Corrigendum: Linear mixed-effects models for within-participant psychology experiments: An introductory tutorial and free, graphical user interface (LMMgui) [Front. Psychol. 6, (2015), (2)] doi: 10.3389/fpsyg.2015.00002;Linear mixed-effects models for within-participant psychology experiments: An introductory tutorial and free, graphical user interface (LMMgui)

Frontiers in Psychology

Volumn 6, Issue JAN, 2015, Pages

  Magezi, David A ^a  

^a University of Fribourg   (Switzerland)

Author keywords

Experimental psychology; Graphical user interface; Linear mixed effects models; R; Within participant design

Indexed keywords

EID: 84926428535     PISSN: None     EISSN: 16641078     Source Type: Journal    
DOI: 10.3389/fpsyg.2019.00489     Document Type: Erratum

References (32)

1. Aarts, E., Verhage, M., Veenvliet, J. V., Dolan, C. V., and van der Sluis, S. (2014). A solution to dependency: using multilevel analysis to accommodate nested data. Nat. Neurosci. 17, 491-496. doi: 10.1038/nn.3648.
2. Baayen, R. H. (2008). Analyzing Linguistic Data: A Practical Introduction to Statistics. Cambridge: Cambridge University Press. doi: 10.1017/CBO9780511801686.
3. Baayen, R. H., Davidson, D. J., and Bates, D. M. (2008). Mixed-effects modeling with crossed random effects for subjects and items. J. Mem. Lang. 59, 390-412. doi: 10.1016/j.jml.2007.12.005.
4. Barr, D. J. (2013). Random effects structure for testing interactions in linear mixed-effects models. Front. Psychol. 4:328. doi: 10.3389/fpsyg.2013.00328.
5. Barr, D. J., Levy, R., Scheepers, C., and Tily, H. J. (2013). Random effects structure for confirmatory hypothesis testing: keep it maximal. J. Mem. Lang. 68, 255-278. doi: 10.1016/j.jml.2012.11.001.
6. Bates, D. M. (2010). lme4: Mixed-Effects Modelling with R. Available online at: http://lme4.r-forge.r-project.org/lMMwR/lrgprt.pdf.
7. Bates, D. M., Mächler, M., Bolker, B. M., and Walker, S. C. (2014a). lme4: Linear Mixed-Effects Models Using Eigen and S4. R Package Version 1.1-7. Available online at: http://CRAN.R-project.org/package=lme4.
8. Bates, D. M., Mächler, M., Bolker, B. M., and Walker, S. C. (2014b). Fitting Linear Mixed-Effects Models Using lme4. Available online at: http://arxiv.org/abs/1406.5823 (submitted to Journal of Statistical Software).
9. Crawley, M. J. (2013). The R Book. Chichester: Wiley. doi: 10.1002/9781118448908.
10. Dixon, P. (2008). Models of accuracy in repeated-measures designs. J. Mem. Lang. 59, 447-456. doi: 10.1016/j.jml.2007.11.004.
11. Gueorguieva, R., and Krystal, J. H. (2004). Move over ANOVA: progress in analyzing repeated-measures data and its reflection in papers published in the archives of general psychiatry. Arch. Gen. Psychiatry 51, 310-317. doi: 10.1001/archpsyc.61.3.310.
12. Howell, D. C. (2009). Statistical Methods for Psychology. Belmont, CA: Wadsworth.
13. Ioannidis, J. P. A. (2005). Why most published research findings are false. PLoS Med 2:e124. doi: 10.1371/journal.pmed.0020124.
14. Ioannidis, J. P. A., Munafo, M. R., Fusar-Poli, P., Nosek, B. A., and David, S. P. (2014). Publication and other reporting biases in cognitive sciences: detection, prevalence, and prevention. Trends Cogn. Sci. 18, 235-241. doi: 10.1016/j.tics.2014.02.010.
15. Jaeger, T. F. (2008). Categorical data analysis: away from ANOVAs (transformation or not) and towards logit mixed models. J. Mem. Lang. 59, 434-446. doi: 10.1016/j.jml.2007.11.007.
16. Judd, C. M., Westfall, J., and Kenny, D. A. (2012). Treating stimuli as a random factor in social psychology: a new and comprehensive solution to a pervasive but largely ignored problem. J. Pers. Soc. Psychol. 103, 54-69. doi: 10.1037/a0028347.
17. Keselman, H. J., Algina, J., and Kowalchuk, R. K. (2001). The analysis of repeated measures designs: a review. Br. J. Math. Stat. Psychol. 54, 1-20. doi: 10.1348/000711001159357.
18. Kristensen, M., and Hansen, T. (2004). Statistical analyses of repeated measures in physiological research: a tutorial. Adv. Physiol. Educ. 28, 2-14. doi: 10.1152/advan.00042.2003.
19. Lazic, S. E. (2010). The problem of pseudoreplication in neuroscientific studies: is it affecting your analysis? BMC Neurosci. 11:5. doi: 10.1186/1471-2202-11-5.
20. Maxwell, S. E., and Delaney, H. D. (2004). Designing Experiments and Anlalyzing Data: A Model Comparison Perspective. New York, NY: Psychology Press.
21. Nimon, K. F. (2012). Statistical assumptions of substantive analyses across the general linear model: a mini-review. Front. Psychol. 3:322. doi: 10.3389/fpsyg.2012.00322.
22. Nuzzo, R. (2014). Scientific method: statistical errors. Nature 13, 150-152. doi: 10.1038/506150a.
23. Oberfeld, D., and Franke, T. (2012). Evaluating the robustness of repeated measures analyses: the case of small sample sizes and nonnormal data. Behav. Res. 45, 792-812. doi: 10.3758/s13428-012-0281-2.
24. Overall, J. E., and Tonidandel, S. (2006). A two-stage analysis of repeated measures with dropouts and/or intermittent missing data. J. Clin. Psychol. 62, 285-291. doi: 10.1002/jclp.20217.
25. Pinheiro, J. C., and Bates, D. M. (2000). Mixed-Effects Models in S and S-Plus. New York, NY: Springer. doi: 10.1007/978-1-4419-0318-1.
26. Quené, H., and van den Bergh, H. (2004). On multi-level modeling of data from repeated measures designs: a tutorial. Speech Commun. 43, 103-121. doi: 10.1016/j.specom.2004.02.004.
27. R Core Team. (2014). R: A Language and Environment for Statistical Computing. Available online at: http://www.R-project.org.
28. Rosenthal, R., and Gaito, J. (1963). The interpretation of levels of significance by psychological researchers. J. Psychol. 55, 33-38.
29. Rosnow, R. L., and Rosenthal, R. (1989). Statistical procedures and the justification of knowledge in psychological science. Am. Psychol. 44, 1276-1284. doi: 10.1037/0003-066X.44.10.1276.
30. Simmons, J. P., Nelson, L. D., and Simonshohn, U. (2011). False-positive psychology: undisclosed flexibility in data collection and analysis allows presenting anything as significant. Psychol. Sci. 11, 1369-1366. doi: 10.1177/0956797611417632.
31. Tukey, J. W. (1980). We need both exploratory and confirmatory. Am. Stat. 34, 23-25.
32. Wagenmakers, E.-J., Wetzels, R., Borsboom, D., van der Maas, H. L. J., and Kievit, R. A. (2012). An agenda for purely confirmatory research. Perspect. Psychol. Sci. 7, 632-638. doi: 10.1177/1745691612463078.