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Shaojie 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 – Applied Measurement in Education, 2022
Much research has been devoted to identification of differential item functioning (DIF), which occurs when the item responses for individuals from two groups differ after they are conditioned on the latent trait being measured by the scale. There has been less work examining differential step functioning (DSF), which is present for polytomous…
Descriptors: Comparative Analysis, Item Response Theory, Item Analysis, Simulation
Song, Yoon Ah; Lee, Won-Chan – Applied Measurement in Education, 2022
This article presents the performance of item response theory (IRT) models when double ratings are used as item scores over single ratings when rater effects are present. Study 1 examined the influence of the number of ratings on the accuracy of proficiency estimation in the generalized partial credit model (GPCM). Study 2 compared the accuracy of…
Descriptors: Item Response Theory, Item Analysis, Scores, Accuracy
Silva Diaz, John Alexander; Köhler, Carmen; Hartig, Johannes – Applied Measurement in Education, 2022
Testing item fit is central in item response theory (IRT) modeling, since a good fit is necessary to draw valid inferences from estimated model parameters. "Infit" and "outfit" fit statistics, widespread indices for detecting deviations from the Rasch model, are affected by data factors, such as sample size. Consequently, the…
Descriptors: Intervals, Item Response Theory, Item Analysis, Inferences
Traditional vs Intersectional DIF Analysis: Considerations and a Comparison Using State Testing Data
Tony Albano; Brian F. French; Thao Thu Vo – Applied Measurement in Education, 2024
Recent research has demonstrated an intersectional approach to the study of differential item functioning (DIF). This approach expands DIF to account for the interactions between what have traditionally been treated as separate grouping variables. In this paper, we compare traditional and intersectional DIF analyses using data from a state testing…
Descriptors: Test Items, Item Analysis, Data Use, Standardized Tests
Abu-Ghazalah, Rashid M.; Dubins, David N.; Poon, Gregory M. K. – Applied Measurement in Education, 2023
Multiple choice results are inherently probabilistic outcomes, as correct responses reflect a combination of knowledge and guessing, while incorrect responses additionally reflect blunder, a confidently committed mistake. To objectively resolve knowledge from responses in an MC test structure, we evaluated probabilistic models that explicitly…
Descriptors: Guessing (Tests), Multiple Choice Tests, Probability, Models
Ferrara, Steve; Steedle, Jeffrey T.; Frantz, Roger S. – Applied Measurement in Education, 2022
Item difficulty modeling studies involve (a) hypothesizing item features, or item response demands, that are likely to predict item difficulty with some degree of accuracy; and (b) entering the features as independent variables into a regression equation or other statistical model to predict difficulty. In this review, we report findings from 13…
Descriptors: Reading Comprehension, Reading Tests, Test Items, Item Response Theory
Pham, Duy N.; Wells, Craig S.; Bauer, Malcolm I.; Wylie, E. Caroline; Monroe, Scott – Applied Measurement in Education, 2021
Assessments built on a theory of learning progressions are promising formative tools to support learning and teaching. The quality and usefulness of those assessments depend, in large part, on the validity of the theory-informed inferences about student learning made from the assessment results. In this study, we introduced an approach to address…
Descriptors: Formative Evaluation, Mathematics Instruction, Mathematics Achievement, Middle School Students
Abulela, Mohammed A. A.; Rios, Joseph A. – Applied Measurement in Education, 2022
When there are no personal consequences associated with test performance for examinees, rapid guessing (RG) is a concern and can differ between subgroups. To date, the impact of differential RG on item-level measurement invariance has received minimal attention. To that end, a simulation study was conducted to examine the robustness of the…
Descriptors: Comparative Analysis, Robustness (Statistics), Nonparametric Statistics, Item Analysis
Papenberg, Martin; Musch, Jochen – Applied Measurement in Education, 2017
In multiple-choice tests, the quality of distractors may be more important than their number. We therefore examined the joint influence of distractor quality and quantity on test functioning by providing a sample of 5,793 participants with five parallel test sets consisting of items that differed in the number and quality of distractors.…
Descriptors: Multiple Choice Tests, Test Items, Test Validity, Test Reliability
Bolt, Daniel; Wang, Yang Caroline; Meyer, Robert H.; Pier, Libby – Applied Measurement in Education, 2020
We illustrate the application of mixture IRT models to evaluate respondent confusion due to the negative wording of certain items on a social-emotional learning (SEL) assessment. Using actual student self-report ratings on four social-emotional learning scales collected from students in grades 3-12 from CORE Districts in the state of California,…
Descriptors: Item Response Theory, Social Emotional Learning, Self Evaluation (Individuals), Measurement Techniques
Wyse, Adam E. – Applied Measurement in Education, 2018
This article discusses regression effects that are commonly observed in Angoff ratings where panelists tend to think that hard items are easier than they are and easy items are more difficult than they are in comparison to estimated item difficulties. Analyses of data from two credentialing exams illustrate these regression effects and the…
Descriptors: Regression (Statistics), Test Items, Difficulty Level, Licensing Examinations (Professions)

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