Estimating power in (generalized) linear mixed models: An open introduction and tutorial in R

Mixed-effects models are a powerful tool for modeling fixed and random effects simultaneously, but do not offer a feasible analytic solution for estimating the probability that a test correctly rejects the null hypothesis. Being able to estimate this probability, however, is critical for sample size...

Ausführliche Beschreibung

Gespeichert in:

Bibliographische Detailangaben
Veröffentlicht in:	Behavior research methods Jg. 53; H. 6; S. 2528 - 2543
Hauptverfasser:	Kumle, Levi, Võ, Melissa L.-H., Draschkow, Dejan
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York Springer US 01.12.2021 Springer Nature B.V
Schlagworte:	Behavioral Science and Psychology Cognitive Psychology Computer Simulation Data Generalized linear models Humans Linear analysis Linear Models Null hypothesis Planning Power Probability Psychology Random effects Reliability Reproducibility of Results Research design Sample Size Simulation World problems lme4 R Simulation mixedpower Power Mixed models
ISSN:	1554-3528, 1554-351X, 1554-3528
Online-Zugang:	Volltext
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!

Abstract	Mixed-effects models are a powerful tool for modeling fixed and random effects simultaneously, but do not offer a feasible analytic solution for estimating the probability that a test correctly rejects the null hypothesis. Being able to estimate this probability, however, is critical for sample size planning, as power is closely linked to the reliability and replicability of empirical findings. A flexible and very intuitive alternative to analytic power solutions are simulation-based power analyses. Although various tools for conducting simulation-based power analyses for mixed-effects models are available, there is lack of guidance on how to appropriately use them. In this tutorial, we discuss how to estimate power for mixed-effects models in different use cases: first, how to use models that were fit on available (e.g. published) data to determine sample size; second, how to determine the number of stimuli required for sufficient power; and finally, how to conduct sample size planning without available data. Our examples cover both linear and generalized linear models and we provide code and resources for performing simulation-based power analyses on openly accessible data sets. The present work therefore helps researchers to navigate sound research design when using mixed-effects models, by summarizing resources, collating available knowledge, providing solutions and tools, and applying them to real-world problems in sample sizing planning when sophisticated analysis procedures like mixed-effects models are outlined as inferential procedures.
AbstractList	Mixed-effects models are a powerful tool for modeling fixed and random effects simultaneously, but do not offer a feasible analytic solution for estimating the probability that a test correctly rejects the null hypothesis. Being able to estimate this probability, however, is critical for sample size planning, as power is closely linked to the reliability and replicability of empirical findings. A flexible and very intuitive alternative to analytic power solutions are simulation-based power analyses. Although various tools for conducting simulation-based power analyses for mixed-effects models are available, there is lack of guidance on how to appropriately use them. In this tutorial, we discuss how to estimate power for mixed-effects models in different use cases: first, how to use models that were fit on available (e.g. published) data to determine sample size; second, how to determine the number of stimuli required for sufficient power; and finally, how to conduct sample size planning without available data. Our examples cover both linear and generalized linear models and we provide code and resources for performing simulation-based power analyses on openly accessible data sets. The present work therefore helps researchers to navigate sound research design when using mixed-effects models, by summarizing resources, collating available knowledge, providing solutions and tools, and applying them to real-world problems in sample sizing planning when sophisticated analysis procedures like mixed-effects models are outlined as inferential procedures.Mixed-effects models are a powerful tool for modeling fixed and random effects simultaneously, but do not offer a feasible analytic solution for estimating the probability that a test correctly rejects the null hypothesis. Being able to estimate this probability, however, is critical for sample size planning, as power is closely linked to the reliability and replicability of empirical findings. A flexible and very intuitive alternative to analytic power solutions are simulation-based power analyses. Although various tools for conducting simulation-based power analyses for mixed-effects models are available, there is lack of guidance on how to appropriately use them. In this tutorial, we discuss how to estimate power for mixed-effects models in different use cases: first, how to use models that were fit on available (e.g. published) data to determine sample size; second, how to determine the number of stimuli required for sufficient power; and finally, how to conduct sample size planning without available data. Our examples cover both linear and generalized linear models and we provide code and resources for performing simulation-based power analyses on openly accessible data sets. The present work therefore helps researchers to navigate sound research design when using mixed-effects models, by summarizing resources, collating available knowledge, providing solutions and tools, and applying them to real-world problems in sample sizing planning when sophisticated analysis procedures like mixed-effects models are outlined as inferential procedures. Mixed-effects models are a powerful tool for modeling fixed and random effects simultaneously, but do not offer a feasible analytic solution for estimating the probability that a test correctly rejects the null hypothesis. Being able to estimate this probability, however, is critical for sample size planning, as power is closely linked to the reliability and replicability of empirical findings. A flexible and very intuitive alternative to analytic power solutions are simulation-based power analyses. Although various tools for conducting simulation-based power analyses for mixed-effects models are available, there is lack of guidance on how to appropriately use them. In this tutorial, we discuss how to estimate power for mixed-effects models in different use cases: first, how to use models that were fit on available (e.g. published) data to determine sample size; second, how to determine the number of stimuli required for sufficient power; and finally, how to conduct sample size planning without available data. Our examples cover both linear and generalized linear models and we provide code and resources for performing simulation-based power analyses on openly accessible data sets. The present work therefore helps researchers to navigate sound research design when using mixed-effects models, by summarizing resources, collating available knowledge, providing solutions and tools, and applying them to real-world problems in sample sizing planning when sophisticated analysis procedures like mixed-effects models are outlined as inferential procedures. Mixed-effects models are a powerful tool for modeling fixed and random effects simultaneously, but do not offer a feasible analytic solution for estimating the probability that a test correctly rejects the null hypothesis. Being able to estimate this probability, however, is critical for sample size planning, as power is closely linked to the reliability and replicability of empirical findings. A flexible and very intuitive alternative to analytic power solutions are simulation-based power analyses. Although various tools for conducting simulation-based power analyses for mixed-effects models are available, there is lack of guidance on how to appropriately use them. In this tutorial, we discuss how to estimate power for mixed-effects models in different use cases: first, how to use models that were fit on available (e.g. published) data to determine sample size; second, how to determine the number of stimuli required for sufficient power; and finally, how to conduct sample size planning without available data. Our examples cover both linear and generalized linear models and we provide code and resources for performing simulation-based power analyses on openly accessible data sets. The present work therefore helps researchers to navigate sound research design when using mixed-effects models, by summarizing resources, collating available knowledge, providing solutions and tools, and applying them to real-world problems in sample sizing planning when sophisticated analysis procedures like mixed-effects models are outlined as inferential procedures.
