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Paul T. von Hippel – Annenberg Institute for School Reform at Brown University, 2023
Longitudinal studies can produce biased estimates of learning if children miss tests. In an application to summer learning, we illustrate how missing test scores can create an illusion of large summer learning gaps when true gaps are close to zero. We demonstrate two methods that reduce bias by exploiting the correlations between missing and…
Descriptors: Testing Problems, Scores, Educational Research, Longitudinal Studies
Ahlgren Reddy, Alison; Harper, Marc – PRIMUS, 2013
Data from the ALEKS-based placement program at the University of Illinois is presented visually in several ways. The placement exam (an ALEKS assessment) contains precise item-specific information and the data show many interesting properties of the student populations of the placement courses, which include Precalculus, Calculus, and Business…
Descriptors: Mathematics Education, College Mathematics, Student Placement, Scores
Brandwein, Ann Cohen; Strawderman, William E. – 1989
This paper presents an expository development of James-Stein estimation with substantial emphasis on exact results for nonnormal location models. The themes of the paper are: (1) the improvement possible over the best invariant estimator via shrinkage estimation is not surprising but expected from a variety of perspectives; (2) the amount of…
Descriptors: Equations (Mathematics), Estimation (Mathematics), Maximum Likelihood Statistics, Statistical Distributions
Kim, Seock-Ho – 1998
The accuracy of the Markov chain Monte Carlo procedure, Gibbs sampling, was considered for estimation of item and ability parameters of the one-parameter logistic model. Four data sets were analyzed to evaluate the Gibbs sampling procedure. Data sets were also analyzed using methods of conditional maximum likelihood, marginal maximum likelihood,…
Descriptors: Ability, Estimation (Mathematics), Item Response Theory, Markov Processes
Yamamoto, Kentaro – 1989
This study developed a hybrid of item response theory (IRT) models and latent class models, which combined the strengths of each type of model. The primary motivation for developing the new model is to describe characteristics of examinees' knowledge at the time of the examination. Hence, the application of the model lies mainly in so-called…
Descriptors: Ability Identification, Achievement Tests, Cognitive Structures, Diagnostic Tests

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