{"id":"W3094300335","doi":"10.5267/j.ijdns.2020.9.003","title":"An extensive comparison of CB-SEM and PLS-SEM for reliability and validity","year":2020,"lang":"en","type":"article","venue":"International Journal of Data and Network Science","topic":"Forecasting Techniques and Applications","field":"Decision Sciences","cited_by":132,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Structural equation modeling; Reliability (semiconductor); Goodness of fit; Construct validity; Confirmatory factor analysis; Construct (python library); Reliability engineering; Set (abstract data type); Mathematics; Statistics; Validity; Computer science; Econometrics; Engineering; Psychometrics","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":[0.003462746,0.00006040159,0.0001975104,0.00006313552,0.0001400727,0.0002266428,0.001440146,0.00002383631,0.000005970073],"category_scores_gemma":[0.002172199,0.00004292063,0.00002082666,0.0002650135,0.0005561797,0.0009300789,0.0004901165,0.00009337655,2.350316e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000008369246,"about_ca_system_score_gemma":0.00007529123,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007599221,"about_ca_topic_score_gemma":0.000005506285,"domain_scores_codex":[0.9982854,0.00004008592,0.000523438,0.0003222477,0.0007290245,0.00009984717],"domain_scores_gemma":[0.9973245,0.0006751757,0.0005080854,0.0002668388,0.001062097,0.0001633242],"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":[0.0006876496,0.0002899508,0.3791372,0.00002254234,0.00003433643,0.000006922721,0.00247021,0.0019342,0.010038,0.01804759,0.0640982,0.5232331],"study_design_scores_gemma":[0.0006880215,0.001162091,0.09640641,0.00009983057,0.00003686902,0.000115368,0.00092319,0.7606055,0.001845879,0.1012223,0.03668633,0.0002082375],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8677744,0.0001823515,0.1282039,0.003381416,0.0001534318,0.0001045944,0.0001539437,0.000006006094,0.0000399161],"genre_scores_gemma":[0.9441137,0.00008878137,0.0553867,0.0002005973,0.0002002406,8.102994e-7,0.000004649694,0.000002064273,0.000002500487],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7586713,"threshold_uncertainty_score":0.2676173,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3844640502346408,"score_gpt":0.511183723802955,"score_spread":0.1267196735683142,"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."}}