Have a personal or library account? Click to login
How many participants do we have to include in properly powered experiments?  A tutorial of power analysis with reference tables Cover

How many participants do we have to include in properly powered experiments? A tutorial of power analysis with reference tables

By: Marc Brysbaert  
Open Access
|Jul 2019

References

  1. 1Albers, 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, 74, 187195. DOI: 10.1016/j.jesp.2017.09.004
  2. 2Anderson, S. F., Kelley, K., & Maxwell, S. E. (2017). Sample-size planning for more accurate statistical power: A method adjusting sample effect sizes for publication bias and uncertainty. Psychological Science, 28(11), 15471562. DOI: 10.1177/0956797617723724
  3. 3Baayen, R. H. (2008). Analyzing Linguistic Data: A practical introduction to statistics using R. Cambridge, UK: Cambridge University Press. DOI: 10.1017/CBO9780511801686
  4. 4Bakker, M., Hartgerink, C. H., Wicherts, J. M., & van der Maas, H. L. (2016). Researchers’ intuitions about power in psychological research. Psychological Science, 27(8), 10691077. DOI: 10.1177/0956797616647519
  5. 5Benjamin, D. J., Berger, J. O., Johannesson, M., Nosek, B. A., Wagenmakers, E. J., Berk, R., Johnson, V. E., et al. (2018). Redefine statistical significance. Nature Human Behaviour, 2(1), 610. DOI: 10.1038/s41562-017-0189-z
  6. 6Birnbaum, M. H. (2004). Human research and data collection via the Internet. Annual Review of Psychology, 55, 803832. DOI: 10.1146/annurev.psych.55.090902.141601
  7. 7Bishop, D. V. M. (2013, June 7). Interpreting unexpected significant results [Blog post]. Retrieved from http://deevybee.blogspot.com/2013/06/interpreting-unexpected-significant.html
  8. 8Bishop, D. V. M. (2018, July 12). One big study or two small studies? Insights from simulations. Retrieved from http://deevybee.blogspot.com/2018/07/one-big-study-or-two-small-studies.html
  9. 9Borsboom, D. (2013, November 20). Theoretical amnesia [Blog post]. Retrieved from http://osc.centerforopenscience.org/2013/11/20/theoretical-amnesia/
  10. 10Bosco, F. A., Aguinis, H., Singh, K., Field, J. G., & Pierce, C. A. (2015). Correlational effect size benchmarks. Journal of Applied Psychology, 100(2), 431449. DOI: 10.1037/a0038047
  11. 11Brooks, G. P., & Barcikowski, R. S. (2012). The PEAR method for sample sizes in multiple linear regression. Multiple Linear Regression Viewpoints, 38(2), 116.
  12. 12Brysbaert, M., & Stevens, M. (2018). Power Analysis and Effect Size in Mixed Effects Models: A Tutorial. Journal of Cognition, 1(9), 120. DOI: 10.5334/joc.10
  13. 13Callens, M., Tops, W., & Brysbaert, M. (2012). Cognitive Profile of Students Who Enter Higher Education with an Indication of Dyslexia. PLoS ONE, 7(6), e38081. DOI: 10.1371/journal.pone.0038081
  14. 14Camerer, C. F., Dreber, A., Holzmeister, F., Ho, T. H., Huber, J., Johannesson, M., Wu, H., et al. (2018). Evaluating the replicability of social science experiments in Nature and Science between 2010 and 2015. Nature Human Behaviour, 2(9), 637644. DOI: 10.1038/s41562-018-0399-z
  15. 15Campbell, J. I., & Thompson, V. A. (2012). MorePower 6.0 for ANOVA with relational confidence intervals and Bayesian analysis. Behavior Research Methods, 44(4), 12551265. DOI: 10.3758/s13428-012-0186-0
  16. 16Chambers, C. D. (2013). Registered reports: A new publishing initiative at Cortex. Cortex, 49, 609610. DOI: 10.1016/j.cortex.2012.12.016
  17. 17Cohen, J. (1962). The statistical power of abnormal-social psychological research: a review. The Journal of Abnormal and Social Psychology, 65(3), 145153. DOI: 10.1037/h0045186
  18. 18Cohen, J. (1988). Statistical Power Analysis for the Behavioral Sciences (2nd ed.). Hillsdale, NJ: Erlbaum.
