{"id":"W2046847367","doi":"10.1177/0013164414523618","title":"Binary Logistic Regression Analysis for Detecting Differential Item Functioning","year":2014,"lang":"en","type":"article","venue":"Educational and Psychological Measurement","topic":"Psychometric Methodologies and Testing","field":"Decision Sciences","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Statistics; Differential item functioning; Sample size determination; Type I and type II errors; Logistic regression; Mathematics; Statistical power; Statistical hypothesis testing; Regression analysis; Psychology; Econometrics; Item response theory; Psychometrics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0840964,0.002154948,0.002668462,0.01453263,0.0008500083,0.002336886,0.002640383,0.001435393,0.003691639],"category_scores_gemma":[0.4065508,0.0008577369,0.003146725,0.009305721,0.001470142,0.003170015,0.002206434,0.003690783,0.001166057],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001074976,"about_ca_system_score_gemma":0.001715625,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009422232,"about_ca_topic_score_gemma":0.001237476,"domain_scores_codex":[0.8691667,0.1059015,0.00760111,0.003626318,0.01306385,0.0006404659],"domain_scores_gemma":[0.4911767,0.4529939,0.02983912,0.01399304,0.01120178,0.0007954811],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.003514422,0.001115108,0.2415622,0.003818809,0.003686292,0.001079012,0.001274819,0.01489759,0.00366213,0.02141131,0.009119377,0.6948589],"study_design_scores_gemma":[0.001576927,0.01292982,0.3447667,0.00418336,0.004405518,0.009797643,0.001862309,0.4928724,0.01901721,0.08146269,0.02609796,0.001027434],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1103429,0.005152935,0.8719209,0.001741183,0.0009244745,0.002248324,0.0008673589,0.001709188,0.005092722],"genre_scores_gemma":[0.4413002,0.001687068,0.5512986,0.0004757117,0.0001744196,0.002875241,0.0004253384,0.0002871847,0.001476271],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0840964,"threshold_uncertainty_score":0.4447496,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.7639754128351917,"score_gpt":0.5122407613224709,"score_spread":0.2517346515127208,"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."}}