Estimating Latent Structure Models with Categorical Variables

One-Step Versus Three-Step Estimators

Bibliographic Data

ID7971052
AuthorsAnnabel Bolck (Netherlands Forensic Institute), Marcel A Croon (Tilburg University), Marcel Croon, Jacques A Hagenaars (Tilburg University), Jacques Hagenaars
Year2004
Volume12
Issue1
Pages3-27
Publication date2004-01-01
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenuePolitical Analysis (JOURNAL)
Journal identifiersISSN: 1047-1987 • E-ISSN: 1476-4989
PublisherCambridge University Press (CUP) (PUBLISHER)
DOI10.1093/pan/mph001
OpenAlexW2061736358
LanguageEN
Citations received445
References cited18

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

Categorical variable · Econometrics · Estimator · Imputation (statistics · Latent class model · Latent variable · Latent variable model · Local independence · Logit · Missing data · Statistics · Structural equation modeling · Advanced Causal Inference Techniques · Bayesian Modeling and Causal Inference · Computer Science · Mathematics · Statistical Methods and Bayesian Inference

Tools

Open DOISci-Hub