Preview

Theory and practice of meat processing

Advanced search

Methods for nonparametric statistics in scientific research. Overview. Part 1.

https://doi.org/10.21323/2414-438X-2021-6-2-151-162

Full Text:

Abstract

Daily, researcher faces the need to compare two or more observation groups obtained under different conditions in order to confirm or argue against a scientific hypothesis. At this stage, it is necessary to choose the right method for statistical analysis. If the statistical prerequisites are not met, it is advisable to choose nonparametric analysis. Statistical analysis consists of two stages: estimating model parameters and testing statistical hypotheses. After that, the interpretation of the mathematical processing results in the context of the research object is mandatory. The article provides an overview of two groups of nonparametric tests: 1) to identify differences in indicator distribution; 2) to assess shift reliability in the values of the studied indicator. The first group includes: 1) Rosenbaum Q-test, which is used to assess the differences by the level of any quantified indicator between two unrelated samplings; 2) Mann-Whitney U-test, which is required to test the statistical homogeneity hypothesis of two unrelated samplings, i. e. to assess the differences by the level of any quantified indicator between two samplings. The second group includes sign G-test and Wilcoxon T-test intended to determine the shift reliability of the related samplings, for example, when measuring the indicator in the same group of subjects before and after some exposure. Examples are given; step-by-step application of each test is described. The first part of the article describes simple nonparametric methods. The second part describes nonparametric tests for testing hypotheses of distribution type (Pearson’s chi-squared test, Kolmogorov test) and nonparametric tests for testing hypotheses of sampling homogeneity (Pearson’s chi-squared test for testing sampling homogeneity, Kolmogorov-Smirnov test).

About the Authors

M. A. Nikitina
V. M. Gorbatov Federal Research Center for Food Systems of Russian Academy of Sciences
Russian Federation

Marina A. Nikitina —   candidate of technical sciences, docent, leading scientific worker, the Head of the Direction of Information Technologies of the Center of Economic and Analytical Research and Information Technologies

26, Talalikhina, 109316, Moscow

Tel: +7–495–676–95–11 extension 297



I. M. Chernukna
V. M. Gorbatov Federal Research Center for Food Systems of Russian Academy of Sciences
Russian Federation

Irina M. Chernukha —  doctor of technical sciences, professor, Academician of the Russian Academy of Sciences, Head of the Department for Coordination of Initiative and International Projects

26, Talalikhina, 109316, Moscow

Tel: +7–495–676–95–11 extension 109



References

1. Fisher, R.A. (1992). Statistical methods for research workers. Chapter in a book: Breakthroughs in statistics. Springer Series in Statistics (Perspectives in Statistics). New York: Springer. 1992. https://doi.org/10.1007/978–1–4612–4380–96

2. Plokhinskiy, N.A. (1978). Mathematical methods in biology. Moscow: Moscow State University. 1978. (In Russian)

3. Glants, S. (1998). Medical and biological statistics. Moscow: Practice. 1998. (In Russian)

4. Lakin, G.F. (1990). Biometrics. Moscow: High School. 1990. (in Russian)

5. Rokitskiy, P.F. (1973). Biological statistics. Minsk: High School. 1973. (In Russian)

6. Snedecor, G.W. (1957). Statistical methods applied to experiments in agriculture and biology. US: The Iowa State college press. 1957.

7. Gubler, E.V., Genkin, A.A. (1973). Application of nonparametric statistical criteria in biomedical research. Leningrad: Medicine. 1973. (In Russian)

8. Prokhorov, Yu.V. (1999). Probability and mathematical statistics: Encyclopedia. Moscow: The Great Russian Encyclopedia. 1999. (In Russian)

9. Hollander, M., Wolfe, D.A. (1999). Nonparametric statistical methods. New York: Wiley-Interscience. 1999.

10. Hettmansperger, T.P. (1991). Statistical inference based on ranks. New York: Krieger Pub Co. 1991.

11. Kendall, M.G. (1962). Rank correlation methods. Chicago: Hafner Publishing Company. 1962.

12. Gibbons, J., Chakraborti, S. (2003). Nonparametric statistical inference. Fourth Edition, Revised and Expanded. New York: Marcel Dekker. 2003.

13. Lehmann, E.L. (1975). Nonparametric statistical methods based on ranks. San Francisco, Düsseldorf etc.: McGraw-Hill. 1975.

