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.

## Cross-Survey Analysis to Estimate Low-Incidence Religious Groups

August 12, 2010## Direct and Indirect Effects for Neighborhood-Based Clustered and Longitudinal Data

June 17, 2010Tyler 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, 2010The 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, 2010The 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