  19. 19Cohen, J. (1992). A power primer. Psychological Bulletin, 112(1), 155159. DOI: 10.1037/0033-2909.112.1.155
  20. 20Colquhoun, D. (2014). An investigation of the false discovery rate and the misinterpretation of p-values. Royal Society Open Science, 1(3), 140216. DOI: 10.1098/rsos.140216
  21. 21Cronbach, L. J. (1957). The two disciplines of scientific psychology. American Psychologist, 12(11), 671684. DOI: 10.1037/h0043943
  22. 22Cumming, G. (2014). The new statistics why and how. Psychological Science, 25(1), 729. DOI: 10.1177/0956797613504966
  23. 23De Deyne, S., Navarro, D. J., Perfors, A., Brysbaert, M., & Storms, G. (2019). The “Small World of Words” English word association norms for over 12,000 cue words. Behavior Research Methods. Preprint available at https://link.springer.com/article/10.3758/s13428-018-1115-7. DOI: 10.3758/s13428-018-1115-7
  24. 24de Jong, T., Marsman, M., & Wagenmakers, E. J. (2019). A Bayesian Approach to the Correction for Multiplicity. Preprint. DOI: 10.31234/osf.io/s56mk
  25. 25Depaoli, S., & van de Schoot, R. (2017). Improving transparency and replication in Bayesian statistics: The WAMBS-Checklist. Psychological Methods, 22(2), 240261. DOI: 10.1037/met0000065
  26. 26Dienes, Z. (2016). How Bayes factors change scientific practice. Journal of Mathematical Psychology, 72, 7889. DOI: 10.1016/j.jmp.2015.10.003
  27. 27Dimitrov, D. M., & Rumrill, P. D., Jr, (2003). Pretest-posttest designs and measurement of change. Work, 20(2), 159165.
  28. 28Dumas-Mallet, E., Button, K. S., Boraud, T., Gonon, F., & Munafò, M. R. (2017). Low statistical power in biomedical science: a review of three human research domains. Royal Society Open Science, 4(2), 160254. DOI: 10.1098/rsos.160254
  29. 29Duval, S., & Tweedie, R. (2000). Trim and fill: a simple funnel-plot–based method of testing and adjusting for publication bias in meta-analysis. Biometrics, 56(2), 455463. DOI: 10.1111/j.0006-341X.2000.00455.x
  30. 30Edwards, M. A., & Roy, S. (2017). Academic research in the 21st century: Maintaining scientific integrity in a climate of perverse incentives and hypercompetition. Environmental Engineering Science, 34(1), 5161. DOI: 10.1089/ees.2016.0223
  31. 31Egbewale, B. E., Lewis, M., & Sim, J. (2014). Bias, precision and statistical power of analysis of covariance in the analysis of randomized trials with baseline imbalance: a simulation study. BMC Medical Research Methodology, 14(1), 49. DOI: 10.1186/1471-2288-14-49
  32. 32Egger, M., Smith, G. D., Schneider, M., & Minder, C. (1997). Bias in meta-analysis detected by a simple, graphical test. BMJ, 315(7109), 629634. DOI: 10.1136/bmj.315.7109.629
  33. 33Etz, A., & Vandekerckhove, J. (2018). Introduction to Bayesian inference for psychology. Psychonomic Bulletin & Review, 25(1), 534. DOI: 10.3758/s13423-017-1262-3
  34. 34Faul, F., Erdfelder, E., Lang, A. G., & Buchner, A. (2007). G* Power 3: A flexible statistical power analysis program for the social, behavioral, and biomedical sciences. Behavior Research Methods, 39(2), 175191. DOI: 10.3758/BF03193146
  35. 35Fletcher, T. D. (2015). Package ‘psychometric’. Available at https://cran.r-project.org/web/packages/psychometric/psychometric.pdf
  36. 36Fraley, R. C., & Vazire, S. (2014). The N-pact factor: Evaluating the quality of empirical journals with respect to sample size and statistical power. PloS one, 9(10), e109019. DOI: 10.1371/journal.pone.0109019
  37. 37Francis, G. (2012). Publication bias and the failure of replication in experimental psychology. Psychonomic Bulletin & Review, 19(6), 975991. DOI: 10.3758/s13423-012-0322-y
  38. 38Fritz, C. O., Morris, P. E., & Richler, J. J. (2012). Effect size estimates: current use, calculations, and interpretation. Journal of Experimental Psychology: General, 141(1), 218. DOI: 10.1037/a0024338
  39. 39Garcia-Marques, L., Garcia-Marques, T., & Brauer, M. (2014). Buy three but get only two: The smallest effect in a 2 × 2 ANOVA is always uninterpretable. Psychonomic Bulletin & Review, 21(6), 14151430. DOI: 10.3758/s13423-014-0640-3
