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.


    • Related Articles

    • Panelist Feedback Analysis

      Purpose Plots summarising 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 or Arithmetic. ...

    • 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 are ...

    • Penalty Analysis

      Purpose To provide a penalty analysis of a consumer data set, that is to investigate how liking or acceptability of product decreases when product attributes are not at the optimal intensity. Data Format Example dataset: Consumer.xlsx Note: for ...

    • Panelist Outliers Analysis

      Purpose To detect panellists that are possible outliers, highlighting those with extremely high or low values. Data Format See the profiling dataset. Options Type of Panel Mean: Adjusted or Arithmetic. Arithmetic means are the well known means, ...

    • Correspondence Analysis (CATA and categorical data)

      Purpose To visualise and summarise analyse tabular data and to highlight the patterns of association in two way tables. It is widely used for mapping pure qualitative variables – e.g cluster by demographic use. This is an example of typical data that ...