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da Silva, Marcelo A.; Liu, Ren; Huggins-Manley, Anne C.; Bazán, Jorge L. – Educational and Psychological Measurement, 2019
Multidimensional item response theory (MIRT) models use data from individual item responses to estimate multiple latent traits of interest, making them useful in educational and psychological measurement, among other areas. When MIRT models are applied in practice, it is not uncommon to see that some items are designed to measure all latent traits…
Descriptors: Item Response Theory, Matrices, Models, Bayesian Statistics
Dimitrov, Dimiter M. – Educational and Psychological Measurement, 2020
This study presents new models for item response functions (IRFs) in the framework of the D-scoring method (DSM) that is gaining attention in the field of educational and psychological measurement and largescale assessments. In a previous work on DSM, the IRFs of binary items were estimated using a logistic regression model (LRM). However, the LRM…
Descriptors: Item Response Theory, Scoring, True Scores, Scaling
Han, Kyung T.; Dimitrov, Dimiter M.; Al-Mashary, Faisal – Educational and Psychological Measurement, 2019
The "D"-scoring method for scoring and equating tests with binary items proposed by Dimitrov offers some of the advantages of item response theory, such as item-level difficulty information and score computation that reflects the item difficulties, while retaining the merits of classical test theory such as the simplicity of number…
Descriptors: Test Construction, Scoring, Test Items, Adaptive Testing
Hecht, Martin; Weirich, Sebastian; Siegle, Thilo; Frey, Andreas – Educational and Psychological Measurement, 2015
Multiple matrix designs are commonly used in large-scale assessments to distribute test items to students. These designs comprise several booklets, each containing a subset of the complete item pool. Besides reducing the test burden of individual students, using various booklets allows aligning the difficulty of the presented items to the assumed…
Descriptors: Measurement, Item Sampling, Statistical Analysis, Models
Huang, Hung-Yu; Wang, Wen-Chung – Educational and Psychological Measurement, 2013
Both testlet design and hierarchical latent traits are fairly common in educational and psychological measurements. This study aimed to develop a new class of higher order testlet response models that consider both local item dependence within testlets and a hierarchy of latent traits. Due to high dimensionality, the authors adopted the Bayesian…
Descriptors: Item Response Theory, Models, Bayesian Statistics, Computation
Cheng, Ying-Yao; Wang, Wen-Chung; Ho, Yi-Hui – Educational and Psychological Measurement, 2009
Educational and psychological tests are often composed of multiple short subtests, each measuring a distinct latent trait. Unfortunately, short subtests suffer from low measurement precision, which makes the bandwidth-fidelity dilemma inevitable. In this study, the authors demonstrate how a multidimensional Rasch analysis can be employed to take…
Descriptors: Item Response Theory, Measurement, Correlation, Measures (Individuals)
Sheng, Yanyan; Wikle, Christopher K. – Educational and Psychological Measurement, 2008
As item response models gain increased popularity in large-scale educational and measurement testing situations, many studies have been conducted on the development and applications of unidimensional and multidimensional models. Recently, attention has been paid to IRT-based models with an overall ability dimension underlying several ability…
Descriptors: Test Items, Individual Testing, Item Response Theory, Evaluation Methods
Wang, Wen-Chung; Chen, Hsueh-Chu – Educational and Psychological Measurement, 2004
As item response theory (IRT) becomes popular in educational and psychological testing, there is a need of reporting IRT-based effect size measures. In this study, we show how the standardized mean difference can be generalized into such a measure. A disattenuation procedure based on the IRT test reliability is proposed to correct the attenuation…
Descriptors: Test Reliability, Rating Scales, Sample Size, Error of Measurement