SAMPLE SIZE AND STRUCTURE FOR MULTILEVEL MODELLING: MONTE CARLO INVESTIGATION FOR THE BALANCED DESIGN

• Authors: Edyta Łaszkiewicz
• Languages of publication: EN

Abstracts

EN

The aim of the study is to examine the robustness of the estimates and standard errors in the case of different structure of the sample and its size. The two-level model with a random intercept, slope and fixed effects, estimated using maximum likelihood, was taken into account. We used Monte Carlo simulation, performed on a sample of the equipotent groups.

Keywords

EN

multilevel model; Monte Carlo; sample size

• Publisher: Katedra Ekonometrii i Statystyki, Szkoła Główna Gospodarstwa Wiejskiego w Warszawie
• Journal: Metody Ilościowe w Badaniach Ekonomicznych
• Year: 2013
• Volume: 14
• Issue: 2
• Pages: 19-28

Dates

published

2013

Contributors

author

Edyta Łaszkiewicz

• Department of Spatial Econometrics, University of Lodz

References

• Bell B. A., Morgan G. B., Kromrey J. D., Ferron, J. M. (2010) The impact of small cluster size on multilevel models: a Monte Carlo examination of two-level models with binary and continuous predictors, JSM Proceedings, Survey Research Methods Section, pp. 4057-4067.
• Browne W. J., Draper D. (2000) Implementation and performance issues in the Bayesian and likelihood fitting of multilevel models, Computational Statistics, 15, pp. 391-420.
• Busing F. (1993) Distribution characteristics of variance estimates in two-level models, Unpublished manuscript, Leiden University.
• Clarke P., Wheaton B. (2007) Addressing data sparseness in contextual population research using cluster analysis to create synthetic neighborhoods, Sociological Methods
• & Research, 35, pp. 311-351.
• Goldstein H. (2010) Multilevel statistical models (4th ed.), New York: Hodder Arnold
• Henderson C. R. (1986) Estimation of singular covariance matrices of random effects, Journal of Dairy Science 69.9, pp. 2379-2385.
• Hoogland J., Boomsma, A. (1998) Robustness studies in covariance structure modeling: An overview and a meta-analysis, Sociological Methods and Research, 26(3), pp. 329-367.
• Hox J. J. (1998) Multilevel modeling: When and why, [in:] Balderjahn I., Mathar R., Schader M., Classification, data analysis, and data highways, New York: Springer Verlag, pp. 147-154.
• Kreft I. G. G. (1996) Are multilevel techniques necessary? An overview, including simulation studies, Unpublished manuscript, California State University at Los Angeles.
• Maas C. J. M., Hox J. J. (2004) Robustness issues in multilevel regression analysis, Statistica Neerlandica, 58, pp. 127-137.
• Maas C. J. M., Hox, J. J. (2005) Sufficient sample sizes for multilevel modeling, Methodology, 1, pp. 86-92.
• Mass C. J., Hox J. J. (2002) Robustness of multilevel parameter estimates against small sample sizes. Unpublished Paper, Utrecht University.
• Moerbeek M., Van Breukelen G. J., Berger M. P. (2001) Optimal experimental designs for multilevel logistic models, Journal of the Royal Statistical Society, Vol.50(1),
• pp. 17-30.
• Mok M. (1995) Sample size requirements for 2-level designs in educational research, Unpublished manuscript, Macquarie University.
• Newsom J. T., Nishishiba M. (2002) Nonconvergence and sample bias in hierarchical linear modeling of dyadic data. Unpublished Manuscript, Portland State University.
• Snijders T. A. B. (2005) Power and Sample Size in Multilevel Linear Models’, [in:] Everitt B.S., Howell D.C. (eds.) Encyclopedia of Statistics in Behavioral Science, Vol. 3, Wiley, pp. 1570–1573.
• Snijders T. A. B., Bosker R. J. (1993) Standard Errors and Sample Sizes for Two-Level Research, Journal of Educational Statistics, Vol. 18, No. 3, pp. 237-259.
• Van der Leeden R., Busing F. (1994) First iteration versus IGLS RIGLS estimates in two-level models: A Monte Carlo study with ML3, Unpublished manuscript, Leiden University.
• Van der Leeden R., Busing F., Meijer E. (1997) Applications of bootstrap methods for two-level models, Paper presented at the Multilevel Conference, Amsterdam.