Author	Kumle, Levi Võ, Melissa L.-H. Draschkow, Dejan
Author_xml	– sequence: 1 givenname: Levi orcidid: 0000-0002-9994-0988 surname: Kumle fullname: Kumle, Levi email: levi.kumle@gmail.com organization: Department of Psychology, Scene Grammar Lab, Goethe University Frankfurt – sequence: 2 givenname: Melissa L.-H. orcidid: 0000-0003-1145-4473 surname: Võ fullname: Võ, Melissa L.-H. organization: Department of Psychology, Scene Grammar Lab, Goethe University Frankfurt – sequence: 3 givenname: Dejan orcidid: 0000-0003-1354-4835 surname: Draschkow fullname: Draschkow, Dejan organization: Oxford Centre for Human Brain Activity, Wellcome Centre for Integrative Neuroimaging, Department of Psychiatry, University of Oxford
BackLink	https://www.ncbi.nlm.nih.gov/pubmed/33954914$$D View this record in MEDLINE/PubMed
BookMark	eNp9kctrFTEYxYNU7EP_ARcScNMuRvO-iQuhlPqAgiC6lJCZfHNNySTXZMbXX99cb621i64SyO-cnO87h2gv5QQIPaXkBV9J_bJSLpjuCKMdoVKojjxAB1RK0XHJ9N6t-z46rPWSEK4ZFY_QPudGCkPFAfpyXucwuTmkNd7kH1BwSPh4DQmKi-E3-BMcQwJX8BR-gsdT9hDrK3yacN5AavRcsl-GOeSEXfJ4XuZcgotbn4-P0cPRxQpPrs8j9PnN-aezd93Fh7fvz04vukGR1dwJMyoCjrteE6AMjJADG3vCZK8HogmRo2dc9cxJYyisvBKuIdybXmgtOT9Cr3e-m6WfwA_QUrloN6WNVn7Z7IL9_yWFr3adv1utKKdCNYPja4OSvy1QZzuFOkCMLkFeqmWSMUWNlLqhz--gl3kpqY1nmSKMcE7NNtGz24luovzdfAPYDhhKrrXAeINQYrf12l29ttVr_9RrSRPpO6IhzG67-zZViPdL-U5a2z9pDeVf7HtUV5HCuSU
CitedBy_id	crossref_primary_10_1038_s41598_024_56139_6 crossref_primary_10_1136_bmjopen_2024_090518 crossref_primary_10_3389_fpsyg_2022_980277 crossref_primary_10_3758_s13423_024_02569_2 crossref_primary_10_1016_j_bbr_2025_115829 crossref_primary_10_1017_ehs_2024_22 crossref_primary_10_1016_j_psychsport_2023_102436 crossref_primary_10_1016_j_psychsport_2022_102159 crossref_primary_10_1016_j_actpsy_2024_104460 crossref_primary_10_1177_09567976241249183 crossref_primary_10_1186_s12984_023_01238_0 crossref_primary_10_1007_s00192_025_06238_3 crossref_primary_10_7554_eLife_89485 crossref_primary_10_3389_fspor_2023_1192737 crossref_primary_10_1016_j_cognition_2022_105104 crossref_primary_10_1038_s41598_023_33307_8 crossref_primary_10_1007_s10936_024_10117_1 crossref_primary_10_1167_iovs_66_4_71 crossref_primary_10_1515_lingty_2025_0034 crossref_primary_10_1017_S1366728923000706 crossref_primary_10_3758_s13421_025_01775_0 crossref_primary_10_1073_pnas_2311116121 crossref_primary_10_3758_s13423_023_02301_6 crossref_primary_10_3389_fpsyg_2025_1652627 crossref_primary_10_5114_pq_174936 crossref_primary_10_1111_bjep_12684 crossref_primary_10_1016_j_cortex_2021_12_017 crossref_primary_10_1016_j_dcn_2025_101614 crossref_primary_10_1016_j_neuroimage_2024_120853 crossref_primary_10_1044_2023_JSLHR_23_00078 crossref_primary_10_1002_alz_70055 crossref_primary_10_1016_j_cognition_2025_106172 crossref_primary_10_1038_s44271_024_00134_0 crossref_primary_10_1016_j_cognition_2023_105533 crossref_primary_10_1080_0361073X_2022_2046948 crossref_primary_10_1515_iral_2023_0155 crossref_primary_10_15626_MP_2021_3078 crossref_primary_10_2139_ssrn_4649792 crossref_primary_10_1016_j_system_2024_103440 crossref_primary_10_1038_s43588_024_00685_6 crossref_primary_10_1371_journal_pone_0315351 crossref_primary_10_1016_j_jml_2023_104478 crossref_primary_10_1017_S0305000924000175 crossref_primary_10_1017_jdm_2024_42 crossref_primary_10_1016_j_jpainsymman_2025_03_006 crossref_primary_10_1080_0284186X_2022_2098680 crossref_primary_10_1016_j_learninstruc_2025_102186 crossref_primary_10_3389_fpsyg_2025_1558689 crossref_primary_10_1007_s10508_024_02875_9 crossref_primary_10_1177_17470218231161310 crossref_primary_10_1186_s12874_025_02546_w crossref_primary_10_1080_00220973_2023_2287444 crossref_primary_10_1007_s11145_024_10536_5 crossref_primary_10_1016_j_jenvp_2024_102377 crossref_primary_10_1057_s41599_024_02842_8 crossref_primary_10_3758_s13421_025_01689_x crossref_primary_10_1038_s41598_025_05745_z crossref_primary_10_3758_s13414_024_02924_4 crossref_primary_10_1038_s41598_025_01293_8 crossref_primary_10_1093_icesjms_fsad097 crossref_primary_10_1016_j_foodres_2024_114702 crossref_primary_10_3758_s13414_024_02891_w crossref_primary_10_1038_s41593_025_01992_5 crossref_primary_10_1080_02699931_2024_2374038 crossref_primary_10_1016_j_jml_2022_104373 crossref_primary_10_1016_j_cct_2025_107955 crossref_primary_10_3389_fnins_2022_803387 crossref_primary_10_3758_s13421_024_01616_6 crossref_primary_10_1098_rsos_220178 crossref_primary_10_3390_jintelligence13010004 crossref_primary_10_1177_0044118X241309356 crossref_primary_10_1007_s11409_021_09273_9 crossref_primary_10_1002_ejp_70067 crossref_primary_10_1038_s41598_023_42006_3 crossref_primary_10_1016_j_jpsychores_2023_111518 crossref_primary_10_1016_j_yhbeh_2023_105445 crossref_primary_10_1016_j_learninstruc_2025_102168 crossref_primary_10_1017_langcog_2023_51 crossref_primary_10_1002_cpp_2929 crossref_primary_10_1016_j_actpsy_2025_105518 crossref_primary_10_1016_j_concog_2024_103769 crossref_primary_10_22599_jesla_95 crossref_primary_10_1016_j_isci_2023_108547 crossref_primary_10_1007_s10608_025_10614_0 crossref_primary_10_1016_j_cbpra_2022_04_005 crossref_primary_10_1111_nyas_15284 crossref_primary_10_1016_j_chbr_2025_100767 crossref_primary_10_1016_j_flora_2022_152062 crossref_primary_10_1177_20438087231175147 crossref_primary_10_3758_s13414_025_03088_5 crossref_primary_10_1007_s40647_025_00447_2 crossref_primary_10_3390_vision8030050 crossref_primary_10_2196_51716 crossref_primary_10_1007_s00426_024_01985_2 crossref_primary_10_1098_rspb_2025_1107 crossref_primary_10_32872_cpe_7495 crossref_primary_10_1016_j_bbi_2023_05_011 crossref_primary_10_1038_s41598_021_96513_2 crossref_primary_10_1057_s41599_025_04806_y crossref_primary_10_1016_j_jenvp_2024_102346 crossref_primary_10_1038_s44271_024_00152_y crossref_primary_10_1371_journal_pone_0306113 crossref_primary_10_1016_j_psychsport_2024_102698 crossref_primary_10_1080_23273798_2024_2390003 crossref_primary_10_1519_JSC_0000000000004223 crossref_primary_10_3758_s13428_021_01686_3 crossref_primary_10_1007_s00125_025_06496_2 crossref_primary_10_1017_S0142716424000444 crossref_primary_10_1007_s10457_023_00871_x crossref_primary_10_1016_j_neuropsychologia_2021_108107 crossref_primary_10_3389_fnins_2024_1407775 crossref_primary_10_1016_j_psyneuen_2024_107020 crossref_primary_10_1038_s41598_022_21917_7 crossref_primary_10_1111_cogs_13099 crossref_primary_10_3758_s13423_023_02382_3 crossref_primary_10_1016_j_jenvp_2024_102337 crossref_primary_10_1371_journal_pone_0308768 crossref_primary_10_1016_j_drugalcdep_2025_112704 crossref_primary_10_1080_15213269_2022_2161576 crossref_primary_10_1007_s10212_025_00951_9 crossref_primary_10_1016_j_ijchp_2024_100476 crossref_primary_10_1016_j_jpsychires_2025_03_046 