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Interaction Effects in Multilevel Models

Abstract Researchers are often interested in estimating interactions in multilevel models, but many researchers assume that the same procedures and interpretations for interactions in single-level models apply to multilevel models. However, estimating interactions in multilevel models is much more complex than in single-level models. Because uncentered (RAS) or grand mean centered (CGM) level-1 predictors in two-level models contain two sources of variability (i.e., within-cluster variability and between-cluster variability), interactions involving RAS or CGM level-1 predictors also contain more than one source of variability. In this Master’s thesis, I use simulations to demonstrate that ignoring the four sources of variability in a total level-... (more)
Created Date 2015
Contributor Mazza, Gina Lynn (Author) / Enders, Craig K. (Advisor) / Aiken, Leona S. (Advisor) / West, Stephen G. (Committee member) / Arizona State University (Publisher)
Subject Statistics / Psychology / centering / clustered data / hierarchical modeling / interaction / moderation / multilevel modeling
Type Masters Thesis
Extent 80 pages
Language English
Reuse Permissions All Rights Reserved
Note Masters Thesis Psychology 2015
Collaborating Institutions Graduate College / ASU Library
Additional Formats MODS / OAI Dublin Core / RIS

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Description Dissertation/Thesis