[Auditor liability is a growing concern of public accounting firms (Collins 1985; Flynn et al. 1990; Mednick 1987; Minow 1984). In lawsuits brought by clients for breach of contract and tort actions for negligent execution of an audit, as well as in an increasing number of lawsuits brought by third parties, a common defense is the auditor's compliance with generally accepted auditing standards (GAAS). In this study, the source of professional auditing standards was manipulated to examine its influence on jurors' decisions. Four fact patterns were presented to members of jury pools called to jury duty. Prospective jurors responded to a fact pattern by voting in favor of either the plaintiff/client or the defendant/auditor. The fact patterns were manipulated to compare jurors' responses to standards of auditing performance established by the federal government versus those established by the auditing profession. In addition, a comparison was made of jurors' responses to two sets of instructions provided by the judge: either that standards alone set the required level of performance or that standards are only a part of the evidence to be considered in establishing the required level of performance. At trial, a judge's instructions to the jury before its deliberations can be influenced by counsel for the public accounting firm as to the evidentiary weight assigned to professional standards. The results that follow indicate that jurors are more likely to rule against a CPA firm when the profession's standards are offered in defense and less likely to rule against it when government standards of performance are offered in defense. Also, as expected, jurors are more likely to accept standards in defense, whether established by the government or the profession, if the judge identifies the standards as the only criterion for an expected level of performance rather than as only part of the evidence.]

[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 Reports on the influence of the source of auditing professional standards on juror decision making concerning auditor liabilities. Manipulation of the source of professional auditing standards to examine its influence on juror's decisions; Indications that jurors are more likely to rule unfavorably when the professsion's standards are offered in defense.

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.