crossref_primary_10_1093_jnen_nlad064 crossref_primary_10_1177_00238309241247210 crossref_primary_10_1007_s00426_023_01833_9 crossref_primary_10_1016_j_psyneuen_2022_105734 crossref_primary_10_3389_fdpys_2024_1429845 crossref_primary_10_5674_jjppp_2505tn crossref_primary_10_1038_s41598_024_82004_7 crossref_primary_10_1093_cercor_bhab360 crossref_primary_10_3758_s13421_022_01383_2 crossref_primary_10_1177_17470218221085943 crossref_primary_10_1016_j_anbehav_2023_09_001 crossref_primary_10_1016_j_jml_2025_104618 crossref_primary_10_1016_j_neuroimage_2023_119888 crossref_primary_10_3390_brainsci12020179 crossref_primary_10_1016_j_bbi_2024_09_010 crossref_primary_10_3758_s13421_025_01760_7 crossref_primary_10_1016_j_rmal_2025_100226 crossref_primary_10_1016_j_concog_2025_103851 crossref_primary_10_1016_j_jenvp_2024_102441 crossref_primary_10_1016_j_cogdev_2024_101462 crossref_primary_10_1111_jre_13269 crossref_primary_10_1016_j_brat_2023_104423 crossref_primary_10_3758_s13414_025_03072_z crossref_primary_10_1016_j_bodyim_2024_101747 crossref_primary_10_1136_bmjopen_2021_054775 crossref_primary_10_1186_s13034_024_00776_2 crossref_primary_10_2174_0115672050370810250430112549 crossref_primary_10_1007_s00426_024_01979_0 crossref_primary_10_1016_j_caeo_2025_100243 crossref_primary_10_1177_25152459241287132 crossref_primary_10_1177_17470218231159787 crossref_primary_10_1177_09567976231225094 crossref_primary_10_2106_JBJS_24_00602 crossref_primary_10_3758_s13428_022_01902_8 crossref_primary_10_1161_JAHA_125_042144 crossref_primary_10_1016_j_jsp_2024_101399 crossref_primary_10_1177_00238309241258162 crossref_primary_10_3758_s13428_023_02137_x crossref_primary_10_1007_s00426_025_02161_w crossref_primary_10_1016_j_asmr_2024_101026 crossref_primary_10_1080_1743727X_2023_2215699 crossref_primary_10_1093_scipol_scaf012 crossref_primary_10_1016_j_aap_2024_107790 crossref_primary_10_1075_lab_24028_ran crossref_primary_10_3758_s13428_025_02737_9 crossref_primary_10_1515_phon_2023_0002 crossref_primary_10_1080_15475441_2024_2313226 crossref_primary_10_3390_languages10020020 crossref_primary_10_1111_weng_12634 crossref_primary_10_1080_02699931_2024_2353775 crossref_primary_10_1115_1_4065269 crossref_primary_10_3168_jds_2024_25793 crossref_primary_10_1007_s00221_025_07060_5 crossref_primary_10_3389_fnint_2025_1454924 crossref_primary_10_1080_0163853X_2023_2185408 crossref_primary_10_3758_s13421_022_01367_2 crossref_primary_10_1016_j_cogdev_2024_101489 crossref_primary_10_3758_s13423_024_02581_6 crossref_primary_10_1016_j_cognition_2024_105731 crossref_primary_10_1162_jocn_a_02078 crossref_primary_10_3389_fpsyg_2023_1139128 crossref_primary_10_1016_j_concog_2025_103872 crossref_primary_10_1002_arcp_1101 crossref_primary_10_1098_rsos_211969 crossref_primary_10_1186_s12984_023_01257_x crossref_primary_10_3758_s13421_025_01684_2 crossref_primary_10_1016_j_chiabu_2024_106874 crossref_primary_10_3758_s13423_022_02061_9 crossref_primary_10_1186_s12913_024_11733_2 crossref_primary_10_1080_09658211_2024_2336161 crossref_primary_10_1177_17479541241242951 crossref_primary_10_7554_eLife_89485_5 crossref_primary_10_1016_j_ijhcs_2023_103119 crossref_primary_10_1177_17470218231217732 crossref_primary_10_3389_fpsyt_2024_1213863 crossref_primary_10_1007_s10936_024_10078_5 crossref_primary_10_1016_j_neuron_2023_04_020 crossref_primary_10_1002_jeab_814 crossref_primary_10_1177_00131644241281049 crossref_primary_10_1080_10503307_2025_2494272 crossref_primary_10_1080_02640414_2024_2408195 crossref_primary_10_1016_j_cognition_2024_105966 crossref_primary_10_1016_j_brat_2022_104233 crossref_primary_10_3758_s13414_024_02992_6 crossref_primary_10_3390_healthcare11030335 crossref_primary_10_1016_j_cogdev_2025_101542 crossref_primary_10_3758_s13428_024_02507_z crossref_primary_10_1016_j_cogdev_2025_101543 crossref_primary_10_1038_s41398_025_03411_6 crossref_primary_10_1016_j_dcn_2025_101599 crossref_primary_10_1121_10_0032400 crossref_primary_10_1038_s41598_025_04122_0 crossref_primary_10_1016_j_neuroimage_2024_120830 crossref_primary_10_1155_hbe2_4765842 crossref_primary_10_1016_j_arthro_2023_10_009 crossref_primary_10_1017_S1366728924000166 crossref_primary_10_1016_j_displa_2025_103146 crossref_primary_10_1177_13621688221140521 crossref_primary_10_1016_j_visres_2024_108535 crossref_primary_10_3758_s13414_025_03107_5 crossref_primary_10_3758_s13428_022_01989_z crossref_primary_10_3390_agriculture14122140 crossref_primary_10_1016_j_bbr_2024_115078 crossref_primary_10_1016_j_brat_2023_104340 crossref_primary_10_1016_j_jml_2024_104535 crossref_primary_10_1080_13825585_2024_2361960 crossref_primary_10_1177_02654075221080128 crossref_primary_10_3758_s13428_023_02176_4 crossref_primary_10_1016_j_jml_2025_104644 crossref_primary_10_1111_bjop_12753 crossref_primary_10_1016_j_ijnsa_2025_100356 crossref_primary_10_1016_j_eja_2023_126984 crossref_primary_10_1111_bjso_12687 crossref_primary_10_1016_j_clinph_2023_09_011 crossref_primary_10_1038_s44271_025_00312_8 crossref_primary_10_1177_01461672241310917 crossref_primary_10_3758_s13421_024_01541_8 crossref_primary_10_3758_s13421_024_01549_0 crossref_primary_10_22430_22565337_2265 crossref_primary_10_3389_fpsyg_2022_845590 crossref_primary_10_3758_s13423_024_02575_4 crossref_primary_10_1016_j_lindif_2024_102593 crossref_primary_10_3758_s13428_024_02523_z crossref_primary_10_1109_TOH_2023_3322557 crossref_primary_10_1177_01461672211067068 crossref_primary_10_3758_s13421_025_01706_z crossref_primary_10_1016_j_neurobiolaging_2025_04_010 crossref_primary_10_1017_S1366728925100370 crossref_primary_10_1177_09567976241279291 crossref_primary_10_1111_jopy_13030 crossref_primary_10_1515_cllt_2023_0127 crossref_primary_10_1098_rsos_220595 crossref_primary_10_1121_10_0020064 crossref_primary_10_1162_jocn_a_02151 crossref_primary_10_1016_j_avrs_2025_100278 crossref_primary_10_1017_S1366728922000529 crossref_primary_10_1016_j_brainres_2025_149897 crossref_primary_10_1177_17470218221091096 crossref_primary_10_1038_s41398_025_03352_0 crossref_primary_10_1210_clinem_dgae488 crossref_primary_10_1111_cogs_70101 crossref_primary_10_1186_s13054_023_04562_y crossref_primary_10_3390_healthcare12030375 crossref_primary_10_1016_j_anbehav_2024_123054 crossref_primary_10_3758_s13428_024_02572_4 crossref_primary_10_1016_j_cub_2025_01_045 crossref_primary_10_1016_j_cognition_2022_105168 crossref_primary_10_1016_j_appet_2022_106105 crossref_primary_10_1016_j_jesp_2025_104783 crossref_primary_10_1080_09658211_2023_2216910 crossref_primary_10_3390_jintelligence11040063 crossref_primary_10_3758_s13428_021_01730_2 crossref_primary_10_3758_s13428_023_02091_8 crossref_primary_10_1016_j_paid_2024_112806 crossref_primary_10_1016_j_janxdis_2024_102847 crossref_primary_10_1016_j_tsc_2025_101881 crossref_primary_10_3758_s13423_023_02348_5 crossref_primary_10_1002_ajpa_24981 crossref_primary_10_1016_j_neuropsychologia_2024_108832 crossref_primary_10_3758_s13415_025_01320_1 crossref_primary_10_1080_09658211_2025_2551232 crossref_primary_10_1109_TVCG_2022_3209443 crossref_primary_10_1007_s12144_022_03276_8 crossref_primary_10_1111_gcb_17168 crossref_primary_10_1017_langcog_2025_10009 crossref_primary_10_3758_s13423_025_02683_9 crossref_primary_10_3390_bs15010063 crossref_primary_10_1038_s41598_022_22866_x crossref_primary_10_1162_opmi_a_00171
