Measures of Reliability in Sports Medicine and Science
- 8.1k Downloads
Reliability refers to the reproducibility of values of a test, assay or other measurement in repeated trials on the same individuals. Better reliability implies better precision of single measurements and better tracking of changes in measurements in research or practical settings. The main measures of reliability are within-subject random variation, systematic change in the mean, and retest correlation. A simple, adaptable form of within-subject variation is the typical (standard) error of measurement: the standard deviation of an individual’s repeated measurements. For many measurements in sports medicine and science, the typical error is best expressed as a coefficient of variation (percentage of the mean). A biased, more limited form of within-subject variation is the limits of agreement: the 95% likely range of change of an individual’s measurements between 2 trials. Systematic changes in the mean of a measure between consecutive trials represent such effects as learning, motivation or fatigue; these changes need to be eliminated from estimates of within-subject variation. Retest correlation is difficult to interpret, mainly because its value is sensitive to the heterogeneity of the sample of participants. Uses of reliability include decision-making when monitoring individuals, comparison of tests or equipment, estimation of sample size in experiments and estimation of the magnitude of individual differences in the response to a treatment. Reasonable precision for estimates of reliability requires approximately 50 study participants and at least 3 trials. Studies aimed at assessing variation in reliability between tests or equipment require complex designs and analyses that researchers seldom perform correctly. A wider understanding of reliability and adoption of the typical error as the standard measure of reliability would improve the assessment of tests and equipment in our disciplines.
KeywordsConfidence Limit Difference Score Typical Error Reliability Study Estimate Sample Size
Chris Gore, John Hawley, Jenny Keating, Michael McMahon, Louis Passfield andAndy Stewart provided valuable feedback on drafts of this article.
- 6.VanLeeuwen DM, Barnes MD, Pase M. Generalizability theory: a unified approach to assessing the dependability (reliability) of measurements in the health sciences. J Outcome Measures 1998; 2: 302–25Google Scholar
- 8.Kovaleski JE, Heitman RJ, Gurchiek LR, et al. Reliability and effects of leg dominance on lower extremity isokinetic force and work using the Closed Chain Rider System. J Sport Rehabil 1997; 6: 319–26Google Scholar
- 10.Kovaleski JE, Ingersoll CD, Knight KL, et al. Reliability of the BTE Dynatrac isotonic dynamometer. Isokinet Exerc Sci 1996; 6: 41–3Google Scholar
- 11.Hopkins WG. A new view of statistics. Available from: http://sportsci.org/resource/stats [Accessed 2000 Apr 18]Google Scholar
- 13.Cohen J. Statistical power analysis for the behavioral sciences. 2nd ed. Mahwah (NJ): Lawrence Erlbaum, 1988Google Scholar
- 15.Clark VR, Hopkins WG, Hawley JA, et al. Placebo effect of carbohydrate feedings during a 40-km cycling time trial. Med Sci Sports Exerc. In pressGoogle Scholar
- 16.Hopkins WG, Wolfinger RD. Estimating ‘individual differences’ in the response to an experimental treatment [abstract]. Med Sci Sports Exerc 1998; 30 (5): S135Google Scholar
- 18.Hopkins WG. Generalizing to a population. Available from: http://sportsci.org/resource/stats/generalize.html [Accessed 2000 Apr 18]Google Scholar
- 19.Hopkins WG. Reliability: calculations and more. Available from: http://sportsci.org/resource/stats/relycalc.html [Accessed 2000 Apr 18]Google Scholar