Effect Of Independence, Auditor Experience, Task Complexity, And Time Budget Pressure On Audit Quality (Study On Surakarta, Yogyakarta, And Semarang KAP)
Bibliographic record
Abstract
The purpose of this study is to find out and provide empirical evidence of the significance of the influence of independence, auditor experience, task complexity, and time budget pressure on audit quality.This research is quantitative research. The population in this study were 74 auditors who worked in Public Accountants Office in Surakarta, Yogyakarta, and Semarang. The sampling technique used was convenience sampling and a sample of 58 auditors was obtained. The analysis technique consists of the classic assumption test, multiple linear regression analysis, t test, and R2 test. The results of testing the questionnaire using Test Validity and Reliability Tests that obtain valid and reliable results. The results of hypothesis testing indicate that the independence variable has a significant positive effect on audit quality. The auditor experience variable has a significant positive effect on audit quality. Task complexity variables have a significant positive effect on audit quality. Variable time budget pressure has a positive significant effect on audit quality. The R2 test results show that the Adjusted R Square value is 0.719. So that independence, auditor experience, task complexity, and time budget pressure can explain the level of audit quality by 71.9%, while 28.1% is influenced by other factors outside the research regression model.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".