Cites_doi	10.18637/jss.v067.i01 10.1016/j.jesp.2017.09.004 10.1017/CBO9780511801686 10.1371/journal.pmed.0020124 10.1006/anbe.1996.0232 10.1348/000711009X441021 10.5334/joc.10 10.1038/485298a 10.7717/peerj.1226 10.2527/jas.2006-449 10.1016/j.jml.2007.12.005 10.1177/2515245918770963 10.1207/S15328007SEM0802 10.1177/1745691614551642 10.5281/zenodo.1341047 10.1371/journal.pbio.2000797 10.1016/j.cognition.2014.03.008 10.7717/peerj.4794 10.5334/joc.72 10.1080/00220970903292876 10.1007/s10211-004-0095-z 10.3758/s13428-016-0809-y 10.1198/000313001300339897 10.3389/fpsyg.2010.00238 10.1111/j.1740-9713.2007.00249.x 10.1016/j.jml.2017.01.001 10.1111/2041-210X.12306 10.1146/annurev.psych.59.103006.093735 10.1111/j.1541-0420.2007.00782.x 10.1111/2041-210X.12504 10.1037/a0031844 10.1016/j.jml.2012.11.001 10.1146/annurev-psych-122414-033702 10.1037/xge0000014 10.1177/2515245920965119 10.17077/f7kk-6w7f
ContentType	Journal Article
Copyright	The Author(s) 2021 2021. The Author(s). The Author(s) 2021. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.
Copyright_xml	– notice: The Author(s) 2021 – notice: 2021. The Author(s). – notice: The Author(s) 2021. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.
DBID	C6C AAYXX CITATION CGR CUY CVF ECM EIF NPM 4T- 7TK K9. 7X8 5PM
DOI	10.3758/s13428-021-01546-0
DatabaseName	SpringerOpen Free (Free internet resource, activated by CARLI) CrossRef Medline MEDLINE MEDLINE (Ovid) MEDLINE MEDLINE PubMed Docstoc Neurosciences Abstracts ProQuest Health & Medical Complete (Alumni) MEDLINE - Academic PubMed Central (Full Participant titles)
DatabaseTitle	CrossRef MEDLINE Medline Complete MEDLINE with Full Text PubMed MEDLINE (Ovid) ProQuest Health & Medical Complete (Alumni) Neurosciences Abstracts Docstoc MEDLINE - Academic
DatabaseTitleList	MEDLINE - Academic CrossRef ProQuest Health & Medical Complete (Alumni) MEDLINE
Database_xml	– sequence: 1 dbid: NPM name: PubMed url: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed sourceTypes: Index Database – sequence: 2 dbid: 7X8 name: MEDLINE - Academic url: https://search.proquest.com/medline sourceTypes: Aggregation Database
DeliveryMethod	fulltext_linktorsrc
Discipline	Psychology
EISSN	1554-3528
EndPage	2543
ExternalDocumentID	PMC8613146 33954914 10_3758_s13428_021_01546_0
Genre	Research Support, Non-U.S. Gov't Journal Article
GrantInformation_xml	– fundername: Wellcome Trust grantid: 203139 – fundername: Wellcome Trust grantid: 104571
GroupedDBID	--- -55 -5G -BR -DZ -EM -ET -~C -~X 0-V 06D 0R~ 0VY 199 1N0 203 23N 2J2 2JN 2JY 2KG 2KM 2LR 2VQ 30V 3V. 4.4 406 408 40E 53G 5GY 7X7 875 88E 8AO 8FI 8FJ 8G5 8TC 8UJ 95. 96X AAAVM AABHQ AACDK AAHNG AAIAL AAJBT AAJKR AAKPC AANZL AARTL AASML AATNV AATVU AAUYE AAWCG AAYIU AAYQN AAYTO AAYZH AAZMS ABAKF ABDZT ABECU ABFTV ABHLI ABIVO ABJNI ABJOX ABJUD ABKCH ABMQK ABNWP ABPLI ABPPZ ABQBU ABQSL ABSXP ABTEG ABTHY ABTKH ABTMW ABUWG ABXPI ACAOD ACBXY ACDTI ACGFS ACHQT ACHSB ACHXU ACIWK ACKIV ACKNC ACMDZ ACMLO ACNCT ACOKC ACPIV ACPRK ACZOJ ADBBV ADHHG ADHIR ADINQ ADKNI ADKPE ADURQ ADYFF ADZKW AEBTG AEFQL AEGAL AEGNC AEJHL AEJRE AEKMD AEMSY AEOHA AEPYU AESKC AETCA AEVLU AEXYK AFBBN AFFNX AFKRA AFQWF AFRAH AFWTZ AFZKB AGAYW AGDGC AGJBK AGMZJ AGQEE AGQMX AGRTI AGWIL AGWZB AGYKE AHAVH AHBYD AHMBA AHSBF AHYZX AIAKS AIGIU AIIXL AILAN AITGF AJBLW AJRNO AJZVZ ALIPV ALMA_UNASSIGNED_HOLDINGS ALSLI AMKLP AMXSW AMYLF AMYQR AOCGG ARALO ARMRJ ASPBG AVWKF AXYYD AYQZM AZFZN AZQEC B-. BAWUL BENPR BGNMA BPHCQ BVXVI C1A C6C CAG CCPQU COF CSCUP DDRTE DIK DNIVK DPUIP DWQXO E3Z EBD EBLON EBS EIOEI EJD EMOBN ESBYG F5P FEDTE FERAY FFXSO FIGPU FINBP FNLPD FRRFC FSGXE FYUFA GGCAI GGRSB GJIRD GNUQQ GNWQR GQ3 GQ6 GQ7 GUQSH H13 HF~ HMCUK HMJXF HRMNR HVGLF HZ~ H~9 IAO IHR IKXTQ INH IPY IRVIT ITC ITM IWAJR J-C JBSCW JZLTJ KOV LLZTM M1P M2M M2O M2R M4Y MVM N2Q N9A NB0 NPVJJ NQJWS NU0 O9- O93 O9G O9J OHT OK1 P2P P9L PADUT PF- PQQKQ PROAC PSQYO PSYQQ PT4 R9I RIG ROL RPV RSV S16 S1Z S27 S3B SBS SBU SCLPG SHX SISQX SJYHP SNE SNPRN SNX SOHCF SOJ SPISZ SRMVM SSLCW SSXJD STPWE SV3 SZN T13 TN5 TR2 TSG TUC TUS U2A U9L UG4 UKHRP UOJIU UPT UTJUX UZXMN VFIZW VXZ W48 WH7 WK8 XJT XOL XSW Z7R Z7S Z7W Z81 Z83 Z88 Z8N Z92 ZMTXR ZOVNA ZUP AAPKM AAYXX ABBRH ABDBE ABFSG ABRTQ ACSTC ADHKG AEZWR AFDZB AFFHD AFHIU AFOHR AGQPQ AHPBZ AHWEU AIXLP ATHPR AYFIA CITATION PHGZM PHGZT PJZUB PPXIY PRQQA CGR CUY CVF ECM EIF NPM 4T- 7TK K9. 7X8 5PM
ID	FETCH-LOGICAL-c607t-49f60ea3ab80e12e945c2fb025b8c08005fd236b2a5991e7d64a45c3d9b488533
IEDL.DBID	RSV
ISICitedReferencesCount	378
ISICitedReferencesURI	http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000647500300002&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
ISSN	1554-3528 1554-351X
IngestDate	Tue Nov 04 02:00:55 EST 2025 Thu Sep 04 18:11:50 EDT 2025 Sat Nov 15 11:51:10 EST 2025 Mon Jul 21 06:00:13 EDT 2025 Sat Nov 29 02:18:10 EST 2025 Tue Nov 18 21:51:48 EST 2025 Fri Feb 21 02:46:05 EST 2025
IsDoiOpenAccess	true
IsOpenAccess	true
IsPeerReviewed	true
IsScholarly	true
Issue	6
Keywords	lme4 R Simulation mixedpower Power Mixed models
Language	English
License	2021. The Author(s). Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
LinkModel	DirectLink
MergedId	FETCHMERGED-LOGICAL-c607t-49f60ea3ab80e12e945c2fb025b8c08005fd236b2a5991e7d64a45c3d9b488533
Notes	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23
ORCID	0000-0003-1145-4473 0000-0002-9994-0988 0000-0003-1354-4835
OpenAccessLink	https://link.springer.com/10.3758/s13428-021-01546-0
PMID	33954914
PQID	2602033193
PQPubID	976348
PageCount	16
ParticipantIDs	pubmedcentral_primary_oai_pubmedcentral_nih_gov_8613146 proquest_miscellaneous_2522619558 proquest_journals_2602033193 pubmed_primary_33954914 crossref_primary_10_3758_s13428_021_01546_0 crossref_citationtrail_10_3758_s13428_021_01546_0 springer_journals_10_3758_s13428_021_01546_0
PublicationCentury	2000
PublicationDate	2021-12-01
PublicationDateYYYYMMDD	2021-12-01
PublicationDate_xml	– month: 12 year: 2021 text: 2021-12-01 day: 01
PublicationDecade	2020
PublicationPlace	New York
