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Mousavi, Amin; Cui, Ying – Education Sciences, 2020
Often, important decisions regarding accountability and placement of students in performance categories are made on the basis of test scores generated from tests, therefore, it is important to evaluate the validity of the inferences derived from test results. One of the threats to the validity of such inferences is aberrant responding. Several…
Descriptors: Student Evaluation, Educational Testing, Psychological Testing, Item Response Theory
Zheng, Chunmei – ProQuest LLC, 2013
Educational and psychological constructs are normally measured by multifaceted dimensions. The measured construct is defined and measured by a set of related subdomains. A bifactor model can accurately describe such data with both the measured construct and the related subdomains. However, a limitation of the bifactor model is the orthogonality…
Descriptors: Educational Testing, Measurement Techniques, Test Items, Models
Kim, Jihye – ProQuest LLC, 2010
In DIF studies, a Type I error refers to the mistake of identifying non-DIF items as DIF items, and a Type I error rate refers to the proportion of Type I errors in a simulation study. The possibility of making a Type I error in DIF studies is always present and high possibility of making such an error can weaken the validity of the assessment.…
Descriptors: Test Bias, Test Length, Simulation, Testing
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Cui, Ying; Leighton, Jacqueline P. – Journal of Educational Measurement, 2009
In this article, we introduce a person-fit statistic called the hierarchy consistency index (HCI) to help detect misfitting item response vectors for tests developed and analyzed based on a cognitive model. The HCI ranges from -1.0 to 1.0, with values close to -1.0 indicating that students respond unexpectedly or differently from the responses…
Descriptors: Test Length, Simulation, Correlation, Research Methodology
Steinheiser, Frederick H., Jr. – 1976
A computer simulation of Bayes' Theorem was conducted in order to determine the probability that an examinee was a master conditional upon his test score. The inputs were: number of mastery states assumed, test length, prior expectation of masters in the examinee population, and conditional probability of a master getting a randomly selected test…
Descriptors: Bayesian Statistics, Classification, Computer Programs, Criterion Referenced Tests