{"id":"W4255033255","doi":"10.1207/s15328007sem1401_5","title":"Multiplicity Control in Structural Equation Modeling","year":2007,"lang":"en","type":"article","venue":"Structural Equation Modeling A Multidisciplinary Journal","topic":"Psychometric Methodologies and Testing","field":"Decision Sciences","cited_by":24,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"","keywords":"Type I and type II errors; Multiplicity (mathematics); Statistical power; Sample size determination; False discovery rate; Statistics; Multiple comparisons problem; Mathematics; Structural equation modeling; Computer science; Algorithm","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":["metaresearch"],"category_scores_codex":[0.3270911,0.002889712,0.004609653,0.00731129,0.004997692,0.007171386,0.005534989,0.004655424,0.007432325],"category_scores_gemma":[0.7744489,0.00229935,0.004389418,0.009394391,0.01220713,0.01176592,0.00846406,0.01004856,0.0008359171],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003247428,"about_ca_system_score_gemma":0.00674853,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002524032,"about_ca_topic_score_gemma":0.002013162,"domain_scores_codex":[0.4798814,0.4409586,0.01972768,0.02281937,0.03446167,0.002151316],"domain_scores_gemma":[0.1327733,0.7881052,0.02341122,0.04078847,0.01377621,0.00114561],"domain_codex":null,"domain_gemma":"methods","domain_candidate":"methods","domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.001506952,0.0005788076,0.07983252,0.003418374,0.003671286,0.001153247,0.01427648,0.02564526,0.001563404,0.5441996,0.00943767,0.3147165],"study_design_scores_gemma":[0.0008070436,0.001884581,0.02434923,0.002265531,0.001886753,0.001320229,0.002078802,0.1079381,0.00511397,0.8292236,0.02264084,0.0004914558],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01884042,0.002107814,0.9684579,0.002324844,0.0008777062,0.001624362,0.0001967084,0.0004623012,0.005107973],"genre_scores_gemma":[0.4390225,0.001495696,0.5485076,0.001406918,0.0009583778,0.006152521,0.0002999224,0.0004117074,0.001744821],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.6729089,"threshold_uncertainty_score":0.8298165,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3975708787621388,"score_gpt":0.4703581866455891,"score_spread":0.07278730788345028,"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."}}