A case of the Bayes factor in a comparative study according to gender of the fear of COVID-19 in Cuba

Authors

Keywords:

fear, COVID-19, mental health, quantitaive analysis, statistical techniques.

Abstract

A complementary analysis is reported using the Bayes Factor to contrast the statistical hypotheses of difference in a recent comparative study of fear of COVID-19 according to gender in Cuba, this methodological contribution allows quantifying the evidence of the alternative hypothesis in relation to the null hypothesis of greater use in clinical research, which confirms the objective of a higher level in women than in men according to the scientific literature. This Bayesian analysis serves as a guide to reinforce the credibility of future research findings in this journal and to spread replication in the health sciences.

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Author Biography

Cristian Antony Ramos Vera, UNIVERSIDAD CESAR VALLEJO

ASOCIADO AL AREA DE INVESTIGACION DE LA UNIVERSIDAD CESAR VALLEJO. FACULTAD DE CIENCIAS DE LA SALUD

MIEMBRO DE LA SOCIEDAD PERUANA DE PSICOMETRIA

References

1. Broche-Pérez Y, Fernández-Fleites Z, Jiménez-Puig E. Fernández-Castillo E, Rodríguez-Martin BC. Gender and Fear of COVID-19 in a Cuban Population Sample. Int J Ment Health Addict. 2020 [acceso: 08/27/2020];1-9. DOI: 10.1007/s11469-020-00343-8

2. Kelter R. Bayesian alternatives to null hypothesis significance testing in biomedical research: a non-technical introduction to Bayesian inference with JASP: BMC Med Res Methodol. 2020 [acceso: 08/27/2020]; 20:1-12. DOI:10.1186/s12874-020-00980-6

3. Marsmamn M, Wagenmakers EJ. Bayesian benefits with JASP. Eur. J. Dev. Psychol. 2017 [acceso: 08/27/2020];14(5):545-55. Disponible en: https://www.tandfonline.com/doi/full/10.1080/17405629.2016.1259614

4. Ly A, Raj A, Etz A, Gronau QF, Wagenmakers EJ. Bayesian reanalyses from summary statistics: a guide for academic consumers. Adv Meth Pract Psychol Sci. 2018 [acceso: 08/27/2020]; 1(3):367-74. Disponible en: https://journals.sagepub.com/doi/full/10.1177/2515245918779348

5. Jeffreys H. Theory of probability. Oxford: Oxford University Press; 1961.

6. Ahorsu DK, Lin CY, Imani V, Saffari M, Griffiths MD, Pakpour AH. The Fear of COVID-19 Scale: Development and Initial Validation. Int J Ment Health Addict. 2020 [acceso: 08/27/2020];1-9. DOI: 10.1007/s11469-020-00270-8

7. Kelter R. Bayesian and frequentist testing for differences between two groups with parametric and nonparametric two‐sample tests. WIREs Comput. Stat. 2020 [acceso: 08/27/2020]; e15235:393-419. DOI:10.1002/wics.1523

8. Ramos-Vera CA. Replicación bayesiana: cuán probable es la hipótesis nula e hipótesis alterna. Educ Med. 2020 [acceso: 08/27/2020] En prensa.
DOI: 10.1016/j.edumed.2020.09.014

9. Brydges CR. Effect Size Guidelines, Sample Size Calculations, and Statistical Power in Gerontology. Innov Aging. 2019; [acceso: 08/27/2020]; 3(4): igz036 DOI:10.1093/geroni/igz036

10. Ramos-Vera CA. The Bayes Factor, a Suitable Complement beyond Values of p<0.05 in Nursing Research and Education. Invest. Educ. Enferm. 2021[acceso: 03/07/2021]; 39(1): e14. DOI: 10.17533/udea.iee.v39n1e14

Published

2021-04-01

How to Cite

1.
Ramos Vera CA. A case of the Bayes factor in a comparative study according to gender of the fear of COVID-19 in Cuba. Rev Cubana Med Milit [Internet]. 2021 Apr. 1 [cited 2025 Mar. 31];50(2):e0210980. Available from: https://revmedmilitar.sld.cu/index.php/mil/article/view/980

Issue

Section

Letter to the Editor