Get 20M+ Full-Text Papers For Less Than $1.50/day. Subscribe now for You or Your Team.

Learn More →

Addressing Data Sparseness in Contextual Population Research

Addressing Data Sparseness in Contextual Population Research 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. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Sociological Methods & Research SAGE

Addressing Data Sparseness in Contextual Population Research

Sociological Methods & Research , Volume 35 (3): 41 – Feb 1, 2007

Loading next page...
 
/lp/sage/addressing-data-sparseness-in-contextual-population-research-8I64k3ERfM

References (35)

Publisher
SAGE
Copyright
Copyright © by SAGE Publications
ISSN
0049-1241
eISSN
1552-8294
DOI
10.1177/0049124106292362
Publisher site
See Article on Publisher Site

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.

Journal

Sociological Methods & ResearchSAGE

Published: Feb 1, 2007

There are no references for this article.