  40. 40Gigerenzer, G., & Marewski, J. N. (2015). Surrogate science: The idol of a universal method for scientific inference. Journal of Management, 41(2), 421440. DOI: 10.1177/0149206314547522
  41. 41Gignac, G. E., & Szodorai, E. T. (2016). Effect size guidelines for individual differences researchers. Personality and Individual Differences, 102, 7478. DOI: 10.1016/j.paid.2016.06.069
  42. 42Giner-Sorolla, R. (2018, January 24). Powering your interaction [Blog post]. Retrieved from https://approachingblog.wordpress.com/2018/01/24/powering-your-interaction-2/
  43. 43Gosling, S. D., & Mason, W. (2015). Internet research in psychology. Annual Review of Psychology, 66, 877902. DOI: 10.1146/annurev-psych-010814-015321
  44. 44Gosling, S. D., Vazire, S., Srivastava, S., & John, O. P. (2004). Should we trust web-based studies? A comparative analysis of six preconceptions about internet questionnaires. American Psychologist, 59(2), 93-104. DOI: 10.1037/0003-066X.59.2.93
  45. 45Hauser, D. J., & Schwarz, N. (2016). Attentive Turkers: MTurk participants perform better on online attention checks than do subject pool participants. Behavior Research Methods, 48(1), 400407. DOI: 10.3758/s13428-015-0578-z
  46. 46Higginson, A. D., & Munafò, M. R. (2016). Current incentives for scientists lead to underpowered studies with erroneous conclusions. PLoS Biology, 14(11), e2000995. DOI: 10.1371/journal.pbio.2000995
  47. 47Hilbig, B. E. (2016). Reaction time effects in lab-versus Web-based research: Experimental evidence. Behavior Research Methods, 48(4), 17181724. DOI: 10.3758/s13428-015-0678-9
  48. 48Hoenig, J. M., & Heisey, D. M. (2001). The abuse of power: the pervasive fallacy of power calculations for data analysis. The American Statistician, 55(1), 1924. DOI: 10.1198/000313001300339897
  49. 49John, L. K., Loewenstein, G., & Prelec, D. (2012). Measuring the prevalence of questionable research practices with incentives for truth telling. Psychological Science, 23(5), 524532. DOI: 10.1177/0956797611430953
  50. 50Johnson, V. E., Payne, R. D., Wang, T., Asher, A., & Mandal, S. (2017). On the reproducibility of psychological science. Journal of the American Statistical Association, 112(517), 110. DOI: 10.1080/01621459.2016.1240079
  51. 51Kelley, K., Maxwell, S. E., & Rausch, J. R. (2003). Obtaining power or obtaining precision: Delineating methods of sample-size planning. Evaluation & the Health Professions, 26(3), 258287. DOI: 10.1177/0163278703255242
  52. 52Kidwell, M. C., Lazarević, L. B., Baranski, E., Hardwicke, T. E., Piechowski, S., Falkenberg, L. S., Nosek, B., et al. (2016). Badges to acknowledge open practices: A simple, low-cost, effective method for increasing transparency. PLoS Biology, 14(5), e1002456. DOI: 10.1371/journal.pbio.1002456
  53. 53Klein, R. A., Vianello, M., Hasselman, F., Adams, B. G., Adams, R. B., Jr., Alper, S., Batra, R., et al. (2018). Many Labs 2: Investigating variation in replicability across samples and settings. Advances in Methods and Practices in Psychological Science, 1(4), 443490. DOI: 10.1177/2515245918810225
  54. 54Knofczynski, G. T., & Mundfrom, D. (2008). Sample sizes when using multiple linear regression for prediction. Educational and Psychological Measurement, 68(3), 431442. DOI: 10.1177/0013164407310131
  55. 55Kraemer, H. C., Mintz, J., Noda, A., Tinklenberg, J., & Yesavage, J. A. (2006). Caution regarding the use of pilot studies to guide power calculations for study proposals. Archives of General Psychiatry, 63(5), 484489. DOI: 10.1001/archpsyc.63.5.484
  56. 56Kruschke, J. K., & Liddell, T. M. (2018). The Bayesian New Statistics: Hypothesis testing, estimation, meta-analysis, and power analysis from a Bayesian perspective. Psychonomic Bulletin & Review, 25(1), 178206. DOI: 10.3758/s13423-016-1221-4
  57. 57Kühberger, A., Fritz, A., & Scherndl, T. (2014). Publication bias in psychology: a diagnosis based on the correlation between effect size and sample size. PloS One, 9(9), e105825. DOI: 10.1371/journal.pone.0105825
