Comparing Friedman versus Mack-Skillings data analyses on duplicated rank data: a case of visual color intensity.
Friedman's test
Mack-Skillings test
color
duplicated ranking test
ranking test
Journal
Journal of the science of food and agriculture
ISSN: 1097-0010
Titre abrégé: J Sci Food Agric
Pays: England
ID NLM: 0376334
Informations de publication
Date de publication:
Oct 2019
Oct 2019
Historique:
received:
08
11
2018
revised:
25
04
2019
accepted:
27
05
2019
pubmed:
1
6
2019
medline:
15
10
2019
entrez:
1
6
2019
Statut:
ppublish
Résumé
In duplicated ranking tests, panelists either rank duplicates separately (2SS) or jointly in a single session (1SS). This study compared data analyses of duplicated yellow color intensity rank data using Friedman versus Mack-Skillings (M-S) tests. Panelists (n = 75) ranked an orange juice set twice - a similar-samples set (100%, 95% versus 90%); samples other than the 100% juice were prepared by dilution with water. Rank sum data were obtained from (a) intermediate ranks from jointly re-ranked scores of 2SS for each panelist, and (b) joint rank data of all panelists from the two replications in 1SS. Both (a) and (b) were analyzed using the M-S test. The median rank data (c) for each panelist from two replications were analyzed using the Friedman test. Comparing M-S with the Friedman tests, the former generally produced higher test statistics and lower P-values than the latter. However, when considering the pattern of post hoc pairwise significant differences, both tests yielded similar conclusions. This study demonstrated that, in a duplicated ranking test with three samples that were very similar in color, separating the two replications into two complete individual ranking tests or serving sessions (2SS) may prevent sensitivity loss due to fatigue that is otherwise experienced when evaluating all samples together in a single session (1SS). We expected to find the M-S test to be more sensitive than the Friedman test; however, this hypothesis was not supported by the post hoc (Tukey's honest significant difference (HSD)) multiple comparison test results under the specific test conditions in this study. © 2019 Society of Chemical Industry.
Sections du résumé
BACKGROUND
BACKGROUND
In duplicated ranking tests, panelists either rank duplicates separately (2SS) or jointly in a single session (1SS). This study compared data analyses of duplicated yellow color intensity rank data using Friedman versus Mack-Skillings (M-S) tests. Panelists (n = 75) ranked an orange juice set twice - a similar-samples set (100%, 95% versus 90%); samples other than the 100% juice were prepared by dilution with water. Rank sum data were obtained from (a) intermediate ranks from jointly re-ranked scores of 2SS for each panelist, and (b) joint rank data of all panelists from the two replications in 1SS. Both (a) and (b) were analyzed using the M-S test. The median rank data (c) for each panelist from two replications were analyzed using the Friedman test.
RESULTS
RESULTS
Comparing M-S with the Friedman tests, the former generally produced higher test statistics and lower P-values than the latter. However, when considering the pattern of post hoc pairwise significant differences, both tests yielded similar conclusions.
CONCLUSION
CONCLUSIONS
This study demonstrated that, in a duplicated ranking test with three samples that were very similar in color, separating the two replications into two complete individual ranking tests or serving sessions (2SS) may prevent sensitivity loss due to fatigue that is otherwise experienced when evaluating all samples together in a single session (1SS). We expected to find the M-S test to be more sensitive than the Friedman test; however, this hypothesis was not supported by the post hoc (Tukey's honest significant difference (HSD)) multiple comparison test results under the specific test conditions in this study. © 2019 Society of Chemical Industry.
Types de publication
Comparative Study
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
5696-5701Informations de copyright
© 2019 Society of Chemical Industry.
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