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

PSC In The News

RSS Feed icon

Stephenson assessing in-home HIV testing and counseling for male couples

Thompson says mass incarceration causes collapse of Detroit neighborhoods

Liberal-conservative gap by education level growing in U.S.

Highlights

Maggie Levenstein named director of ISR's Inter-university Consortium for Political and Social Research

Arline Geronimus receives 2016 Harold R. Johnson Diversity Service Award

PSC spring 2016 newsletter: Kristin Seefeldt, Brady West, newly funded projects, ISR Runs for Bob, and more

AAUP reports on faculty compensation by category, affiliation, and academic rank

Next Brown Bag

PSC Brown Bags
will resume fall 2016

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 and 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