Home > Publications . Search All . Browse All . Country . Browse PSC Pubs . PSC Report Series

PSC In The News

RSS Feed icon

Frey says China is source country of most new U.S. immigrants

Rodriguez, Geronimus, Bound and Dorling find excess mortality among blacks influences key elections

Kruger says high concentration of local fast food outlets is risk factor for obesity

Highlights

Cheng wins ASA Outstanding Graduate Student Paper Award

Hicken wins 2015 UROP Outstanding Research Mentor Award

U-M ranked #1 in Sociology of Population by USN&WR's "Best Graduate Schools"

PAA 2015 Annual Meeting: Preliminary program and list of UM participants

Next Brown Bag

Mon, May 18
Lois Verbrugge, Disability Experience & Measurement

Philippa J. Clarke photo

Addressing data sparseness in contextual population research - Using cluster analysis to create synthetic neighborhoods

Publication Abstract

Clarke, Philippa J., and B. Wheaton. 2007. "Addressing data sparseness in contextual population research - Using cluster analysis to create synthetic neighborhoods." Sociological Methods & Research, 35(3): 311-351.

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.

DOI:10.1177/0049124106292362 (Full Text)

Licensed Access Link

Public Access Link

Browse | Search : All Pubs | Next