Types of Correlation Coefficients in Medical Science Research Using SPSS and R Software: A Review Article

Document Type : Review Article

Authors

1 Professor, Department of Biostatistics, School of Health, Health Data Science Research Center, Health Research Institute, Kermanshah University of Medical Sciences, Kermanshah, Iran

2 PhD Student, Department of Biostatistics, School of Health, Kermanshah University of Medical Sciences, Kermanshah, Iran

3 Professor, Department of Biostatistics, School of Health, Kermanshah University of Medical Sciences, Kermanshah, Iran

4 PhD Student, Health Data Science Research Center, Health Research Institute, Department of Biostatistics, School of Health,Kermanshah University of Medical Sciences, Kermanshah, Iran

Abstract

Background: The correlation coefficient is a statistical measure that measures the degree of correlation between two variables and takes values between -1 and +1, which indicates the strength and direction of the linear relationship between two variables. Correlation coefficients play a significant role in medical science studies. These coefficients help researchers to identify and measure the relationship between different clinical variables and health outcomes.
Description of the Article: Considering the importance of calculating correlation coefficients in medical research, the purpose of this paper is to introduce various types of correlation coefficients, simple and applied statistical concepts and, methods to investigate the relationship between variables according to their nature and how to calculate them. Types of commonly used correlation coefficients, including Pearson, Spearman, Kendall, Phi, V Kramer, Summers Delta, Gamma, agreement coefficient and, Landa correlation coefficient will be analyzed using SPSS and R software. Also, the conditions of using each of the correlation coefficients will be discussed according to the different assumptions and the unique interpretation of each of the coefficients. Finally, examples of the calculation and interpretation of each of the correlation coefficients in medical sciences are provided.
Conclusion: Correlation is the most widely used statistical measure to evaluate the relationships between variables. However, it should be used with caution. Otherwise, it can lead to erroneous interpretations and results.

