Hostname: page-component-5f745c7db-f9j5r Total loading time: 0 Render date: 2025-01-06T07:39:00.455Z Has data issue: true hasContentIssue false
  • English
  • Français

A General Model for the Analysis of Multilevel Data

Published online by Cambridge University Press:  01 January 2025

Harvey Goldstein  and
Roderick P. McDonald
Harvey Goldstein*
Affiliation:
University of London Institute of Education
Roderick P. McDonald
Affiliation:
Macquarie University
*
Requests for reprints should be sent to Harvey Goldstein, Institute of Education, 20 Bedford Way, London, UNITED KINGDOM WC1H OAL.
Get access Rights & Permissions [Opens in a new window]

Abstract

A general model is developed for the analysis of multivariate multilevel data structures. Special cases of the model include repeated measures designs, multiple matrix samples, multilevel latent variable models, multiple time series, and variance and covariance component models.

Keywords

complex surveysmixed effects modelsmultivariate linear modelrepeated measurestime seriesvariance component models
Type
Original Paper
Information
Psychometrika , Volume 53 , Issue 4 , December 1988 , pp. 455 - 467
Copyright
Copyright © 1988 The Psychometric Society

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Footnotes

We would like to acknowledge the helpful comments of Ruth Silver. We also wish to thank the referees for helping to clarify the paper. This work was partly carried out with research funds provided by the Economic and Social Research Council (U.K.).

References

Aitkin, M. A., & Longford, N. (1986). Statistical Modelling in School effectiveness studies. (with discussion). Journal of the Royal Statistical Society, Series A, 149, 1–43.CrossRefGoogle Scholar
Goldstein, H. (1979). The Design and Analysis of longitudinal Studies, London: Academic Press.Google Scholar
Goldstein, H. (1986). Multilevel mixed linear model analysis using iterative generalized least squares. Biometrika, 73, 43–56.CrossRefGoogle Scholar
Goldstein, H. (1986). Efficient statistical modelling of longitudinal data. Annals of Human Biology, 13, 129–142.CrossRefGoogle ScholarPubMed
Goldstein, H., & Silver (in press). In Holt, D. & Smith, F. (Ed.), Multilevel and multivariate models in survey analysis. In The Analysis of Complex Surveys.Google Scholar
Johnson, S., & Bell, M. (1985). Evaluating and predicting survey efficiency using generalisability theory. Journal of Educational Measurement, 22, 107–120.CrossRefGoogle Scholar
Jones, R. G. (1980). Best linear unbiased estimation in repeated surveys. Journal of the Royal Statistical Society, Series B, 42, 221–226.CrossRefGoogle Scholar
Jöreskog, K. G. and Sörbom, D. (1979). Advances in factor analysis and structural equation models, Cambridge, MA: Abt Books.Google Scholar
McArdle, J. J., & McDonald, R. P. (1984). Some algebraic properties of the reticular action model for moment structures. British Journal of the Mathematical and Statistical Psychology, 37, 234–251.CrossRefGoogle ScholarPubMed
McCullagh, P., & Nelder, J. A. (1983). Generalized linear models, London: Chapman and Hall.CrossRefGoogle Scholar
McDonald, R. P. (1985). Factor analysis and related methods, Hillsdale, NJ: Lawrence Earlbaum.Google Scholar
McDonald, R. P., & Goldstein, H. (in preparation). Applications of multilevel latent variable models.Google Scholar
Shavelson, P., & Webb, R. (1981). Generalisability theory: 1973–1980. British Journal of Mathematical and Statistical Psychology, 34, 133–166.CrossRefGoogle Scholar
Strenio, T., Weisberg, H. I., & Bryk, A. S. (1983). Empirical bayes estimation of individual growth curve parameters and their relationship to covariates. Biometrics, 39, 71–86.CrossRefGoogle ScholarPubMed