[As behavioral accounting research has progressed, research designs have become more complex. New topic areas are initially investigated using single-factor designs, which may result in significant effects and a substantial amount of explained variation. In subsequent investigations, explained variation is increased by employing factorial designs and/or multiple-level explanatory variables. Traditionally, ANOVA is the statistical analysis approach employed to test research hypotheses. A limitation of ANOVA is that it only detects significant differences among cell means, but does not indicate the functional form of the relationship among cell means. We propose that researchers employ contrast coding-a refinement of ANOVA-to test research hypotheses. Contrast coding requires researchers to specify a priori the functional form of the relationship among cell means. This article demonstrates that contrast coding provides greater statistical power than the conventional ANOVA without increasing Type I error rates. Examples from recent accounting literature illustrate when the use of contrast coding is most advantageous. The examples include a factorial design and a single-factor, multiple-level experiment. Formulas for calculating the net benefit of employing contrast coding and the significance of the net benefit are presented. The examples and analysis support the use of contrast coding with certain research designs. The primary benefit of the a priori specification required by contrast coding is greater statistical power.]

Abstract Illustrates the increased statistical power of contrast coding over the conventional ANOVA on behavioral analysis, when multiple-level factors are involved or when certain types of interactive relationships among factors are hypothesized. Effect on Type I error rates; Limitations of ANOVA; Advantages of contrast coding; Empirical examples; Multi-level factors.