Estudios de casos y controles: Propuesta de robustez de análisis para ciencias de la conducta
Resumen
En el campo de las ciencias de la conducta resulta indispensable realizar interpretaciones adecuadas de los resultados obtenidos que permitan el rechazo de la hipótesis nula. Se conoce que las características propias de la disciplina cuentan con desventajas para el abordaje estadístico en comparación con otras áreas aplicadas del conocimiento, donde por naturaleza se tiene mayor probabilidad de obtener datos con distribuciones normales y, por lo tanto, usar técnicas paramétricas. Dado lo anterior es que se realiza una recopilación de información de los elementos de mayor importancia sugeridos para ampliar mejorar la robustez de la interpretación tanto para técnicas paramétricas como para las no paramétricas.
Descargas
Citas
Al-Saleh, M. F., & Samawi, H. M. (2007). Interference on Overlapping Coefficients in Two Exponential Populations. Journal of Modern Applied Statistical Methods, 6(2), 503–516. https://doi.org/10.22237/jmasm/1193890440
APA. (2008). Reporting standards for research in psychology: Why do we need them? What might they be? American Psychologist, 63(9), 839–851. https://doi.org/10.1037/0003-066X.63.9.839
Appelbaum, M., Cooper, H., Kline, R. B., Mayo-Wilson, E., Nezu, A. M., & Rao, S. M. (2018). Journal article reporting standards for quantitative research in psychology: The APA Publications and Communications Board task force report. American Psychologist, 73(1), 3–25. https://doi.org/10.1037/amp0000191
Baguley, T. (2009). Standardized or simple effect size: What should be reported? British Journal of Psychology, 100(3), 603–617. https://doi.org/10.1348/000712608X377117
Begley, C. G., & Ellis, L. M. (2012). Raise standards for preclinical cancer research. Nature, 483(7391), 531–533. https://doi.org/10.1038/483531a
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
Chernoff, H., & Savage, I. R. (1958). Asymptotic Normality and Efficiency of Certain Nonparametric Test Statistics. The Annals of Mathematical Statistics, 29(4), 972–994. http://www.jstor.org/stable/2236941
Chmura-Kraemer, H. (2014). Wiley StatsRef: Statistics Reference Online (N. Balakrishnan, T. Colton, B. Everitt, W. Piegorsch, F. Ruggeri, & J. L. Teugels (eds.)). Wiley. https://doi.org/10.1002/9781118445112
Cohen, J. (2013). Statistical Power Analysis for the Behavioral Sciences. Routledge. https://doi.org/10.4324/9780203771587
Colquhoun, D. (2017). The reproducibility of research and the misinterpretation of p -values. Royal Society Open Science, 4(12), 171085. https://doi.org/10.1098/rsos.171085
Cureton, E. E. (1956). Rank-biserial correlation. Psychometrika, 21(3), 287–290. https://doi.org/10.1007/BF02289138
De la Fuente, E. I., Cañadas, G. R., Guàrdia, J., & Lozano, L. M. (2009). Hypothesis Probability or Statistical Significance? Methodology, 5(1), 35–39. https://doi.org/10.1027/1614-2241.5.1.35
Dixon, W. J. (1954). Power Under Normality of Several Nonparametric Tests. The Annals of Mathematical Statistics, 25(3), 610–614.
Dudley-Marling, C. (2010). The myth of the normal curve (1st ed.). Peter Lang.
Ellis, P. D. (2010). The Essential Guide to Effect Sizes. In The Essential Guide to Effect Sizes. Cambridge University Press. https://doi.org/10.1017/cbo9780511761676
Friedman, H. (1968). Magnitude of experimental effect and a table for its rapid estimation. Psychological Bulletin, 70(4), 245–251. https://doi.org/10.1037/h0026258
Galvez-Contreras, A. Y., Guzmán-Muñiz, J., Moy-López, N. A., & Gonzalez-Perez, O. (2022). Contributions of Latin America to scientific research in neuroscience and psychology. Revista Mexicana de Neurociencia, 23(2). https://doi.org/10.24875/RMN.21000034
García, J., Ortega, E., & De la Fuente, L. (2008). Tamaño del efecto en las revistas de Psicología indizadas en Redalyc. Informes Psicológicos, 10(11), 173–188.
Glass, G. V., McGaw, B., & G.V., S. (1981). Meta-Analysis in Social Research. SAGE publiclations inc.
Greenland, S., Senn, S. J., Rothman, K. J., Carlin, J. B., Poole, C., Goodman, S. N., & Altman, D. G. (2016). Statistical tests, P values, confidence intervals, and power: a guide to misinterpretations. European Journal of Epidemiology, 31(4), 337–350. https://doi.org/10.1007/s10654-016-0149-3
Gurnsey, R. (2017). Statistics for research in Psychology: A modern approach using estimation. SAGE publiclations inc.
Guzmán-González, J. I., Sánchez-García, F., Madera-Carrillo, H., & Medina-Aguayo, F. (2019). Propuesta de verificación de la robustez del análisis y comprobación de hipótesis en los resultados de estudios en neurociencia cognitiva, psicología y medicina. Revista Mexicana de Investigación En Psicología, 11(2).
