However, there are also estimators of the population crossvalidated correlation. The resulting measure is a sample crossvalidated correlation (e.g. It can be estimated by splitting the available observations into an estimation sample and a validation (or holdout) sample (and computing the Pearson correlation between the actual Y-values of the objects in the validation sample with the Y-values predicted with the regression parameters estimated in the estimation sample). An appropriate measure of predictive validity is the crossvalidated correlation (rather than the mean squared error of prediction). Hence, relative prediction is what matters (rather than absolute prediction). a consumer's utility for a product or for a concept) rather than the absolute Y-value of an object. In most instances, one is interested in predicting the Y-value of an object compared to other objects (e.g. In the social sciences in general (and in consumer research in particular) it is often valuable to measure the predictive validity of a regression model. An example of the use of these estimators in consumer research is presented. The advantage of these, estimators (over a sample crossvalidated correlation) is that they produce more precise estimates. A few such estimators can be found in the psychology literature. Estimators of the population crossvalidated correlation can be used. Philippe Cattin, University of ConnecticutĪ frequent measure of the predictive validity of a regression model is the crossvalidated correlation. ON THE USE OF FORMULAS OF THE PREDICTIVE VALIDITY OF REGRESSION IN CONSUMER RESEARCH Wilkie, Ann Abor, MI : Association for Consumer Research, Pages: 284-287.Īdvances in Consumer Research VolPages 284-287 Philippe Cattin (1979) ,"On the Use of Formulas of the Predictive Validity of Regression in Consumer Research", in NA - Advances in Consumer Research Volume 06, eds.
ABSTRACT - A frequent measure of the predictive validity of a regression model is the crossvalidated correlation.