14. Puri, M.L., Sen, P.K. (1985). Nonparametric methods in general linear models. New York etc.: Wiley. 1985.

15. Pratt, J.W., Gibbons, J.D. (1981). Concepts of nonparametric theory. New York: Springer-Verlag. 1981. https://doi.org/10.1007/978–1–4612–5931–2

16. Sidak, Z., Sen, P.K., Hajek, J. (1999). Theory of rank tests. US: Academic Press. 1999.

17. Tyurin, Yu.N., Makarov, A.A. (1998). Statistical analysis of data on a computer. Moscow: Infra-M. 1998. (In Russian)

18. Tyurin, Yu.N., Shmerling, D.S. (2004). Nonparametric methods of statistics. Sociology: Methodology, Methods, Mathematical models, 18, 154–166. (In Russian)

19. Siegel, S. (1957). Nonparametric statistics. The American Statistician, 11 (3), 13–19. https://doi.org/10.1080/01621459.1957.10501392

20. Mann, H.B., Whitney, D.R. (1947). On a test of whether one of two random variables is stochastically larger than the other. Annals of Mathematical Statistics, 18, 50–60. https://doi.org/10.1214/aoms/1177730491

21. Rosenbaum, S. (1954). Tables for a nonparametric test of location. Annals of Mathematical Statistics, 25 (1), 146–150. https://doi.org/10.1214/aoms/1177728854

22. Wilcoxon, F. (1945). Individual Comparisons by ranking methods. Biometrics Bulletin, 1(6), 80–83. http://www.jstor.org/stable/3001968.

23. Wilcoxin, F. (1947). Probability tables for individual comparisons by ranking methods. Biometrics,3(3), 119–122. https://doi.org/10.2307/3001946

24. Wilcoxon, F. (1946). Individual comparisons of grouped data by ranking methods. Journal of Economic Entomology, 39, 269. https://doi.org/10.1093/jee/39.2.269

25. Stepanov, V.G. (2019). Application of nonparametric statistical methods in agricultural biology and veterinary medicine research. St-Petersburg: Lan. 2019. (In Russian)

26. Edelbaeva, N.A., Lebedinskaya, O.G., Kovanova, E.S., Tenetova, E.P., Timofeev, A.G. (2019). Fundamentals of nonparametric statistics. Moscow: YUNITY-DANA. 2019. (In Russian)

27. Kuz’mina, E.V., P’yankova, N.G., Tret’yakova, N.V., Botsoeva, A.V. (2020). Using data analysis methodology to foster professional competencies in business informaticians. European Journal of Contemporary Education, 9(1), 54–66. https://doi.org/10.13187/ejced.2020.1.54

28. Podrihalo, O., Savina, S., Podrigalo, L., Iermakov, S., Jagiełło, W., Rydzik, Ł. et al. (2020). Influence of health-related fitness on the morphofunctional condition of second mature aged women. International Journal of Environmental Research and Public Health, 17(22), Article 8465, 1–9. https://doi.org/10.3390/ijerph17228465

29. Kotlyarova, I., Chuvashova, A. (2020). Educational imitation of basic job function using the knowledge of English among technical Major students. International Journal of Instruction, 14(1), 303–324. https://doi.org/10.29333/IJI.2021.14118A

30. Mueller, S. K., Nocera, A. L., Dillon, S. T., Libermann, T. A., Wendler, O., Bleier, B. S. (2019). Tissue and exosomal serine protease inhibitors are significantly overexpressed in chronic rhinosinusitis with nasal polyps. American Journal of Rhinology and Allergy, 33(4), 359–368. https://doi.org/10.1177/1945892419831108

31. Gilic, F., Schultz, K., Sempowski, I., Blagojevic, A. (2019). “Nightmares-family medicine” course is an effective acute care teaching tool for family medicine residents. Simulation in Healthcare, 14(3), 157–162. https://doi.org/10.1097/SIH.0000000000000355

32. Zabetian, H., Sadeghi, F., Falah, A., Kalani, N. (2018). Studying the Effect of Intravenous Injections of Ketorolac (IVIK) on analgesia control before and after using tourniquet in orthopedic surgery of femur and tibia by general anesthesia. Journal of Research in Medical and Dental Science, 6(2), 227–232. https://doi.org/10.24896/jrmds.20186235

33. Spielberger, J., Heid, F., Schmidtmann, I., Drees, P., Betz, U., Schwaderlapp, W. et al. (2021). Patient-centered perioperative vigilance: perioperative process quality, effectiveness of pain treatment and mobilization progress after implementation of a treatment bundle for total knee endoprosthesis. Anaesthesist, 70(3), 213–222. https://doi.org/10.1007/s00101–020–00874–8

34. Regnery, S., Behl, N. G. R., Platt, T., Weinfurtner, N., Windisch, P., Deike-Hofmann, K. et al. (2020). Ultra-high-field sodium MRI as biomarker for tumor extent, grade and IDH mutation status in glioma patients. NeuroImage: Clinical, 28, Article 102427. https://doi.org/10.1016/j.nicl.2020.102427

