Sampling Practices of Internal Auditors at Corporations on the Standard & Poor's Toronto Stock Exchange Composite Index*
Bibliographic record
Abstract
ABSTRACT The purpose of this study is to find out how often statistical and nonstatistical audit sampling practices are used by internal auditors in companies listed on the Standard and Poor's (S&P) Toronto Stock Exchange (TSX) Composite Index and how such practices are related to the training and background of the respondents. We adapted the questionnaire used by Hall, Hunton, and Pierce (2002) in their survey of U.S. auditors in public accounting, industry, and government. Although 20 percent of companies responding do not have an internal audit department, the other 80 percent use statistical methods to plan sample sizes 15 percent (+5 percent) of the time, random sample selection methods 23 percent (+5 percent) of the time, but statistical evaluation methods only 10% (+4%) of the time. Despite the low percentage use, almost half of the respondents reported substantial training in statistical sampling and evaluation methods. Moreover, we found statistically significantly higher proportions of respondents with substantial training in audit sampling methods among companies cross‐listed on U.S. exchanges compared with companies listed only on the TSX. Finally, respondents with a chartered accountant designation tend to have a negative impact on the use of statistical methods in audit sampling, and companies cross‐listed on U.S. exchanges tend to have larger internal audit departments than companies listed only on the TSX.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".