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Generalised Procrustes Analysis

Generalised Procrustes Analysis

Discover a powerful multivariate technique for mapping the consensus among assessors rating series of products/concepts, and for quantifying and mapping the redundancy in sensory descriptors.

Course Details

Learning Objectives

Learning Objectives:

Upon completion of this module, participants will be able:

  • To understand the context of use for GPA
  • To know which exploratory analyses should be carried out on the data
  • To prepare the dataset in the required format for GPA
  • To read and interpret numeric and graphical software output
  • To report and communicate the results of the GPA

Target Audience

Target Audience:

This module is intended for scientific staff who collect 3-dimensional datasets and who wish to graphically summarizing them as well as assess agreement between assessors.



Participants must have attended the session Principal Component Analysis and its Applications or have working knowledge of this multivariate technique.

Course Outline

Course Outline:
  • Purpose of GPA
  • Principle of GPA
  • GPA Output and Interpretation
  • Case Studies
  • Software Packages for GPA
  • Summary

Practical Info

Practical Info:

Recommended Course Duration: 1 day

Related Sessions

  • An applied set of modules with focus on the most widely used multivariate methods and their applications in several fields of application. Learn about the principle of the methods, the data needed, and the information they provide.

  • Learn about preference mapping techniques to explore and understand consumer preferences. Applications dealing with segmentation and the identification of niche markets are discussed. Focus on pitfalls and good practices.

  • Understanding consumers and their preferences is crucial for competitive businesses. Learn about commonly used methods to analyse consumer test data, the way to interpret results and to communicate them with insightful graphical summaries.

  • This workshop on Measuring and Reporting Descriptive Panel Performance first introduces indicators designed to capture the desirable attributes of descriptive data collected by a trained sensory panel (agreement, repeatability and discrimination power). A suite of statistical tools are used to depict and quantify the performance of the panel.

    Real-life case studies are used to illustrate the principles of the statistical tools and the insights that may be extracted from the data.