Panelist Strip Plot Analysis

Panelist Strip Plot Analysis

Purpose

Plots for each attribute the panellists means across the products, for visual comparison and interpretation.

Data Format

  1. See the profiling dataset.

Background

This analysis calculates the mean response from each assessor on each attribute then for each attribute plots these as strip, where a strip represents a panellist.

Options

  1. 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.
  2. Include Panel Mean: Should the panel level mean be included for each attribute?
  3. Y-axis Scale: Automatic or Manual. Automatic chooses the plotting range from the range of each attribute. Manual uses the plotting range you specified.
  4. Y-axis min value: The smallest value that will appear on all plots when specifying the plotting range manually.
  5. Y-axis max value: The largest value that will appear on all plots when specifying the plotting range manually.
  6. Anonymise Assessors? Choose to replace the assessor names or not. There are options for randomly generated names or names from the assessor metadata.
  7. Anonymise Products? Choose to replace the product names or not. There are options for randomly generated names or names from the product metadata.
  8. 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

A strip plot for each attribute, with the panellist average for each product plotted and if “Include Panel Mean” was set to “Yes” then the panel level mean is also plotted as a strip for each attribute and product.

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 + Product:Assessor + Residuals.

A small spread between panellists suggests good agreement, whereas similar means across products suggests poor discrimination or that the products are similar on that attribute.

Technical Information

  1. R packages: This analysis uses SensoMineR to calculate the adjusted means.


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