Panelist Outliers Analysis
To detect panellists that are possible outliers,
highlighting those with extremely high or low values.
- See the profiling dataset.
- Type of Panel Mean: Adjusted or
Arithmetic. Arithmetic means are the well known means, calculated by summing
the observations then dividing by the number of observations. Adjusted means,
commonly called Least-Squares means (LSmeans) or Estimated Marginal Means
(EMMs), use a regression model to calculate the means adjusting for the balance
of the data.
- Number of standard deviations: Assessors
whose mean falls outside plus or minus this number of standard deviations from
the panel mean are highlighted as possible outliers.
- Y-axis Scale: Automatic or Manual.
Automatic chooses the plotting range from the range of each attribute. Manual
uses the plotting range you specified.
min value: The smallest value that will appear on all plots when specifying
the plotting range manually.
- Y-axis max value: The largest value that
will appear on all plots when specifying the plotting range manually.
- Anonymise Assessors? Choose to replace
the assessor names or not. There are options for randomly generated names or
names from the assessor metadata.
- Anonymise Products? Choose to replace the
product names or not. There are options for randomly generated names or names
from the product metadata.
- Anonymise Attributes? Choose to replace
the attribute names or not. There are options for randomly generated names or
names from the attribute metadata.
Results and Interpretation
- For each product and attribute a bar plot of
panellist means is displayed. These plots overlay the panel mean as a black
line and the chosen number of standard deviations from this mean as two dotted
lines. Any panellist outside this range is highlighted as a possible outlier.
- If “Type of Panel Mean” was set to “Adjusted”
then the panel means will be adjusted means from models of the form: Attribute
= Product + Assessor + Replicate + Residuals or Attribute = Product + Assessor
+ Residuals if replications are not present.
- Outliers should not necessarily be removed.
- For a large number of draws from a normal
distribution it is expected that around 5% of observations would be outside of plus
or minus 1.96 standard deviations from the mean.
- R packages: This analysis uses SensoMineR to
calculate the adjusted means.
Direct Feedback, Panelist Feedback on a scale, Panelist Feedback
Direct Feedback The direct feedback function provides the feedback live to panelists. The mean and sd need to be specified for each attribute for each product, so you need to know ahead of the mean and the standard deviation value. When panelists ...
Panelist Feedback Analysis
Mar 2022 Author: JM Purpose Plots summarising of the scoring range for each judge on each of the samples and attributes. Data Format See the profiling dataset. The attributes should be of scale or interval type. Options Type of Panel Mean: Adjusted ...
Panelist Performance Analysis
Mar 2022 Author: JM Purpose This analysis looks at the overall panel performance in terms of Discrimination, Agreement and Repeatability or Reproducibility, and then the performance of each individual in the panel in these terms. Data Format See the ...
Panelist Strip Plot Analysis
Mar 2022 Author: JM Purpose Plots for each attribute the panellists means across the products, for visual comparison and interpretation. Data Format See the profiling dataset. Background This analysis calculates the mean response from each assessor ...
Why are the results from panel and panelist performance different?
Although the panel performance and the panellist performance seems to be linked, at least from an interpretation point of view (one could expect that the panel is discriminating if panellists are discriminated), the analyses involved different data ...