The impact of community-level variables on individual-level - Outcomes theoretical results and applications

被引:49
|
作者
Angeles, G [1 ]
Guilkey, DK [1 ]
Mroz, TA [1 ]
机构
[1] Univ N Carolina, Dept Maternal & Child Hlth, Chapel Hill, NC 27514 USA
关键词
multilevel models; hierarchical models; multilevel error structure; Monte Carlo simulations;
D O I
10.1177/0049124104273069
中图分类号
O1 [数学]; C [社会科学总论];
学科分类号
03 ; 0303 ; 0701 ; 070101 ;
摘要
The authors studs, 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 oil 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 call 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.
引用
收藏
页码:76 / 121
页数:46
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