  58. 58Lachin, J. M. (1981). Introduction to sample size determination and power analysis for clinical trials. Controlled Clinical Trials, 2(2), 93113. DOI: 10.1016/0197-2456(81)90001-5
  59. 59Lakens, D. (2013). Calculating and reporting effect sizes to facilitate cumulative science: a practical primer for t-tests and ANOVAs. Frontiers in Psychology, 4, 863. DOI: 10.3389/fpsyg.2013.00863
  60. 60Lakens, D. (2014). Performing high-powered studies efficiently with sequential analyses. European Journal of Social Psychology, 44, 701710. DOI: 10.1002/ejsp.2023
  61. 61Lakens, D., Adolfi, F. G., Albers, C. J., Anvari, F., Apps, M. A., Argamon, S. E., Zwaan, R. A., et al. (2018). Justify your alpha. Nature Human Behaviour, 2(3), 168171. DOI: 10.1038/s41562-018-0311-x
  62. 62Lakens, D., Scheel, A. M., & Isager, P. M. (2018). Equivalence testing for psychological research: A tutorial. Advances in Methods and Practices in Psychological Science, 1(2), 259269. DOI: 10.1177/2515245918770963
  63. 63LeBel, E. P., McCarthy, R., Earp, B. D., Elson, M., & Vanpaemel, W. (in press). A unified framework to quantify the credibility of scientific findings. Advances in Methods and Practices in Psychological Science. Advance publication. DOI: 10.1177/2515245918787489
  64. 64Lenth, R. V. (2006). Java Applets for Power and Sample Size [Computer software]. Retrieved November, 9, 2018 from http://www.stat.uiowa.edu/~rlenth/Power
  65. 65Lindsay, D. S. (2015). Replication in psychological science. Psychological Science, 26(12), 18271832. DOI: 10.1177/0956797615616374
  66. 66Lindsay, D. S. (2017). Sharing data and materials in psychological science. Psychological Science, 28(6), 699702. DOI: 10.1177/0956797617704015
  67. 67Litman, L., Robinson, J., & Abberbock, T. (2017). TurkPrime.com: A versatile crowdsourcing data acquisition platform for the behavioral sciences. Behavior Research Methods, 49(2), 433442. DOI: 10.3758/s13428-016-0727-z
  68. 68Loiselle, D., & Ramchandra, R. (2015). A counterview of ‘An investigation of the false discovery rate and the misinterpretation of p-values’ by Colquhoun (2014). Royal Society Open Science, 2(8), 150217. DOI: 10.1098/rsos.150217
  69. 69Maxwell, S. E. (2000). Sample size and multiple regression analysis. Psychological Methods, 5(4), 434. DOI: 10.1037/1082-989X.5.4.434
  70. 70Maxwell, S. E. (2004). The persistence of underpowered studies in psychological research: causes, consequences, and remedies. Psychological Methods, 9(2), 147163. DOI: 10.1037/1082-989X.9.2.147
  71. 71Morey, R. D., Hoekstra, R., Rouder, J. N., Lee, M. D., & Wagenmakers, E. J. (2016). The fallacy of placing confidence in confidence intervals. Psychonomic Bulletin & Review, 23(1), 103123. DOI: 10.3758/s13423-015-0947-8
  72. 72Morey, R. D., Rouder, J. N., Jamil, T., Urbanek, S., Forner, K., & Ly, A. (2018). Package ‘BayesFactor’. Available at https://cran.r-project.org/web/packages/BayesFactor/BayesFactor.pdf
  73. 73Morris, S. B., & DeShon, R. P. (2002). Combining effect size estimates in meta-analysis with repeated-measures and independent-groups designs. Psychological Methods, 7(1), 105125. DOI: 10.1037/1082-989X.7.1.105
  74. 74Munafò, M. R., Nosek, B. A., Bishop, D. V., Button, K. S., Chambers, C. D., Du Sert, N. P., Ioannidis, J. P., et al. (2017). A manifesto for reproducible science. Nature Human Behaviour, 1(1), 0021. DOI: 10.1038/s41562-016-0021
  75. 75Murphy, K. R., Myors, B., & Wolach, A. (2014). Statistical power analysis: A simple and general model for traditional and modern hypothesis tests. Routledge. DOI: 10.4324/9781315773155
  76. 76Nosek, B. A., & Lakens, D. (2014). Registered reports. Social Psychology, 45, 137141. DOI: 10.1027/1864-9335/a000192
  77. 77O’Connell, N. S., Dai, L., Jiang, Y., Speiser, J. L., & Ward, R. (2017). Methods for Analysis of Pre-Post Data in Clinical Research: A Comparison of Five Common Methods. Journal of Biometrics & Biostatistics, 8(1). DOI: 10.4172/2155-6180.1000334
