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JSTOR is a not-for-profit service that helps scholars, researchers, and students discover, use, and build upon a wide range of content in a trusted digital archive. We use information technology and tools to increase productivity and facilitate new forms of scholarship. For more information about JSTOR, please contact support@jstor.org.American Marketing Association is collaborating with JSTOR to digitize, preserve and extend access to Multiple regression analysis is one of the most widely used statistical procedures for both scholarly and applied marketing research. Yet, correlated predictor variables-and potential collinearity effects-are a common concern in interpretation of regression estimates. Though the literature on ways of coping with collinearity is extensive, relatively little effort has been made to clarify the conditions under which collinearity affects estimates developed with multiple regression analysis-or how pronounced those effects are. The authors report research designed to address these issues. The results show, in many situations typical of published cross-sectional marketing research, that fears about the harmful effects of collinear predictors often are exaggerated. The authors demonstrate that collinearity cannot be viewed in isolation. Rather, the potential deleterious effect of a given level of collinearity should be viewed in conjunction with other factors known to affect estimation accuracy.Multiple regression analysis is one of the most widely used statistical procedures for both scholarly and applied marketing research. Its popularity is fostered by its applicability to varied types of data and problems, ease of interpretation, robustness to violations of the underlying assumptions, and widespread availability.Multiple regression is used in marketing research for two related, but distinct, purposes. One is simply for prediction per se. In such applications, the researcher is interested in finding the linear combination of a set of predictors that provides the best point estimates of the dependent variable across a set of observations. Predictive accuracy is calibrated by the magnitude of the R2 and the statistical significance of the overall model.The second purpose-conditional on statistically significant overall prediction-is to draw conclusions about individual predictor variables. In such applications, the focus is on the size of the (standardized) regression coefficients, their estimated standard errors, and the associated t-test probabilities. These statistics are used to test hypotheses about the effect of individual predictors on the dependent variable or to evaluate their relative "importance."Problems may arise when two or more predictor variables are correlated. Overall prediction is not affected, but interpretation of and conclusions based on the size of the regression coefficients, their standard errors, or the associated t-tests may be misleading because of the potentially confounding effects of collinearity. This point is well known among researchers who use multiple...

Multiple regression analysis is one of the most widely used statistical procedures for both scholarly and applied marketing research. Yet, correlated predictor variables—and potential collinearity effects—are a common concern in interpretation of regression estimates. Though the literature on ways of coping with collinearity is extensive, relatively little effort has been made to clarify the conditions under which collinearity affects estimates developed with multiple regression analysis—or how pronounced those effects are. The authors report research designed to address these issues. The results show, in many situations typical of published cross-sectional marketing research, that fears about the harmful effects of collinear predictors often are exaggerated. The authors demonstrate that collinearity cannot be viewed in isolation. Rather, the potential deleterious effect of a given level of collinearity should be viewed in conjunction with other factors known to affect estimation accuracy.

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