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Published Online:https://doi.org/10.3928/01484834-20140325-04Cited by:29

Abstract

Nurse investigators often collect study data in the form of counts. Traditional methods of data analysis have historically approached analysis of count data either as if the count data were continuous and normally distributed or with dichotomization of the counts into the categories of occurred or did not occur. These outdated methods for analyzing count data have been replaced with more appropriate statistical methods that make use of the Poisson probability distribution, which is useful for analyzing count data. The purpose of this article is to provide an overview of the Poisson distribution and its use in Poisson regression. Assumption violations for the standard Poisson regression model are addressed with alternative approaches, including addition of an overdispersion parameter or negative binomial regression. An illustrative example is presented with an application from the ENSPIRE study, and regression modeling of comorbidity data is included for illustrative purposes. [J Nurs Educ. 2014;53(4):207–215.]

  • Akaike H. (1974). A new look at the statistical model identification. IEEE Transactions on Automatic Control, 19, 716–723.10.1109/TAC.1974.1100705

    CrossrefGoogle Scholar
  • Bowers L., Crowder M. (2012). Nursing staff numbers and their relationship to conflict and containment rates on psychiatric wards—A cross sectional time series Poisson regression study. International Journal of Nursing Studies, 49(1), 15–20.10.1016/j.ijnurstu.2011.07.005

    Crossref MedlineGoogle Scholar
  • Chang Y., Mark B. (2011). Effects of learning climate and registered nurse staffing on medication errors. Nursing Research, 60, 32–39.10.1097/NNR.0b013e3181ff73cc

    Crossref MedlineGoogle Scholar
  • Charlson M.E., Pompei P., Ales K.L., MacKenzie C.R. (1987). A new method of classifying prognostic comorbidity in longitudinal studies: Development and validation. Journal of Chronic Diseases, 40, 373–383.10.1016/0021-9681(87)90171-8

    Crossref MedlineGoogle Scholar
  • Chen A.C., Thompson E.A., Morrison-Beedy D. (2010). Multi-system influences on adolescent risky sexual behavior. Research in Nursing and Health, 33, 512–527.10.1002/nur.20409

    Crossref MedlineGoogle Scholar
  • Cohen J., Cohen P., West S.G., Aiken L.S. (2003). Applied multiple regression/correlation analysis for the behavioral sciences (3rd ed.). Mahwah, NJ: Lawrence Erlbaum.

    Google Scholar
  • Coxe S., West S.G., Aiken L.S. (2009). The analysis of count data: A gentle introduction to Poisson regression and its alternatives. Journal of Personality Assessment, 91, 121–136.10.1080/00223890802634175

    Crossref MedlineGoogle Scholar
  • da Cruz Ede P., Toporcov T.N., Rotundo L.D., Biazevic M.G., Brasileiro R.S., de Carvalho M.B., Antunes J.L. (2012). Food restrictions of patients who are undergoing treatment for oral and oropharyngeal cancer. European Journal of Oncology Nursing, 16, 253–257.10.1016/j.ejon.2011.06.002

    Crossref MedlineGoogle Scholar
  • Diggle P.J., Heagerty P., Liang K.Y., Zeger S.L. (2002). Analysis of longitudinal data (2nd ed.). New York, NY: Oxford University Press.

    Google Scholar
  • Dunbar S.B., Clark P.C., Reilly C.M., Gary R.A., Smith A., McCarty F., Ryan R. (2013). A trial of family partnership and education interventions in heart failure. Journal of Cardiac Failure, 19, 829–841.10.1016/j.cardfail.2013.10.007

    Crossref MedlineGoogle Scholar
  • Eisbach S.S., Cluxton-Keller F., Harrison J., Krall J.R., Hayat M.J., Gross D. (2014). Characteristics of temper tantrums in preschoolers with disruptive behavior in a clinical setting. Journal of Psychosocial Nursing and Mental Health Services. Advance online publication.10.3928/02793695-20140110-02

    LinkGoogle Scholar
  • Hall D.B. (2000). Zero-inflated Poisson and binomial regression with random effects: A case study. Biometrics, 56, 1030–1039.10.1111/j.0006-341X.2000.01030.x

    Crossref MedlineGoogle Scholar
  • Hayat M.J., Eckardt P., Higgins M., Kim M., Schmiege S. (2013). Teaching statistics to nursing students: An expert panel consensus. Journal of Nursing Education, 52, 330–334.10.3928/01484834-20130430-01

    LinkGoogle Scholar
  • Hayat M.J., Hedlin H. (2012). Modern statistical modeling approaches for analyzing repeated-measures data. Nursing Research, 61, 188–194.10.1097/NNR.0b013e31824f5f58

    Crossref MedlineGoogle Scholar
  • Hutchinson M.K., Holtman M.C. (2005). Analysis of count data using Poisson regression. Research in Nursing and Health, 28, 408–418.10.1002/nur.20093

    Crossref MedlineGoogle Scholar
  • Johnson-Masotti A.P., Laud P.W., Hoffmann R.G., Hayat M.J., Pinkerton S.D. (2004). A Bayesian approach to net health benefits: An illustration and application to modeling HIV prevention. Medical Decision Making, 24, 634–653.10.1177/0272989X04271040

    Crossref MedlineGoogle Scholar
  • Krause M.R. (2012). Director of nursing current job tenure and past experience and quality of care in nursing homes. Health Care Management Review, 37, 98–108.10.1097/HMR.0b013e318222429a

    Crossref MedlineGoogle Scholar
  • Lambert D. (1992). Zero-inflated Poisson regression, with an application to defects in manufacturing. Technometrics, 34, 1–14.10.2307/1269547

