Bibliometric Analysis of the Studies Determined By the Monte Carlo Simulation Technique of the Power of the Test

Abstract

In order to give detailed information about the characteristics of scientific studies related to the power of the test, a bibliometric analysis of studies in which the power of the tests used in statistical data analysis was determined with the aid of Monte Carlo simulation techniques was carried out. The Web of Science (WoS) database's 1309 scientific studies in which the power of the test was determined by using Monte Carlo simulation techniques served as the study's material of data. The analysis includes the number of studies by year, average citation count by year, the number of articles published in scientific journals, countries of responsible authors, and the most relevant words in the studies. It was observed that Communications in Statistics-Simulation and Computation and Journal of Statistical Computation and Simulation are the journals with the highest number of published articles. The United States of America (USA) takes the lead when considering the countries of corresponding authors, while Türkiye is in 8th place. The most used keywords in the ten-year time period of scientific studies were respectively “power”, “test”, “skewness”, “model” and “inference”. As a result, it can be concluded that test power studies can be obtained safely using the Monte Carlo simulation technique.

Keywords

• Type I error probability
• Test power
• Bibliometric analysis
• Monte Carlo
• Simulation

References

1. Aria M, Cuccurullo C. 2017. Bibliometrix: An R-tool for comprehensive science mapping analysis. J Informetr, 11(4): 959-975.
2. Baspinar E, Gurbuz F. 2000. The power of the test in the samples of various sample sizes were taken from the binary combinations of the normal, beta, gamma and Weibull distributions. J Agric Sci, 6(1): 116-127.
3. Burton A, Altman DG, Royston P, Holder RL. 2006. The design of simulation studies in medical statistics. Stat Med, 25(24): 4279-4292.
4. Büyükkidik S. 2022. A Bibliometric Analysis: A tutorial for the bibliometrix package in r using IRT literature. J Meas Eval, 13(3): 164-193.
5. Çavuş M. 2020. The Computational approach tests for testing the equality of population means under two-parameter exponential distribution. PhD thesis, Eskisehir Technical University, Institute of Graduate Programs, Eskisehir, Türkiye, pp: 105.
6. Donthu N, Kumar S, Mukherjee D, Pandey N, Lim WM. 2021. How to conduct a bibliometric analysis: An overview and guidelines. J Bus Res, 133: 285-296.
7. Ferreira EB, Rocha MC, Mequelino DB. 2012. Monte Carlo evaluation of the ANOVA's F and Kruskal-Wallis tests under binomial distribution. Sigmae, 1(1): 126-139.
8. Han J, Kang HJ, Kim M, Kwon GH. 2020. Mapping the intellectual structure of research on surgery with mixed reality: Bibliometric network analysis (2000–2019). J Biomed Inform, 109: 103516.

9. Koşkan Ö, Gürbüz F. 2009. Comparison of F test and resampling approach for type i error rate and test power by simulation method. J Agric Sci, 15(1): 105-111.
10. Kroese DP, Brereton T, Taimre T, Botev ZI. 2014. Why the Monte Carlo method is so important today. Interdiscip Rev Comput Stat, 6: 386-392.
11. Lantz B. 2013. The impact of sample nonnormality on ANOVA and alternative methods. Br J Math Stat Psychol, 66(2): 224–244.
12. Lloyd CJ. 2005. Estimating test power adjusted for size. J Stat Comput Simul, 75(11): 921-933.
13. Mahapoonyanont N, Mahapoonyanont T, Pengkaew N, Kamhangkit R. 2010. Power of the test of one-way anova after transforming with large sample size data. Procedia Soc, 9: 993-937.
14. Moral-Muñoz JA, Herrera-Viedma E, Santisteban-Espejo A, Cobo MJ. 2020. Software tools for conducting bibliometric analysis in science: An up-to-date review. Prof de la Inf, 29(1).
15. Morris TP, White IR., Crowther MJ. 2019. Using simulation studies to evaluate statistical methods. Stat Med, 38: 2074–2102.
16. Murphy KR, Myors B, Wolach A. 2014. Statistical power analysis: a simple and general model for traditional and modern hypothesis tests. Routledge, New York, USA, pp: 244.
17. Öztuna D, Elhan AH, Tüccar E. 2006. Investigation of four different normality tests in terms of type I error rate and power under different distributions. Turk J Med Sci, 36(3): 171-176.
18. Patric JD. 2009. Simulations to analyze type I error and power in the anova F test and nonparametric alternatives. Master Thesis, University of West Florida, Florida, USA, pp: 80.
19. Zhang J, Boos DD. 1994. Adjusted power estimates in Monte Carlo experiments. Commun Stat Simul, 23(1): 165-173.