{"id":"W3163249028","doi":"10.31234/osf.io/wprg8","title":"We need to change how we compute RMSEA for nested model comparisons in structural equation modeling","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Psychometric Methodologies and Testing","field":"Decision Sciences","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Structural equation modeling; Nested set model; Context (archaeology); Mathematics; Square root; Index (typography); Computer science; Applied mathematics; Econometrics; Statistics; Data mining","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":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch","metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.005002421,0.0004284428,0.001206108,0.001758245,0.0001710634,0.001194914,0.001546655,0.0004047036,0.00003747486],"category_scores_gemma":[0.02282522,0.0003368653,0.0003091083,0.002623277,0.00002658226,0.0003316413,0.001782607,0.0005891625,0.000004199158],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001489027,"about_ca_system_score_gemma":0.0001929391,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000395169,"about_ca_topic_score_gemma":0.0003884629,"domain_scores_codex":[0.9948936,0.0005232594,0.001188784,0.00147224,0.001306988,0.0006151975],"domain_scores_gemma":[0.985638,0.01166205,0.0005344014,0.0011171,0.0008223669,0.0002261351],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00003146956,0.00001841601,0.001480386,0.00004203775,0.0000157642,0.000001781738,0.002066685,0.9222134,0.00004265987,0.0004802325,0.001589713,0.0720174],"study_design_scores_gemma":[0.0004186616,0.00003327184,0.0007674032,0.0001770635,0.00001444547,0.000002087895,0.004982459,0.9509722,0.00001519453,0.04208735,0.0001395757,0.0003902747],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1407548,0.0006667889,0.8330863,0.02211658,0.001674361,0.001325011,0.00004461454,0.0001167051,0.0002148454],"genre_scores_gemma":[0.5324447,0.00004404735,0.4666674,0.0002822987,0.000170052,0.000135331,0.00004258721,0.00001829569,0.0001953233],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.3916899,"threshold_uncertainty_score":0.9999083,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.8713736100893348,"score_gpt":0.5306916632316865,"score_spread":0.3406819468576483,"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."}}