Cross-Survey Analysis to Estimate Low-Incidence Religious Groups

August 12, 2010

Elizabeth Tighe, David Livert, Melissa Barnett, and Leonard Saxe, Cross-Survey Analysis to Estimate Low-Incidence Religious Groups, Sociological Methods & Research 2010 39: 56-82.

Population-based surveys are of limited utility to estimate rare or low-incidence groups, particularly for those defined by religion or ethnicity not included in the U.S. Census. Methods of cross-survey analysis Read the rest of this entry »

Direct and Indirect Effects for Neighborhood-Based Clustered and Longitudinal Data

June 17, 2010

Tyler J. VanderWeele, Sociological Methods & Research 2010 38: 515-544.


Definitions of direct and indirect effects are given for settings in which individuals are clustered in groups or neighborhoods and in which treatments are administered at the group level. A particular intervention may Read the rest of this entry »

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

Gustavo Angeles, David K. Guilkey, and Thomas A. Mroz, The Impact of Community-Level Variables on Individual-Level Outcomes: Theoretical Results and Applications, Sociological Methods & Research 2005 34: 76-121.

February 7, 2010

The authors study alternative estimators of the impacts of higher level variables in multilevel models. This is important since many of the important variables in social science research arehigher level factors having impacts on many lower level outcomes such as school achievement and contraceptive use. While the large sample properties of alternative estimators for these models are well known, there is little evidence about the relative performance of these estimators in the sample sizes typical in social science research. The authors attempt to fill this gap by presenting evidence about point estimation and standard error estimation for both two-and three-level models. A majorconclusion of the article is that readily available commercial software can be used to obtain both reliable point estimates and coefficient standard errors in models with two or more levels as long as appropriate corrections are made for possible error correlations at the highest level.

Key Words: multilevel models • hierarchical models • multilevel error structure • Monte Carlo simulations

Gary Goertz and James Mahoney, Two-Level Theories and Fuzzy-Set Analysis, Sociological Methods & Research 2005 33: 497-538.

February 7, 2010
Abstract: Two-level theories explain outcomes with causal variables at two levels of analysis that are systematically related to one another. Although many prominent scholars in the field of comparativeanalysis have developed two-level theories, the empirical and methodological issues that these theories raise have yet to be investigated. In this article, the authors explore different structures of two-level theories and consider the issues involved in testing these theories with fuzzy-set methods. They show that grasping the overall structure of two-level theories requires both specifying the particular type of relationship that exists between and within levels of analysis and specifying the logical linkages between levels in terms of necessary and sufficient conditions. They argue that for the purposes of testing these theories, fuzzy-set analysis provides a powerful set of tools. However, to realize this potential, investigators using fuzzy-set methods must be clear about the two-level structure of their theories from the onset.

Key Words: fuzzy sets • multilevel models • concepts • revolution