Philippa Clarke and Blair Wheaton Addressing Data Sparseness in Contextual Population Research: Using Cluster Analysis to Create Synthetic Neighborhoods Sociological Methods & Research 2007 35: 311-351.

February 7, 2010

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.

Key Words: multilevel models • data sparseness • cluster analysis • Monte Carlo simulations • survey research

Joachim R. Frick, Jan Goebel, Edna Schechtman, Gert G. Wagner, and Shlomo Yitzhaki, Using Analysis of Gini (ANOGI) for Detecting Whether Two Subsamples Represent the Same Universe: The German Socio-Economic Panel Study (SOEP) Experience, Sociological Methods & Research 2006 34: 427-468.

February 7, 2010
A wildly discussed shortcoming of panel surveys is a potential bias arising from selective attrition. Based on data of the German Socio-Economic Panel Study (SOEP), the authors analyze potential artifacts (level, structure, income inequality) by comparing results for two independently drawn panel subsamples started in 1984 and 2000. They apply ANOGI (analysis of Gini) techniques, the equivalent of ANOVA performed with the Gini coefficient. They rearrange, reinterpret, and use the decomposition in the comparison of subpopulations from which the different samples were drawn. Taking into account indicators for income, significant differences between these two samples with respect to income inequality are found in the first year, which start to fade away in Wave 2 and disappear in Wave 3. The authors find credible indication for these differences to be driven by changes in response behavior of short-term panel members rather than by attrition among members of the longer running sample.

Key Words: panel studies • survey research • inequality decomposition • Gini

Emilio A. Parrado, Chris McQuiston, and Chenoa A. Flippen, Participatory Survey Research: Integrating Community Collaboration and Quantitative Methods for the Study of Gender and HIV Risks Among Hispanic Migrants, Sociological Methods & Research 2005 34: 204-239.

February 7, 2010

This article outlines a research strategy for studying difficult-to-reach migrant populations that combines community collaboration, targeted random sampling, and parallel sampling in sending and receiving areas. The authors describe how this methodology was applied to the study of gender, migration, and HIV risks among Hispanic migrants in Durham, North Carolina. They illustrate the usefulness of community collaboration for informing survey design and providing a contextual understanding of research findings. They likewise demonstrate the importance of parallel sampling and assess the bias that would have resulted from conducting their study withconvenience samples as opposed to a targeted random sampling technique. While the authors describe its application to HIV risks among Hispanic migrants, the methodology can easily beextended to other migrant groups as well as to other sensitive topics pertaining to migration and social adaptation.

Key Words: immigrant health • mixed methodology • participatory research • survey research • Mexicans