%0 Journal Article
%T Could Sequential Residual Centering Resolve Low Sensitivity in Moderated Regression? Simulations and Cancer Symptom Clusters
%A Richard B. Francoeur
%J Open Journal of Statistics
%P 24-44
%@ 2161-7198
%D 2013
%I Scientific Research Publishing
%R 10.4236/ojs.2013.36A004
%X <p>
	Multicollinearity constitutes shared variat<span>ion among predictors that inflates standard errors of regression coefficients. Several years ago, it was proven that the common practice of <i>mean centering</i> in moderated regression cannot alleviate multicollinearity among variables comprising an interaction, but merely masks it. <i>Residual centering</i> (orthogonalizing) is unacceptable because it biases parameters for predictors from which the interaction derives, thus precluding interpre</span><span>tation of moderator effects. I propose and validate residual centering in <i>sequential re-estimations</i> of a moderated regres</span><span>sion¡ª<i>sequential residual centering</i> (SRC)¡ªby revealing unbiased multicollinearity conditioning across the interaction <i>and</i> its related terms. Across simulations, SRC reduces variance inflation factors (VIF) regardless of distribution shape or pattern of regression coefficients across predictors. For any predictor, the reduced VIF is used <i>to derive a lower standard error of its regression coefficient. </i>A cancer sample illustrates SRC, which allows unbiased interpretations of symptom clusters. SRC can be applied efficiently to alleviate multicollinearity after data collection and shows promise for advancing synergistic frontiers of research.</span><span></span> 
</p>
%K Mean Centering
%K Multicollinearity
%K Moderated Regression
%K Statistical Interaction
%K Effect Modifier
%K Residual Centering
%K Symptom Cluster
%K Sickness Behavior
%K Malaise
%K Cancer
%U http://www.scirp.org/journal/PaperInformation.aspx?PaperID=41365