PublicationPlace_xml	– name: New York – name: United States
PublicationTitle	Behavior research methods
PublicationTitleAbbrev	Behav Res
PublicationTitleAlternate	Behav Res Methods
PublicationYear	2021
Publisher	Springer US Springer Nature B.V
Publisher_xml	– name: Springer US – name: Springer Nature B.V
References	HoenigJMHeiseyDMThe abuse of power: The pervasive fallacy of power calculations for data analysisAmerican Statistician2001551192410.1198/000313001300339897 MaxwellSEKelleyKRauschJRSample Size Planning for Statistical Power and Accuracy in Parameter EstimationAnnual Review of Psychology200859153756310.1146/annurev.psych.59.103006.09373517937603 BrysbaertMStevensMPower Analysis and Effect Size in Mixed Effects Models: A TutorialJournal of Cognition20181112010.5334/joc.10 Ioannidis, J. P. A. (2005). Why most published research findings are false. PLoS Medicine. https://doi.org/10.1371/journal.pmed.0020124 Cohen, J. (1988). Statistical Power Analysis for the Behavioral Sciences. Retrieved from http://utstat.toronto.edu/~brunner/oldclass/378f16/readings/CohenPower.pdf von OertzenTBrandmaierAMOptimal study design with identical power: An application of power equivalence to latent growth curve modelsPsychology and Aging201328241442810.1037/a0031844 Albers, C., & Lakens, D. (2018). When power analyses based on pilot data are biased: Inaccurate effect size estimators and follow-up bias. Journal of Experimental Social Psychology. https://doi.org/10.1016/j.jesp.2017.09.004 LitièreSAlonsoAMolenberghsGType I and type II error under random-effects misspecification in generalized linear mixed modelsBiometrics20076341038104410.1111/j.1541-0420.2007.00782.x17425642 GelmanACarlinJBeyond Power Calculations: Assessing Type S (Sign) and Type M (Magnitude) ErrorsPerspectives on Psychological Science20149664165110.1177/174569161455164226186114 JohnsonPCDBarrySJEFergusonHMMüllerPPower analysis for generalized linear mixed models in ecology and evolutionMethods in Ecology and Evolution20156213314210.1111/2041-210X.1230625893088 LeBeau, B. (2019). Power Analysis by Simulation using R and simglm. Retrieved from https://ir.uiowa.edu/pq_pubs/3 Barr, D. J., Levy, R., Scheepers, C., & Tily, H. J. (2013). Random effects structure for confirmatory hypothesis testing: Keep it maximal. Journal of Memory and Language, 68(3). https://doi.org/10.1016/j.jml.2012.11.001 Bates, D. M., Mächler, M., Bolker, B., & Walker, S. (2014). Fitting Linear Mixed-Effects Models using lme4. Journal of Staistical Software, 67(1). https://doi.org/10.18637/jss.v067.i01 O’Brien, R., & Castelloe, J. (2007). Sample size analysis for traditional hypothesis testing: concepts and issues. In Pharmaceutical Statistics using SAS: A Practical Guide. Lakens, D., Scheel, A. M., & Isager, P. M. (2018). Equivalence Testing for Psychological Research : A Tutorial. https://doi.org/10.1177/2515245918770963 Nakagawa, S., & Foster, T. M. (2004). The case against retrospective statistical power analyses with an introduction to power analysis, 103–108. https://doi.org/10.1007/s10211-004-0095-z Magnusson, K. (2018). Powerlmm: Power analysis for longitudinal multilevel models. Lenth, R. V. (2007). Statistical power calculations. Journal of Animal Science, 85(13 Suppl). https://doi.org/10.2527/jas.2006-449 YanMZhouWShuHYusupuRMiaoDKrügelAKlieglREye movements guided by morphological structure: Evidence from the Uighur languageCognition2014132218121510.1016/j.cognition.2014.03.00824813572 WestfallJKennyDAJuddCMStatistical power and optimal design in experiments in which samples of participants respond to samples of stimuliJournal of Experimental Psychology: General201414352020204510.1037/xge0000014 YongEReplication studies: Bad copyNature2012485739829830010.1038/485298a22596136 Brysbaert, M. (2019). How Many Participants Do We Have to Include in Properly Powered Experiments ? A Tutorial of Power Analysis with Reference Tables, 2(1), 1–38. MatuschekHKlieglRVasishthSBaayenHBatesDBalancing Type I error and power in linear mixed modelsJournal of Memory and Language20179430531510.1016/j.jml.2017.01.001 GreenPMacLeodCJSIMR: An R package for power analysis of generalized linear mixed models by simulationMethods in Ecology and Evolution20167449349810.1111/2041-210X.12504 Bates, D. M., Mächler, M., Zurich, E., Bolker, B. M., & Walker, S. C. (2015b). Fitting Linear Mixed-Effects Models Using lme4. JSS Journal of Statistical Software, 67. https://doi.org/10.18637/jss.v067.i01 Paxton, P., Curran, P. J., & Bollen, K. A. (2001). Monte Carlo Experiments : Design and Implementation Monte Carlo Experiments : Design and Implementation University of North Carolina at Chapel Hill, (April). https://doi.org/10.1207/S15328007SEM0802 KainMPBolkerBMMcCoyMWA practical guide and power analysis for GLMMs: detecting among treatment variation in random effectsPeerJ20153e122610.7717/peerj.1226264014464579019 von OertzenTPower equivalence in structural equation modellingBritish Journal of Mathematical and Statistical Psychology201063225727210.1348/000711009X441021 Konstantopoulos, S., & Taylor, P. (2020). Power Analysis in Two-Level Unbalanced Designs, 78(3), 291–317. https://doi.org/10.1080/00220970903292876 Coppock, A. (2013). 10 Things to Know About Statistical Power. Retrieved September 20, 2018, from http://egap.org/methods-guides/10-things-you-need-know-about statistical-power JuddCMWestfallJKennyDAExperiments with More Than One Random Factor: Designs, Analytic Models, and Statistical PowerAnnual Review of Psychology201768January60162510.1146/annurev-psych-122414-03370227687116 Martin, J. (2012). PAMM: power analysis for random effects in mixed models. HarrisonXADonaldsonLCorrea-CanoMEEvansJFisherDNGoodwinCEDA brief introduction to mixed effects modelling and multi-model inference in ecologyPeerJ20182018513210.7717/peerj.4794 Baayen, H. (2007). Analyzing linguistic data: a practical introduction to statistics using R. BaayenHDavidsonDJBatesDMMixed-effects modeling with crossed random effects for subjects and itemsJournal of Memory and Language200859439041210.1016/j.jml.2007.12.005 Bates, D. M., Kliegl, R., Vasishth, S., & Baayen, R. H. (2015a). Parsimonious Mixed Models. Journal of Memory and Language, 27. Methodology. Luke, S. G. (2017). Evaluating significance in linear mixed-effects models in R, (September 2016), 1494–1502. https://doi.org/10.3758/s13428-016-0809-y DeBruine, L. M., & Barr, D. J. (2021). Understanding mixed-effects models through data simulation. Advances in Methods and Practices in Psychological Science. https://doi.org/10.1177/2515245920965119 KlieglRWeiPDambacherMYanMZhouXExperimental effects and individual differences in linear mixed models: Estimating the relationship between spatial, object, and attraction effects in visual attentionFrontiers in Psychology20111JAN11210.3389/fpsyg.2010.00238 Goldstein, H. (2007). Becoming familiar with multilevel modelling, 133–135. Retrieved from https://harveygoldsteinweb.files.wordpress.com/2019/02/becoming-familiar-with-multilevel-modelling.pdf SzucsDIoannidisJPAEmpirical assessment of published effect sizes and power in the recent cognitive neuroscience and psychology literaturePLoS Biology201715311810.1371/journal.pbio.2000797 Kumle, L., Võ, M. L.