Applied Nursing Research
Volume 20, Issue 1 , Pages 50-53 , February 2007

Is power everything? What can we learn from large data sets

  • Susan Lacey, PhD, RN
    • ,
  • Ronda G. Hughes, PhD, MHS, RN

      Affiliations

    • Corresponding Author InformationCorresponding author. Center for Primary Care, Prevention, & Clinical Partnerships, Agency for Healthcare Research & Quality, Rockville, MD 20852, USA. Tel: (301) 427 1578; fax: (301) 427 1595.

References 

  1. Castle JE. Maximizing research opportunities: Secondary data analysis. Journal of Neuroscience Nursing. 2003;35(5):287–290
  2. Chen L. The importance of the normality assumption in large public health data sets. Annual Review of Public Health. 2002;23:151–169(electronic publication 2001 Oct 25)
  3. E-Masri MM, Fox-Wasylyshyn SM. Missing data: An introductory conceptual overview for the novice researcher. Canadian Journal of Nursing Research. 2005, Dec;37(4):156–171
  4. Iezzoni LI. Assessing quality using administrative data. Annals of Internal Medicine. 1997, Oct 15;127(8)(Pt. 2):666–674
  5. Patrician PA. Multiple imputation for missing data. Research in Nursing & Health. 2002, Feb;25(1):76–84
  6. Schwartz RM, Gagnon DE, Muri JH, Zhao QR, Kellogg R. Administrative data for quality improvement, section 2. Pediatrics. 1999, Jan;103(1):291–301
  7. Wunsch H, Linde-Zwirble WT, Angus DC. Methods to adjust for bias and confounding in critical care health services research involving observational data. Journal of Critical Care. 2006, Mar;21(1):1–7
  8. Zhan, C., Miller, M. R. (2006). Administrative data based patient safety research; a critical review. Downloaded from qhc.bmjjournals.com on 27 Sept 2006.

 Susan Lacey is a 2006 Robert Wood Johnson Executive Nurse Fellow.

PII: S0897-1897(06)00138-8

doi: 10.1016/j.apnr.2006.10.007

Applied Nursing Research
Volume 20, Issue 1 , Pages 50-53 , February 2007

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