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The Case for Using the Repeatability Coefficient When Calculating Test–Retest Reliability

Overview of attention for article published in PLOS ONE, September 2013
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Title
The Case for Using the Repeatability Coefficient When Calculating Test–Retest Reliability
Published in
PLOS ONE, September 2013
DOI 10.1371/journal.pone.0073990
Pubmed ID
Authors

Sharmila Vaz, Torbjörn Falkmer, Anne Elizabeth Passmore, Richard Parsons, Pantelis Andreou

Abstract

The use of standardised tools is an essential component of evidence-based practice. Reliance on standardised tools places demands on clinicians to understand their properties, strengths, and weaknesses, in order to interpret results and make clinical decisions. This paper makes a case for clinicians to consider measurement error (ME) indices Coefficient of Repeatability (CR) or the Smallest Real Difference (SRD) over relative reliability coefficients like the Pearson's (r) and the Intraclass Correlation Coefficient (ICC), while selecting tools to measure change and inferring change as true. The authors present statistical methods that are part of the current approach to evaluate test-retest reliability of assessment tools and outcome measurements. Selected examples from a previous test-retest study are used to elucidate the added advantages of knowledge of the ME of an assessment tool in clinical decision making. The CR is computed in the same units as the assessment tool and sets the boundary of the minimal detectable true change that can be measured by the tool.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 582 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 5 <1%
United States 4 <1%
Switzerland 2 <1%
Singapore 2 <1%
Netherlands 1 <1%
Sweden 1 <1%
Canada 1 <1%
Malaysia 1 <1%
Denmark 1 <1%
Other 1 <1%
Unknown 563 97%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 124 21%
Researcher 91 16%
Student > Master 74 13%
Student > Bachelor 39 7%
Student > Doctoral Student 31 5%
Other 113 19%
Unknown 110 19%
Readers by discipline Count As %
Medicine and Dentistry 128 22%
Psychology 62 11%
Engineering 44 8%
Agricultural and Biological Sciences 32 5%
Nursing and Health Professions 30 5%
Other 151 26%
Unknown 135 23%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 25 May 2020.
All research outputs
#20,793,949
of 25,547,904 outputs
Outputs from PLOS ONE
#182,780
of 222,786 outputs
Outputs of similar age
#159,438
of 210,760 outputs
Outputs of similar age from PLOS ONE
#3,827
of 5,035 outputs
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