{"id":"W4233550164","doi":"10.31234/osf.io/vr8ut","title":"How Many Tiers Do We Need? Type I Errors and Power in Multiple Baseline Designs","year":2020,"lang":"en","type":"preprint","venue":"","topic":"Behavioral and Psychological Studies","field":"Psychology","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal; Institut universitaire en santé mentale de Montréal; Institut Universitaire en Santé Mentale de Québec","funders":"","keywords":"Baseline (sea); Type I and type II errors; Multiple baseline design; Power (physics); Computer science; Replicate; Statistical power; Statistics; Empirical research; Reliability engineering; Mathematics; Psychology; Engineering","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":["metaresearch"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.5431716,0.002603144,0.007497136,0.002945659,0.003782686,0.009565916,0.007869079,0.009200381,0.009638839],"category_scores_gemma":[0.8180302,0.003142345,0.007543727,0.004579588,0.01165271,0.01379138,0.005639812,0.01035862,0.00271201],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00445071,"about_ca_system_score_gemma":0.006354128,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002136277,"about_ca_topic_score_gemma":0.003110414,"domain_scores_codex":[0.3231375,0.5145866,0.05542449,0.04235169,0.06099865,0.003501019],"domain_scores_gemma":[0.1252747,0.685638,0.04332862,0.1091993,0.03332327,0.003236175],"domain_codex":"methods","domain_gemma":"methods","domain_candidate":"methods","domain_consensus":"methods","study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.01747344,0.001774666,0.04941224,0.01508479,0.009962474,0.0009718005,0.0182702,0.007841087,0.01276846,0.13695,0.07032271,0.6591681],"study_design_scores_gemma":[0.008398279,0.01057762,0.07756875,0.01477916,0.005900098,0.001280823,0.005033064,0.04206408,0.02209407,0.6607891,0.1500021,0.001512843],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03996809,0.006768799,0.8835116,0.0292784,0.008867732,0.01687925,0.001542181,0.0026231,0.01056086],"genre_scores_gemma":[0.1859125,0.001060149,0.7635472,0.0110062,0.001020753,0.03449504,0.0004478483,0.0008810195,0.001629206],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.4568284,"threshold_uncertainty_score":0.5633507,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.318544135642937,"score_gpt":0.3610885468397447,"score_spread":0.04254441119680769,"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."}}