The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.
Hierarchical linear models
Abstract
Hierarchical regression models are useful as soon as there are predictors at different levels of variation. For example, in studying scholastic achievement we may have information about individual students (for example, family background), class-level information (characteristics of the teacher), and also information about the school (educational policy, type of neighborhood). Another situation in which hierarchical modeling arises naturally is in the analysis of data obtained by stratified or cluster sampling. A natural family of models is regression of y on indicator variables for strata or clusters, in addition to any measured predictors x. With cluster sampling, hierarchical modeling is in fact necessary in order to generalize to the unsampled clusters.
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Cites
A new look at the statistical model identification
Akaike, H. (1974) · IEEE Transactions on Automatic Control · doi:10.1109/tac.1974.1100705 · Cited by 51,879
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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
Cited by
Centering Decisions in Hierarchical Linear Models: Implications for Research in Organizations
Hofmann, D. (1998) · Journal of Management · doi:10.1016/s0149-2063(99)80077-4 · Cited by 2,109
Alternative methods for assessing mediation in multilevel data: The advantages of multilevel SEM
Preacher, K. J., Zhang, Z., & Zyphur, M. J. (2011) · Structural Equation Modeling: A Multidisciplinary Journal · doi:10.1080/10705511.2011.557329 · Cited by 889
Factors associated with success in medical school: systematic review of the literature
Ferguson, E., James, D., & Madeley, L. (2002) · BMJ · doi:10.1136/bmj.324.7343.952 · Cited by 811
Flynn, K. E., Piña, I. L., Whellan, D. J., Lin, L., Blumenthal, J. A., Ellis, S. J., Fine, L. J., Howlett, J. G., Keteyian, S. J., Kitzman, D. W., et al. (2009) · JAMA · doi:10.1001/jama.2009.457 · Cited by 737
The use of multiple imputation for the analysis of missing data.
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Metadata
- Venue
- Bayesian Data Analysis
- Publisher
- Chapman and Hall/CRC
- Published
- 2013-11-27
- Pages
- 397-420
- OpenAlex
- W2337193626
- Cited by
- 631
- References
- 4
- FWCI
- 131.14
- Type
- book chapter
- Language
- en
- Open access
- closed
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