The Extent of Use of Analytical Procedures by External Auditors in Jordan in the Light of ISA 520
Bibliographic record
Abstract
The aim of this study was to identify the substance of the Audit Standard No. 520 concerning the analytical procedures, and evaluating the use of auditors in Jordan for the analytical procedures. Moreover, the study sought to make valuable recommendations to develop audit methods and address weaknesses to take advantage of financial analysis methods. To achieve the objectives of the study, questionnaire was developed included (38) questions distributed to a sample of auditors practitioners of the audit profession and numbered (46) auditors, have been recovered, (42) of them were collected, (41) Questionnaires were used in analysis; One sample t-test was used to test the hypotheses of the study. the researcher found: that the auditors are not using enough analytical procedures as the arithmetic average (3.49). Because they use financial ratios moderately where the arithmetic average (3.45), the arithmetic average of the comparable financial ratios and information extracted with pre-specified criteria (3.69). Finally, researchers suggested some of the recommendations of the most important need for the competent authorities to hold training courses, workshops and seminars related to the application of analytical procedures, and to verify the commitment and the work of audit firms auditors applying quality control standards, and be subject to peer review.
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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 | 0.025 | 0.066 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".