Inferring Latent Structure in Polytomous Data with a Higher-Order Diagnostic Model

Datos Bibliográficos

ID21363079
AutoresSteven Andrew Culpepper (0000-0003-4226-6176, Department of Statistics, University of Illinois at Urbana-Champaign, Champaign, IL, autor de correspondencia), James J Balamuta (Departments of Informatics and Statistics, University of Illinois at Urbana-Champaign), James Balamuta (0000-0003-2826-8458, University of Illinois Urbana-Champaign)
Año2023
Volumen58
Número2
Páginas368-386
Fecha de publicación2023-03-04
Peer ReviewedSí
Open AccessNo
TipoARTICLE
RevistaMultivariate Behavioral Research (JOURNAL)
Identificadores de la revistaISSN: 0027-3171 • E-ISSN: 1532-7906
EditorialInforma UK Limited (PUBLISHER • GB)
DOI10.1080/00273171.2021.1985949
PMID34699299
OpenAlexW3208714234
IdiomaEN
Citas recibidas2
Referencias citadas50

Researchers continue to develop and advance models for diagnostic research in the social and behavioral sciences. These diagnostic models (DMs) provide researchers with a framework for providing a fine-grained classification of respondents into substantively meaningful latent classes as defined by a multivariate collection of binary attributes. A central concern for DMs is advancing exploratory methods for uncovering the latent structure, which corresponds with the relationship between unobserved binary attributes and observed polytomous items with two or more response options. Multivariate behavioral polytomous data are often collected within a higher-order design where general factors underlying first-order latent variables. This study advances existing exploratory DMs for polytomous data by proposing a new method for inferring the latent structure underlying polytomous response data using a higher-order model to describe dependence among the discrete latent attributes. We report a novel Bayesian formulation that uses variable selection techniques for inferring the latent structure along with a higher-order factor model for attributes. We report evidence of accurate parameter recovery in a Monte Carlo simulation study and present results from an application to the 2012 Programme for International Student Assessment (PISA) problem-solving vignettes to demonstrate the method

Bayesian inference · Bayesian probability · Data mining · Econometrics · Item response theory · Latent class model · Latent variable · Latent variable model · Machine learning · Model selection · Multivariate statistics · Polytomous Rasch model · Psychometrics · Statistics · Structural equation modeling · Advanced Statistical Methods and Models · Artificial Intelligence · Computer Science · Mathematics · Sensory Analysis and Statistical Methods · Statistical Methods and Bayesian Inference