Modeling and Interpreting Interactions in Multiple Regression

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Modeling and Interpreting Interactions in Multiple Regression Donald F. Burrill The Ontario Institute for Studies in Education Toronto, Ontario Canada A method of constructing Interactions in Multiple Regression models is described which produces interaction variables that are uncorrelated with their component variables and with any lower-order interaction variables. The method is, in essence, a partial Gram-Schmidt orthogonalization that makes use of standard Regression procedures, requiring neither special programming nor the use of special-purpose programs before proceeding with the analysis. Advantages of the method include clarity of tests of Regression coefficients, and efficiency of winnowing out uninformative predictors (in the form of Interactions ) in reducing a full model to a satisfactory reduced model. The method is illustrated by applying it to a convenient data set.

Modeling and Interpreting Interactions in Multiple Regression Donald F. Burrill The Ontario Institute for Studies in Education Toronto, Ontario Canada

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