TY - JOUR
T1 - Orthogonalizing Through Residual Centering
T2 - Extended Applications and Caveats
AU - Geldhof, G. John
AU - Pornprasertmanit, Sunthud
AU - Schoemann, Alexander M.
AU - Little, Todd D.
N1 - Funding Information:
The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported in part by the Center for Research Methods and Data Analysis (Todd D. Little, Director) in the College of Liberal Arts and Sciences at the University of Kansas (for more information visit www.Quant.KU.edu ) and a grant from the John Templeton Foundation.
PY - 2013/2
Y1 - 2013/2
N2 - Residual centering is a useful tool for orthogonalizing variables and latent constructs, yet it is underused in the literature. The purpose of this article is to encourage residual centering's use by highlighting instances where it can be helpful: modeling higher order latent variable interactions, removing collinearity from latent constructs, creating phantom indicators for multiple group models, and controlling for covariates prior to latent variable analysis. Residual centering is not without its limitations, however, and the authors also discuss caveats to be mindful of when implementing this technique. They discuss the perils of double orthogonalization (i.e., simultaneously orthogonalizing A relative to B and B relative to the original A), the unintended consequences of orthogonalization on model fit, the removal of a mean structure, and the effects of nonnormal data on residual centering.
AB - Residual centering is a useful tool for orthogonalizing variables and latent constructs, yet it is underused in the literature. The purpose of this article is to encourage residual centering's use by highlighting instances where it can be helpful: modeling higher order latent variable interactions, removing collinearity from latent constructs, creating phantom indicators for multiple group models, and controlling for covariates prior to latent variable analysis. Residual centering is not without its limitations, however, and the authors also discuss caveats to be mindful of when implementing this technique. They discuss the perils of double orthogonalization (i.e., simultaneously orthogonalizing A relative to B and B relative to the original A), the unintended consequences of orthogonalization on model fit, the removal of a mean structure, and the effects of nonnormal data on residual centering.
KW - collinearity
KW - confirmatory factor analysis
KW - covariates
KW - latent interaction
KW - residual centering
KW - structural equation modeling
UR - http://www.scopus.com/inward/record.url?scp=84871319048&partnerID=8YFLogxK
U2 - 10.1177/0013164412445473
DO - 10.1177/0013164412445473
M3 - Article
AN - SCOPUS:84871319048
VL - 73
SP - 27
EP - 46
JO - Educational and Psychological Measurement
JF - Educational and Psychological Measurement
SN - 0013-1644
IS - 1
ER -