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Pohl, Steffi; Gräfe, Linda; Rose, Norman – Educational and Psychological Measurement, 2014
Data from competence tests usually show a number of missing responses on test items due to both omitted and not-reached items. Different approaches for dealing with missing responses exist, and there are no clear guidelines on which of those to use. While classical approaches rely on an ignorable missing data mechanism, the most recently developed…
Descriptors: Test Items, Achievement Tests, Item Response Theory, Models
Foorman, Barbara R.; Petscher, Yaacov – Society for Research on Educational Effectiveness, 2011
In Florida, mean proficiency scores are reported on the Florida Comprehensive Achievement Test (FCAT) as well as recommended learning gains from the developmental scale score. Florida now has another within-year measure of growth in reading comprehension from the Florida Assessments for Instruction in Reading (FAIR). The FAIR reading comprehension…
Descriptors: Achievement Tests, Reading Comprehension, Reading Tests, Scores
Fukuhara, Hirotaka; Kamata, Akihito – Applied Psychological Measurement, 2011
A differential item functioning (DIF) detection method for testlet-based data was proposed and evaluated in this study. The proposed DIF model is an extension of a bifactor multidimensional item response theory (MIRT) model for testlets. Unlike traditional item response theory (IRT) DIF models, the proposed model takes testlet effects into…
Descriptors: Item Response Theory, Test Bias, Test Items, Bayesian Statistics
Sinharay, Sandip; Dorans, Neil J.; Grant, Mary C.; Blew, Edwin O. – Journal of Educational and Behavioral Statistics, 2009
Test administrators often face the challenge of detecting differential item functioning (DIF) with samples of size smaller than that recommended by experts. A Bayesian approach can incorporate, in the form of a prior distribution, existing information on the inference problem at hand, which yields more stable estimation, especially for small…
Descriptors: Test Bias, Computation, Bayesian Statistics, Data
Johnson, Matthew S.; Jenkins, Frank – ETS Research Report Series, 2005
Large-scale educational assessments such as the National Assessment of Educational Progress (NAEP) sample examinees to whom an exam will be administered. In most situations the sampling design is not a simple random sample and must be accounted for in the estimating model. After reviewing the current operational estimation procedure for NAEP, this…
Descriptors: Bayesian Statistics, Hierarchical Linear Modeling, National Competency Tests, Sampling

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