Multilevel analysis in public health research.
Over the past few years there has been growing interest in considering factors defined at multiple levels in public health research. Multilevel analysis has emerged as one analytical strategy that may partly address this need, by allowing the simultaneous examination of group-level and individual-level factors. This paper reviews the rationale for using multilevel analysis in public health research, summarizes the statistical methodology, and highlights some of the research questions that have been addressed using these methods. The advantages and disadvantages of multilevel analysis compared with standard methods are reviewed. The use of multilevel analysis raises theoretical and methodological issues related to the theoretical model being tested, the conceptual distinction between group- and individual-level variables, the ability to differentiate "independent" effects, the reciprocal relationships between factors at different levels, and the increased complexity that these models imply. The potentialities and limitations of multilevel analysis, within the broader context of understanding the role of factors defined at multiple levels in shaping health outcomes, are discussed.
Diez-Roux
A V
AV
Division of General Medicine, Columbia College of Physicians and Surgeons, New York, New York, USA. diezrou@medicine1.cpmc.columbia.edu
D000704:Analysis of Variance; D015982:Bias; D015984:Causality; D003627:Data Interpretation, Statistical; D015987:Effect Modifier, Epidemiologic; D006801:Humans; D016014:Linear Models; D015980:Public Health Practice; D012107:Research Design