{"id":"W2093403136","doi":"10.1002/bimj.200390053","title":"Incorporating Inter‐item Correlations in Item Response Data Analysis","year":2003,"lang":"en","type":"article","venue":"Biometrical Journal","topic":"Advanced Statistical Modeling Techniques","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; Alberta Heritage Foundation for Medical Research","keywords":"Rasch model; Ordinal data; Categorical variable; Polytomous Rasch model; Item response theory; Ordinal Scale; Statistics; Ordinal regression; Latent variable; Econometrics; Rating scale; Mathematics; Computer science; Psychometrics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.002699673,0.0001344135,0.0002766998,0.003937906,0.0001614156,0.0002978863,0.001549313,0.00008831436,0.00001918285],"category_scores_gemma":[0.01618897,0.0001187987,0.00007738546,0.01503965,0.0000471839,0.0008067943,0.0004688158,0.00051326,0.00001371012],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002199831,"about_ca_system_score_gemma":0.0001178236,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007717736,"about_ca_topic_score_gemma":0.000005594669,"domain_scores_codex":[0.9975523,0.0005990544,0.0006728173,0.000436769,0.0004390435,0.0003000178],"domain_scores_gemma":[0.9957579,0.002713866,0.0002611742,0.0008769575,0.0001559068,0.0002342313],"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":[0.0003294708,0.001290502,0.1169139,0.00001748035,0.0005767162,0.001700139,0.0006014243,0.00636389,0.002717396,0.2877267,0.004421377,0.577341],"study_design_scores_gemma":[0.0003224163,0.0001508168,0.01462835,0.00002791458,0.00004749816,0.0001819629,0.00002484967,0.9300302,0.0001035305,0.05187054,0.002350678,0.0002612405],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.004186068,0.0001519943,0.9947622,0.0004273352,0.0001410647,0.00006293992,0.00001683875,0.0001062719,0.0001452941],"genre_scores_gemma":[0.4883643,0.000007328705,0.5115334,0.00005679704,0.00001399158,0.000001149858,0.000004187829,0.000004932615,0.00001386387],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9236663,"threshold_uncertainty_score":0.9920981,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09435964257504734,"score_gpt":0.3701971385103444,"score_spread":0.2758374959352971,"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."}}