35. McDonald, J.H. (2014). G-test of goodness-of-fit. Baltimore, Maryland: Sparky House Publishing. 2014.

36. McDonald, J.H. (2014). Small numbers in chi-square and G-tests. Baltimore, Maryland: Sparky House Publishing. 2014.

37. Mangiaterra, G., Amiri, M., Di Cesare, A., Pasquaroli, S., Manso, E., Cirilli, N., Citterio, B. et al. (2018). Detection of viable but non-culturable Pseudomonas aeruginosa in cystic fibrosis by qPCR: a validation study. BMC Infectious Diseases, 18, Article 701. https://doi.org/10.1186/s12879–018–3612–9

38. Bilello, L.A., Scuderi, C., Haddad, I.C.J., Smotherman, C., Shahady, E. (2018). Practice transformation: using teambased care training to improve diabetes outcomes. Journal of primary care and community health, 9. https://doi.org/10.1177/2150132718817952

39. Taetzsch, A., Das, S. K., Brown, C., Krauss, A., Silver, R. E., Roberts, S. B. (2018). Are gluten-free diets more nutritious? An evaluation of self-selected and recommended gluten-free and gluten-containing dietary patterns. Nutrients,10(12), Article 1881. https://doi.org/10.3390/nu10121881

40. Raurich, J. M., Llompart-Pou, J. A., García-de-Lorenzo, A., Buño Soto, A., Marsé, P., Frontera, G. et al. (2018). Effect of the route of nutrition and l-alanyl-l-glutamine supplementation in amino acids’ concentration in trauma patients. European Journal of Trauma and Emergency Surgery,44(6), 869–876. https://doi.org/10.1007/s00068–017–0851–1

41. Runge, T.M., Jirapinyo, P., Chan, W.W., Thompson, C.C. (2019). Dysphagia predicts greater weight regain after Roux-enY gastric bypass: a longitudinal case-matched study. Surgery for obesity and related diseases, 15(12), 2045–2051. https://doi.org/10.1016/j.soard.2019.06.041

42. Lee, D., May, K., Faramarzi, B. (2019). Comparison of first and second acupuncture treatments in horses with chronic laminitis. Iranian Journal of Veterinary Research, 20(1), 9–12.

43. Avena, V., Messina, D., Corte, C., Mussi, J., Saez, A., Boarelli, P. et al. (2019). Association between consumption of yerba mate and lipid profile in overweight women. Nutricion hospitalaria, 36(6), 1300–1306. https://doi.org/10.20960/nh.02599

44. Reaver, A., Hewlings, S., Westerman, K., Blander, G., Schmeller, T., Heer, M. et al. (2019). A randomized, placebo-controlled, double-blind crossover study to assess a unique phytosterol ester formulation in lowering LDL cholesterol utilizing a novel virtual tracking tool. Nutrients, 11(9), Article 2108. https://doi.org/10.3390/nu11092108

45. Shi, L., Rong, Y., Daly, M., Dyer, B., Benedict, S., Qiu, J. et al. (2020). Cone-beam computed tomography-based delta-radiomics for early response assessment in radiotherapy for locally advanced lung cancer. Physics in Medicine and Biology, 65(1), Article 015009. https://doi.org/10.1088/1361–6560/ab3247

46. Shauly-Aharonov, M. (2020). An exact test with high power and robustness to unmeasured confounding effects. Statistics in Medicine, 39(8), 1041–1053. https://doi.org/10.1002/sim.8460

47. Mou, T., Deng, W.J., Gu, F.Y., Pawitan, Y., Vu, T.N. (2020). Reproducibility of methods to detect differentially expressed genes from single-cell RNA sequencing. Frontiers in Genetics, 10, Article 1331. https://doi.org/10.3389/fgene.2019.01331

48. Sasaki, C.A.L., da Costa, T.H.M. (2021). Micronutrient deficiency in the diets of para-athletes participating in a sports scholarship program. Nutrition, 81, Article 110992. https://doi.org/10.1016/j.nut.2020.110992

49. Anderson, D., Sturt, J., McDonald, N., White, C., Porter-Steele, J., Rogers, R. et al. (2021). International feasibility study for the women’s wellness with type 2 diabetes programme (WWDP): An eHealth enabled 12-week intervention programme for midlife women with type 2 diabetes. Diabetes Research and Clinical Practice, 171, Article 108541. https://doi.org/10.1016/j.diabres.2020.108541


For citation:


Nikitina M.A., Chernukna I.M. Methods for nonparametric statistics in scientific research. Overview. Part 1. Theory and practice of meat processing. 2021;6(2):151-162. https://doi.org/10.21323/2414-438X-2021-6-2-151-162

Views: 115


Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.


ISSN 2414-438X (Print)
ISSN 2414-441X (Online)