{"id":"W2098365234","doi":"10.5539/ass.v10n10p76","title":"Internal Audit Effectiveness: Data Screening and Preliminary Analysis","year":2014,"lang":"en","type":"article","venue":"Asian Social Science","topic":"Forecasting Techniques and Applications","field":"Decision Sciences","cited_by":20,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Missing data; Univariate; Statistic; Multicollinearity; Principal component analysis; Statistics; Exploratory data analysis; Descriptive statistics; Multivariate analysis; Audit; Multivariate statistics; Stratified sampling; Exploratory factor analysis; Computer science; Mathematics; Accounting; Regression analysis; Business; Structural equation modeling","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.09806958,0.0007407573,0.001582751,0.008984384,0.001702973,0.002781381,0.001400786,0.00071086,0.003555765],"category_scores_gemma":[0.1766561,0.0007413243,0.00141983,0.006553565,0.001564564,0.001951699,0.002623365,0.0014656,0.001040172],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00420645,"about_ca_system_score_gemma":0.008501193,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002624229,"about_ca_topic_score_gemma":0.003274993,"domain_scores_codex":[0.9203349,0.03841996,0.01266433,0.002145215,0.02296813,0.003467464],"domain_scores_gemma":[0.712613,0.157984,0.02706779,0.01126891,0.0873451,0.003721121],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.002180255,0.004461322,0.5118816,0.006334193,0.0001660085,0.0009643741,0.06708924,0.00174447,0.005508204,0.003499945,0.01018982,0.3859805],"study_design_scores_gemma":[0.000240967,0.01126076,0.8409784,0.002590545,0.0003678667,0.0006426285,0.09253608,0.005120535,0.0156548,0.001345699,0.02907001,0.0001916376],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9286375,0.0004404354,0.01516724,0.001112604,0.00007893729,0.03648193,0.005693367,0.0002171006,0.01217097],"genre_scores_gemma":[0.8995207,0.0007992181,0.04826772,0.0004366524,0.0001089225,0.04087167,0.004983221,0.0001007028,0.004911235],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.09806958,"threshold_uncertainty_score":0.5186477,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.108408374190136,"score_gpt":0.423667204839842,"score_spread":0.3152588306497061,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}