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Showing all 5 results
Weitzman, R. A. – Journal of Educational and Behavioral Statistics, 2006
Focusing on a single sample obtained randomly with replacement from a single population, this article examines the regression of population on sample proportions and develops an unbiased estimator of the square of the correlation between them. This estimator turns out to be the regression coefficient. Use of the squared-correlation estimator as a…
Descriptors: Sample Size, Intervals, Credibility, Computation
Peer reviewedKaplan, David – Journal of Educational and Behavioral Statistics, 2002
Considers the problem of modeling sustained educational change through the use of dynamic multipliers applied to panel data and attempts to develop and advocate dynamic multiplier analysis for educational research. Presents three examples to illustrate the approach and closes with a discussion of the implications of dynamic multiplier analysis for…
Descriptors: Educational Change, Educational Research, Models, Policy Formation
Peer reviewedKim, Seock-Ho; Cohen, Allan S. – Journal of Educational and Behavioral Statistics, 1998
Presents a review of fundamental concepts and applications used to address the Behrens-Fisher problem (W. Behrens, 1929 and R. Fisher, 1935), a problem in testing the difference between two population means, through fiducial, Bayesian, and frequentist approaches. (Contains 86 references.) (SLD)
Descriptors: Bayesian Statistics, Statistical Significance
Peer reviewedBecker, Betsy Jane – Journal of Educational and Behavioral Statistics, 1996
A review of 501 articles and 29 dissertations on teaching statistics found that less than 30% reported the results of empirical studies. A large part of the nonempirical literature is devoted to descriptions of statistics courses and lessons that, although untested, can be a resource for teaching. (SLD)
Descriptors: Course Content, Higher Education, Literature Reviews, Nonprint Media
Peer reviewedMorris, Carl N. – Journal of Educational and Behavioral Statistics, 1995
Hierarchical models are extremely promising tools for data analysis, but it is important not to lessen hard thinking about data and iterative model checking when fitting hierarchical models. More and better software, methods to assure proper calibration, and materials in support of hierarchical model use are all needed. (SLD)
Descriptors: Computer Software Development, Educational Research, Research Methodology, Robustness (Statistics)

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