    CrossrefGoogle Scholar
  • Li J., Galatsch M., Siegrist J., Müller B.H., Hasselhorn H.M.European NEXT Study Group. (2011). Reward frustration at work and intention to leave the nursing profession—Prospective results from the European longitudinal NEXT study. International Journal of Nursing Studies, 48, 628–635.10.1016/j.ijnurstu.2010.09.011

    Crossref MedlineGoogle Scholar
  • Manojlovich M., Sidani S., Covell C.L., Antonakos C.L. (2011). Nurse dose: Linking staffing variables to adverse patient outcomes. Nursing Research, 60, 214–220.10.1097/NNR.0b013e31822228dc

    Crossref MedlineGoogle Scholar
  • McCullagh P., Nelder J.A. (1989). Generalized linear models (2nd ed.). London, United Kingdom: Chapman & Hall.10.1007/978-1-4899-3242-6

    CrossrefGoogle Scholar
  • Neelon B.H., O’Malley A.J., Normand S.T. (2010). A Bayesian model for repeated measures zero-inflated count data with application to outpatient psychiatric service use. Statistical Modeling, 10, 421–439.10.1177/1471082X0901000404

    Crossref MedlineGoogle Scholar
  • Owen S.V., Froman R.D. (2005). Why carve up your continuous data?Research in Nursing and Health, 28, 496–503.10.1002/nur.20107

    Crossref MedlineGoogle Scholar
  • Raftery A. (1995). Bayesian model selection in social research. Sociological Methodology, 25, 111–196.10.2307/271063

    CrossrefGoogle Scholar
  • Ratner P.A., Spinelli J.J., Beking K., Lorenzi M., Chow Y., Teschke K., Dimich-Ward H. (2010). Cancer incidence and adverse pregnancy outcome in registered nurses potentially exposed to antineoplastic drugs. BMC Nursing, 9, 9–15.10.1186/1472-6955-9-15

    Crossref MedlineGoogle Scholar
  • Schreuder J.A., Plat N., Magerøy N., Moen B.E., van der Klink J.J., Groothoff J.W., Roelen C.A. (2011). Self-rated coping styles and registered sickness absence among nurses working in hospital care: A prospective 1-year cohort study. International Journal of Nursing Studies, 48, 838–846.10.1016/j.ijnurstu.2010.12.008

    Crossref MedlineGoogle Scholar
  • Schwartz S.J., Forthun L.F, Ravert R.D., Zamboanga B.L., Umaña-Taylor A.J., Filton B.J., Hudson M. (2010). Identity consolidation and health risk behaviors in college students. American Journal of Health Behavior, 34, 214–224.10.5993/AJHB.34.2.9

    Crossref MedlineGoogle Scholar
  • Sears K., Goldsworthy S., Goodman W.M. (2010). The relationship between simulation in nursing education and medication safety. Journal of Nursing Education, 49, 52–55.10.3928/01484834-20090918-12

    LinkGoogle Scholar
  • Shang J., Wenzel J., Krumm S., Griffith K., Stewart K. (2012). Who will drop out and who will drop in: Exercise adherence in a randomized clinical trial among patients receiving active cancer treatment. Cancer Nursing, 35, 312–322.10.1097/NCC.0b013e318236a3b3

    Crossref MedlineGoogle Scholar
  • Staggs V.S. (2013). Nurse staffing, RN mix, and assault rates on psychiatric units. Research in Nursing and Health, 36, 26–37.10.1002/nur.21511

    Crossref MedlineGoogle Scholar
  • Staggs V.S., Dunton N. (2012). Hospital and unit characteristics associated with nursing turnover include skill mix but not staffing level: An observational cross-sectional study. International Journal of Nursing Studies, 49, 1138–1145.10.1016/j.ijnurstu.2012.03.009

    Crossref MedlineGoogle Scholar
  • Theisen S., Drabik A., Stock S. (2012). Pressure ulcers in older hospitalised patients and its impact on length of stay: A retrospective observational study. Journal of Clinical Nursing, 21, 380–387.10.1111/j.1365-2702.2011.03915.x

    Crossref MedlineGoogle Scholar
  • van Gaal B.G., Schoonhoven L., Mintjes J.A., Borm G.F., Hulscher M.E., Defloor T., van Achterberg T. (2011). Fewer adverse events as a result of the SAFE or SORRY? programme in hospitals and nursing homes. Part I: Primary outcome of a cluster randomised trial. International Journal of Nursing Studies, 48, 1040–1048.10.1016/j.ijnurstu.2011.02.017

    Crossref MedlineGoogle Scholar
  • Ver Hoef J.M., Boveng P.L. (2007). Quasi-Poisson vs. negative binomial regression: How should we model overdispersed count data?Ecology, 88, 2766–2772.10.1890/07-0043.1

    Crossref MedlineGoogle Scholar
  • Vitolo M.R., Bortolini G.A., Campagnolo P.D., Hoffman D.J. (2012). Maternal dietary counseling reduces consumption of energy-dense foods among infants: A randomized controlled trial. Journal of Nutrition Education and Behavior, 44, 140–147.10.1016/j.jneb.2011.06.012

    Crossref MedlineGoogle Scholar
  • Xie H., McHugo G., Sengupta A., Clark R., Drake R. (2004). A method for analyzing longitudinal outcomes with many zeros. Mental Health Services Research, 6, 239–246.10.1023/B:MHSR.0000044749.39484.1b

    Crossref MedlineGoogle Scholar

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