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10.3390/pharmacy5020026 http://scihub22266oqcxt.onion/10.3390/pharmacy5020026 C5597151/?report=reader!5597151!28970438     free     free     free Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Pharmacy+(Basel) 2017 ; 5 (2): ä Nephropedia Template TP {{tp|p=28970438|t=2017. A Comparison of Parametric and Non-Parametric Methods Applied to a Likert Scale |pdf=|usr=}}{{28970438}} gab.com Text A Comparison of Parametric and Non-Parametric Methods Applied to a Likert Scale JO: Pharmacy (Basel) http://www.kidney.de/pget.php?&tw=1&pmid=28970438 #FOAvip A trenchant and passionate dispute over the use of parametric versus non-parametric methods for the analysis of Likert scale ordinal data has raged for the past eight decades. The answer is not a simple ?yes? or ?no? but is related to hypotheses, objectives, risks, and paradigms. In this paper, we took a pragmatic approach. We applied both types of methods to the analysis of actual Likert data on responses from different professional subgroups of European pharmacists regarding competencies for practice. Results obtained show that with ?large? (>15) numbers of responses and similar (but clearly not normal) distributions from different subgroups, parametric and non-parametric analyses give in almost all cases the same significant or non-significant results for inter-subgroup comparisons. Parametric methods were more discriminant in the cases of non-similar conclusions. Considering that the largest differences in opinions occurred in the upper part of the 4-point Likert scale (ranks 3 ?very important? and 4 ?essential?), a ?score analysis? based on this part of the data was undertaken. This transformation of the ordinal Likert data into binary scores produced a graphical representation that was visually easier to understand as differences were accentuated. In conclusion, in this case of Likert ordinal data with high response rates, restraining the analysis to non-parametric methods leads to a loss of information. The addition of parametric methods, graphical analysis, analysis of subsets, and transformation of data leads to more in-depth analyses. Twit Text FOAVip A Comparison of Parametric and Non-Parametric Methods Applied to a Likert Scale ????Pharmacy (Basel) http://www.kidney.de/pget.php?&tw=1&pmid=28970438 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=28970438 #FOAvip English Wikipedia <ref>{{Cite pmid|28970438}}</ref> A Comparison of Parametric and Non-Parametric Methods Applied to a Likert Scale #MMPMID28970438 Mircioiu C; Atkinson J Pharmacy (Basel) 2017[Jun]; 5 (2): ä PMID28970438show ga A trenchant and passionate dispute over the use of parametric versus non-parametric methods for the analysis of Likert scale ordinal data has raged for the past eight decades. The answer is not a simple ?yes? or ?no? but is related to hypotheses, objectives, risks, and paradigms. In this paper, we took a pragmatic approach. We applied both types of methods to the analysis of actual Likert data on responses from different professional subgroups of European pharmacists regarding competencies for practice. Results obtained show that with ?large? (>15) numbers of responses and similar (but clearly not normal) distributions from different subgroups, parametric and non-parametric analyses give in almost all cases the same significant or non-significant results for inter-subgroup comparisons. Parametric methods were more discriminant in the cases of non-similar conclusions. Considering that the largest differences in opinions occurred in the upper part of the 4-point Likert scale (ranks 3 ?very important? and 4 ?essential?), a ?score analysis? based on this part of the data was undertaken. This transformation of the ordinal Likert data into binary scores produced a graphical representation that was visually easier to understand as differences were accentuated. In conclusion, in this case of Likert ordinal data with high response rates, restraining the analysis to non-parametric methods leads to a loss of information. The addition of parametric methods, graphical analysis, analysis of subsets, and transformation of data leads to more in-depth analyses. äA Comparison of Parametric and Non-Parametric Methods Applied to a Lik(epmc).pdf DeepDyve Pubget Overpricing