{"id":"W4206938451","doi":"10.5267/j.ijdns.2021.12.015","title":"A comparative study on the performance of maximum likelihood, generalized least square, scale-free least square, partial least square and consistent partial least square estimators in structural equation modeling","year":2022,"lang":"en","type":"article","venue":"International Journal of Data and Network Science","topic":"Technology and Data Analysis","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Universiti Teknologi MARA","keywords":"Partial least squares regression; Structural equation modeling; Statistics; Estimator; Mathematics; Covariance; Least-squares function approximation; Multivariate statistics; Applied mathematics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.06894672,0.001718881,0.002479813,0.006259806,0.0008910338,0.003075705,0.002405448,0.002225648,0.003231497],"category_scores_gemma":[0.1899935,0.0007833051,0.002201573,0.008839761,0.001849824,0.006364933,0.002375967,0.002442318,0.001308705],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001911459,"about_ca_system_score_gemma":0.002498501,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004306072,"about_ca_topic_score_gemma":0.004876107,"domain_scores_codex":[0.9529052,0.03448284,0.002143191,0.002701389,0.007219356,0.0005481527],"domain_scores_gemma":[0.8365456,0.1381364,0.004805457,0.006529142,0.01306361,0.0009197163],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001345649,0.0003321639,0.05076892,0.003889857,0.001651297,0.000252218,0.00338438,0.06530233,0.001455465,0.0674172,0.008957189,0.7952434],"study_design_scores_gemma":[0.0004494439,0.002862262,0.09039139,0.003593126,0.001835132,0.001562802,0.005613779,0.7297093,0.009616805,0.09577334,0.0578619,0.0007307603],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.2067169,0.06786049,0.6915359,0.006469205,0.0007418625,0.0004493521,0.001539459,0.002569549,0.02211722],"genre_scores_gemma":[0.5014019,0.02039386,0.4714866,0.0005002758,0.0003457934,0.0004935565,0.00180265,0.0009098868,0.002665573],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.06894672,"threshold_uncertainty_score":0.3646294,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05249066502804415,"score_gpt":0.3095113366993233,"score_spread":0.2570206716712792,"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."}}