English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

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.

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