-H., & Draschkow, D. (2018). Mixedpower: a library for estimating simulation-based power for mixed models in R. https://doi.org/10.5281/zenodo.1341047 ThomasLJuanesFThe importance of statistical power analysis: An example from Animal BehaviourAnimal Behaviour199652485685910.1006/anbe.1996.0232 XA Harrison (1546_CR16) 2018; 2018 D Szucs (1546_CR37) 2017; 15 T von Oertzen (1546_CR40) 2013; 28 JM Hoenig (1546_CR17) 2001; 55 1546_CR25 J Westfall (1546_CR41) 2014; 143 1546_CR24 1546_CR23 SE Maxwell (1546_CR33) 2008; 59 1546_CR29 T von Oertzen (1546_CR39) 2010; 63 1546_CR27 H Matuschek (1546_CR32) 2017; 94 1546_CR26 M Brysbaert (1546_CR9) 2018; 1 CM Judd (1546_CR20) 2017; 68 L Thomas (1546_CR38) 1996; 52 E Yong (1546_CR43) 2012; 485 P Green (1546_CR15) 2016; 7 1546_CR5 R Kliegl (1546_CR22) 2011; 1 1546_CR4 H Baayen (1546_CR2) 2008; 59 1546_CR7 1546_CR6 1546_CR1 MP Kain (1546_CR21) 2015; 3 1546_CR3 1546_CR14 1546_CR36 1546_CR35 1546_CR12 1546_CR34 1546_CR11 S Litière (1546_CR28) 2007; 63 1546_CR18 PCD Johnson (1546_CR19) 2015; 6 M Yan (1546_CR42) 2014; 132 1546_CR8 A Gelman (1546_CR13) 2014; 9 1546_CR10 1546_CR31 1546_CR30
References_xml	– reference: HarrisonXADonaldsonLCorrea-CanoMEEvansJFisherDNGoodwinCEDA brief introduction to mixed effects modelling and multi-model inference in ecologyPeerJ20182018513210.7717/peerj.4794 – reference: MatuschekHKlieglRVasishthSBaayenHBatesDBalancing Type I error and power in linear mixed modelsJournal of Memory and Language20179430531510.1016/j.jml.2017.01.001 – reference: DeBruine, L. M., & Barr, D. J. (2021). Understanding mixed-effects models through data simulation. Advances in Methods and Practices in Psychological Science. https://doi.org/10.1177/2515245920965119 – reference: YongEReplication studies: Bad copyNature2012485739829830010.1038/485298a22596136 – reference: Baayen, H. (2007). Analyzing linguistic data: a practical introduction to statistics using R. – reference: Kumle, L., Võ, M. L.-H., & Draschkow, D. (2018). Mixedpower: a library for estimating simulation-based power for mixed models in R. https://doi.org/10.5281/zenodo.1341047 – reference: Bates, D. M., Kliegl, R., Vasishth, S., & Baayen, R. H. (2015a). Parsimonious Mixed Models. Journal of Memory and Language, 27. Methodology. – reference: Luke, S. G. (2017). Evaluating significance in linear mixed-effects models in R, (September 2016), 1494–1502. https://doi.org/10.3758/s13428-016-0809-y – reference: HoenigJMHeiseyDMThe abuse of power: The pervasive fallacy of power calculations for data analysisAmerican Statistician2001551192410.1198/000313001300339897 – reference: LitièreSAlonsoAMolenberghsGType I and type II error under random-effects misspecification in generalized linear mixed modelsBiometrics20076341038104410.1111/j.1541-0420.2007.00782.x17425642 – reference: KlieglRWeiPDambacherMYanMZhouXExperimental effects and individual differences in linear mixed models: Estimating the relationship between spatial, object, and attraction effects in visual attentionFrontiers in Psychology20111JAN11210.3389/fpsyg.2010.00238 – reference: LeBeau, B. (2019). Power Analysis by Simulation using R and simglm. Retrieved from https://ir.uiowa.edu/pq_pubs/3/ – reference: JuddCMWestfallJKennyDAExperiments with More Than One Random Factor: Designs, Analytic Models, and Statistical PowerAnnual Review of Psychology201768January60162510.1146/annurev-psych-122414-03370227687116 – reference: KainMPBolkerBMMcCoyMWA practical guide and power analysis for GLMMs: detecting among treatment variation in random effectsPeerJ20153e122610.7717/peerj.1226264014464579019 – reference: Ioannidis, J. P. A. (2005). Why most published research findings are false. PLoS Medicine. https://doi.org/10.1371/journal.pmed.0020124 – reference: Cohen, J. (1988). Statistical Power Analysis for the Behavioral Sciences. Retrieved from http://utstat.toronto.edu/~brunner/oldclass/378f16/readings/CohenPower.pdf – reference: JohnsonPCDBarrySJEFergusonHMMüllerPPower analysis for generalized linear mixed models in ecology and evolutionMethods in Ecology and Evolution20156213314210.1111/2041-210X.1230625893088 – reference: Albers, C., & Lakens, D. (2018). When power analyses based on pilot data are biased: Inaccurate effect size estimators and follow-up bias. Journal of Experimental Social Psychology. https://doi.org/10.1016/j.jesp.2017.09.004 – reference: Paxton, P., Curran, P. J., & Bollen, K. A. (2001). Monte Carlo Experiments : Design and Implementation Monte Carlo Experiments : Design and Implementation University of North Carolina at Chapel Hill, (April). https://doi.org/10.1207/S15328007SEM0802 – reference: O’Brien, R., & Castelloe, J. (2007). Sample size analysis for traditional hypothesis testing: concepts and issues. In Pharmaceutical Statistics using SAS: A Practical Guide. – reference: BrysbaertMStevensMPower Analysis and Effect Size in Mixed Effects Models: A TutorialJournal of Cognition20181112010.5334/joc.10 – reference: Coppock, A. (2013). 10 Things to Know About Statistical Power. Retrieved September 20, 2018, from http://egap.org/methods-guides/10-things-you-need-know-about statistical-power – reference: Brysbaert, M. (2019). How Many Participants Do We Have to Include in Properly Powered Experiments ? A Tutorial of Power Analysis with Reference Tables, 2(1), 1–38. – reference: Martin, J. (2012). PAMM: power analysis for random effects in mixed models. – reference: Nakagawa, S., & Foster, T. M. (2004). The case against retrospective statistical power analyses with an introduction to power analysis, 103–108. https://doi.org/10.1007/s10211-004-0095-z – reference: ThomasLJuanesFThe importance of statistical power analysis: An example from Animal BehaviourAnimal Behaviour199652485685910.1006/anbe.1996.0232 – reference: von OertzenTBrandmaierAMOptimal study design with identical power: An application of power equivalence to latent growth curve modelsPsychology and Aging201328241442810.1037/a0031844 – reference: Bates, D. M., Mächler, M., Zurich, E., Bolker, B. M., & Walker, S. C. (2015b). Fitting Linear Mixed-Effects Models Using lme4. JSS Journal of Statistical Software, 