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Dardick, William R.; Mislevy, Robert J. – Educational and Psychological Measurement, 2016
A new variant of the iterative "data = fit + residual" data-analytical approach described by Mosteller and Tukey is proposed and implemented in the context of item response theory psychometric models. Posterior probabilities from a Bayesian mixture model of a Rasch item response theory model and an unscalable latent class are expressed…
Descriptors: Bayesian Statistics, Probability, Data Analysis, Item Response Theory
Gilstrap, Donald L. – Complicity: An International Journal of Complexity and Education, 2013
In addition to qualitative methods presented in chaos and complexity theories in educational research, this article addresses quantitative methods that may show potential for future research studies. Although much in the social and behavioral sciences literature has focused on computer simulations, this article explores current chaos and…
Descriptors: Educational Research, Social Science Research, Behavioral Science Research, Statistical Analysis
Thompson, Nathan A. – Practical Assessment, Research & Evaluation, 2011
Computerized classification testing (CCT) is an approach to designing tests with intelligent algorithms, similar to adaptive testing, but specifically designed for the purpose of classifying examinees into categories such as "pass" and "fail." Like adaptive testing for point estimation of ability, the key component is the…
Descriptors: Adaptive Testing, Computer Assisted Testing, Classification, Probability
Atar, Burcu; Kamata, Akihito – Hacettepe University Journal of Education, 2011
The Type I error rates and the power of IRT likelihood ratio test and cumulative logit ordinal logistic regression procedures in detecting differential item functioning (DIF) for polytomously scored items were investigated in this Monte Carlo simulation study. For this purpose, 54 simulation conditions (combinations of 3 sample sizes, 2 sample…
Descriptors: Test Bias, Sample Size, Monte Carlo Methods, Item Response Theory
Owston, Ronald D. – 1979
The development of a probabilistic model for validating Gange's learning hierarchies is described. Learning hierarchies are defined as paired networks of intellectual tasks arranged so that a substantial amount of positive transfer occurs from tasks in a lower position to connected ones in a higher position. This probabilistic validation technique…
Descriptors: Associative Learning, Classification, Difficulty Level, Mathematical Models