Modeling Multilevel Data Structures

2002/01/01 by Marco R. Steenbergen, Bradford S. Jones, Bradford Jones · 1,521 citations
Business, Management and Accounting · Social Sciences · #Cluster analysis #Computer science #Data mining #Data science #Electoral Systems and Political Participation #Exploit #Focus (optics) #Gender Politics and Representation #Machine learning #Multilevel model #Political Influence and Corporate Strategies #Statistical model #Theoretical computer science

paper · doi:10.2307/3088424

published in American Journal of Political Science 46(1), 218 (Wiley)

openalex publication_date 2002/01/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/31

Abstract

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 impor? tant. To exploit the opportunities of? fered 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 atten? tion to some of the pitfalls in multilevel analysis.

Citations

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• Longitudinal data analysis using generalized linear models
• Prejudice as a Response to Perceived Group Threat: Population Composition and Anti-Immigrant and Racial Prejudice in Europe

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