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Leckie, George – Journal of Educational and Behavioral Statistics, 2018
The traditional approach to estimating the consistency of school effects across subject areas and the stability of school effects across time is to fit separate value-added multilevel models to each subject or cohort and to correlate the resulting empirical Bayes predictions. We show that this gives biased correlations and these biases cannot be…
Descriptors: Value Added Models, Reliability, Statistical Bias, Computation
France, Stephen L.; Batchelder, William H. – Educational and Psychological Measurement, 2015
Cultural consensus theory (CCT) is a data aggregation technique with many applications in the social and behavioral sciences. We describe the intuition and theory behind a set of CCT models for continuous type data using maximum likelihood inference methodology. We describe how bias parameters can be incorporated into these models. We introduce…
Descriptors: Maximum Likelihood Statistics, Test Items, Difficulty Level, Test Theory
Peer reviewedvan Zyl, J. M.; Neudecker, H.; Nel, D. G. – Psychometrika, 2000
Derives the asymptotic normal distribution of the maximum likelihood estimator of Cronbach's alpha (under normality) for the case when no assumptions are made about the covariances among items. Also considers the asymptotic distribution for the special case of compound symmetry and when compared to the exact distribution. (Author/SLD)
Descriptors: Equations (Mathematics), Maximum Likelihood Statistics, Reliability, Statistical Distributions
Camilli, Gregory – Journal of Educational and Behavioral Statistics, 2006
A simple errors-in-variables regression model is given in this article for illustrating the method of marginal maximum likelihood (MML). Given suitable estimates of reliability, error variables, as nuisance variables, can be integrated out of likelihood equations. Given the closed form expression of the resulting marginal likelihood, the effects…
Descriptors: Maximum Likelihood Statistics, Regression (Statistics), Reliability, Error of Measurement

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