Abstract Evaluates the extent to which analytical procedures used by auditors provide correct signals as to the existence of material misstatements under `best case' circumstances. Expectation models; Materiality definitions for error seedlings; Accounting error types.
[This study extends the existing research on the audit effectiveness of analytical procedures in a setting that used actual accounting data seeded with "material" simulated accounting errors. Five sample companies, whose revenues represented a wide range of time-series behavior, were selected to analyze the effects of eight commonly encountered accounting errors on 15 often-used analytical procedures (eight ratios and seven accounts). A "best case" scenario was induced by using, among other factors, single-industry companies, quarterly data, and more sophisticated expectation models than had been used in prior studies. The best predicting of six candidate models (four naive, a regression, and the Census X-11 time-series model) was used to generate quarterly predictions for comparison with actual data seeded with the largest of four empirically based materiality measures. Five investigation rules, including two simple percentage change rules and a statistical rule using three different alpha levels, were applied to prediction errors to determine whether error investigations were correctly signaled. The results of prediction model selection were dominated by X-11, followed by regression. Also, X-11 emerged as the "best" model more often for ratios than for accounts, while the reverse was true for the regression models. Overall, the analytical procedures examined did not signal (Type I and Type II error rates) very well when applied in isolation to quarterly data. However, when the quarterly signaling resulting were "annualized," and when an annual material error was seeded into an individual quarter's data, the results were much more encouraging. The lowest error rates were observed for instances where the primary substantive test would have been direct recomputation (i.e., interest and depreciation errors). The assertion of SAS No. 56 that income statement accounts should be more predictable than balance sheet accounts was contradicted, but the evidence is limited. The seeded quarterly material errors were generally swamped by prediction errors of the best-predicting expectation models. A significant correlation was observed between the ratio (prediction error/materiality) and the incidence of Type II signaling errors, indicating that this relationship might be used as a filter to determine when analytical procedures are likely to be effective audit tests.]
[This paper builds upon the work of Burgstahler and Jiambalvo (1986a, 1986b) on auditors' estimation of error through sampling. Burgstahler and Jiambalvo used a ball-and-urn inference model to argue that the isolation of sample errors violates professional auditing standards. They speculated that context-specific data about sample errors induces uniqueness perceptions that, in turn, cause auditors non-normatively to isolate the errors. We extend their research by manipulating two variables they suggested, but did not test: (1) containment information, which indicates whether the discovered error is limited to a well-defined subpopulation of unsampled items; and (2) perceived frequency of errors, which is directly related to the similarity of an error to other potential errors. Our results show that containment information strongly affects the projection-isolation decision, regardless of the perceived frequency of the error. In our empirical work, error frequency also affects the judgment. These findings suggest that containment information does not affect the decision solely by inducing perceptions of uniqueness but rather has a direct effect on it. Additional research is needed to describe and understand more fully this important judgment.]