{"id":"W2054681858","doi":"10.3389/fpsyg.2015.00474","title":"A cautionary note on the use of the Analysis of Covariance (ANCOVA) in classification designs with and without within-subject factors","year":2015,"lang":"en","type":"article","venue":"Frontiers in Psychology","topic":"Advanced Statistical Methods and Models","field":"Mathematics","cited_by":177,"is_retracted":false,"has_abstract":true,"ca_institutions":"Amgen (Canada)","funders":"","keywords":"Covariate; Analysis of covariance; Statistics; Covariance; Psychology; Statistical power; Econometrics; Subject (documents); Population; Interpretation (philosophy); Mathematics; Computer science; Demography","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":[0.0005873254,0.0001013208,0.0003589437,0.0001727768,0.00002340509,0.000003459838,0.0001317333,0.00008375661,0.000001834811],"category_scores_gemma":[0.0008167429,0.00005439487,0.00003206384,0.0006638283,0.000346309,0.00004962968,0.00001672591,0.0001824667,6.546431e-8],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003401101,"about_ca_system_score_gemma":0.00003747273,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003362076,"about_ca_topic_score_gemma":0.0001213454,"domain_scores_codex":[0.9987248,0.0004759436,0.0003159612,0.0002180274,0.0001472609,0.0001180713],"domain_scores_gemma":[0.9986596,0.0006418167,0.0002592772,0.0003490917,0.00006088813,0.00002933573],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.002246858,0.0006121336,0.7258652,0.00005527351,0.0006022766,0.000004258881,0.01065676,0.008505579,0.001169816,0.2368788,0.002690877,0.01071218],"study_design_scores_gemma":[0.001229342,0.0002651561,0.2722861,0.00008994089,0.0003362939,0.000003148522,0.001237215,0.07143159,0.0002413317,0.6525617,0.0001430396,0.0001751826],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1912256,0.00002426407,0.8080821,0.0002017722,0.0001180029,0.0002244372,0.00002457662,0.000003998897,0.00009525388],"genre_scores_gemma":[0.5471013,0.000009533132,0.4527578,0.00009247207,0.000002616217,0.00001391557,0.000001303153,0.000006681937,0.00001439191],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.4535792,"threshold_uncertainty_score":0.2218158,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.331853442799546,"score_gpt":0.4418659844653024,"score_spread":0.1100125416657564,"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."}}