Dimension: A Program to Generate Unidimensional and Multidimensional Item Response Data

1993 ◽  
Vol 17 (3) ◽  
pp. 252-252 ◽  
Author(s):  
John Hattie ◽  
Krzysztof Krakowski
Keyword(s):  
Item Response ◽  
Multidimensional Item Response ◽  
Response Data

Robustness of Irt Parameter Estimation to Violations of The Unidimensionality Assumption

1986 ◽  
Vol 11 (2) ◽  
pp. 91-115 ◽  
Author(s):  
David A. Harrison
Keyword(s):  
Item Response ◽  
Computerized Adaptive Testing ◽  
Factor Model ◽  
Common Factors ◽  
General Factor ◽  
Multidimensional Item Response ◽  
Experimental Conditions ◽  
Response Data ◽  
Item Parameters ◽  
Item Response Theory Models

Multidimensional item response data were created from a hierarchical factor model under a variety of conditions. The strength of a second-order general factor, the number of first-order common factors, the distribution of items loading on those common factors, and the number of items in simulated tests were systematically manipulated. The computer program LOGIST effectively recovered both item parameters and trait parameters implied by the general factor in nearly all of the experimental conditions. Implications of these findings for computerized adaptive testing, investigations of item bias, and other applications of item response theory models are discussed.

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

2018 ◽  
Vol 43 (4) ◽  
pp. 322-335 ◽  
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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