Article

Multilevel Models

Andrew S. Fullerton

in Sociology

ISBN: 9780199756384
Published online November 2012 | | DOI: http://dx.doi.org/10.1093/obo/9780199756384-0088
Multilevel Models

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  • Sociology
  • Comparative and Historical Sociology
  • Economic Sociology
  • Gender and Sexuality
  • Health, Illness, and Medicine
  • Population and Demography
  • Race and Ethnicity
  • Social Movements and Social Change
  • Social Stratification, Inequality, and Mobility
  • Social Theory

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Multilevel models are a set of statistical techniques for analyzing quantitative data measured at two or more levels of analysis. In the basic two-level linear model, level 1 units (e.g., students) are nested within level 2 units (e.g., schools) and variables are included in the model from both levels of analysis. Although one can specify separate equations for each level, they are linked through a random intercept and random slopes. Prior to the development of multilevel models, scholars used methods such as Ordinary Least Squares (OLS) regression to examine data from multiple levels of analysis. However, the clustering of micro-level units within meso- and macro-level units violates several key assumptions of the OLS regression model. This led statisticians to develop a set of models that explicitly incorporates the multilevel nature of the data into the statistical models. Although the earliest formulations and applications of the multilevel model focused on two levels of analysis and a continuous outcome, the basic model has been extended in recent years to include three or more levels of analysis and a wide variety of different outcome types. The prevalence of categorical variables in quantitative sociological research makes hierarchical generalized linear models essential for multilevel scholarship in sociology. Longitudinal data analysis represents another major application of multilevel modeling dating back to some of the earliest multilevel research on the effects of schools on student achievement over time. The alternative “fixed effects” approach to longitudinal analysis captures (time-invariant) between-subject variation with a set of nuisance parameters. In other words, fixed effects models control for between-subject variation but do not allow one to model this variation based on one or more variables. However, researchers may use multilevel models to predict variation within and between subjects. Multilevel models are an extension of the “random effects” approach to longitudinal analysis, which allows one to predict between-subject variation based on subject-level characteristics. The intercept is the only random coefficient in a random effects model, but multilevel models for longitudinal data may have random slopes as well. There is now a much broader range of multilevel models available for the analysis of longitudinal data. This article reviews the literature on multilevel models from the classic works to recent methodological advances and applications in an attempt to trace the development of multilevel models since the 1970s and provide an overview of the potential of this diverse set of methods for analyzing multilevel data structures.

Article.  10518 words. 

Subjects: Sociology ; Comparative and Historical Sociology ; Economic Sociology ; Gender and Sexuality ; Health, Illness, and Medicine ; Population and Demography ; Race and Ethnicity ; Social Movements and Social Change ; Social Stratification, Inequality, and Mobility ; Social Theory

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