Between-Item Multidimensional IRT: How Far Can the Estimation Methods Go?

Multidimensional item response models are known to be difficult to estimate, with a variety of estimation and modeling strategies being proposed to handle the difficulties. While some previous studies have considered the performance of these estimation methods, they typically include only one or two methods, or a small number of factors. In this paper, we report on a large simulation study of between-item multidimensional IRT estimation methods, considering five different methods, a variety of sample sizes, and up to eight factors. This study provides a comprehensive picture of the methods’ relative performance, as well as each individual method’s strengths and weaknesses. The study results lead us to make recommendations for applied research, related to which estimation methods should be used under various scenarios.

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Estimating General Multidimensional Item-Response Models Using Markov Chain Monte Carlo Estimation Methods: Simulation Study and Empirical Example

PsycEXTRA Dataset ◽

10.1037/e680662011-001 ◽

2011 ◽

Author(s):

Jinsong Chen

Keyword(s):

Monte Carlo ◽

Markov Chain ◽

Markov Chain Monte Carlo ◽

Item Response ◽

Simulation Study ◽

Estimation Methods ◽

Multidimensional Item Response ◽

Response Models ◽

Item Response Models ◽

Monte Carlo Estimation

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Extreme Response Style: A Simulation Study Comparison of Three Multidimensional Item Response Models

Applied Psychological Measurement ◽

10.1177/0146621618789392 ◽

2018 ◽

Vol 43 (4) ◽

pp. 322-335 ◽

Cited By ~ 2

Author(s):

Brian C. Leventhal

Keyword(s):

Item Response ◽

Partial Credit Model ◽

Response Model ◽

Multidimensional Item Response ◽

Generalized Partial Credit Model ◽

Response Models ◽

Item Response Models ◽

Nominal Response Model ◽

Extreme Response ◽

Generalized Partial Credit

Several multidimensional item response models have been proposed for survey responses affected by response styles. Through simulation, this study compares three models designed to account for extreme response tendencies: the IRTree Model, the multidimensional nominal response model, and the modified generalized partial credit model. The modified generalized partial credit model results in the lowest item mean squared error (MSE) across simulation conditions of sample size (500, 1,000), survey length (10, 20), and number of response options (4, 6). The multidimensional nominal response model is equally suitable for surveys measuring one substantive trait using responses to 10 four-option, forced-choice Likert-type items. Based on data validation, comparison of item MSE, and posterior predictive model checking, the IRTree Model is hypothesized to account for additional sources of construct-irrelevant variance.

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