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Addressing Data Sparseness in Contextual Population Research

Author

Listed:
  • Philippa Clarke

    (University of Michigan, Ann Arbor)

  • Blair Wheaton

    (University of Toronto, Canada)

Abstract

The use of multilevel modeling with data from population-based surveys is often limited by the small number of cases per Level 2 unit, prompting a recent trend in the neighborhood literature to apply cluster techniques to address the problem of data sparseness. In this study, the authors use Monte Carlo simulations to investigate the effects of marginal group sizes on multilevel model performance, bias, and efficiency. They then employ cluster analysis techniques to minimize data sparseness and examine the consequences in the simulations. They find that estimates of the fixed effects are robust at the extremes of data sparseness, while cluster analysis is an effective strategy to increase group size and prevent the overestimation of variance components. However, researchers should be cautious about the degree to which they use such clustering techniques due to the introduction of artificial within-group heterogeneity.

Suggested Citation

  • Philippa Clarke & Blair Wheaton, 2007. "Addressing Data Sparseness in Contextual Population Research," Sociological Methods & Research, , vol. 35(3), pages 311-351, February.
    • Handle: RePEc:sae:somere:v:35:y:2007:i:3:p:311-351
    DOI: 10.1177/0049124106292362
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    References listed on IDEAS

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    1. Soobader, Mah-Jabeen & LeClere, Felicia B., 1999. "Aggregation and the measurement of income inequality: effects on morbidity," Social Science & Medicine, Elsevier, vol. 48(6), pages 733-744, March.
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