Group-mean-centering independent variables in multi-level models is dangerous

Group-mean centering of independent variables in multi-level models is widely practiced and widely recommended. For example, in cross-national studies of educational performance, family background is scored as a deviation from the country mean for student’s family background. We argue that this is u...

Full description

Saved in:
Bibliographic Details
Published in:Quality & quantity 2017, Vol.51 (1), p.261-283
Main Authors: Kelley, Jonathan, Evans, M. D. R., Lowman, Jennifer, Lykes, Valerie
Format: Article
Language:English
Subjects:
Academic achievement
Bias
Correlation analysis
Estimates
Groups
Independent variables
Mean square errors
Measurement errors
Methodology of the Social Sciences
Regression analysis
Social processes
Social Sciences
Variables
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Group-mean centering of independent variables in multi-level models is widely practiced and widely recommended. For example, in cross-national studies of educational performance, family background is scored as a deviation from the country mean for student’s family background. We argue that this is usually a serious mis-specification, introducing bias and random measurement error with all their attendant vices. We examine five diverse examples of “real world” analyses using large, high quality datasets on topics of broad interest in the social sciences. In all of them, consistent with much (but not all) of the technical literature, group-mean centering substantially distorts results. Moreover the distortions are large, substantively important differences pointing towards seriously incorrect interpretations of important social processes. We therefore recommend that group-mean centering be abandoned.
ISSN:0033-5177
1573-7845
DOI:10.1007/s11135-015-0304-z