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Christine E. DeMars; Paulius Satkus – Educational and Psychological Measurement, 2024
Marginal maximum likelihood, a common estimation method for item response theory models, is not inherently a Bayesian procedure. However, due to estimation difficulties, Bayesian priors are often applied to the likelihood when estimating 3PL models, especially with small samples. Little focus has been placed on choosing the priors for marginal…
Descriptors: Item Response Theory, Statistical Distributions, Error of Measurement, Bayesian Statistics
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von Davier, Matthias; Bezirhan, Ummugul – Educational and Psychological Measurement, 2023
Viable methods for the identification of item misfit or Differential Item Functioning (DIF) are central to scale construction and sound measurement. Many approaches rely on the derivation of a limiting distribution under the assumption that a certain model fits the data perfectly. Typical DIF assumptions such as the monotonicity and population…
Descriptors: Robustness (Statistics), Test Items, Item Analysis, Goodness of Fit
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Wallin, Gabriel; Wiberg, Marie – Journal of Educational and Behavioral Statistics, 2023
This study explores the usefulness of covariates on equating test scores from nonequivalent test groups. The covariates are captured by an estimated propensity score, which is used as a proxy for latent ability to balance the test groups. The objective is to assess the sensitivity of the equated scores to various misspecifications in the…
Descriptors: Models, Error of Measurement, Robustness (Statistics), Equated Scores
MacDonald, George T. – ProQuest LLC, 2014
A simulation study was conducted to explore the performance of the linear logistic test model (LLTM) when the relationships between items and cognitive components were misspecified. Factors manipulated included percent of misspecification (0%, 1%, 5%, 10%, and 15%), form of misspecification (under-specification, balanced misspecification, and…
Descriptors: Simulation, Item Response Theory, Models, Test Items
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Tate, Richard L. – Journal of Educational Measurement, 1995
Robustness of the school-level item response theoretic (IRT) model to violations of distributional assumptions was studied in a computer simulation. In situations where school-level precision might be acceptable for real school comparisons, expected a posteriori estimates of school ability were robust over a range of violations and conditions.…
Descriptors: Comparative Analysis, Computer Simulation, Estimation (Mathematics), Item Response Theory