Analysis of Variance

Experiments are becoming increasingly important in marketing research. Supposea company has to decide which of three potential new brand logos should beused in the future. An experiment in which three groups of participants rate theirliking of one of the logos would provide the necessary information to make thisdecision. The statistical challenge is to determine which (if any) of the three logosis liked significantly more than the others. The adequate statistical technique to assess the statistical significance of such mean differences between groups ofparticipants is called analysis of variance (ANOVA). The present chapter providesan introduction to the key statistical principles of ANOVA and comparesthis method to the closely related t-test, which can alternatively be used if exactlytwo means need to be compared. Moreover, it provides introductions to the keyvariants of ANOVA that have been developed for use when participants areexposed to more than one experimental condition (repeated-measuresANOVA), when more than one dependent variable is measured (multivariateANOVA), or when a continuous control variable is considered (analysis ofcovariance). This chapter is intended to provide an applied introduction toANOVA and its variants. Therefore, it is accompanied by an exemplary datasetand self-explanatory command scripts for the statistical software packages R andSPSS, which can be found in the Web-Appendix