  78. 78Onghena, P., Michiels, B., Jamshidi, L., Moeyaert, M., & Van den Noortgate, W. (2018). One by one: Accumulating evidence by using meta-analytical procedures for single-case experiments. Brain Impairment, 19(1), 3358. DOI: 10.1017/BrImp.2017.25
  79. 79Open Science Collaboration. (2015). Estimating the reproducibility of psychological science. Science, 349(6251): aac4716. DOI: 10.1126/science.aac4716
  80. 80Paolacci, G., & Chandler, J. (2014). Inside the Turk: Understanding Mechanical Turk as a participant pool. Current Directions in Psychological Science, 23(3), 184188. DOI: 10.1177/0963721414531598
  81. 81Perugini, M., Gallucci, M., & Costantini, G. (2018). A Practical Primer to Power Analysis for Simple Experimental Designs. International Review of Social Psychology, 31(1): 20. DOI: 10.5334/irsp.181
  82. 82Pollatsek, A., & Well, A. D. (1995). On the use of counterbalanced designs in cognitive research: A suggestion for a better and more powerful analysis. Journal of Experimental Psychology: Learning, Memory, and Cognition, 21(3), 785794. DOI: 10.1037//0278-7393.21.3.785
  83. 83Pyc, M. A., & Rawson, K. A. (2010). Why testing improves memory: Mediator effectiveness hypothesis. Science, 330(6002), 335335. DOI: 10.1126/science.1191465
  84. 84Rouder, J. N., & Haaf, J. M. (2018). Power, dominance, and constraint: A note on the appeal of different design traditions. Advances in Methods and Practices in Psychological Science, 1(1), 1926. Preprint. DOI: 10.1177/2515245917745058
  85. 85Rouder, J. N., Morey, R. D., Verhagen, J., Swagman, A. R., & Wagenmakers, E. J. (2017). Bayesian analysis of factorial designs. Psychological Methods, 22(2), 304. DOI: 10.1037/met0000057
  86. 86Rouder, J. N., Speckman, P. L., Sun, D., Morey, R. D., & Iverson, G. (2009). Bayesian t tests for accepting and rejecting the null hypothesis. Psychonomic Bulletin & Review, 16(2), 225237. DOI: 10.3758/PBR.16.2.225
  87. 87Schimmack, U. (2015, September 5). The Replicability of Cognitive Psychology in the OSF-Reproducibility-Project [Blog post]. Retrieved from https://replicationindex.wordpress.com/2015/09/05/comparison-of-php-curve-predictions-and-outcomes-in-the-osf-reproducibility-project-part-2-cognitive-psychology/
  88. 88Schönbrodt, F. D., Wagenmakers, E. J., Zehetleitner, M., & Perugini, M. (2017). Sequential hypothesis testing with Bayes factors: Efficiently testing mean differences. Psychological Methods, 22(2), 322339. DOI: 10.1037/met0000061
  89. 89Shadish, W. R., Hedges, L. V., & Pustejovsky, J. E. (2014). Analysis and meta-analysis of single-case designs with a standardized mean difference statistic: A primer and applications. Journal of School Psychology, 52(2), 123147. DOI: 10.1016/j.jsp.2013.11.005
  90. 90Shrout, P. E., & Fleiss, J. L. (1979). Intraclass correlations: uses in assessing rater reliability. Psychological Bulletin, 86(2), 420428. DOI: 10.1037/0033-2909.86.2.420
  91. 91Simmons, J. P., Nelson, L. D., & Simonsohn, U. (2011). False-positive psychology: Undisclosed flexibility in data collection and analysis allows presenting anything as significant. Psychological Science, 22(11), 13591366. DOI: 10.1177/0956797611417632
  92. 92Simonsohn, U. (2014, March 12). No-way interactions [Blog post]. Retrieved from http://datacolada.org/17. DOI: 10.15200/winn.142559.90552
  93. 93Smaldino, P. E., & McElreath, R. (2016). The natural selection of bad science. Royal Society Open Science, 3(9), 160384. DOI: 10.1098/rsos.160384
  94. 94Smith, J. D. (2012). Single-case experimental designs: A systematic review of published research and current standards. Psychological Methods, 17(4), 510. DOI: 10.1037/a0029312
  95. 95Stanley, T. D., Carter, E. C., & Doucouliagos, H. (2018). What meta-analyses reveal about the replicability of psychological research. Psychological Bulletin, 144(12), 13251346. DOI: 10.1037/bul0000169
  96. 96Stevens, M., & Brysbaert, M. (2016). A simple solution for missing observations based on random effects models. Unpublished manuscript available at http://crr.ugent.be/members/marc-brysbaert.
