{"id":"W2601443004","doi":"10.1027/1614-2241/a000122","title":"Using the Errors-in-Variables Method in Two-Group Pretest-Posttest Designs","year":2017,"lang":"en","type":"article","venue":"Methodology","topic":"Statistical Methods in Clinical Trials","field":"Mathematics","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"","keywords":"Analysis of covariance; Covariate; Statistics; Reliability (semiconductor); Covariance; Mathematics; Econometrics; Observational error; Sample size determination; Psychology; Power (physics)","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":["metaresearch"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.05701825,0.0003228902,0.001390287,0.0001675948,0.0003006976,0.00009650548,0.001336324,0.0003358175,0.0002629937],"category_scores_gemma":[0.5003332,0.0002321059,0.0001619683,0.0002333884,0.0005796097,0.0001247228,0.0005094077,0.0009187646,0.00001241675],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001126456,"about_ca_system_score_gemma":0.0001015463,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009251228,"about_ca_topic_score_gemma":0.0007141522,"domain_scores_codex":[0.9686999,0.02797033,0.001469911,0.0007637896,0.0002926082,0.000803408],"domain_scores_gemma":[0.7083924,0.2891886,0.0006824357,0.001565117,0.00007596838,0.00009553904],"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":[0.0004929897,0.0003769779,0.02743119,0.0001732353,0.0001228149,0.0002086867,0.0009659617,0.0003318671,0.03769069,0.8935692,0.0002013432,0.03843502],"study_design_scores_gemma":[0.001506163,0.00009365105,0.006239912,0.0001050654,0.0001125703,0.0000413259,0.0001300156,0.006769084,0.003294202,0.9813048,0.0001367707,0.0002664664],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01968444,0.00006100656,0.9745602,0.0009381879,0.001279552,0.0008113489,0.00001806858,0.00004991569,0.002597263],"genre_scores_gemma":[0.01153765,0.0000124885,0.9874957,0.0003922417,0.0003236956,0.00008618701,5.414395e-7,0.00006075435,0.00009074742],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.4433149,"threshold_uncertainty_score":0.9709982,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.918746052008132,"score_gpt":0.6893558059739121,"score_spread":0.2293902460342199,"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."}}