{"id":"W4380537177","doi":"10.5267/j.ijdns.2023.6.004","title":"The performance of unweighted least squares and regularized unweighted least squares in estimating factor loadings in structural equation modeling","year":2023,"lang":"en","type":"article","venue":"International Journal of Data and Network Science","topic":"Advanced Statistical Modeling Techniques","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Structural equation modeling; Statistics; Covariance; Monte Carlo method; Multivariate statistics; Variance (accounting); Mathematics; Covariance matrix; Multivariate normal distribution; Factor analysis; Population; Econometrics; Observational error","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001477665,0.0001007276,0.0001707134,0.0002977411,0.0001695749,0.0002353636,0.001807303,0.00002966913,6.928315e-7],"category_scores_gemma":[0.0005180719,0.000072408,0.00001378787,0.0007459152,0.0002627465,0.002286486,0.0007611213,0.0002143969,2.216512e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005900509,"about_ca_system_score_gemma":0.0001127109,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002703625,"about_ca_topic_score_gemma":0.00002137175,"domain_scores_codex":[0.9981946,0.00005193948,0.000538973,0.000275963,0.0006952683,0.0002432555],"domain_scores_gemma":[0.9986231,0.0004246182,0.0003224009,0.0002392317,0.0003299045,0.0000606868],"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":[0.0003012718,0.00003375332,0.03094188,0.00004120548,0.00003175927,0.00004636323,0.001991564,0.572639,0.007815644,0.0422736,0.00006248298,0.3438215],"study_design_scores_gemma":[0.0002753451,0.00005758874,0.006713906,0.0002937544,0.000001836732,0.00002817892,0.00003998532,0.96764,0.0001919547,0.02467602,0.000005050995,0.00007630591],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5221157,0.00008723554,0.477203,0.0003536091,0.0001706739,0.00004599509,0.000009612179,0.00001170528,0.000002445714],"genre_scores_gemma":[0.7994432,0.000167751,0.2003069,0.00001582102,0.00005516725,0.00000104322,0.000005258882,0.000003373975,0.000001525252],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3950011,"threshold_uncertainty_score":0.3358448,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04644598109613306,"score_gpt":0.3389767834544907,"score_spread":0.2925308023583577,"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."}}