Results 231 to 240 of about 19,101,034 (262)

Bayesian inference for multidimensional graded response model using Pólya-Gamma latent variables

Journal of Statistical Computation and Simulation, 2023
In the context of both cognitive and affective tests, items are usually designed to involve more than two responses, for which polytomous models are applicable.
Zhihui Fu, Meilan Lu
exaly   +4 more sources

A Metropolis–Hastings Robbins–Monro algorithm via variational inference for estimating the multidimensional graded response model: a calculationally efficient estimation scheme to deal with complex test structures

Computational Statistics
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Xue Wang, Jing Lu, Jiwei Zhang 0021
exaly   +6 more sources

A multidimensional zero-inflated graded response model for ordinal symptom data.

Psychological Methods, 2022
Zero responses and their equivalents-for example, never, none, not at all-are commonly observed on measures of psychopathology inquiring about symptom frequencies, particularly when these measures are administered to community samples. Measurement researchers typically accommodate multivariate zero inflation by including a nonpathological class of ...
Brooke E. Magnus, Mauricio Garnier-Villarreal   +1 more
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Multidimensional graded response models with hierarchical structure and Q-matrix

Brazilian Journal of Probability and Statistics
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
da Silva, Marcelo, Bazán, Jorge, Liu, Ren, Possan, Edna, Vincenzi, Silvana   +4 more
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Supplemental Material - Detecting Differential Item Functioning in Multidimensional Graded Response Models With Recursive Partitioning

Supplemental Material for Detecting Differential Item Functioning in Multidimensional Graded Response Models With Recursive Partitioning by Franz Classe, and Christoph Kern in Applied Psychological Measurement.
Classe, Franz, Kern, Christoph
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ANALISIS RESPON ITEM PADA INSTRUMEN WORLD VALUES SURVEY BERDASARKAN MODEL MULTIDIMENSIONAL GRADED RESPONSE [PDF]

, 2023
Multidimensional Graded Response Model (MGRM) merupakan salah satu pemodelan IRT multidimensional untuk data respon bertipe politomus ordinal yang mengasumsikan daya pembeda (item discrimination) bervariasi antar item. Pada penelitian ini, MGRM digunakan untuk menganalisis instrumen World Values Survey (WVS), yang merupakan program penelitian ...
NOVIA SUCY ARISTAWIDYA, .
core   +3 more sources

Variational Estimation for Multidimensional Graded Response Model

Journal of Computational and Graphical Statistics
Qian-Zhen Zheng, Laixu Shang, Ping-Feng Xu, Na Shan, Zhigen Gao   +4 more
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A Gibbs‐INLA algorithm for multidimensional graded response model analysis

British Journal of Mathematical and Statistical Psychology, 2023
Abstract In this paper, we propose a novel Gibbs‐INLA algorithm for the Bayesian inference of graded response models with ordinal response based on multidimensional item response theory. With the combination of the Gibbs sampling and the integrated nested Laplace approximation (INLA), the new framework avoids the cumbersome tuning ...
Xiaofan Lin, Siliang Zhang, Yincai Tang, Xuan Li   +3 more
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Symptom Presence and Symptom Severity as Unique Indicators of Psychopathology: An Application of Multidimensional Zero-Inflated and Hurdle Graded Response Models

Educational and Psychological Measurement, 2021
Questionnaires inquiring about psychopathology symptoms often produce data with excess zeros or the equivalent (e.g., none, never, and not at all). This type of zero inflation is especially common in nonclinical samples in which many people do not exhibit psychopathology, and if unaccounted for, can result in biased parameter estimates when fitting ...
Brooke E. Magnus, Yang Liu
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The Influence of Multidimensionality on the Graded Response Model

Applied Psychological Measurement, 1994
Most item response theory models assume a uni-dimensional latent space. This study extended previous work on the effects of dimensionality on parameter estimation for dichotomous models to the polytomous graded response model. A multidimensional graded response model was developed to generate data in one, two, and three dimensions.
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