Azuero, Andres

Budhathoki, Chakra

Chandrasekhar, Rameela

Cook, Paul

Cramer, Emily

Dietrich, Mary S.

Garnier-Villarreal, Mauricio

Hanlon, Alexandra

Hayat, Matthew J.

He, Jinghua

Higgins, Melinda

Hu, Jinxiang

Kim, Myoung Jin

Mueller, Martina

Nolan, Joseph R.

Perkhounkova, Yelena

Rothers, Janet

Schluck, Glenna

Schwartz, Todd A.

Staggs, Vincent S.

Su, Xiaogang

Templin, Thomas N.

Weaver, Michael T.

Yang, Qing

Ye, Sangbeak

English

epublications@Marquette

Marquette University e-Publications@Marquette College of Nursing Faculty Research and Publications Nursing, College of 7-2019 Moving nursing beyond p < .05 Matthew J. Hayat Baydine F. Lewis College of Nursing Vincent S. Staggs University of Missouri - Kansas City Todd A. Schwartz University of North Carolina Melinda Higgins Emory University Andres Azuero University of Alabama at Birmingham See next page for additional authors Follow this and additional works at: https://epublications.marquette.edu/nursing_fac  Part of the Nursing Commons Recommended Citation Hayat, Matthew J.; Staggs, Vincent S.; Schwartz, Todd A.; Higgins, Melinda; Azuero, Andres; Budhathoki, Chakra; Chandrasekhar, Rameela; Cook, Paul; Cramer, Emily; Dietrich, Mary S.; Garnier-Villarreal, Mauricio; Hanlon, Alexandra; He, Jinghua; Hu, Jinxiang; Kim, Myoung Jin; Mueller, Martina; Nolan, Joseph R.; Perkhounkova, Yelena; Rothers, Janet; Schluck, Glenna; Su, Xiaogang; Templin, Thomas N.; Weaver, Michael T.; Yang, Qing; and Ye, Sangbeak, "Moving nursing beyond p < .05" (2019). College of Nursing Faculty Research and Publications. 658. https://epublications.marquette.edu/nursing_fac/658 Authors Matthew J. Hayat, Vincent S. Staggs, Todd A. Schwartz, Melinda Higgins, Andres Azuero, Chakra Budhathoki, Rameela Chandrasekhar, Paul Cook, Emily Cramer, Mary S. Dietrich, Mauricio Garnier-Villarreal, Alexandra Hanlon, Jinghua He, Jinxiang Hu, Myoung Jin Kim, Martina Mueller, Joseph R. Nolan, Yelena Perkhounkova, Janet Rothers, Glenna Schluck, Xiaogang Su, Thomas N. Templin, Michael T. Weaver, Qing Yang, and Sangbeak Ye This article is available at e-Publications@Marquette: https://epublications.marquette.edu/nursing_fac/658  Marquette University e-Publications@Marquette  Nursing Faculty Research and Publications/College of Nursing  This paper is NOT THE PUBLISHED VERSION; but the author’s final, peer-reviewed manuscript. The published version may be accessed by following the link in the citation below.  International Journal of Nursing Studies, Vol. 95 (July 2019): A1-A2. DOI. This article is © Elsevier and permission has been granted for this version to appear in e-Publications@Marquette. Elsevier does not grant permission for this article to be further copied/distributed or hosted elsewhere without the express permission from Elsevier.   Moving nursing beyond p < .05  Matthew J. Hayat Department of Population Health Sciences, School of Public Health, Baydine F. Lewis College of Nursing & Health Professions (Joint), Georgia State University, Atlanta, GA, USA Vincent S. Staggs Department of Pediatrics, University of Missouri-Kansas City, Biostatistics & Epidemiology Core, Children’s Mercy Kansas City, Kansas City, MO, USA Todd A. Schwartz Department of Biostatistics, Gillings School of Global Public Health, School of Nursing, University of North Carolina, Chapel Hill, NC, USA Melinda Higgins School of Nursing, Emory University, Atlanta, GA, USA Andres Azuero School of Nursing, The University of Alabama at Birmingham, Birmingham, AL, USA Chakra Budhathoki School of Nursing, Johns Hopkins University, Baltimore, MD, USA Rameela Chandrasekhar Department of Biostatistics, Vanderbilt University School of Medicine, School of Nursing, Vanderbilt University, Nashville, TN, USA Paul Cook College of Nursing, University of Colorado, Aurora, CO, USA Emily Cramer School of Nursing, University of Kansas Medical Center, Kansas City, KS, USA Mary S. Dietrich School of Medicine (Biostatistics, VICC, Psychiatry), School of Nursing, Vanderbilt University, Nashville, TN, USA Mauricio Garnier-Villarreal College of Nursing, Marquette University, Milwaukee, WI, USA Alexandra Hanlon Center for Biostatistics and Health Data Science, Department of Statistics, Virginia Polytechnic Institute and State University, Blacksburg, VA, USA Jinghua He Jinxiang Hu Department of Biostatistics and Data Science, University of Kansas Medical Center, Kansas City, KS, USA Myoung Jin Kim Office of Nursing Research, Scholarship, and Innovation, Mennonite College of Nursing, Illinois State University, Normal, IL, USA Martina Mueller College of Nursing and Department of Public Health Sciences (Joint), Medical University of South Carolina, Charleston, SC, USA Joseph R. Nolan Department of Statistics, Northern Kentucky University, Highland Heights, KY, USA Yelena Perkhounkova Office for Nursing Research and Scholarship, College of Nursing, University of Iowa, Iowa City, IA, USA Janet Rothers College of Nursing, University of Arizona, Tucson, AZ, USA Glenna Schluck College of Nursing, Florida State University, Tallahassee, FL, USA Xiaogang Su Department of Mathematical Sciences, University of Texas at El Paso, El Paso, TX, USA Thomas N. Templin College of Nursing, Wayne State University, Detroit, MI, USA Michael T. Weaver College of Nursing, University of Florida, Gainesville, FL, USA Qing