{"id":"W201352042","doi":"10.22237/jmasm/1367382480","title":"JMASM 32: Multiple Imputation of Missing Multilevel, Longitudinal Data: A Case When Practical Considerations Trump Best Practices?","year":2013,"lang":"en","type":"article","venue":"Journal of Modern Applied Statistical Methods","topic":"Intergenerational and Educational Inequality Studies","field":"Social Sciences","cited_by":26,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"European Social Fund; Canadian Institutes of Health Research; National and Kapodistrian University of Athens; Michael Smith Health Research BC; Jacobs Foundation; Canadian Institute for Advanced Research","keywords":"Missing data; Imputation (statistics); Longitudinal data; Multilevel model; Statistics; Computer science; Syntax; Econometrics; Data mining; Mathematics; Artificial intelligence","routes":{"ca_aff":true,"ca_fund":true,"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":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.004142799,0.0001675557,0.0004552835,0.0001217316,0.000644761,0.0002166333,0.0001573743,0.0001203757,0.000683309],"category_scores_gemma":[0.03117813,0.0001445335,0.00006569701,0.0001028063,0.0004127327,0.0009164537,0.00009984437,0.000405689,0.00001851801],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001266346,"about_ca_system_score_gemma":0.0009592443,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001885178,"about_ca_topic_score_gemma":0.0005238555,"domain_scores_codex":[0.9964021,0.001188033,0.001129498,0.0002870457,0.0007008378,0.0002924465],"domain_scores_gemma":[0.9817039,0.01481654,0.001558871,0.000217123,0.00143009,0.0002734076],"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":[0.0003359802,0.002140654,0.0007735507,0.0001622087,0.0007993419,0.0004855352,0.03019051,0.0006286189,0.0100216,0.6561731,0.03100363,0.2672853],"study_design_scores_gemma":[0.001047223,0.000214359,0.002135705,0.00006813294,0.0004953215,0.001684802,0.01196729,0.1082333,0.0006543588,0.8679595,0.005122033,0.0004179556],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.007854712,0.0001312602,0.9781193,0.01119787,0.0004467523,0.0003224924,0.0001330543,0.00001019994,0.001784331],"genre_scores_gemma":[0.4438989,0.00001387407,0.5554655,0.0001493195,0.0003825659,0.00001403298,0.00001551898,0.000008610956,0.00005168856],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.4360442,"threshold_uncertainty_score":0.9769827,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.4745486544071172,"score_gpt":0.5553026850984688,"score_spread":0.08075403069135162,"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."}}