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Bayesian Item Response Modeling

Jean-Paul Fox
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The article was published on 2010-01-01. It has received 331 citations till now. The article focuses on the topics: Bayesian probability.

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Multiple-Group Factor Analysis Alignment

TL;DR: In this article, the alignment method is used to estimate group-specific factor means and variances without requiring exact measurement invariance, which is a valuable alternative to the currently used multiple-group CFA methods that require multiple manual model adjustments guided by modification indexes.
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Crowd-sourced Text Analysis: Reproducible and Agile Production of Political Data

TL;DR: Crowd-sourcing to distribute text for reading and interpretation by massive numbers of nonexperts generates results comparable to those using experts to read and interpret the same texts, but do so far more quickly and flexibly, making crowd-sourced datasets intrinsically reproducible.
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Recent Methods for the Study of Measurement Invariance With Many Groups

TL;DR: In this paper, a comparison of factor analytic and item response theory approaches to the study of invariance across groups is presented, and a list of considerations for choosing between the two methods is presented.
Journal ArticleDOI

A Bayesian Approach to Multilevel Structural Equation Modeling With Continuous and Dichotomous Outcomes

TL;DR: In this article, a simulation study compared estimation quality using Bayesian and frequentist approaches in the context of a multilevel latent covariate model and found that Bayesian estimation may be used to overcome convergence problems and improve parameter estimate bias.
Journal ArticleDOI

On the Complexity of Item Response Theory Models

TL;DR: In this paper, the authors examined four popular IRT models-exploratory factor analytic, bifactor, DINA, and DINO-with different functional forms but the same number of free parameters.
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