There is a newer version of the record available.

Published September 8, 2020 | Version 2
Other Open

Análisis de poder estadístico y cálculo de tamaño de muestra en R: Guía práctica

Authors/Creators

  • 1. Universidad El Bosque

Description

Esta guía práctica acompaña la serie de videos Poder estadístico y tamaño de muestra en R, de mi canal de YouTube Investigación Abierta, que recomiendo ver antes de leer este documento. Contiene una explicación general del análisis de poder estadístico y cálculo de tamaño de muestra, centrándose en el procedimiento para realizar análisis de poder y tamaños de muestra en jamovi y particularmente en R, usando los paquetes pwr (para diseños sencillos) y Superpower (para diseños factoriales más complejos). La sección dedicada a pwr está ampliamente basada en este video de Daniel S. Quintana (2019).

Notes

Fuentes y citas: Con la intención de sustentar claramente, pero de forma sencilla, la información presentada, incluyo varias citas a lo largo del documento que, creo, podrían servir a estudiantes, docentes e investigadores para explorar un tema particular con mayor profundidad, o soportar una decisión en un proyecto de investigación. Las referencias completas de todas las citas (incluyendo hipervínculos a las fuentes), están al final del documento.

Files

Análisis de Poder en R.pdf

Files (1.4 MB)

Name Size Download all
md5:179626f5d8302e3945fa76de30906143
1.4 MB Preview Download

Additional details

Related works

Is supplemented by
Other: 10.17605/OSF.IO/3QX6A (DOI)

