{"id":"W2758319537","doi":"10.1177/0013164417719111","title":"Recommendations on the Sample Sizes for Multilevel Latent Class Models","year":2017,"lang":"en","type":"article","venue":"Educational and Psychological Measurement","topic":"Bayesian Methods and Mixture Models","field":"Computer Science","cited_by":93,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Latent class model; Sample size determination; Statistics; Sample (material); Rule of thumb; Nested set model; Computer science; Model selection; Econometrics; Data mining; Mathematics; Algorithm","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":[],"consensus_categories":[],"category_scores_codex":[0.0009958328,0.0001037531,0.00009412987,0.00001959352,0.0008100831,0.0002198267,0.0005848045,0.00004648841,0.00005520288],"category_scores_gemma":[0.0004660402,0.00005898724,0.0000568991,0.00002178265,0.00006113881,0.0001328984,0.00005893615,0.0001007465,0.000006570171],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002751907,"about_ca_system_score_gemma":0.000030499,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000009636351,"about_ca_topic_score_gemma":0.00000373865,"domain_scores_codex":[0.9990324,0.00008753361,0.0001496927,0.0003368294,0.0002325653,0.0001609124],"domain_scores_gemma":[0.9986386,0.0005082146,0.00009706865,0.000518464,0.0001559582,0.0000817043],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.000009863841,0.0002523706,0.00003456768,0.000002297084,0.00001163451,4.116742e-8,0.0001206943,0.000006344914,0.0000720485,0.8193682,0.01352469,0.1665972],"study_design_scores_gemma":[0.0002696629,0.0001298434,0.02560972,0.00002363389,0.000005034115,0.000001867537,0.00000506956,0.008538443,0.00006374146,0.951153,0.01408371,0.0001162132],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0009032149,0.00006229416,0.8258319,0.1686484,0.000681794,0.00029396,0.00001538592,0.00001434169,0.003548719],"genre_scores_gemma":[0.6427089,0.00003468111,0.3535258,0.003058024,0.0001857965,0.000269798,0.000003273268,0.000003671835,0.0002101295],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.6418056,"threshold_uncertainty_score":0.6230586,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.4657980968857349,"score_gpt":0.4110614061339838,"score_spread":0.05473669075175114,"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."}}