Descriptive Analysis Workbook Panelist Consistency
What is it?
Panelist Consistency is one of the sheets in the Descriptive Analysis Workbook . Determines consistency by looking at the range of responses given across the reps.
Why would I use it?
Identify panelists, attributes or samples that were not repeated successfully across reps.
Setup options
To generate the Panelist Consistency sheet, under 2. Select Options , select Panelist consistency and adjust the thresholds as needed. |
The Panelist Consistency sheet is designed to work with a 100 pt scale.
Analysis Overview
The range is calculated by subtracting the minimum value from the maximum value of all the reps.
- Large Threshold . Includes all differences greater than the threshold value.
- Medium Threshold . Includes all differences between the medium threshold value and larger threshold value.
- Small Threshold . Includes all differences between the small threshold value and medium threshold value.
- Number of sessions . The total number of reps included in the report.
- Percentages . The total number within the threshold/ the total number *100.
Difference scores with the difference sizes highlighted. If the value falls below the small difference threshold, it is left white.
- #Large Difference . Number of attributes with a large difference.
- #Medium Difference . Number of attributes with a medium difference.
- #Small Difference . Number of attributes with a small difference.
Example Conclusion
There are several factors to consider
when reviewing potential differences between replications. For instance, if a
particular attribute or sample has several responses in the large threshold, we
might wonder whether something happened with one of the samples or perhaps that
attribute was not well understood by the panel.
In the summary by attribute table, we see
that Sweetness Flavour is causing some issues for our
panel.
In
the summary by panelist table, we see that Tabitha has issues with replication
in several attributes/samples which can be identified by looking at the
breakdown by sample tables.
In the summary by sample table, we see
that the number of differences found are fairly even across the
samples and therefore might consider that the attributes are causing more
issues to our panelists than the samples.
In the breakdown by sample table for Apple Juice 1 we see that Tabitha Rouse is experiencing issues with Sweetness Flavour and we will want to look at the other samples to see if there is something going on in general with this attribute or only with Apple Juice 1.
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