{"id":"W2320131044","doi":"10.1037/a0034524","title":"Longitudinal design considerations to optimize power to detect variances and covariances among rates of change: Simulation results based on actual longitudinal studies.","year":2013,"lang":"en","type":"article","venue":"Psychological Methods","topic":"Cognitive Abilities and Testing","field":"Psychology","cited_by":98,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"National Institute on Aging; National Institutes of Health; Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung","keywords":"Sample size determination; Statistics; Statistical power; Context (archaeology); Variance (accounting); Growth curve (statistics); Bivariate analysis; Monte Carlo method; Econometrics; Reliability (semiconductor); Mathematics; Covariance; Power (physics); Sample (material)","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":[],"category_scores_codex":[0.1012721,0.0008838708,0.001201009,0.0008228606,0.0005839799,0.001532883,0.001517979,0.001775673,0.00386861],"category_scores_gemma":[0.3701179,0.0007092279,0.001920466,0.0009659174,0.001101045,0.002757276,0.001353972,0.002655895,0.0003002666],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00131998,"about_ca_system_score_gemma":0.002636802,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003028347,"about_ca_topic_score_gemma":0.002964782,"domain_scores_codex":[0.9526438,0.04378757,0.0008614239,0.000962969,0.001234482,0.0005097882],"domain_scores_gemma":[0.497413,0.478622,0.006517573,0.008620102,0.007973903,0.0008533365],"domain_codex":null,"domain_gemma":"methods","domain_candidate":"methods","domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.002625559,0.0008099211,0.04688545,0.001512196,0.001461401,0.0005424589,0.001095698,0.7866517,0.003961918,0.08769231,0.005381866,0.0613795],"study_design_scores_gemma":[0.001404869,0.002890212,0.01111514,0.0006808221,0.0009973927,0.0004127829,0.000356433,0.8524935,0.00573448,0.1152674,0.008512971,0.0001340275],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.166609,0.001634656,0.8160899,0.002836815,0.0002179465,0.002550473,0.001073116,0.0003109759,0.008677083],"genre_scores_gemma":[0.662828,0.0005940126,0.3293926,0.0009650313,0.0000517297,0.004487395,0.0005379372,0.00008112506,0.001062058],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.898728,"threshold_uncertainty_score":0.5355842,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.5016745736120142,"score_gpt":0.5277738604236346,"score_spread":0.02609928681162044,"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."}}