Abstract
Purpose
To do nursing research effectively requires an understanding of fundamental principles of statistical methods. In this article, some key statistical methods which are commonly used in nursing research are identified and summarized.
Methods
Ninety-two original articles from the Journal of Korean Academy of Nursing Administration were reviewed. Statistical methods were classified and summarized for usage in research and occurrence of common errors.
Results
Among the original articles reviewed, 58 statistical usages contained errors. Most errors were found in linear regression analysis, Pearson correlation analysis, and chi-square test. From the detection of statistical errors in usage, suggestions for appropriate statistical methods were made.
Conclusion
In order to improve validity of original articles in the Journal of Korean Academy of Nursing Administration, clearly stated statistical usage and close editorial attention to statistical methods are needed. Understanding statistical methods is part of the process that researchers must use to determine both quality and usefulness of the research. Research findings will be used to guide nursing practice and reduce uncertainty in decision making. However, to understand how to interpret research results, it is important to be able to understand basic statistical concepts. Researchers should also choose statistical methods that match their purposes.
References
1. Agresti A. Categorical data analysis. 2002. 2nd ed. New York: John Wiley & Sons.
2. Altman DG. Statistical reviewing for medical journals. Stat Med. 1998. 17:2661–2674. http://dx.doi.org/10.1002/(SICI)1097-0258(19981215)17:23<2661::AID-SIM33>3.0.CO;2-B.
3. Altman DG. Statistics in medical journals: Some recent trends. Stat Med. 2000. 19:3275–3289. http://dx.doi.org/10.1002/1097-0258(20001215)19:23<3275::AID-SIM626>3.0.CO;2-M.
4. Altman DG, Goodman SN, Schroter S. How statistical expertise is used in medical research. JAMA. 2002. 287:2817–2820. http://dx.doi.org/10.1001/jama.287.21.2817.
5. Altman DG. The scandal of poor medical research. BMJ. 1994. 308:283–284. http://dx.doi.org/10.1136/bmj.308.6924.283.
6. Altman DG. Poor-quality medical research: What can journals do? JAMA. 2002. 287:2765–2767. http://dx.doi.org/10.1001/jama.287.21.2765.
7. Armitage P, Colton T. Encyclopedia of biostatistics. 1998. New York: John Wiley & Sons.
8. Bailar JC 3rd, Mosteller F. Guidelines for statistical reporting in articles for medical journals: Amplifications and explanations. Ann Intern Med. 1988. 108:266–273.
9. Dillon WR, Goldsrein M. Multivariate analysis. 1984. New York: John Wiley & Sons.
10. Emerson JD, Colditz GA. Use of statistical analysis in the New England Journal of Medicine. N Engl J Med. 1983. 309:709–713. http://dx.doi.org/10.1056/NEJM198309223091206.
11. Fisher VB, Lumley H. Biostatistics: A methodology for the health sciences. 2004. 2nd ed. New York: John Wiley & Sons.
12. Forthofer RN, Lee ES, Hernandez M. Biostatistics: A guide to design, analysis, and discovery. 2007. New York: ELSEVIER.
13. Hair JF, Anderson RE. Multivariate data analysis. 1995. 4th ed. New Jersey: Prentice Hall.
14. Hosmer DW, Lemeshow S. Applies logistic regression. 2000. 2nd ed. New York: John Wiley & Sons.
15. Lachin JM. Biostatistical methods: The assessment of relative risks. 2000. New York: John Wiley & Sons.
16. Landis JR, Koch GG. The measurement of observer agreement for categorical data. Biometrics. 1977. 33:159–174.
17. MacArthur RD, Jackson GG. An evaluation of the use of statistical methodology in the Journal of Infectious Disease. J Infect Dis. 1984. 149:349–354. http://dx.doi.org/10.1093/infdis/149.3.349.
18. Redmond CK, Colton T. Biostatistics in clinical research. 2001. New York: John Wiley & Sons.
19. Rennie D. Freedom and responsibility in medical publication: Setting the balancing right. JAMA. 1998. 280:300–302. http://dx.doi.org/10.1001/jama.280.3.300.
20. Rosner B. Fundamentals of biostatistics. 2011. 7th ed. Pacific Grove: Duxbury.
21. Shoukri MM, Pause MM. Statistical methods for health sciences. 1998. 2nd. ed. New York: CRC Press.
22. Sterne JA, Davey SG. Sifting the evidence: What's wrong with significance tests? BMJ. 2001. 322:226–231.