Skip to main content

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
Copyright
Reuse Permissions All Rights Reserved
Note Masters Thesis Psychology 2015
Collaborating Institutions Graduate College / ASU Library
Additional Formats MODS / OAI Dublin Core / RIS


  Full Text
684.2 KB application/pdf
Download Count: 2465

Description Dissertation/Thesis