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32 - Analyzing Nested Data

Multilevel Modeling and Alternative Approaches

from Part VII - General Analytic Considerations

Published online by Cambridge University Press:  23 March 2020

By
Daniel J. Bauer ,
Daniel M. McNeish ,
Scott A. Baldwin  and
Patrick J. Curran
Edited by
Aidan G. C. Wright  and
Michael N. Hallquist
Aidan G. C. Wright
Affiliation:
University of Pittsburgh
Michael N. Hallquist
Affiliation:
Pennsylvania State University
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Summary

Nested data arise frequently in clinical research. The nesting might be hierarchical, such as patients nested within clinicians, or it might be longitudinal, such as repeated assessments over time nested within individuals. As articulated in this chapter, whenever and however nesting occurs, it is necessary to account for the statistical dependence of observations within units when analyzing the data. Further, it is important to determine the level(s) of the data at which predictors exert their effects. Multilevel models are a particularly popular and useful approach for addressing these issues. We thus describe these models in detail, illustrating the application of multilevel models in clinical research via two examples. The first example considers nesting of siblings within families and demonstrates the importance of separating within- versus between-family effects. The second example focuses on the application of multilevel models with repeated measures to evaluate within-person change over time. Additionally, we provide a brief survey of other approaches to the analysis of nested data (e.g., cluster-robust standard errors, generalized estimating equations, fixed-effects models).

Keywords

Nested dataclusteringmultilevel modelingdependencerandom effectsfixed effectshierarchicallongitudinalgrowth
Type
Chapter
Information
The Cambridge Handbook of Research Methods in Clinical Psychology , pp. 426 - 443
Publisher: Cambridge University Press
Print publication year: 2020

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