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

PSC In The News

RSS Feed icon

Geronimus says black-white differences in mortality "help silence black voices in the electorate"

Do universities need more conservative thinkers?

Starr critical of risk assessment scores for sentencing

Highlights

Presentation on multilevel modeling using Stata, July 26th, noon, 6050 ISR

Frey's new report explores how the changing US electorate could shape the next 5 presidential elections, 2016 to 2032

U-M's Data Science Initiative offers expanded consulting services via CSCAR

Elizabeth Bruch promoted to Associate Professor

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