Hartgerink, C. H. J., Wicherts, J. M., & van Assen, M. A. L. M. (2017). Too Good to be False: Nonsignificant Results Revisited. Collabra: Psychology, 3(1). https://doi.org/10.1525/collabra.71
Hedges, L. V. (1981). Distribution Theory for Glass’s Estimator of Effect size and Related Estimators. Journal of Educational Statistics, 6(2), 107–128. https://doi.org/10.3102/10769986006002107
Hill, M., & Dixon, W. J. (1982). Robustness in Real Life: A Study of Clinical Laboratory Data. Biometrics, 38(2), 377. https://doi.org/10.2307/2530452
Hodges, J. L., & Lehmann, E. L. (1956). The Efficiency of Some Nonparametric Competitors of the t-Test. The Annals of Mathematical Statistics, 27(2), 324–335. http://www.jstor.org/stable/2236996
Ioannidis, J. P. A. (2005). Why Most Published Research Findings Are False. PLoS Medicine, 2(8), e124. https://doi.org/10.1371/journal.pmed.0020124
Kerlinger, F. N. (1966). Foundations of behavioral research (Vol. 1). Holt, Rinehart and Winston. https://psycnet.apa.org/record/1966-35003-000
Kerri A. Goodwin, C., & James Goodwin. (2017). Research in Psychology: Methods and Design (8th ed.). John Wiley & Sons.
Kirk, R. E. (2003). The importance of effect magnitude. In S. Davis (Ed.), Handbook of Research Methods in Experimental Psychology (pp. 83–105). Blackwell.
Kitchen, C. M. R. (2009). Nonparametric vs Parametric Tests of Location in Biomedical Research. American Journal of Ophthalmology, 147(4), 571–572. https://doi.org/10.1016/j.ajo.2008.06.031
Luce, M. F., & Kahn, B. E. (1999). Avoidance Or Vigilance? the Psychology of False‐Positive Test Results. Journal of Consumer Research, 26(3), 242–259. https://doi.org/10.1086/209561
Mann, H. B., & Whitney, D. R. (1947). On a Test of Whether one of Two Random Variables is Stochastically Larger than the Other. The Annals of Mathematical Statistics, 18(1), 50–60. https://doi.org/10.1214/aoms/1177730491
Micceri, T. (1989). The unicorn, the normal curve, and other improbable creatures. Psychological Bulletin, 105(1), 156–166. https://doi.org/10.1037/0033-2909.105.1.156
Nosek, B. A., Spies, J. R., & Motyl, M. (2012). Scientific Utopia. Perspectives on Psychological Science, 7(6), 615–631. https://doi.org/10.1177/1745691612459058
Podsakoff, P. M., MacKenzie, S. B., Lee, J.-Y., & Podsakoff, N. P. (2003). Common method biases in behavioral research: A critical review of the literature and recommended remedies. Journal of Applied Psychology, 88(5), 879–903. https://psycnet.apa.org/buy/2003-08045-010
Rouder, 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), 225–237. https://doi.org/10.3758/PBR.16.2.225
Schönbrodt, F. D., & Wagenmakers, E.-J. (2018). Bayes factor design analysis: Planning for compelling evidence. Psychonomic Bulletin & Review, 25(1), 128–142. https://doi.org/10.3758/s13423-017-1230-y
Simmons, J. P., Nelson, L. D., & Simonsohn, U. (2011). False-Positive Psychology. Psychological Science, 22(11), 1359–1366. https://doi.org/10.1177/0956797611417632
SJR. (2022, September 29). International Science Ranking. https://www.scimagojr.com/countryrank.php?area=3200
Smith, P. L., & Little, D. R. (2018). Small is beautiful: In defense of the small-N design. Psychonomic Bulletin & Review, 25(6), 2083–2101. https://doi.org/10.3758/s13423-018-1451-8
Stigler, S. M. (1977). Do Robust Estimators Work with Real Data? The Annals of Statistics, 5(6). https://doi.org/10.1214/aos/1176343997
Szucs, D., & Ioannidis, J. P. A. (2017). Empirical assessment of published effect sizes and power in the recent cognitive neuroscience and psychology literature. PLOS Biology, 15(3), e2000797. https://doi.org/10.1371/journal.pbio.2000797
Tanizaki, H. (1997). Power comparison of non-parametric tests: Small-sample properties from Monte Carlo experiments. Journal of Applied Statistics, 24(5), 603–632. https://doi.org/10.1080/02664769723576
The jamovi project. (2020). Jamovi (1.2). https://www.jamovi.org
Wasserstein, R. L., Schirm, A. L., & Lazar, N. A. (2019). Moving to a World Beyond “p < 0.05.” American Statistician, 73(1), 1–19. https://doi.org/10.1080/00031305.2019.1583913
Wetzels, R., Matzke, D., Lee, M. D., Rouder, J. N., Iverson, G. J., & Wagenmakers, E.-J. (2011). Statistical Evidence in Experimental Psychology. Perspectives on Psychological Science, 6(3), 291–298. https://doi.org/10.1177/1745691611406923
Wilcoxon, F. (1945). Individual Comparisons by Ranking Methods. Biometrics Bulletin, 1(6), 80. https://doi.org/10.2307/3001968
Derechos de autor 2023 Jesua I. Guzmán-González, Franco G. Sánchez-García, Luis M. Sánchez-Loyo, Saúl Ramírez-de los Santos
Esta obra está bajo licencia internacional Creative Commons Reconocimiento-NoComercial-SinObrasDerivadas 4.0.