  97. 97Szucs, D., & Ioannidis, J. P. (2017a). Empirical assessment of published effect sizes and power in the recent cognitive neuroscience and psychology literature. PLoS Biology, 15(3), e2000797. DOI: 10.1371/journal.pbio.2000797
  98. 98Szucs, D., & Ioannidis, J. (2017b). When null hypothesis significance testing is unsuitable for research: A reassessment. Frontiers in Human Neuroscience, 11, 390. DOI: 10.3389/fnhum.2017.00390
  99. 99Tomczak, M., Tomczak, E., Kleka, P., & Lew, R. (2014). Using power analysis to estimate appropriate sample size. Trends in Sport Sciences, 21(4), 195206. DOI: 10.1007/978-3-8349-3752-0_5
  100. 100Trafimow, D., & Myüz, H. A. (in press). The sampling precision of research in five major areas of psychology. Behavior Research Methods. Preprint available at https://link.springer.com/article/10.3758%2Fs13428-018-1173-x
  101. 101Vankov, I., Bowers, J., & Munafò, M. R. (2014). Article Commentary: On the Persistence of Low Power in Psychological Science. Quarterly Journal of Experimental Psychology, 67(5), 10371040. DOI: 10.1080/17470218.2014.885986
  102. 102Vasilev, M. R., Kirkby, J. A., & Angele, B. (2018). Auditory distraction during reading: A Bayesian meta-analysis of a continuing controversy. Perspectives on Psychological Science, 13(5), 567597. DOI: 10.1177/1745691617747398
  103. 103Verhaeghen, P. (2003). Aging and vocabulary score: A meta-analysis. Psychology and aging, 18(2), 332339. DOI: 10.1037/0882-7974.18.2.332
  104. 104Wagenmakers, E. J., Love, J., Marsman, M., Jamil, T., Ly, A., Morey, R. D., et al. (2018). Bayesian inference for psychology, Part II: Example applications with JASP. Psychonomic Bulletin and Review, 25(1), 5876. DOI: 10.3758/s13423-017-1323-7
  105. 105Wilkinson, L. (1999). Statistical methods in psychology journals: Guidelines and explanations. American Psychologist, 54(8), 594604. DOI: 10.1037/0003-066X.54.8.594
  106. 106Wilson, T. D., Reinhard, D. A., Westgate, E. C., Gilbert, D. T., Ellerbeck, N., Hahn, C., Shaked, A., et al. (2014). Just think: The challenges of the disengaged mind. Science, 345>(6192), 7577. DOI: 10.1126/science.1250830
  107. 107Zwaan, R. A., Pecher, D., Paolacci, G., Bouwmeester, S., Verkoeijen, P., Dijkstra, K., & Zeelenberg, R. (2018). Participant nonnaiveté and the reproducibility of cognitive psychology. Psychonomic Bulletin & Review, 25(5), 19681972. DOI: 10.3758/s13423-017-1348-y
DOI: https://doi.org/10.5334/joc.72 | Journal eISSN: 2514-4820
Language: English
Submitted on: Dec 17, 2018
Accepted on: May 22, 2019
Published on: Jul 19, 2019
Published by: Ubiquity Press
In partnership with: Paradigm Publishing Services
Publication frequency: 1 issue per year

© 2019 Marc Brysbaert, published by Ubiquity Press
This work is licensed under the Creative Commons Attribution 4.0 License.