{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007269623,0.00007772467,0.0001817898,0.0002636878,0.0008028241,0.0004164132,0.002100825,0.00003714929,0.00003505531],"category_scores_gemma":[0.002146597,0.00006072428,0.00005202742,0.003070633,0.0008568486,0.0005465203,0.001131005,0.0000974665,0.00001913802],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002044267,"about_ca_system_score_gemma":0.00004006299,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001282828,"about_ca_topic_score_gemma":0.00002672725,"domain_scores_codex":[0.9978802,0.0001421872,0.0002228724,0.0006781641,0.0008563275,0.0002202683],"domain_scores_gemma":[0.9983714,0.0005584611,0.0001568158,0.0006672802,0.0001438927,0.0001021049],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.000008799742,0.00001418308,0.01268631,0.000001222043,0.00001211578,5.456e-7,0.0003179858,0.000002469207,0.0003691042,0.01695345,0.001122392,0.9685114],"study_design_scores_gemma":[0.0000841756,0.00006748095,0.9353518,0.00001142032,0.00005618736,0.000003953885,0.0003696967,0.02590044,0.0001975694,0.02964707,0.008177313,0.0001328456],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06279305,0.00001468778,0.8276794,0.001528444,0.00007115934,0.0001682245,0.00002882345,0.0001023625,0.1076138],"genre_scores_gemma":[0.9869752,8.10078e-7,0.01275786,0.00006632443,0.00009284284,0.000008180174,0.000003923594,0.000003341084,0.00009150531],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9683786,"threshold_uncertainty_score":0.6174755,"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."}}