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Hanauer, Matthew; Yel, Nedim – Research in the Schools, 2018
Bayesian analysts use informed priors to improve analytic precision and prediction; however, rarely have they applied a mixed methods approach that uses qualitative data to develop these priors. Yet, using qualitatively informed priors can be useful when making predictions in the context of small sample sizes, which is common in school-based…
Descriptors: Decision Making, Response to Intervention, Mixed Methods Research, Bayesian Statistics
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What Works Clearinghouse, 2023
The appendices accompany the full report "Using Bayesian Meta-Analysis to Explore the Components of Early Literacy Interventions. WWC 2023-008," (ED630495), which pilots a new taxonomy developed by early literacy experts and intervention developers as part of a larger effort to develop standard nomenclature for the components of literacy…
Descriptors: Bayesian Statistics, Meta Analysis, Early Intervention, Literacy
Clotfelter, Charles T.; Ladd, Helen F.; Vigdor, Jacob L. – National Center for Analysis of Longitudinal Data in Education Research, 2008
Using detailed administrative data for the public K-12 schools of North Carolina, we measure racial segregation in its public schools. With data for the 2005-2006 school year, we update previously published calculations that measure segregation by unevenness in racial enrollment patterns, both between schools and within schools. We find that…
Descriptors: Teacher Effectiveness, Elementary Secondary Education, Racial Segregation, School Segregation
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van der Linden, Wim J. – 1984
The classification problem in educational testing is a decision problem. One must assign subjects to one of several available treatments on the basis of test scores, where the success of each treatment is measured by a different criterion. Examples of classification decisions include individualized instruction, counseling, and clinical settings.…
Descriptors: Bayesian Statistics, Classification, Cutting Scores, Decision Making
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Wedman, Ingemar – Scandinavian Journal of Educational Research, 1981
Describes a procedure, called a full Bayesian procedure, for making decisions in connection with criterion-referenced measurements. The procedure uses continuous utility functions instead of a dichotomized utility structure and combines the posterior distribution for a certain person with utility functions for "advance" and "retain" decisions…
Descriptors: Bayesian Statistics, Criterion Referenced Tests, Elementary Secondary Education, Expectancy Tables
Vos, Hans J. – 1994
A method is proposed for optimizing cutting scores for a selection-placement-mastery problem simultaneously. A simultaneous approach has two advantages over separate optimization. First, test scores used in previous decisions can be used as "prior data" in later decisions, increasing the efficiency of the decisions. Then, more realistic…
Descriptors: Bayesian Statistics, Computer Assisted Instruction, Criteria, Cutting Scores
van der Linden, Wim J. – 1985
This paper reviews recent research in the Netherlands on the application of decision theory to test-based decision making about personnel selection and student placement. The review is based on an earlier model proposed for the classification of decision problems, and emphasizes an empirical Bayesian framework. Classification decisions with…
Descriptors: Bayesian Statistics, Classification, Cutting Scores, Decision Making
Spray, Judith A.; Reckase, Mark D. – 1994
The issue of test-item selection in support of decision making in adaptive testing is considered. The number of items needed to make a decision is compared for two approaches: selecting items from an item pool that are most informative at the decision point or selecting items that are most informative at the examinee's ability level. The first…
Descriptors: Ability, Adaptive Testing, Bayesian Statistics, Computer Assisted Testing
Vos, Hans J. – 1994
As part of a project formulating optimal rules for decision making in computer assisted instructional systems in which the computer is used as a decision support tool, an approach that simultaneously optimizes classification of students into two treatments, each followed by a mastery decision, is presented using the framework of Bayesian decision…
Descriptors: Achievement Tests, Bayesian Statistics, Classification, Computer Managed Instruction
Noble, Julie P.; Sawyer, Richard – 1988
The validity of American College Testing Program (ACT) test scores and self-reported high school grades for predicting grades in specific college freshman courses was studied. Specific course grades are typically used to place students in remedial, standard, or advanced classes. These placement decisions, in turn, have immediate implications for…
Descriptors: Bayesian Statistics, College Freshmen, Comparative Analysis, Evaluation Methods