{"id":"W2912880345","doi":"10.1177/0013164419829855","title":"Item Response Tree Models to Investigate Acquiescence and Extreme Response Styles in Likert-Type Rating Scales","year":2019,"lang":"en","type":"article","venue":"Educational and Psychological Measurement","topic":"Psychometric Methodologies and Testing","field":"Decision Sciences","cited_by":39,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Acquiescence; Psychology; Likert scale; Rating scale; Item response theory; Social psychology; Explanatory model; Scale (ratio); Psychometrics; Statistics; Developmental psychology; Mathematics","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":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.02036774,0.0002083406,0.00034326,0.0005136818,0.0001489678,0.0002020068,0.0004364091,0.0001059247,0.0001967638],"category_scores_gemma":[0.07539219,0.0001422982,0.00004151578,0.001543644,0.0001454443,0.0002125412,0.0001460738,0.0002049378,0.00007136424],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007438178,"about_ca_system_score_gemma":0.00009923058,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001711011,"about_ca_topic_score_gemma":0.00001807516,"domain_scores_codex":[0.9945995,0.002099877,0.00070328,0.0009236594,0.001321693,0.0003520384],"domain_scores_gemma":[0.9777385,0.02090774,0.0001833791,0.0004444535,0.00041189,0.0003140045],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.01225237,0.0007334612,0.7199343,0.00002208412,0.00002427036,0.000009036084,0.002987203,0.0004888008,0.1205586,0.004461916,0.008088537,0.1304395],"study_design_scores_gemma":[0.0004295992,0.0005287902,0.9520442,0.00009241828,0.000002373401,0.00001733903,0.001022192,0.0004757549,0.00008869696,0.04367717,0.001418799,0.0002026428],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9806734,0.001268786,0.0004874561,0.01522847,0.0005318762,0.0003795493,0.000003696863,0.00002380219,0.001402962],"genre_scores_gemma":[0.9776695,0.00003598116,0.02005758,0.001459073,0.00007116534,0.00003437386,7.394071e-7,0.000007588339,0.0006639539],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2321099,"threshold_uncertainty_score":0.9323962,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.7658315465227954,"score_gpt":0.4754891438004715,"score_spread":0.2903424027223239,"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."}}