The DOI matches its Crossref record; 5 of the 5 references it cites were found in a registry or an index.
A Hierarchical Model for Studying School Effects
Abstract
When researchers investigate how school policies, practices, or climates affect student outcomes, they use multilevel, hierarchical data. Though methodologists have consistently warned of the formidable inferential problems such data pose for traditional statistical methods, no comprehensive alternative analytic strategy has been available. This paper presents a general statistical methodology for such hierarchically structured data and illustrates its use by reexamining the High School and Beyond data and the controversy over the effectiveness of public and Catholic schools. The model enables the researcher to utilize mean achievement and certain structural parameters that characterize the equity in the social distribution of achievement as multivariate outcomes for each school. Variation in these school-level outcomes is then explained as a function of school characteristics.
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Citations
Cites
Maximum Likelihood from Incomplete Data Via the EM Algorithm
Dempster, A. P., Laird, N. M., & Rubin, D. B. (1977) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1977.tb01600.x · Cited by 50,444
Maximum Likelihood Approaches to Variance Component Estimation and to Related Problems
Harville, D. A. (1977) · Journal of the American Statistical Association · doi:10.1080/01621459.1977.10480998 · Cited by 2,667
Bayes Estimates for the Linear Model
Lindley, D. V., & Smith, A. F. M. (1972) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1972.tb00885.x · Cited by 2,025
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Empirical bayes meta-analysis.
Raudenbush, S. W., & Bryk, A. S. (1985) · Journal of Educational Statistics · doi:10.3102/10769986010002075 · Cited by 299
Self-citation
Cited by
A general and simple method for obtaining R 2 from generalized linear mixed‐effects models
Nakagawa, S., & Schielzeth, H. (2013) · Methods in Ecology and Evolution · doi:10.1111/j.2041-210x.2012.00261.x · Cited by 10,356
Lower level mediation in multilevel models.
Kenny, D. A., Korchmaros, J. D., & Bolger, N. (2003) · Psychological Methods · doi:10.1037/1082-989x.8.2.115 · Cited by 687
Multilevel Modeling: When and Why
Hox, J. (1998) · Studies in Classification, Data Analysis, and Knowledge Organization · doi:10.1007/978-3-642-72087-1_17 · Cited by 500
Standard Errors and Sample Sizes for Two-Level Research
Snijders, T. A. B., & Bosker, R. J. (1993) · Journal of Educational Statistics · doi:10.3102/10769986018003237 · Cited by 487
McNeish, D., & Kelley, K. (2019) · Psychological Methods · doi:10.1037/met0000182 · Cited by 336
Metadata
- Authors
- Raudenbush, S., & Bryk, A. S.
- Venue
- Sociology of Education
- Publisher
- JSTOR
- Published
- 1986-01
- Volume
- 59
- Issue
- 1
- Pages
- 1
- ISSN
- 0038-0407 (ISSN-L)
- OpenAlex
- W2080983475
- Cited by
- 818
- References
- 5
- FWCI
- 20.62
- Type
- journal article
- Language
- en
- Open access
- closed
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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