{"id":"W2612770846","doi":"","title":"PAPER: Examining Position Effects in Large-Scale Assessments Using an SEM Approach","year":2016,"lang":"en","type":"article","venue":"ITC 2016 Conference","topic":"Psychometric Methodologies and Testing","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Rasch model; Position (finance); Reading (process); Test (biology); Structural equation modeling; Scale (ratio); Psychology; Item response theory; Sample (material); Computer science; Statistics; Econometrics; Psychometrics; Social psychology; Cognitive psychology; Artificial intelligence; Natural language processing; Mathematics; Developmental psychology; Linguistics","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":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.02924811,0.001236377,0.0009132183,0.002295801,0.0006903188,0.002085286,0.001224667,0.0006038253,0.004705667],"category_scores_gemma":[0.1405342,0.0005313638,0.001486378,0.003557046,0.001436892,0.001988184,0.002048442,0.001152768,0.0006297427],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009956965,"about_ca_system_score_gemma":0.00221254,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001832271,"about_ca_topic_score_gemma":0.003214291,"domain_scores_codex":[0.9657129,0.02666682,0.001424058,0.00285888,0.003139896,0.000197464],"domain_scores_gemma":[0.7443389,0.2252214,0.01214899,0.01034091,0.007364833,0.0005849114],"domain_codex":null,"domain_gemma":"methods","domain_candidate":"methods","domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004695309,0.0006397751,0.4507149,0.001608206,0.003580801,0.000578784,0.006704047,0.04066685,0.005570923,0.02405838,0.008141628,0.4572662],"study_design_scores_gemma":[0.0002053251,0.002429798,0.5385437,0.001243727,0.001378455,0.0009050974,0.00661851,0.3549066,0.01033019,0.06607521,0.01715819,0.0002051534],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4103326,0.0004040033,0.5778269,0.001000745,0.0002569174,0.0009960153,0.001152101,0.001259906,0.006770844],"genre_scores_gemma":[0.6869673,0.0001488877,0.3099477,0.000210065,0.00006582098,0.0009597139,0.0006877938,0.0001636853,0.0008489934],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9707519,"threshold_uncertainty_score":0.1546806,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.5208722029508508,"score_gpt":0.4857174030503449,"score_spread":0.03515479990050591,"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."}}