The Impact of Community-Level Variables on Individual-Level Outcomes

Gustavo Angeles, David K. Guilkey and Thomas Mroz
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Gustavo Angeles: University of North Carolina at Chapel Hill
David K. Guilkey: University of North Carolina at Chapel Hill

Sociological Methods & Research, 2005, vol. 34, issue 1, 76-121

Abstract: 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 are higher 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 major conclusion 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.

Keywords: multilevel models; hierarchical models; multilevel error structure; Monte Carlo simulations (search for similar items in EconPapers)
Date: 2005
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Citations: View citations in EconPapers (14)

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Persistent link: https://EconPapers.repec.org/RePEc:sae:somere:v:34:y:2005:i:1:p:76-121

DOI: 10.1177/0049124104273069

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