Methods for Analyzing Experiments with Multiple Critiera

Field experimentation as a means of aiding business decision making has become increasingly popular during the past decade, with test marketing of products serving as a good example. The problem that arises is the need to define and subsequently analyze more than one dependent variable simultaneously.An approach to analyzing experimental data with multiple criteria is explained and demonstrated from a test of the effectiveness of 2 posters. As a supplement to traditional multivariate analysis of variance and covariance, the application of a stepdown F test is advocated when an ordering of the criterion is meaningful. Further, an analysis of contrasts is recommended when such an ordering is not managerially relevant. The step-down procedure enjoys the advantage of simultaneously testing an overall hypothesis and hypotheses on each criterion variable. In general, the argument is supported that multivariate analysis of variance and covariance are powerful techniques for the analysis of experimental data. Appendices.