Descriptive Analysis Workbook Attribute Performance Summary

Descriptive Analysis Workbook Attribute Performance Summary

What is it?

 Attribute Performance Summary is one of the sheets in the Descriptive Analysis Workbook .  An overview of your panel's performance and their ability to discriminate by attribute. 

Why would I use it?

Indicate possible reasons for potential performance issues.

Setup options



To generate the Attribute Performance Summary sheet, under 2. Select Options , select Attribute performance summary.  Adjust the thresholds as required.
 
The Analysis Options from Defaults area indicates what your default selections for ANOVA model and post hoc test are. If a change is necessary, click Change Advanced Analysis Options .




Analysis Overview




  1. Sig: based on the ANOVA selected in the defaults.
  2. Performance Status .  Indicates whether your panel is in alignment or disagreement.
Criteria for Panel Alignment
​
For good Panel Alignment, all of the following must be true:
  1. The analysis determined that this attribute was significant.
  2. Good agreement threshold (default 3) or fewer panelists had a crossover score greater than Panelist crossover (default 0).
  3. Good agreement threshold (default 3) or fewer panelists had a panelist p-value for product greater than Panelist p-value (default 0.25).
If all 3 criteria are not met, it is considered Panel Disagreement.
If neither alignment nor disagreement are detected in an attribute, the cell is left blank.



Criteria for Determining the # of Samples With Means > % of Scale
Panel product means that are > sample mean % value set (default 25%).

Criteria for Determining the # of Discriminating Panelists

On the Panelist Mean Scores tab, verify whether the means for every sample, for each attribute, for each panelists are less than Panelist Product Mean x the scale (default 15).

Criteria for Determining # of Panelists in Agreement

Number of panelists who have a Pearson’s Correlation Coefficient above the set Correlation threshold (default 0.70).  


Example Conclusion

As our panel is near the end of their training, we can see that they are in alignment, using all samples and panelists for differentiation.



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