Journal ArticleDOI
Higher-Order Item Response Models for Hierarchical Latent Traits
TLDR
In this article, a new class of higher order item response theory models for hierarchical latent traits that are flexible in accommodating both dichotomous and polytomous items, to estimate both item and person parameters jointly, to allow users to specify customized item response functions, and to go beyond two orders of latent traits and the linear relationship between latent traits.Abstract:
Many latent traits in the human sciences have a hierarchical structure. This study aimed to develop a new class of higher order item response theory models for hierarchical latent traits that are flexible in accommodating both dichotomous and polytomous items, to estimate both item and person parameters jointly, to allow users to specify customized item response functions, and to go beyond two orders of latent traits and the linear relationship between latent traits. Parameters of the new class of models can be estimated using the Bayesian approach with Markov chain Monte Carlo methods. Through a series of simulations, the authors demonstrated that the parameters in the new class of models can be well recovered with the computer software WinBUGS, and the joint estimation approach was more efficient than multistaged or consecutive approaches. Two empirical examples of achievement and personality assessments were given to demonstrate applications and implications of the new models.read more
Citations
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Journal ArticleDOI
School factors that are related to school principals’ job satisfaction and organizational commitment
Yan Liu,Mehmet Şükrü Bellibaş +1 more
TL;DR: In this paper, a secondary analysis using the TALIS 2013 dataset, and applied a rigorous quantitative approach applied a Latent Trait method was first applied to construct latent variables of principals' job satisfaction and organizational commitment to compare the interests across countries.
Journal ArticleDOI
Mixture Random-Effect IRT Models for Controlling Extreme Response Style on Rating Scales
TL;DR: Mixture random-effect item response theory (IRT) models for ERS are developed in this study to simultaneously identify the mixtures of latent classes from different ERS levels and detect the possible differential functioning items that result from different latent mixtures.
Journal ArticleDOI
Multilevel Cognitive Diagnosis Models for Assessing Changes in Latent Attributes.
Journal ArticleDOI
A Multilevel Higher Order Item Response Theory Model for Measuring Latent Growth in Longitudinal Data
TL;DR: Various multilevel higher order item response theory (ML-HIRT) models for simultaneously measuring growth in the second- and first-order latent traits of dichotomous and polytomous items are proposed and reveal that the parameters could be recovered satisfactorily and that latent trait estimation was reliable across measurement times.
Journal ArticleDOI
Mixture IRT Model With a Higher-Order Structure for Latent Traits:
TL;DR: The proposed higher-order mixture IRT models can accommodate both linear and nonlinear models for latent traits and incorporate diverse item response functions and can be recovered fairly well using WinBUGS with Bayesian estimation.
References
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Book
Explanatory item response models : a generalized linear and nonlinear approach
Paul De Boeck,Mark Wilson +1 more
TL;DR: In this article, a framework for item response models is presented, and a generalized (non-linear) mixed model for polytomous data is presented. But it does not address the problem of item response modeling.
Journal ArticleDOI
Full-information item bi-factor analysis
Robert D. Gibbons,Donald Hedeker +1 more
TL;DR: The authors derived a bi-factor item-response model for binary response data, where each item has a nonzero loading on the primary dimension and at most one of the s − 1 group factors.
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