67. https://doi.org/10.18637/jss.v067.i01 – reference: Bates, D. M., Mächler, M., Bolker, B., & Walker, S. (2014). Fitting Linear Mixed-Effects Models using lme4. Journal of Staistical Software, 67(1). https://doi.org/10.18637/jss.v067.i01 – reference: Magnusson, K. (2018). Powerlmm: Power analysis for longitudinal multilevel models. – reference: BaayenHDavidsonDJBatesDMMixed-effects modeling with crossed random effects for subjects and itemsJournal of Memory and Language200859439041210.1016/j.jml.2007.12.005 – reference: GreenPMacLeodCJSIMR: An R package for power analysis of generalized linear mixed models by simulationMethods in Ecology and Evolution20167449349810.1111/2041-210X.12504 – reference: GelmanACarlinJBeyond Power Calculations: Assessing Type S (Sign) and Type M (Magnitude) ErrorsPerspectives on Psychological Science20149664165110.1177/174569161455164226186114 – reference: Goldstein, H. (2007). Becoming familiar with multilevel modelling, 133–135. Retrieved from https://harveygoldsteinweb.files.wordpress.com/2019/02/becoming-familiar-with-multilevel-modelling.pdf – reference: Lakens, D., Scheel, A. M., & Isager, P. M. (2018). Equivalence Testing for Psychological Research : A Tutorial. https://doi.org/10.1177/2515245918770963 – reference: Barr, D. J., Levy, R., Scheepers, C., & Tily, H. J. (2013). Random effects structure for confirmatory hypothesis testing: Keep it maximal. Journal of Memory and Language, 68(3). https://doi.org/10.1016/j.jml.2012.11.001 – reference: Konstantopoulos, S., & Taylor, P. (2020). Power Analysis in Two-Level Unbalanced Designs, 78(3), 291–317. https://doi.org/10.1080/00220970903292876 – reference: von OertzenTPower equivalence in structural equation modellingBritish Journal of Mathematical and Statistical Psychology201063225727210.1348/000711009X441021 – reference: Lenth, R. V. (2007). Statistical power calculations. Journal of Animal Science, 85(13 Suppl). https://doi.org/10.2527/jas.2006-449 – reference: WestfallJKennyDAJuddCMStatistical power and optimal design in experiments in which samples of participants respond to samples of stimuliJournal of Experimental Psychology: General201414352020204510.1037/xge0000014 – reference: MaxwellSEKelleyKRauschJRSample Size Planning for Statistical Power and Accuracy in Parameter EstimationAnnual Review of Psychology200859153756310.1146/annurev.psych.59.103006.09373517937603 – reference: SzucsDIoannidisJPAEmpirical assessment of published effect sizes and power in the recent cognitive neuroscience and psychology literaturePLoS Biology201715311810.1371/journal.pbio.2000797 – reference: YanMZhouWShuHYusupuRMiaoDKrügelAKlieglREye movements guided by morphological structure: Evidence from the Uighur languageCognition2014132218121510.1016/j.cognition.2014.03.00824813572 – ident: 1546_CR7 doi: 10.18637/jss.v067.i01 – ident: 1546_CR1 doi: 10.1016/j.jesp.2017.09.004 – ident: 1546_CR3 doi: 10.1017/CBO9780511801686 – ident: 1546_CR18 doi: 10.1371/journal.pmed.0020124 – volume: 52 start-page: 856 issue: 4 year: 1996 ident: 1546_CR38 publication-title: Animal Behaviour doi: 10.1006/anbe.1996.0232 – volume: 63 start-page: 257 issue: 2 year: 2010 ident: 1546_CR39 publication-title: British Journal of Mathematical and Statistical Psychology doi: 10.1348/000711009X441021 – ident: 1546_CR5 – volume: 1 start-page: 1 issue: 1 year: 2018 ident: 1546_CR9 publication-title: Journal of Cognition doi: 10.5334/joc.10 – ident: 1546_CR30 – volume: 485 start-page: 298 issue: 7398 year: 2012 ident: 1546_CR43 publication-title: Nature doi: 10.1038/485298a – ident: 1546_CR10 – volume: 3 start-page: e1226 year: 2015 ident: 1546_CR21 publication-title: PeerJ doi: 10.7717/peerj.1226 – ident: 1546_CR6 doi: 10.18637/jss.v067.i01 – ident: 1546_CR27 doi: 10.2527/jas.2006-449 – volume: 59 start-page: 390 issue: 4 year: 2008 ident: 1546_CR2 publication-title: Journal of Memory and Language doi: 10.1016/j.jml.2007.12.005 – ident: 1546_CR25 doi: 10.1177/2515245918770963 – ident: 1546_CR36 doi: 10.1207/S15328007SEM0802 – volume: 9 start-page: 641 issue: 6 year: 2014 ident: 1546_CR13 publication-title: Perspectives on Psychological Science doi: 10.1177/1745691614551642 – ident: 1546_CR24 doi: 10.5281/zenodo.1341047 – volume: 15 start-page: 1 issue: 3 year: 2017 ident: 1546_CR37 publication-title: PLoS Biology doi: 10.1371/journal.pbio.2000797 – volume: 132 start-page: 181 issue: 2 year: 2014 ident: 1546_CR42 publication-title: Cognition doi: 10.1016/j.cognition.2014.03.008 – volume: 2018 start-page: 1 issue: 5 year: 2018 ident: 1546_CR16 publication-title: PeerJ doi: 10.7717/peerj.4794 – ident: 1546_CR8 doi: 10.5334/joc.72 – ident: 1546_CR23 doi: 10.1080/00220970903292876 – ident: 1546_CR34 doi: 10.1007/s10211-004-0095-z – ident: 1546_CR29 doi: 10.3758/s13428-016-0809-y – volume: 55 start-page: 19 issue: 1 year: 2001 ident: 1546_CR17 publication-title: American Statistician doi: 10.1198/000313001300339897 – ident: 1546_CR11 – volume: 1 start-page: 1 issue: JAN year: 2011 ident: 1546_CR22 publication-title: Frontiers in Psychology doi: 10.3389/fpsyg.2010.00238 – ident: 1546_CR14 doi: 10.1111/j.1740-9713.2007.00249.x – volume: 94 start-page: 305 year: 2017 ident: 1546_CR32 publication-title: Journal of Memory and Language doi: 10.1016/j.jml.2017.01.001 – volume: 6 start-page: 133 issue: 2 year: 2015 ident: 1546_CR19 publication-title: Methods in Ecology and Evolution doi: 10.1111/2041-210X.12306 – ident: 1546_CR31 – volume: 59 start-page: 537 issue: 1 year: 2008 ident: 1546_CR33 publication-title: Annual Review of Psychology doi: 10.1146/annurev.psych.59.103006.093735 – volume: 63 start-page: 1038 issue: 4 year: 2007 ident: 1546_CR28 publication-title: Biometrics doi: 10.1111/j.1541-0420.2007.00782.x – ident: 1546_CR35 – volume: 7 start-page: 493 issue: 4 year: 2016 ident: 1546_CR15 publication-title: Methods in Ecology and Evolution doi: 10.1111/2041-210X.12504 – volume: 28 start-page: 414 issue: 2 year: 2013 ident: 1546_CR40 publication-title: Psychology and Aging doi: 10.1037/a0031844 – ident: 1546_CR4 doi: 10.1016/j.jml.2012.11.001 – volume: 68 start-page: 601 issue: January year: 2017 ident: 1546_CR20 publication-title: Annual Review of Psychology doi: 10.1146/annurev-psych-122414-033702 – volume: 143 start-page: 2020 issue: 5 year: 2014 ident: 1546_CR41 publication-title: Journal of Experimental Psychology: General doi: 10.1037/xge0000014 – ident: 1546_CR12 doi: 10.1177/2515245920965119 – ident: 1546_CR26 doi: 10.17077/f7kk-6w7f
SSID	ssj0038214
Score	2.7094498
Snippet	Mixed-effects models are a powerful tool for modeling fixed and random effects simultaneously, but do not offer a feasible analytic solution for estimating the...
