out the regularized latent class model resulted in five partially ordered latent classes. In total, three out of five latent classes are ordered for all items. For the remaining two classes, violations for two and three items were found, respectively, which can be interpreted as a kind of latent differential item functioning.

scholarly journals Regularized Latent Class Analysis for Polytomous Item Responses: An Application to SPM-LS Data

2020 ◽  
Author(s):  
Alexander Robitzsch
Keyword(s):  
Latent Class Analysis ◽  
Latent Class ◽  
Latent Class Model ◽  
Simulated Data ◽  
Latent Class Models ◽  
Class Analysis ◽  
Polytomous Item ◽  
Item Responses ◽  
Polytomous Item Responses ◽  
Raven's Standard Progressive Matrices

The last series of Raven's standard progressive matrices (SPM-LS) test were studied with respect to its psychometric properties in a series of recent papers. In this paper, the SPM-LS dataset is analyzed with regularized latent class models (RLCM). For dichotomous item response data, an alternative estimation approach for RLCMs is proposed. For polytomous item responses, different alternatives for performing regularized latent class analysis are proposed. The usefulness of the proposed methods is demonstrated in a simulated data illustration and for the SPM-LS dataset. For the SPM-LS dataset, it turned out the regularized latent class model resulted in five partially ordered latent classes.

Bayesian nonparametric latent class model for longitudinal data

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.

scholarly journals AN ALTERNATIVE TO CLASSICAL LATENT CLASS MODELS SELECTION METHODS FOR SPARSE BINARY DATA: AN ILLUSTRATION WITH SIMULATED DATA

2017 ◽  
Vol 23 (1) ◽  
pp. 199-220
Author(s):  
Carlomagno Araya Alpizar
Keyword(s):  
Binary Data ◽  
Goodness Of Fit ◽  
Latent Class ◽  
Empirical Distribution ◽  
Latent Class Model ◽  
Simulated Data ◽  
Latent Class Models ◽  
Type I ◽  
Chi Square ◽  
Goodness Of Fit Tests

Within the context of a latent class model with manifest binary variables, we propose an alternative method that solves the problem of estimating empirical distribution with sparse contingency tables and the chi-square approximation for goodness-of-fit will not be valid. We analyze sparse binary data, where there are many response patterns with very small expected frequencies in several data sets varying in degree of sparseness from 1 to 5 defined d = n/2p = n/R is a factor that is mentioned in almost all prior literature as being an important determinant of how well the distribution is represented by the chi-squared.The proposed approach produced results that were valid and reliable under the mentioned problematic data conditions. Results from the proposal presented compare the rates of Type I for traditional goodness-of-fit tests. We also show that with data density d ≤ 5, Pearson’s statistic

scholarly journals Hybridizing Machine Learning Methods and Finite Mixture Models for Estimating Heterogeneous Treatment Effects in Latent Classes

2020 ◽  
pp. 107699862095198
Author(s):  
Youmi Suk ◽  
Jee-Seon Kim ◽  
Hyunseung Kang
Keyword(s):  
Machine Learning ◽  
Treatment Effects ◽  
Latent Class ◽  
Finite Mixture Models ◽  
Likelihood Estimation ◽  
Study Data ◽  
Latent Class Models ◽  
Finite Mixture ◽  
Latent Classes ◽  
Heterogeneous Treatment Effects

There has been increasing interest in exploring heterogeneous treatment effects using machine learning (ML) methods such as causal forests, Bayesian additive regression trees, and targeted maximum likelihood estimation. However, there is little work on applying these methods to estimate treatment effects in latent classes defined by well-established finite mixture/latent class models. This article proposes a hybrid method, a combination of finite mixture modeling and ML methods from causal inference to discover effect heterogeneity in latent classes. Our simulation study reveals that hybrid ML methods produced more precise and accurate estimates of treatment effects in latent classes. We also use hybrid ML methods to estimate the differential effects of private lessons across latent classes from Trends in International Mathematics and Science Study data.

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

1980 ◽  
Vol 5 (1) ◽  
pp. 65-81 ◽  
Author(s):  
John R. Bergan ◽  
Anthony A. Cancelli ◽  
John W. Luiten
Keyword(s):  
Latent Class ◽  
Decision Rules ◽  
Latent Class Models ◽  
Latent Classes ◽  
Classification Techniques ◽  
Complex Models ◽  
Class Models

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.

Latent Class Models for the Analysis of Behavioral Hierarchies

1987 ◽  
Vol 24 (2) ◽  
pp. 174-186 ◽  
Author(s):  
Lawrence F. Feick
Keyword(s):  
Item Response ◽  
Latent Class ◽  
Latent Class Models ◽  
Scale Model ◽  
Guttman Scale ◽  
Cross Sectional Survey ◽  
Cross Sectional ◽  
Consumer Complaint ◽  
Item Responses ◽  
Class Models

The author develops and describes latent class models useful for the analysis of behavioral hierarchies. The latent class models investigated are generalizations of the Guttman scale model and consider probabilistic relationships of item response to scale type and multiple hierarchical orderings of item responses. In addition, the author develops models for hierarchies that are present at the level of sets of items rather than at the level of individual items. He calls them “characteristics models” and examines their relationship to models for hierarchies of items. The models are illustrated on consumer complaint data gathered from a cross-sectional survey.

Explanatory Secondary Dimension Modeling of Latent Differential Item Functioning

2011 ◽  
Vol 35 (8) ◽  
pp. 583-603 ◽  
Author(s):  
Paul De Boeck ◽  
Sun-Joo Cho ◽  
Mark Wilson
Keyword(s):  
Differential Item Functioning ◽  
Mixture Models ◽  
Latent Class ◽  
The Other ◽  
Model Parameters ◽  
Latent Classes ◽  
Property A ◽  
Arithmetic Operations ◽  
Item Functioning ◽  
Time Specific

The models used in this article are secondary dimension mixture models with the potential to explain differential item functioning (DIF) between latent classes, called latent DIF. The focus is on models with a secondary dimension that is at the same time specific to the DIF latent class and linked to an item property. A description of the models is provided along with a means of estimating model parameters using easily available software and a description of how the models behave in two applications. One application concerns a test that is sensitive to speededness and the other is based on an arithmetic operations test where the division items show latent DIF.

Reliable Inference in Highly Stratified Contingency Tables: Using Latent Class Models as Density Estimators

2011 ◽  
Vol 19 (2) ◽  
pp. 173-187 ◽  
Author(s):  
Drew A. Linzer
Keyword(s):  
Latent Class ◽  
Latent Class Model ◽  
Contingency Tables ◽  
Latent Class Models ◽  
Small Samples ◽  
Categorical Variables ◽  
Density Estimator ◽  
Class Model ◽  
Wide Range ◽  
Demographic Subgroups

Contingency tables are among the most basic and useful techniques available for analyzing categorical data, but they produce highly imprecise estimates in small samples or for population subgroups that arise following repeated stratification. I demonstrate that preprocessing an observed set of categorical variables using a latent class model can greatly improve the quality of table-based inferences. As a density estimator, the latent class model closely approximates the underlying joint distribution of the variables of interest, which enables reliable estimation of conditional probabilities and marginal effects, even among subgroups containing fewer than 40 observations. Though here focused on applications to public opinion, the procedure has a wide range of potential uses. I illustrate the benefits of the latent class model—based approach for greatly improved accuracy in estimating and forecasting vote preferences within small demographic subgroups using survey data from the 2004 and 2008 U.S. presidential election campaigns.

Sign in / Sign up

Export Citation Format

Share Document