Undetected Deviations in Tests of Controls: Experimental Evidence of Nonsampling Risk*
Bibliographic record
Abstract
ABSTRACT The objective of the study was to examine the effects of three independent variables ‐ accountability, audit workpaper structure, and type of control deviations ‐ on auditors' detection failure rates during control tests in a purchases, payables, and payments cycle. The experimental design used a between‐subjects manipulation of accountability and workpaper structure, and a within‐subjects manipulation of deviation type. Consistent with prior research, we observed an alarmingly high detection failure rate of 42.3 percent. This failure rate was not affected by levels of accountability or workpaper structure, although postexperiment evidence suggests that these variables were successfully manipulated. Failure rates did depend on the type of seeded control deviation, with nonmonetary deviations being overlooked most frequently. In addition to replicating prior research, our study makes two further contributions. First, we provide empirical evidence that supports Hirst's (1992) speculation that successful manipulations of accountability may not affect auditor performance because auditors may self‐induce levels of accountability that create a ceiling effect on auditor performance. Second, we observe that although auditors perceived that highly structured workpapers allowed them to be more effective and efficient when performing tests of controls, their actual audit performance was not more effective and, on average, was less efficient.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.019 | 0.150 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".