References

  • 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, 74, 187–195. https://doi.org/10. 1016/j.jesp.2017.09.004
  • Baker, M. (2016). 1,500 scientists lift the lid on reproducibility. Nature News, 533 (7604), 452. https://doi.org/10. 1038/533452a
  • Bakker, A., Cai, J., English, L., Kaiser, G., Mesa, V., & Van Dooren, W. (2019). Beyond small, medium, or large: Points of consideration when interpreting effect sizes. Educational Studies in Mathematics, 102 (1), 1–8. https://doi.org/10.1007/s10649-019-09908-4
  • Benjamin, D. J., Berger, J. O., Johannesson, M., Nosek, B. A., Wagenmakers, E.-J., Berk, R., Bollen, K. A., Brembs, B., Brown, L., Camerer, C., Cesarini, D., Chambers, C. D., Clyde, M., Cook, T. D., De Boeck, P., Dienes, Z., Dreber, A., Easwaran, K., Efferson, C., . . . Johnson, V. E. (2018). Redefine statistical significance. Nature Human Behaviour, 2 (1), 6–10. https://doi.org/10.1038/s41562-017-0189-z
  • Blakesley, R. E., Mazumdar, S., Dew, M. A., Houck, P. R., Tang, G., Reynolds III, C. F., & Butters, M. A. (2009). Comparisons of methods for multiple hypothesis testing in neuropsychological research. Neuropsychology, 23 (2), 255–264. https://doi.org/10.1037/a0012850
  • Bonferroni, C. E. (1936). Teoria statistica delle classi e calcolo delle probabilità. Pubblicazioni Del R Istituto Superiore Di Scienze Economiche E Commerciali Di Firenze.
  • Button, K. S., Ioannidis, J. P. A., Mokrysz, C., Nosek, B. A., Flint, J., Robinson, E. S. J., & Munafò, M. R. (2013). Power failure: Why small sample size undermines the reliability of neuroscience. Nature Reviews Neuroscience, 14 (5), 365–376. https://doi.org/10.1038/nrn3475
  • Caldwell, A. R., & Lakens, D. (2020). Power Analysis with Superpower. https://aaroncaldwell.us/SuperpowerBook/.
  • Caldwell, A. R., Lakens, D., DeBruine, L., & Love, J. (2020). Superpower: Simulation-Based Power Analysis for Factorial Designs.
  • Champely, S., Ekstrom, C., Dalgaard, P., Gill, J., Weibelzahl, S., Anandkumar, A., Ford, C., Volcic, R., & Rosario, H. D. (2020). Pwr: Basic Functions for Power Analysis.
  • Chatham, K. (1999). Planned Contrasts: An Overview of Comparison Methods.
  • Cliff, N. (1996). Ordinal Methods for Behavioral Data Analysis. Psychology Press. https://doi.org/10.4324/ 9781315806730
  • Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Erlbaum.
  • Cohen, J. (1992). A power primer. Psychological Bulletin, 112 (1), 155–159. https://doi.org/10.1037/0033-2909. 112.1.155
  • Correa, J. C. (2020). Scripts en R [Video]. In YouTube. https://www.youtube.com/watch?v=ejQ0BS2gVJI.
  • Correll, J., Mellinger, C., McClelland, G. H., & Judd, C. M. (2020). Avoid Cohen's "Small", "Medium", and "Large" for Power Analysis. Trends in Cognitive Sciences, 24 (3), 200–207. https://doi.org/10.1016/j.tics.2019.12.009
  • Faul, F., Erdfelder, E., Buchner, A., & Lang, A.-G. (2009). Statistical power analyses using G*Power 3.1: Tests for correlation and regression analyses. Behavior Research Methods, 41 (4), 1149–1160. https://doi.org/10.3758/ BRM.41.4.1149
  • Faul, 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), 175–191. https: //doi.org/10.3758/BF03193146
  • Goedhart, J. (2016). Calculation of a distribution free estimate of effect size and confidence intervals using VBA/Excel. bioRxiv, 073999. https://doi.org/10.1101/073999
  • Holm, S. (1979). A Simple Sequentially Rejective Multiple Test Procedure. Scandinavian Journal of Statistics, 6 (2), 65–70.
  • Huberty, C. J., & Lowman, L. L. (2000). Group Overlap as a Basis for Effect Size. Educational and Psychological Measurement, 60 (4), 543–563. https://doi.org/10.1177/0013164400604004
  • Lakens, D. (2017). Equivalence Tests: A Practical Primer for t Tests, Correlations, and Meta-Analyses. Social Psychological and Personality Science. https://doi.org/10.1177/1948550617697177
  • Lakens, D., Adolfi, F. G., Albers, C. J., Anvari, F., Apps, M. A. J., Argamon, S. E., Baguley, T., Becker, R. B., Benning, S. D., Bradford, D. E., Buchanan, E. M., Caldwell, A. R., Van Calster, B., Carlsson, R., Chen, S.-C., Chung, B., Colling, L. J., Collins, G. S., Crook, Z., . . . Zwaan, R. A. (2018). Justify your alpha. Nature Human Behaviour, 2 (3), 168–171. https://doi.org/10.1038/s41562-018-0311-x
  • Lakens, D., & Caldwell, A. R. (2020). Introduction to Superpower. In The Comprehensive R Archive Network. http://shorturl.at/fnDX6.
  • Lakens, 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), 259–269. https://doi.org/10.1177/ 2515245918770963
  • Loken, E., & Gelman, A. (2017). Measurement error and the replication crisis. Science, 355 (6325), 584–585. https://doi.org/10.1126/science.aal3618
  • Macbeth, G., Razumiejczyk, E., & Ledesma, R. D. (2011). Cliffs Delta Calculator: A non-parametric effect size program for two groups of observations. Universitas Psychologica, 10 (2), 545–555. https://doi.org/10.11144/ Javeriana.upsy10-2.cdcp
  • Quintana, D. S. (2017). Statistical considerations for reporting and planning heart rate variability case-control studies. Psychophysiology, 54 (3), 344–349. https://doi.org/10.1111/psyp.12798
  • Quintana, D. S. (2019). A non-technical guide to performing power analysis in R [Video]. In YouTube. https://youtu.be/ZIjOG8LTTh8.
  • Selya, A. S., Rose, J. S., Dierker, L. C., Hedeker, D., & Mermelstein, R. J. (2012). A practical guide to calculating Cohen's f2, a measure of local effect size, from PROC MIXED. Frontiers in Psychology, 3, 111. https://doi.org/ 10.3389/fpsyg.2012.00111
  • Streiner, D. L. (2015). Best (but oft-forgotten) practices: The multiple problems of multiplicityWhether and how to correct for many statistical tests. The American Journal of Clinical Nutrition, 102 (4), 721–728. https: //doi.org/10.3945/ajcn.115.113548