Multilevel analysis of educational data / edited by R. Darrell Bock.
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TextPublication details: San Diego : Academic Press, c1989.Description: xii, 354 p. : ill. ; 24 cmISBN: - 0121088405 (alk. paper)
- 370.727 BOC 23
- LB2822.75 .M85 1989
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Papers and discussions presented at an international conference, organized by the Center for Research in Evaluation, Standards, and Student Testing of UCLA, and by NORC; held Apr. 26-28, 1987, at the Princeton Hyatt Regency Hotel.
Includes bibliographies and indexes.
D.B. Rubin, Some Applications of Multilevel Models of Educational Data.
H.I. Braun, Empirical Bayes Methods: A Tool for Exploratory Analysis.
R.J. Mislevy and R.D. Bock, A Hierarchical Item-response Model for Educational Testing.
C. Lewis, Difficulties with Bayesian Inference for Random Effects.
B. Muthéen and A. Satorra, Multilevel Aspects of Varying Parameters in Structural Models.
R.K. Tsutakawa--Discussant, Discussions of Papers by Mislevy and Bock, Muthéen and Satorra, and Lewis.
H. Goldstein, Models for Multilevel Response Variables with an Application to Growth Curves.
J. Gray, Multilevel Models: Issues and Problems Emerging from their Recent Application in British Students of School Effectiveness.
P. McCullagh, Discussant, What Can Go Wrong with Iteratively Re-weighted Least Squares?
A.S. Bryk and S.W. Raudenbush, Toward a More Appropriate Conceptualization of Research on School Effects: A Three-Level Hierarchical Linear Model.
S.W. Raudenbush and A.S. Bryk, Quantitative Models for Estimating Teacher and School Effectiveness.
L. Burstein, K.-S. Kim, and G. Delandshere, Multilevel Investigations of Systematically Varying Slopes: Issues, Alternatives, and Consequences.
H. Swaminathan, -Discussant, Multilevel Data Analysis: A Discussion.
M. Aitkin, Profile Predictive Likelihood for Random Effects in the Two-Level Model.
N.T. Longford, Fisher Scoring Algorithm for Variance Component Analysis of Data with Multilevel Structure.
P.W. Holland, Discussant, Discussion of Aitkin's and Longford's Paper.
R.D. Bock, Addendum-Measurement of Human Variation: A Two-Stage Model.
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