Yang School of Nursing, Duke University, Durham, NC, USA Sangbeak Ye School of Nursing and Health Studies, University of Missouri-Kansas City, Kansas City, MO, USA  We write as a community of professional statisticians and quantitative methodologists with extensive collective experience in nursing research. Our group includes faculty in academic nursing programs, statistics reviewers for nursing research journals, statistics educators who teach nursing students, and statistics collaborators on nursing research studies. Members of our group have participated in expert panel discussions and presentations at international statistics conferences about the use of statistics in nursing research and education (Hayat et al., 2013, 2014). Our efforts are further described in the opening editorial for a special issue of Nursing Research devoted to statistics in nursing (Hayat, 2012). Since 2011 we have continued to maintain an email listserv for statisticians in nursing. Over the years, members of our group have routinely encountered, and tried to address, misuses and misunderstandings of p-values and significance testing. Fortunately, the American Statistical Association (ASA) recently launched a large-scale effort aimed at “Moving to a World Beyond ‘p < 0.05’” (Wasserstein et al., 2019), publishing a 19-page editorial with this title in the ASA-sponsored journal The American Statistician, along with 43 thought-provoking papers from prominent statisticians and other experts on the topic. Wasserstein et al. (2019) call for abandoning the phrase “statistically significant” and discontinuing the practice of categorizing p-values based on an arbitrary threshold such as .05, noting that, “Regardless of whether it was ever useful, a declaration of ‘statistical significance’ has today become meaningless.” It is unfortunate that the term “significant” was ever attached to “p < .05.” Webster’s dictionary defines the word “significance” as “importance.” Yet, statistical significance is not synonymous with importance. This misleading label has led to misinterpretations and poor decisions that have cost lives, money, and resources (Ziliak and McCloskey, 2008). Patients and healthcare providers have paid a heavy price for the science community’s reliance on statistical significance as a criterion for importance. Decisions should never be made based solely on a significance test or p-value. The deep issues with inappropriately using p-values as a measure of importance were addressed nearly a decade ago by Hayat (2010) in Nursing Research, but the reality is that p-values continue to be poorly understood and widely misused. Our hope is that editorial boards of nursing journals will consider revising their author submission and publication guidelines to provide clear guidance more consistent with the promotion of rigorous, reproducible science. Specifically, in line with the recent effort by the ASA, we recommend the following manuscript submission requirements: 1) When a p-value is reported, state its value regardless of how small or large it may be. 2) Avoid using .05 or any other cutoff for a p-value as the basis for a decision about the meaningfulness/importance of an effect. 3) In reporting a p-value, a measure of the effect size should be included, along with a corresponding interval estimate (e.g., confidence interval).  The “p < 0.05” standard and concomitant language of “statistical significance” are well-entrenched, despite the damage that has resulted from their use. This recent initiative among statisticians to move beyond “p < 0.05” is an escalation of a longstanding agenda in the statistics discipline to shift science away from “statistical significance” and toward measures of clinical and practical importance. We would like to see the nursing science community take this important step forward, abandoning the language of “statistical significance” in favor of meaningful statistics, which are essential for sound science and healthcare decision-making. References Hayat, 2010. M.J. Hayat. Understanding statistical significance. Nurs. Res., 59 (3) (2010), pp. 219-223, 10.1097/NNR.0b013e3181dbb2cc Hayat, 2012. M.J. Hayat. Statistics in nursing research. Nurs. Res., 61 (3) (2012), pp. 147-148, 10.1097/NNR.0b013e318257f5dc Hayat et al., 2013. M.J. Hayat, P. Eckardt, M. Higgins, M. Kim, S. Schmiege. Teaching statistics to nursing students: an expert panel consensus. J. Nurs. Educ., 52 (6) (2013), pp. 330-334, 10.3928/01484834-20130430-01 Hayat et al., 2014. M.J. Hayat, M. Higgins, T.A. Schwartz, V.S. Staggs. Statistical challenges in nursing education and research: an expert panel consensus. Nurse Educ., 40 (1) (2014), pp. 21-25, 10.1097/NNE.0000000000000080 Wasserstein et al., 2019. R.L. Wasserstein, A.L. Schirm, N.A. Lazar. Moving to a world beyond “p < 0.05”. Am. Stat., 73 (2019), pp. 1-19, 10.1080/00031305.2019.1583913 Ziliak and McCloskey, 2008. S.T. Ziliak, D.N. McCloskey. The Cult of Statistical Significance: How the Standard Error Costs Us Jobs, Justice, and Lives. The University of Michigan Press, Ann Arbor, MI (2008) 

Moving nursing beyond \u3cem\u3ep\u3c/em\u3e \u3c .05

https://epublications.marquette.edu/cgi/viewcontent.cgi?article=1659&amp;context=nursing_fac

Moving nursing beyond \u3cem\u3ep\u3c/em\u3e \u3c .05

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