NotesFAQContact Us
Collection
Advanced
Search Tips
Showing all 7 results Save | Export
Peer reviewed Peer reviewed
Direct linkDirect link
Harold Doran; Testsuhiro Yamada; Ted Diaz; Emre Gonulates; Vanessa Culver – Journal of Educational Measurement, 2025
Computer adaptive testing (CAT) is an increasingly common mode of test administration offering improved test security, better measurement precision, and the potential for shorter testing experiences. This article presents a new item selection algorithm based on a generalized objective function to support multiple types of testing conditions and…
Descriptors: Computer Assisted Testing, Adaptive Testing, Test Items, Algorithms
Peer reviewed Peer reviewed
Direct linkDirect link
Kuan-Yu Jin; Wai-Lok Siu – Journal of Educational Measurement, 2025
Educational tests often have a cluster of items linked by a common stimulus ("testlet"). In such a design, the dependencies caused between items are called "testlet effects." In particular, the directional testlet effect (DTE) refers to a recursive influence whereby responses to earlier items can positively or negatively affect…
Descriptors: Models, Test Items, Educational Assessment, Scores
Peer reviewed Peer reviewed
Direct linkDirect link
Debeer, Dries; Janssen, Rianne; De Boeck, Paul – Journal of Educational Measurement, 2017
When dealing with missing responses, two types of omissions can be discerned: items can be skipped or not reached by the test taker. When the occurrence of these omissions is related to the proficiency process the missingness is nonignorable. The purpose of this article is to present a tree-based IRT framework for modeling responses and omissions…
Descriptors: Item Response Theory, Test Items, Responses, Testing Problems
Peer reviewed Peer reviewed
Direct linkDirect link
Jin, Kuan-Yu; Wang, Wen-Chung – Journal of Educational Measurement, 2014
Sometimes, test-takers may not be able to attempt all items to the best of their ability (with full effort) due to personal factors (e.g., low motivation) or testing conditions (e.g., time limit), resulting in poor performances on certain items, especially those located toward the end of a test. Standard item response theory (IRT) models fail to…
Descriptors: Student Evaluation, Item Response Theory, Models, Simulation
Peer reviewed Peer reviewed
Direct linkDirect link
Wang, Wen-Chung; Jin, Kuan-Yu; Qiu, Xue-Lan; Wang, Lei – Journal of Educational Measurement, 2012
In some tests, examinees are required to choose a fixed number of items from a set of given items to answer. This practice creates a challenge to standard item response models, because more capable examinees may have an advantage by making wiser choices. In this study, we developed a new class of item response models to account for the choice…
Descriptors: Item Response Theory, Test Items, Selection, Models
Peer reviewed Peer reviewed
Direct linkDirect link
Jiao, Hong; Wang, Shudong; He, Wei – Journal of Educational Measurement, 2013
This study demonstrated the equivalence between the Rasch testlet model and the three-level one-parameter testlet model and explored the Markov Chain Monte Carlo (MCMC) method for model parameter estimation in WINBUGS. The estimation accuracy from the MCMC method was compared with those from the marginalized maximum likelihood estimation (MMLE)…
Descriptors: Computation, Item Response Theory, Models, Monte Carlo Methods
Peer reviewed Peer reviewed
Bolt, Daniel M. – Journal of Educational Measurement, 2000
Reviewed aspects of the SIBTEST procedure through three studies. Study 1 examined the effects of item format using 40 mathematics items from the Scholastic Assessment Test. Study 2 considered the effects of a problem type factor and its interaction with item format for eight items, and study 3 evaluated the degree to which factors varied in the…
Descriptors: Computer Software, Hypothesis Testing, Item Bias, Mathematics