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Record W2918238771 · doi:10.5539/ijef.v11n3p77

The Extent of Use of Analytical Procedures by External Auditors in Jordan in the Light of ISA 520

2019· article· en· W2918238771 on OpenAlexvenueno aff
Hasan Flayyeh Al Qtaish, Mohammed Hassan Makhlouf

Bibliographic record

VenueInternational Journal of Economics and Finance · 2019
Typearticle
Languageen
FieldComputer Science
TopicOrganizational and Employee Performance
Canadian institutionsnot available
Fundersnot available
KeywordsAuditAccountingSample (material)Test (biology)Analytical proceduresQuality (philosophy)Financial AuditControl (management)Actuarial sciencePsychologyBusinessComputer science

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.461
Threshold uncertainty score0.131

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.009
GPT teacher head0.218
Teacher spread0.209 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations3
Published2019
Admission routes1
Has abstractyes

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