{"id":"W2093729006","doi":"10.1080/10705511.2013.742385","title":"Multiplicity Control in Structural Equation Modeling: Incorporating Parameter Dependencies","year":2013,"lang":"en","type":"article","venue":"Structural Equation Modeling A Multidisciplinary Journal","topic":"Psychometric Methodologies and Testing","field":"Decision Sciences","cited_by":58,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"","keywords":"Bonferroni correction; Mathematics; Statistics; Type I and type II errors; Multiplicity (mathematics); Multiple comparisons problem; Structural equation modeling; Econometrics; Mathematical analysis","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":[0.2045768,0.004099875,0.004374988,0.005999249,0.003468121,0.005554785,0.006264794,0.004423241,0.009098556],"category_scores_gemma":[0.6226934,0.002501869,0.005412521,0.007436273,0.006797091,0.01294778,0.008490038,0.01000661,0.0008954719],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00251628,"about_ca_system_score_gemma":0.006495285,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003312952,"about_ca_topic_score_gemma":0.003462494,"domain_scores_codex":[0.7399312,0.2151721,0.01002496,0.01852174,0.01476374,0.001586179],"domain_scores_gemma":[0.3259221,0.6009368,0.0205181,0.03960082,0.01148427,0.001537822],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.001912694,0.001166053,0.07520883,0.002644122,0.006434141,0.001286242,0.01433569,0.07183813,0.002977443,0.3828483,0.00714525,0.4322032],"study_design_scores_gemma":[0.0007041203,0.002469559,0.01675205,0.001453362,0.002165994,0.0007030786,0.001444425,0.3572637,0.004491608,0.5987761,0.01331743,0.0004584493],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01263431,0.0005936214,0.9829112,0.0007006621,0.0002994227,0.0008708889,0.00009003284,0.0003466409,0.001553357],"genre_scores_gemma":[0.2912226,0.0007021655,0.7011436,0.0005694662,0.0003620274,0.004391518,0.0002279807,0.0003722113,0.001008342],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.2045768,"threshold_uncertainty_score":0.9808984,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3600477337163394,"score_gpt":0.4263533006677822,"score_spread":0.0663055669514428,"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."}}