2008
DOI: 10.1016/j.jclinepi.2007.05.019
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A parametric analysis of ordinal quality-of-life data can lead to erroneous results

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Cited by 42 publications
(31 citation statements)
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“…Much deliberation appears in the literature about the nature of Likert-type data and the assumptions needed to be met for this Figure 1. Implementation of the SSI program type of data to be analyzed parametrically (Clason & Dormody, 1994;Jamieson, 2004;Kahler, Rogausch, Brunner, & Himmel, 2008;Nanna & Sawilosky, 1998). Due to the ordinal nature of our data, particularly when analyzing data consisting of two or three combined Likert items, appropriate non-parametric tests of significance were used to determine the extent the SSI program facilitated changes in the students' character and values as global citizens (Cohen, 1988;Conover, 1999;Grissom & Kim, 2005).…”
Section: Data Collection and Analysismentioning
confidence: 99%
“…Much deliberation appears in the literature about the nature of Likert-type data and the assumptions needed to be met for this Figure 1. Implementation of the SSI program type of data to be analyzed parametrically (Clason & Dormody, 1994;Jamieson, 2004;Kahler, Rogausch, Brunner, & Himmel, 2008;Nanna & Sawilosky, 1998). Due to the ordinal nature of our data, particularly when analyzing data consisting of two or three combined Likert items, appropriate non-parametric tests of significance were used to determine the extent the SSI program facilitated changes in the students' character and values as global citizens (Cohen, 1988;Conover, 1999;Grissom & Kim, 2005).…”
Section: Data Collection and Analysismentioning
confidence: 99%
“…Interval measures are preferable to ordinal scores: They are characterized by measurement units that maintain the same size over the entire domain so that the measurement of change is more precise. Misusing ordinal scores as they were interval measures can lead to erroneous conclusions in clinical trials [5]. A method that produces interval measures from ordinal scores is desirable.…”
Section: Introductionmentioning
confidence: 99%
“…Literature warned researchers against the practice of misusing ordinal raw scores as they were interval measures (eg, calculating means, standard deviations and effect sizes) [31,32], and showed erroneous conclusions that can derive from applying parametric analyses inappropriately [33]. Since Rasch models allow for the transformation of ordinal raw scores into interval measures, they have been suggested as a valuable tool in both the analysis of clinical data, and the development and evaluation of instruments [30,34,35].…”
Section: Discussionmentioning
confidence: 99%