SourceID	pubmedcentral proquest pubmed crossref springer
SourceType	Open Access Repository Aggregation Database Index Database Enrichment Source Publisher
StartPage	2528
SubjectTerms	Behavioral Science and Psychology Cognitive Psychology Computer Simulation Data Generalized linear models Humans Linear analysis Linear Models Null hypothesis Planning Power Probability Psychology Random effects Reliability Reproducibility of Results Research design Sample Size Simulation World problems
Title	Estimating power in (generalized) linear mixed models: An open introduction and tutorial in R
URI	https://link.springer.com/article/10.3758/s13428-021-01546-0 https://www.ncbi.nlm.nih.gov/pubmed/33954914 https://www.proquest.com/docview/2602033193 https://www.proquest.com/docview/2522619558 https://pubmed.ncbi.nlm.nih.gov/PMC8613146
Volume	53
WOSCitedRecordID	wos000647500300002&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
hasFullText	1
inHoldings	1
isFullTextHit
isPrint
journalDatabaseRights	– providerCode: PRVAVX databaseName: SpringerLINK Contemporary 1997-Present customDbUrl: eissn: 1554-3528 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0038214 issn: 1554-3528 databaseCode: RSV dateStart: 20050201 isFulltext: true titleUrlDefault: https://link.springer.com/search?facet-content-type=%22Journal%22 providerName: Springer Nature
link	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnV1Jb9UwEB5B4dBLKWsDbWUkDiCIGttx7PRWVa04oAqVRe-CIid2SiRwq5cUAb-emWzoURUJzh47jmfxN7L9DcAzUXMnvTKxw-QrTk2m4rzkFv2qLvM0r03me02_0ScnZrHI346Pwtrptvt0JNlHasorEdTutVymxKYsKP1VODAm6rdwuzPkjqfvPk7xVxrB0-F5zDX9VregK7jy6vXIP85I-63n-M7_TXoTNkaoyQ4G27gLN3y4B-tzxPtxHz4doYMTZA1n7ILKpbEmsOdnAxN189O7F4xQqF2yr81371hfNqfdZweBUdUtlO4GwlhULrPBsY4YEdCiaZzTB_Dh-Oj94et4rLcQV1miuxhVkyXeSluaxHPh81RVoi4RFZWGCMkTVTshs1JYhajSa5elFkWky0sMA4gbH8JaOA9-C1hWcZdZbm1uMCWyrkzq2laV0ForjSgnAj6poKhGMnKqifGlwKSEVq4YVq7AlSv6lSuSCF7OfS4GKo6_Sm9Pmi1Gt2wLTN5EIjHqyAiezs3oUHRKYoM_v0QZQqQ8V8pE8GgwhPlzUtKpKE8j0CsmMgsQWfdqS2g-96TdBnET7koRvJoM5fe0rv-Lx_8m_gTWBdlaf91mG9a65aXfgdvVt65pl7twUy_Mbu8svwBZDQ6w
linkProvider	Springer Nature
linkToHtml	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnV1baxUxEB6kCvZF662uVo3gg6JLN8lesr4Vaal4PEit0hcJ2U22XWjTcnYr6q_vzN7kWCrocybZbOaSb0jyDcALUXErXaJCi8lXGKs0CfOCG_SrqsjjvFKp6zQ9y-ZzdXCQfxoehTXjbffxSLKL1JRXIqjdbLiMiU1ZUPqb4MCYqF-Pcceii3x7n7-O8VcqweP-ecwV_Za3oEu48vL1yD_OSLutZ-f2_016DW4NUJNt9bZxB645fxdWp4j38x5820YHJ8jqD9kZlUtjtWcvD3sm6vqXs68YoVCzYCf1D2dZVzanecu2PKOqWyjd9oSxqFxmvGUtMSKgRdM4e_fhy872_rvdcKi3EJZplLUhqiaNnJGmUJHjwuVxUoqqQFRUKCIkj5LKCpkWwiSIKl1m09igiLR5gWEAceMDWPGn3j0ElpbcpoYbkytMiYwtoqoyZSmyLEsyRDkB8FEFuhzIyKkmxrHGpIRWTvcrp3HldLdyOgrg9dTnrKfi-Kv0xqhZPbhlozF5E5HEqCMDeD41o0PRKYnx7vQcZQiR8jxJVADrvSFMn5OSTkV5HEC2ZCKTAJF1L7f4-qgj7VaIm3BXCuDNaCi_p3X1Xzz6N_FncHN3_-NMz97PPzyGVUF211292YCVdnHunsCN8ntbN4unnctcAK_HEKw
linkToPdf	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV1Lb9QwEB6hglAvUF4lUMBIHEAQNbbjxOmtgq5AVKuKl3pBlhM7JRK4q90UQX99Z5JNYKmKhDh7HMeeGfsbjf0NwBNRcye90rHD4CtOdabiouQW_aoui7SodeY7Te_n06k-PCwOfnvF3912H1KS_ZsGYmkK7fbM1eTiEgHu9oLLlJiVBYXCCgfBoP1ySkWDKF5__2nYi6UWPO2fylzQb_U4Oocxz1-V_CNf2h1Dk-v_P4ENuLaEoGy3t5kbcMmHm7A-7oQ_b8HnPXR8grLhiM2ojBprAnt61DNUN6fePWOETu2cfWt-eMe6cjqLHbYbGFXjQum2J5JFpTMbHGuJKQEtnb7z7jZ8nOx9ePk6XtZhiKssydsYVZYl3kpb6sRz4YtUVaIuES2VmojKE1U7IbNSWIVo0-cuSy2KSFeUuD0gnrwDa-E4-LvAsoq7zHJrC42hknVlUte2qkSe5ypH9BMBH9RhqiVJOdXK-GowWKGVM_3KGVw5062cSSJ4PvaZ9RQdf5XeGrRslu66MBjUiUTibiQjeDw2o6NR9sQGf3yCMoRUeaGUjmCzN4pxOCkpW8rTCPIVcxkFiMR7tSU0Xzoyb414Ck-rCF4MRvPrty6exb1_E38EVw9eTcz-m-nb-7AuyOy6GzlbsNbOT_wDuFJ9b5vF_GHnPWcBCRmQ
openUrl	ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Estimating+power+in+%28generalized%29+linear+mixed+models%3A+An+open+introduction+and+tutorial+in+R&rft.jtitle=Behavior+research+methods&rft.au=Kumle%2C+Levi&rft.au=V%C3%B5%2C+Melissa+L.-H.&rft.au=Draschkow%2C+Dejan&rft.date=2021-12-01&rft.pub=Springer+US&rft.issn=1554-351X&rft.eissn=1554-3528&rft.volume=53&rft.issue=6&rft.spage=2528&rft.epage=2543&rft_id=info:doi/10.3758%2Fs13428-021-01546-0&rft_id=info%3Apmid%2F33954914&rft.externalDocID=PMC8613146
thumbnail_l	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1554-3528&client=summon
thumbnail_m	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1554-3528&client=summon
thumbnail_s	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1554-3528&client=summon