{"id":"W1983043858","doi":"10.1080/15305058.2001.9669474","title":"Illustrating the Use of Nonparametric Regression to Assess Differential Item and Bundle Functioning Among Multiple Groups","year":2001,"lang":"en","type":"article","venue":"International Journal of Testing","topic":"Psychometric Methodologies and Testing","field":"Decision Sciences","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Nonparametric statistics; Differential item functioning; Bundle; Nonparametric regression; Differential (mechanical device); Statistics; Regression analysis; Regression; Smoothing; Psychology; Mathematics; 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":[0.02344842,0.0009637806,0.000978173,0.00237133,0.0007120112,0.001137061,0.001140366,0.00116007,0.003882654],"category_scores_gemma":[0.07151907,0.0003611837,0.001022406,0.00350588,0.001575477,0.001458557,0.002198332,0.002168772,0.001014791],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006076327,"about_ca_system_score_gemma":0.001193266,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004265834,"about_ca_topic_score_gemma":0.004118616,"domain_scores_codex":[0.9840091,0.01282834,0.0004315347,0.0007126469,0.001674243,0.0003440487],"domain_scores_gemma":[0.9365717,0.05473804,0.002055869,0.003516247,0.00286988,0.000248196],"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":[0.0008490105,0.0007435362,0.05374749,0.0005846064,0.0005144472,0.001677874,0.005513666,0.1111519,0.01134158,0.202057,0.0118575,0.5999613],"study_design_scores_gemma":[0.0001736405,0.0008415909,0.0510978,0.0002283718,0.0001380728,0.002843538,0.001633465,0.6673836,0.01169833,0.2327791,0.03086143,0.0003211814],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02633488,0.0002402594,0.9663532,0.0005948885,0.00005301656,0.0001187243,0.0001478242,0.0008316708,0.005325493],"genre_scores_gemma":[0.3107259,0.0003469388,0.6854911,0.0002307226,0.00005276468,0.0005067617,0.0002160585,0.0003927439,0.00203697],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.02344842,"threshold_uncertainty_score":0.1240086,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.6770030026306908,"score_gpt":0.4656857700680094,"score_spread":0.2113172325626814,"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."}}