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Data auditing is the process of conducting a data audit to assess how company's data is fit for given purpose. This involves profiling the data and assessing the impact of poor quality data on the organization's performance and profits.
The audit module offers planning, selection and evaluation of statistical audit samples, methods for data auditing (e.g., Benford’s law) and algorithm auditing (e.g., model fairness). Bain : Bayesian informative hypotheses evaluation [ 8 ] for t-tests, ANOVA , ANCOVA , linear regression and structural equation modeling .
Audit technology is a general term used for computer-aided audit techniques (CAATs) used by accounting firms to enhance an engagement. These techniques improve the efficiency and effectiveness of audit findings by allowing auditors to analyze much larger sets of data, sometimes using entire populations of data, rather than taking a sample.
Concerns include increasingly deterministic and rigid processes, privileging of coding, and retrieval methods; reification of data, increased pressure on researchers to focus on volume and breadth rather than on depth and meaning, time and energy spent learning to use computer packages, increased commercialism, and distraction from the real ...
Audit log: Specifies whether the product logs activity performed by the user (the auditor) for later reference (e.g., inclusion into audit report). Data graph: Specifies whether the product provides graphs of results. Export (CSV): Specifies whether the product support exporting selected rows to a comma-separated values formatted file.
Computer-based test interpretation (CBTI) programs are technological tools that have been commonly used to interpret data in psychological assessments since the 1960s. CBTI programs are used for a myriad of psychological tests, like clinical interviews or problem rating, but are most frequently exercised in psychological and neuropsychological ...
Audit studies have been conducted to test the existence of discrimination in numerous occupations and services and in regards to multiple characteristics. For example, studies have been conducted to measure discrimination against racial minorities by real estate agents , [ 5 ] as well as gender discrimination against women applying for ...
Data reconciliation is a technique that targets at correcting measurement errors that are due to measurement noise, i.e. random errors.From a statistical point of view the main assumption is that no systematic errors exist in the set of measurements, since they may bias the reconciliation results and reduce the robustness of the reconciliation.