J Korean Acad Nurs.  2012 Feb;42(1):48-55. 10.4040/jkan.2012.42.1.48.

Statistical Methods to Control Response Bias in Nursing Activity Surveys

Affiliations
  • 1Department of Nursing, Inha University, Incheon, Korea. lim20712@inha.ac.kr
  • 2College of Nursing, University of Illinois at Chicago, Chicago, USA.

Abstract

PURPOSE
The aim of this study was to compare statistical methods to control response bias in nursing activity surveys.
METHODS
Data were collected at a medical unit of a general hospital. The number of nursing activities and consumed activity time were measured using self-report questionnaires. Descriptive statistics were used to identify general characteristics of the units. Average, Z-standardization, gamma regression, finite mixture model, and stochastic frontier model were adopted to estimate true activity time controlling for response bias.
RESULTS
The nursing activity time data were highly skewed and had non-normal distributions. Among the 4 different methods, only gamma regression and stochastic frontier model controlled response bias effectively and the estimated total nursing activity time did not exceeded total work time. However, in gamma regression, estimated total nursing activity time was too small to use in real clinical settings. Thus stochastic frontier model was the most appropriate method to control response bias when compared with the other methods.
CONCLUSION
According to these results, we recommend the use of a stochastic frontier model to estimate true nursing activity time when using self-report surveys.

Keyword

Nursing; Time; Bias

MeSH Terms

Adult
Female
Humans
*Models, Statistical
Nursing Staff, Hospital/*statistics & numerical data
Questionnaires
Task Performance and Analysis
Time Factors

Reference

1. Barber J., Thompson S. Multiple regression of cost data: Use of generalised linear models. Journal of Health Services Research and Policy. 2004. 9:197–204. http://dx.doi.org/10.1258/1355819042250249.
2. Coyle D. Statistical analysis in pharmacoeconomic studies: A review of current issues and standards. Pharmacoeconomics. 1996. 9:506–516.
3. Groot W., van den Brink H.M. Optimism, pessimism and the compensating income variation of cardiovascular disease: A two-tiered quality of life stochastic frontier model. Social Science and Medicine. 2007. 65:1479–1489. http://dx.doi.org/10.1016/j.socscimed.2007.05.009.
4. Hardin J.W., Hilbe J.M. Generalized linear models and extensions. 2007. 2nd ed. College Station, TX: Stata Press.
5. Hofter R.A., List J.A. Valuation on the frontier: Calibrating actual and hypothetical statements of value. American Journal of Agricultural Economics. 2004. 86:213–221. http://dx.doi.org/10.1111/j.0092-5853.2004.00573.x.
6. Kang K.H. Analysis of nursing activities and cost of nursing service based on the ABC system. Journal of Korean Academy of Nursing Administration. 1999. 5:389–400.
7. Kang Y., Kim K., Kim Y., Park H., Seo K., Song S., et al. Analysis of anesthesia and recovery room nurses' activities. Journal of Korean Academy of Nursing Administration. 2006. 12:63–75.
8. Kaplan R.S., Anderson S.R. Time-driven activity-based costing. Harvard Business Review. 2004. 82:131–138.
9. Kim G.W. Photography in scientific management. 2002. Seoul: Chung-Ang University;Unpublished master's thesis.
10. Kim J., Ham U., Rhieu S. Analysis of efficiency in hospitals by stochastic frontier approach. Daehan Journal of Business. 2009. 22:1867–1889.
11. Korea Hospital Association & Korean Institute of Hospital Management. 2009 Hospital management statistics. 2011. 04. Seoul: Author.
12. Lee S.J. Cost analysis of home health care with activity-based costing (ABC). 2003. Seoul: Yonsei University;Unpublished doctoral dissertation.
13. Lee S.J., Lee S.H., Seo J.H. Hospital cost accounting. 2001. Seoul: Imagination Pub.
14. Lim J.Y. An analysis of cost and profits of a nursing unit using performance-based costing: Case of a general surgical ward in a general hospital. Journal of Korean Academy of Nursing. 2008. 38:161–171.
15. Louie G.H., Ward M.M. Sex disparities in self-reported physical functioning: True differences, reporting bias, or incomplete adjustment for confounding? Journal of the American Geriatrics Society. 2010. 58:1117–1122. http://dx.doi.org/10.1111/j.1532-5415.2010.02858.x.
16. MaCleod J., Hickman M., Smith G.D. Reporting bias and selfreported drug use. Addiction. 2005. 100:562–563. http://dx.doi.org/10.1111/j.1360-0443.2005.01099.x.
17. Manning W.G., Basu A., Mullahy J. Generalized modeling approaches to risk adjustment of skewed outcomes data. Journal of Health Economics. 2005. 24:465–488. http://dx.doi.org/10.1016/j.jhealeco.2004.09.011.
18. McLachlan G., Peel D. Finite mixture models. 2000. NY: Wiley.
19. Moran J.L., Solomon P.J., Peisach A.R., Martin J. New models for old questions: Generalized linear models for cost prediction. Journal of Evaluation in Clinical Practice. 2007. 13:381–389. http://dx.doi.org/10.1111/j.1365-2753.2006.00711.x.
20. Oh D., Lee J., Min I. Analysis on efficiency and productivity of Korean regional public hospital between before and after the separation of dispensary from medical practice: Using parametric and non-parametric statistical approaches. Korea Journal of Health Economics and Policy. 2007. 13:173–198.
21. Park J.H., Sung Y.H., Song M.S., Cho J.S., Sim W.H. The classification of standard nursing activities in Korea. Journal of Korean Academy of Nursing. 2000. 30:1411–1426.
22. Ryu H., Park E., Park Y., Han K., Lim J. A workload analysis of a visiting nursing service based on a health center in Seoul. Journal of Korean Academy of Nursing. 2003. 33:1018–1027.
23. Seo S. A review and comparison of methods for detecting outliers in univariate data sets. 2006. Pittsburgh, USA: University of Pittsburgh;Unpublished doctoral dissertation.
24. Shedden K., Zucker R.A. Regularized finite mixture models for probability trajectories. Psychometrika. 2008. 73:625–646. http://dx.doi.org/10.1007/s11336-008-9077-9.
25. Sterken E. A stochastic frontier approach to running performance. IMA Journal of Management Mathematics. 2005. 16:141–149. http://dx.doi.org/10.1093/imaman/dpi007.
26. Storfjell J.L., Ohlson S., Omoike O., Fitzpatrick T., Wetasin K. Non-value-added time: The million dollar nursing opportunity. Journal of Nursing Administration. 2009. 39:38–45. http://dx.doi.org/10.1097/NNA.0b013e31818e9cd4.
27. Yoon K.J. An comparison of DEA and SFM to evaluate the performance of public department. Korean Public Administration Review. 1998. 32:257–273.
28. Zhou X.H., Melfi C.A., Hui S.L. Methods for comparison of cost data. Annals of Internal Medicine. 1997. 127:752–756.
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