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Park, Seohee; Kim, Kyung Yong; Lee, Won-Chan – Journal of Educational Measurement, 2023
Multiple measures, such as multiple content domains or multiple types of performance, are used in various testing programs to classify examinees for screening or selection. Despite the popular usages of multiple measures, there is little research on classification consistency and accuracy of multiple measures. Accordingly, this study introduces an…
Descriptors: Testing, Computation, Classification, Accuracy
Kim, Stella Y.; Lee, Won-Chan – Journal of Educational Measurement, 2023
The current study proposed several variants of simple-structure multidimensional item response theory equating procedures. Four distinct sets of data were used to demonstrate feasibility of proposed equating methods for two different equating designs: a random groups design and a common-item nonequivalent groups design. Findings indicated some…
Descriptors: Item Response Theory, Equated Scores, Monte Carlo Methods, Research Methodology
Kim, Hyung Jin; Lee, Won-Chan – Journal of Educational Measurement, 2022
Orlando and Thissen (2000) introduced the "S - X[superscript 2]" item-fit index for testing goodness-of-fit with dichotomous item response theory (IRT) models. This study considers and evaluates an alternative approach for computing "S - X[superscript 2]" values and other factors associated with collapsing tables of observed…
Descriptors: Goodness of Fit, Test Items, Item Response Theory, Computation
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
Kim, Stella Yun; Lee, Won-Chan – Applied Measurement in Education, 2023
This study evaluates various scoring methods including number-correct scoring, IRT theta scoring, and hybrid scoring in terms of scale-score stability over time. A simulation study was conducted to examine the relative performance of five scoring methods in terms of preserving the first two moments of scale scores for a population in a chain of…
Descriptors: Scoring, Comparative Analysis, Item Response Theory, Simulation
Brennan, Robert L.; Kim, Stella Y.; Lee, Won-Chan – Educational and Psychological Measurement, 2022
This article extends multivariate generalizability theory (MGT) to tests with different random-effects designs for each level of a fixed facet. There are numerous situations in which the design of a test and the resulting data structure are not definable by a single design. One example is mixed-format tests that are composed of multiple-choice and…
Descriptors: Multivariate Analysis, Generalizability Theory, Multiple Choice Tests, Test Construction
Wang, Shaojie; Zhang, Minqiang; Lee, Won-Chan; Huang, Feifei; Li, Zonglong; Li, Yixing; Yu, Sufang – Journal of Educational Measurement, 2022
Traditional IRT characteristic curve linking methods ignore parameter estimation errors, which may undermine the accuracy of estimated linking constants. Two new linking methods are proposed that take into account parameter estimation errors. The item- (IWCC) and test-information-weighted characteristic curve (TWCC) methods employ weighting…
Descriptors: Item Response Theory, Error of Measurement, Accuracy, Monte Carlo Methods