{"id":"W4240653878","doi":"10.31234/osf.io/wmuqj","title":"Computational options for standard errors and test statistics with incomplete normal and nonnormal data in SEM","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Bayesian Modeling and Causal Inference","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Inference; Statistics; Standard error; Matrix (chemical analysis); Statistical inference; Computer science; Statistical hypothesis testing; Test (biology); Mathematics; Structural equation modeling; Econometrics; Fisher information; Variety (cybernetics); Applied mathematics; Algorithm; Artificial intelligence","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":[],"consensus_categories":[],"category_scores_codex":[0.04621579,0.002152545,0.001977744,0.003453169,0.001292116,0.005672592,0.004976701,0.003721173,0.01249696],"category_scores_gemma":[0.2536478,0.001597417,0.002604301,0.004603286,0.005693581,0.01077639,0.005808413,0.006901112,0.002946779],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002417488,"about_ca_system_score_gemma":0.002732023,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002322024,"about_ca_topic_score_gemma":0.003752545,"domain_scores_codex":[0.9541491,0.03894136,0.001598677,0.001726368,0.003256629,0.0003279138],"domain_scores_gemma":[0.7731559,0.2116511,0.002617779,0.009214004,0.002855244,0.0005060957],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00008500822,0.00003765557,0.0005454685,0.0002740869,0.00007084687,0.0001223497,0.0003273514,0.0303296,0.0001818495,0.9068449,0.00219824,0.05898266],"study_design_scores_gemma":[0.00003015868,0.00001964922,0.0001120834,0.0001270342,0.00001691555,0.00007054171,0.00005611925,0.07556512,0.0002992549,0.9209383,0.002735123,0.00002962137],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001430652,0.0008611559,0.9937803,0.001295059,0.00006413733,0.00004186028,0.00009357601,0.0001881288,0.002245096],"genre_scores_gemma":[0.04932145,0.00143733,0.9458802,0.0004623538,0.0002272091,0.0005616661,0.0002197346,0.000419239,0.001470743],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.04621579,"threshold_uncertainty_score":0.2444153,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05336164965101582,"score_gpt":0.3043241847029391,"score_spread":0.2509625350519233,"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."}}