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Direct linkShaojie Wang; Won-Chan Lee; Minqiang Zhang; Lixin Yuan – Applied Measurement in Education, 2024
To reduce the impact of parameter estimation errors on IRT linking results, recent work introduced two information-weighted characteristic curve methods for dichotomous items. These two methods showed outstanding performance in both simulation and pseudo-form pseudo-group analysis. The current study expands upon the concept of information…
Descriptors: Item Response Theory, Test Format, Test Length, Error of Measurement
Finch, Holmes; French, Brian F. – Applied Measurement in Education, 2019
The usefulness of item response theory (IRT) models depends, in large part, on the accuracy of item and person parameter estimates. For the standard 3 parameter logistic model, for example, these parameters include the item parameters of difficulty, discrimination, and pseudo-chance, as well as the person ability parameter. Several factors impact…
Descriptors: Item Response Theory, Accuracy, Test Items, Difficulty Level
Finch, W. Holmes – Applied Measurement in Education, 2016
Differential item functioning (DIF) assessment is a crucial component in test construction, serving as the primary way in which instrument developers ensure that measures perform in the same way for multiple groups within the population. When such is not the case, scores may not accurately reflect the trait of interest for all individuals in the…
Descriptors: Test Bias, Monte Carlo Methods, Comparative Analysis, Population Groups
Lee, Wooyeol; Cho, Sun-Joo – Applied Measurement in Education, 2017
Utilizing a longitudinal item response model, this study investigated the effect of item parameter drift (IPD) on item parameters and person scores via a Monte Carlo study. Item parameter recovery was investigated for various IPD patterns in terms of bias and root mean-square error (RMSE), and percentage of time the 95% confidence interval covered…
Descriptors: Item Response Theory, Test Items, Bias, Computation
Liang, Tie; Wells, Craig S. – Applied Measurement in Education, 2015
Investigating the fit of a parametric model plays a vital role in validating an item response theory (IRT) model. An area that has received little attention is the assessment of multiple IRT models used in a mixed-format test. The present study extends the nonparametric approach, proposed by Douglas and Cohen (2001), to assess model fit of three…
Descriptors: Nonparametric Statistics, Goodness of Fit, Item Response Theory, Test Format
Koziol, Natalie A. – Applied Measurement in Education, 2016
Testlets, or groups of related items, are commonly included in educational assessments due to their many logistical and conceptual advantages. Despite their advantages, testlets introduce complications into the theory and practice of educational measurement. Responses to items within a testlet tend to be correlated even after controlling for…
Descriptors: Classification, Accuracy, Comparative Analysis, Models
Murphy, Daniel L.; Beretvas, S. Natasha – Applied Measurement in Education, 2015
This study examines the use of cross-classified random effects models (CCrem) and cross-classified multiple membership random effects models (CCMMrem) to model rater bias and estimate teacher effectiveness. Effect estimates are compared using CTT versus item response theory (IRT) scaling methods and three models (i.e., conventional multilevel…
Descriptors: Teacher Effectiveness, Comparative Analysis, Hierarchical Linear Modeling, Test Theory
Finch, Holmes; Stage, Alan Kirk; Monahan, Patrick – Applied Measurement in Education, 2008
A primary assumption underlying several of the common methods for modeling item response data is unidimensionality, that is, test items tap into only one latent trait. This assumption can be assessed several ways, using nonlinear factor analysis and DETECT, a method based on the item conditional covariances. When multidimensionality is identified,…
Descriptors: Test Items, Factor Analysis, Item Response Theory, Comparative Analysis
Peer reviewedBolt, Daniel M. – Applied Measurement in Education, 2002
Compared two parametric procedures for detecting differential item functioning (DIF) using the graded response model (GRM), the GRM-likelihood ratio test and the GRM-differential functioning of items and tests, with a nonparametric DIF detection procedure, Poly-SIBTEST. Monte Carlo simulation results show that Poly-SIBTEST showed the least amount…
Descriptors: Comparative Analysis, Item Bias, Monte Carlo Methods, Nonparametric Statistics
