Journal ArticleDOI
Estimating Latent Structure Models with Categorical Variables: One-Step Versus Three-Step Estimators
TLDR
In this article, the authors study the properties of a three-step approach to estimating the parameters of a latent structure model for categorical data and propose a simple correction for a common source of bias.Abstract:
We study the properties of a three-step approach to estimating the parameters of a latent structure model for categorical data and propose a simple correction for a common source of bias. Such models have a measurement part (essentially the latent class model) and a structural (causal) part (essentially a system of logit equations). In the three-step approach, a stand-alone measurement model is first defined and its parameters are estimated. Individual predicted scores on the latent variables are then computed from the parameter estimates of the measurement model and the individual observed scoring patterns on the indicators. Finally, these predicted scores are used in the causal part and treated as observed variables. We show that such a naive use of predicted latent scores cannot be recommended since it leads to a systematic underestimation of the strength of the association among the variables in the structural part of the models. However, a simple correction procedure can eliminate this systematic bias. This approach is illustrated on simulated and real data. A method that uses multiple imputation to account for the fact that the predicted latent variables are random variables can produce standard errors for the parameters in the structural part of the model.read more
Citations
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Journal ArticleDOI
Auxiliary Variables in Mixture Modeling: Three-Step Approaches Using Mplus
Tihomir Asparouhov,Bengt Muthén +1 more
TL;DR: This article discusses alternatives to single-step mixture modeling for latent class predictor variables in several different settings, including latent class analysis, latent transition analysis, and growth mixture modeling.
Journal ArticleDOI
Latent Class Modeling with Covariates: Two Improved Three-Step Approaches
TL;DR: The correction method of Bolck, Croon, and Hagenaars is extended by showing that it involves maximizing a weighted log-likelihood function for clustered data, which makes it possible to apply the method not only with categorical but also with continuous explanatory variables.
Journal ArticleDOI
Understanding the Mechanisms through Which an Influential Early Childhood Program Boosted Adult Outcomes
TL;DR: The authors used longitudinal data on cognitive and personality traits from an experimental evaluation of the influential Perry Preschool program to analyze the channels through which the program boosted both male and female participant outcomes.
Journal ArticleDOI
poLCA: An R Package for Polytomous Variable Latent Class Analysis
Drew A. Linzer,Jeffrey B. Lewis +1 more
TL;DR: poLCA is a software package for the estimation of latent class and latent class regression models for polytomous outcome variables, implemented in the R statistical computing environment using expectation-maximization and Newton-Raphson algorithms to find maximum likelihood estimates of the model parameters.
Journal ArticleDOI
Robustness of Stepwise Latent Class Modeling With Continuous Distal Outcomes
Zsuzsa Bakk,Jeroen K. Vermunt +1 more
TL;DR: Although each of the 4 investigated methods yields unbiased estimates of the class-specific means of distal outcomes when the underlying assumptions hold, 3 of the methods could fail to different degrees when assumptions are violated.
References
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Book
Structural Equations with Latent Variables
TL;DR: The General Model, Part I: Latent Variable and Measurement Models Combined, Part II: Extensions, Part III: Extensions and Part IV: Confirmatory Factor Analysis as discussed by the authors.
Book
Multiple imputation for nonresponse in surveys
TL;DR: In this article, a survey of drinking behavior among men of retirement age was conducted and the results showed that the majority of the participants reported that they did not receive any benefits from the Social Security Administration.
Journal ArticleDOI
Multiple Imputation for Nonresponse in Surveys.
C. D. Kershaw,Donald B. Rubin +1 more
TL;DR: This work focuses on the development of Imputation Models for Social Security Benefit Reconciliation in the context of a Finite Population and examines the role of Bayesian and Randomization--Based Inferences in these models.
Journal ArticleDOI
Latent Structure Analysis.
Book
Graphical Models in Applied Multivariate Statistics
TL;DR: This introduction to the use of graphical models in the description and modeling of multivariate systems covers conditional independence, several types of independence graphs, Gaussian models, issues in model selection, regression and decomposition.
Related Papers (5)
Deciding on the Number of Classes in Latent Class Analysis and Growth Mixture Modeling: A Monte Carlo Simulation Study
Auxiliary Variables in Mixture Modeling: Three-Step Approaches Using Mplus
Tihomir Asparouhov,Bengt Muthén +1 more