An Empirical Investigation of the Influence of Qualitative Risk Factors on Canadian Auditors’ Determination of Performance Materiality*
Bibliographic record
Abstract
This paper presents the results of a field experiment that tested the effects of various qualitative risk factors suggested by auditing standards and prior literature on practicing Canadian auditors’ estimates of performance materiality, a concept introduced by Canadian Auditing Standard (CAS) 320, in the audit of specific accounts in a financial statement audit. Ninety-four practicing auditors responded to four scenarios and, based on “good” and “bad” versions of six qualitative risk factors, revised or not, as they deemed appropriate, initially established performance materiality for the audit of four different transaction streams/account balances. For all four scenarios, on average, the auditors revised, to a statistically significant degree, performance materiality, downward on the basis of “bad” information and upward on the basis of “good” information. Different combinations of transaction streams/accounts and risk factors were associated with different magnitudes of revision. However, at the level of individual participants, responses were quite varied. Some participants did not revise performance materiality and some even stated that performance materiality should not be revised based on risk-related information. It may be that the concept of performance materiality as promulgated in CAS 320 and the relationship between overall materiality, performance materiality, and risk requires clarification to provide appropriate guidance for auditors to make performance materiality judgments.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.062 | 0.220 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".