title: Power, effects, confidence, and significance: an investigation of statistical practices in nursing research.
abstract: OBJECTIVE
  To (a) assess the statistical power of nursing research to detect small, medium, and large effect sizes; (b) estimate the experiment-wise Type I error rate in these studies; and (c) assess the extent to which (i) a priori power analyses, (ii) effect sizes (and interpretations thereof), and (iii) confidence intervals were reported.
  
  
  METHODS
  Statistical review.
  
  
  METHODS
  Papers published in the 2011 volumes of the 10 highest ranked nursing journals, based on their 5-year impact factors.
  
  
  METHODS
  Papers were assessed for statistical power, control of experiment-wise Type I error, reporting of a priori power analyses, reporting and interpretation of effect sizes, and reporting of confidence intervals. The analyses were based on 333 papers, from which 10,337 inferential statistics were identified.
  
  
  RESULTS
  The median power to detect small, medium, and large effect sizes was .40 (interquartile range [IQR]=.24-.71), .98 (IQR=.85-1.00), and 1.00 (IQR=1.00-1.00), respectively. The median experiment-wise Type I error rate was .54 (IQR=.26-.80). A priori power analyses were reported in 28% of papers. Effect sizes were routinely reported for Spearman's rank correlations (100% of papers in which this test was used), Poisson regressions (100%), odds ratios (100%), Kendall's tau correlations (100%), Pearson's correlations (99%), logistic regressions (98%), structural equation modelling/confirmatory factor analyses/path analyses (97%), and linear regressions (83%), but were reported less often for two-proportion z tests (50%), analyses of variance/analyses of covariance/multivariate analyses of variance (18%), t tests (8%), Wilcoxon's tests (8%), Chi-squared tests (8%), and Fisher's exact tests (7%), and not reported for sign tests, Friedman's tests, McNemar's tests, multi-level models, and Kruskal-Wallis tests. Effect sizes were infrequently interpreted. Confidence intervals were reported in 28% of papers.
  
  
  CONCLUSIONS
  The use, reporting, and interpretation of inferential statistics in nursing research need substantial improvement. Most importantly, researchers should abandon the misleading practice of interpreting the results from inferential tests based solely on whether they are statistically significant (or not) and, instead, focus on reporting and interpreting effect sizes, confidence intervals, and significance levels. Nursing researchers also need to conduct and report a priori power analyses, and to address the issue of Type I experiment-wise error inflation in their studies.
authors.affiliation: Gaskin Research, Melbourne, Victoria, Australia; Central Queensland University, Institute for Health and Social Science Research, Centre for Mental Health Nursing Innovation, School of Nursing and Midwifery, Melbourne, Victoria, Australia.
authors.forename: Cadeyrn J
authors.initials: CJ
authors.lastname: Gaskin
authors.affiliation: Central Queensland University, Institute for Health and Social Science Research, Centre for Mental Health Nursing Innovation, Rockhampton, Queensland, Australia; Central Queensland University, School of Nursing and Midwifery, Rockhampton, Queensland, Australia. Electronic address: b.happell@cqu.edu.au.
authors.forename: Brenda
authors.initials: B
authors.lastname: Happell
mesh_terms: D003627:Data Interpretation, Statistical; D015399:Nursing Research; D011643:Publishing
keywords: Nursing research; Research design; Review literature as topic; Statistics; Statistics as topic