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

PSC In The News

RSS Feed icon

Weir's 2009 report on NFL brain injuries got more attention than neurological findings published in 2005

Edin and Shaefer's book a call to action for Americans to deal with poverty

Weir says pain may underlie rise in suicide and substance-related deaths among white middle-aged Americans


MCubed opens for new round of seed funding, November 4-18

PSC News, fall 2015 now available

Barbara Anderson appointed chair of Census Scientific Advisory Committee

John Knodel honored by Thailand's Chulalongkorn University

Next Brown Bag

Monday, Dec 7 at noon, 6050 ISR-Thompson
Daniel Eisenberg, "Healthy Minds Network: Mental Health among College-Age Populations"

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