.png){kind=icon}
返回
Modeling multilevel data structures
.png){kind=icon}
PRE
AI
DOI：10.2307/3088424.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
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.
Keyword:
HIERARCHICAL LINEAR-MODELS
PUBLIC SUPPORT
COGNITIVE MOBILIZATION
EUROPEAN INTEGRATION
REGRESSION-MODEL
REFERENDUM
INFERENCE
TIME
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