Highlights

Behzad Mahaki: PubMed , Google Scholar

Samira Jafari: Google Scholar

Mansour Rezaei: PubMed ,Google Schola

Leila Solouki: Google Scholar

Keywords

Main Subjects

References

  1. Liu J, Tang W, Chen G, Lu Y, Feng C, Tu XM. Correlation and agreement: overview and clarification of competing concepts and measures. Shanghai Arch Psychiatry 2016; 28(2): 115-20.
  2. Pal M, Bharati P, Pal M, Bharati P. Introduction to correlation and linear regression analysis.[online 2019] Available from: https://sphweb.bumc.bu.edu/otlt/mph-modules/bs/bs704_multivariable/bs704_multivariable5.html
  3. Nishimura A, Tabuchi Y, Kikuchi M, Masuda R, Goto K, Iijima T. The amount of fluid given during surgery that leaks into the interstitium correlates with infused fluid volume and varies widely between patients. Anesth Analg 2016; 123(4): 925-32.
  4. Senthilnathan S. Usefulness of correlation analysis. [July 9 2019]. Available from: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3416918.
  5. Altman N, Krzywinski M. Association, correlation and causation. Nat Methods 2015;12(10): 899-900.
  6. Rezaee Z, Aliabadi S, Dorestani A, Rezaee NJ. Application of time series models in business research: correlation, association, causation. Sustainability 2020; 12(12): 4833.
  7. Miot HA. Assessing normality of data in clinical and experimental trials.[ Apr-Jun 2017]. Available from: https://www.scielo.br/j/jvb/a/FPW5hwZ6DTH4gvj5mJYpt6B/?lang=en.
  8. Scott M, Flaherty D, Currall J. Statistics: are we related? Journal of Small Animal Practice. 2013; 54(3): 124-8.
  9. Pearson K, Lee A. On the laws of inheritance in man: I. Inheritance of physical characters. Biometrika. 1903; 2(4): 357-462.
  10. Schober P, Boer C, Schwarte LA. Correlation coefficients: appropriate use and interpretation. Anesth Analg 2018; 126(5): 1763-8.
  11. Akoglu H. User's guide to correlation coefficients. Turk J Emerg Med 2018; 18(3): 91-3.
  12. Sedgwick P. Pearson’s correlation coefficient. BMJ2012; 345: e4483.
  13. Taghizadeh S, Abbasalizad Farhangi M, Poorali F. Correlation between blood pressure, body mass index, life style and dietary habits in children and adolescents aged 6 to 18 years in Tabriz, Iran [in Persian]. J Shahid Sadoughi Univ Med Sci 2020; 28(2): 2399-410.
  14. Ghaderi R, Mahmoodi-Shan GR, Taziki MH, Behnampour N. Association between satisfaction with hearing aid use and quality of life among elderly individuals with hearing loss. J Res Dev Nurs Midw 2023; 20(2): 30-3.
  15. Hauke J, Kossowski T. Comparison of values of Pearson's and Spearman's correlation coefficients on the same sets of data. Quaestiones geographicae 2011; 30(2): 87-93.
  16. He YL, Li Y, Lin CY, Qi YF, Wang X, Zhou HL, et al. Three‐dimensional turbo‐spin‐echo amide proton transfer‐weighted MRI for cervical cancer: A preliminary study. J Magn Reson Imaging 2019; 50(4): 1318-25.
  17. Xiao C, Ye J, Esteves RM, Rong C. Using Spearman's correlation coefficients for exploratory data analysis on big dataset. Concurrency and Computation: Practice and Experience 2016; 28(14): 3866-78.
  18. Vincze A, Herczeg-Lisztes E, Szabó K, Béldi TG, Nagy-Vincze M, Pór Á, et al. Pruritogenic molecules in the skin of patients with dermatomyositis. Front Med (Lausanne) 2023; 10: 1168359.
  19. Soydan Z, Bayramoglu E, Urut DU, Iplikcioglu AC, Sen C. Tracing the disc: The novel qualitative morphometric MRI based disc degeneration classification system. JOR Spine 2024; 7(1): e1321.
  20. Field A. Discovering statistics using IBM SPSS statistics. 4th Ed. New York, NY: SAGE Publications Ltd; 2013.
  21. Sadiq M, Mehmood T, Aslam M. Identifying the factors associated with cesarean section modeled with categorical correlation coefficients in partial least squares. PLoS One 2019; 14(7): e0219427.
  22. Shanbehzadeh M, Nopour R. Determination of the most important diagnostic criteria for COVID-19: A step forward to design an intelligent clinical decision support system. J Adv Med Biomed Res 2021; 29(134): 176-82.
  23. Piaw CY. Mastering research statistics. 1st Ed. Malaysia: McGraw Hill Education; 2013.
  24. Somers RH. A new asymmetric measure of association for ordinal variables. Am Sociol Rev 1962; 27(6): 799-811.
  25. Metsämuuronen J. Directional nature of Goodman–Kruskal gamma and some consequences: identity of Goodman–Kruskal gamma and Somers delta, and their connection to Jonckheere–Terpstra test statistic. Behaviormetrika 2021; 48(2): 283-307.
  26. Ciuhu AN, Spircu T, Padure L, Badiu C, Poroch V, Omer S, et al. Relationships between communicating bad news, cancer disease adaptation, and related mood disorders. Proc Rom Acad Series B 2016; 18(2): 79-88.
  27. Goodman LA, Kruskal WH. Measures of association for cross classifications. II: Further discussion and references. Journal of the American Statistical Association 1959; 54(285): 123-63.
  28. Goodman LA, Kruskal WH. Measures of association for cross classifications III: Approximate sampling theory. Journal of the American Statistical Association 1963; 58(302): 310-64.
  29. Goodman LA, Kruskal WH. Measures of association for cross classifications, IV: Simplification of asymptotic variances. Journal of the American Statistical Association 1972; 67(338): 415-21.
  30. Goodman LA, Kruskal WH, Goodman LA, Kruskal WH. Measures of association for cross classifications. New York, NY: Springer; 1979.
  31. Fang J, Liu L, Fang P. What is the most important factor affecting patient satisfaction–a study based on gamma coefficient. Patient Prefer Adherence 2019; 13: 515-25.
  32. Kvålseth TO. An alternative measure of ordinal association as a value-validity correction of the Goodman–Kruskal gamma. Communications in Statistics-Theory and Methods 2017; 46(21): 10582-9.
  33. Batchelder AW, Stanton AM, Kirakosian N, King D, Grasso C, Potter J, et al. Mental health and substance use diagnoses and treatment disparities by sexual orientation and gender in a community health center sample. LGBT Health 2021; 8(4): 290-9.
  34. Dimitriu M, Ionescu CA, Matei A, Viezuina R, Rosu G, Ilinca C, et al. The problems associated with adolescent pregnancy in Romania: A cross‐sectional study. J Eval Clin Pract 2019; 25(1): 117-24.
Journal of Isfahan Medical School
Volume 42, Issue 773
1st Week, September
September and October 2024
Pages 560-573

Files

History

  • Receive Date: 16 April 2024
  • Accept Date: 05 August 2024

Share

How to cite

Statistics