M. Steenbergen, and B. Jones. American Journal of Political Science, 46 (1):
218--237(2002)
Abstract
Multilevel data are structures that consist of multiple
units of analysis, one nested within the other. Such data
are becoming quite common in political science and provide
numerous opportunities for theory testing and development.
Unfortunately, this type of data typically generates a
number of statistical problems, of which clustering is
particularly important. To exploit the opportunities
offered by multilevel data, and to solve the statistical
problems inherent in them, special statistical techniques
are required. In this article, we focus on a technique that
has become popular in educational statistics and
sociology-multilevel analysis. In multilevel analysis,
researchers build models that capture the layered structure
of multilevel data, and determine how layers interact and
impact a dependent variable of interest. Our objective in
this article is to introduce the logic and statistical
theory behind multilevel models, to illustrate how such
models can be applied fruitfully in political science, and
to call attention to some of the pitfalls in multilevel
analysis.
%0 Journal Article
%1 SteeJone:02
%A Steenbergen, Marco R.
%A Jones, Bradford S.
%D 2002
%J American Journal of Political Science
%K methodology multilevel political_science statistics
%N 1
%P 218--237
%T Modeling Multilevel Data Structures
%V 46
%X Multilevel data are structures that consist of multiple
units of analysis, one nested within the other. Such data
are becoming quite common in political science and provide
numerous opportunities for theory testing and development.
Unfortunately, this type of data typically generates a
number of statistical problems, of which clustering is
particularly important. To exploit the opportunities
offered by multilevel data, and to solve the statistical
problems inherent in them, special statistical techniques
are required. In this article, we focus on a technique that
has become popular in educational statistics and
sociology-multilevel analysis. In multilevel analysis,
researchers build models that capture the layered structure
of multilevel data, and determine how layers interact and
impact a dependent variable of interest. Our objective in
this article is to introduce the logic and statistical
theory behind multilevel models, to illustrate how such
models can be applied fruitfully in political science, and
to call attention to some of the pitfalls in multilevel
analysis.
@article{SteeJone:02,
abstract = {Multilevel data are structures that consist of multiple
units of analysis, one nested within the other. Such data
are becoming quite common in political science and provide
numerous opportunities for theory testing and development.
Unfortunately, this type of data typically generates a
number of statistical problems, of which clustering is
particularly important. To exploit the opportunities
offered by multilevel data, and to solve the statistical
problems inherent in them, special statistical techniques
are required. In this article, we focus on a technique that
has become popular in educational statistics and
sociology-multilevel analysis. In multilevel analysis,
researchers build models that capture the layered structure
of multilevel data, and determine how layers interact and
impact a dependent variable of interest. Our objective in
this article is to introduce the logic and statistical
theory behind multilevel models, to illustrate how such
models can be applied fruitfully in political science, and
to call attention to some of the pitfalls in multilevel
analysis.},
added-at = {2009-10-28T04:42:52.000+0100},
author = {Steenbergen, Marco R. and Jones, Bradford S.},
biburl = {https://www.bibsonomy.org/bibtex/2feb7ee9204aa5da3ba52f2b6b1c78833/jwbowers},
citeulike-article-id = {100217},
date-added = {2007-09-03 22:45:16 -0500},
date-modified = {2007-09-03 22:45:16 -0500},
interhash = {a34b87e86cd36bee3d59a44993ed3686},
intrahash = {feb7ee9204aa5da3ba52f2b6b1c78833},
journal = {American Journal of Political Science},
keywords = {methodology multilevel political_science statistics},
number = 1,
opturl = {http://links.jstor.org/sici?sici=0092-5853%28200201%2946%3A1%3C218%3AMMDS%3E2.0.CO%3B2-4},
pages = {218--237},
priority = {0},
timestamp = {2009-10-28T04:43:02.000+0100},
title = {Modeling Multilevel Data Structures},
volume = 46,
year = 2002
}