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Session Name+ |
Summary |
Length (days) |
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Principal Component Analysis and its Applications |
Principal Component Analysis (PCA) is a multivariate method which can identify redundancy or correlation among a set of measurements or variables for the purpose of data reduction. This powerful exploratory tool provides insightful graphical summaries with ability to include additional information as well.
This training course discusses the limitations of traditional descriptive tools for exploring datasets with several variables and presents how PCA can:
Summarize large sets of data
Identify structure, trends in the data
Identify redundancy, correlation in the data
Produce insightful graphical displays of the results |
1.0 |
 |
Cluster Analysis and its Applications |
This training session covers a powerful multivariate technique, cluster analysis, that comprises a diverse collection of techniques that can be used to classify objects (e.g. individuals, countries, species, cells, genes, etc).
Cluster analysis is an exploratory tool, with the ability to form homogeneous groups of individuals or objects. |
1.0 |
 |
Introduction to Generalized Procrustes Analysis |
This course covers Generalized Procrustes Analysis (GPA), a powerful multivariate technique developped in psychometrics and used extensively in sensory evaluation to:
Summarize large sets of 3-dimensional data (objects, characteristics and assessors)
Identify structure and trends in the data
Identify agreement between assessors and correlation in the data
Produce graphical displays of the results |
1.0 |
 |
Multivariate Data Analysis School |
This 5-day course focuses on the practical aspects of the most widely used multivariate methods: Principal Component Analysis (PCA), Factor Analysis, Correspondance Analysis, Cluster Analysis, Discriminant Analysis and Canonical Analysis.
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5.0 |
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Multivariate Techniques for Sensory and Consumer Studies |
This 3-day session is targeted towards sensory and consumer scientists interested in an applied workshop on multivariate data analysis of sensory and consumer test data with a strong emphasis on graphical summary representations.
During the first two days, classical multivariate techniques including Principal Component Analysis (PCA), Factor Analysis, Correspondence Analysis, Cluster Analysis and Discriminant Analysis are presented and their usefulness for sensory and consumer data is illustrated with case studies based on real data.
The last day is dedicated to alternative and less known applications of these methods to address specific issues and get more actionable results from the collected data. For instance:
PCA to measure panelist agreement in sensory attributes understanding
Extended Preference Mapping for the identification of niche products & sensory drivers
Discriminant analysis for product and concept mapping
A combination of PCA and cluster analysis to select a subset of representative products in a larger series of samples.
Throughout the workshop, participants are encouraged to use their own software and data to apply the multivariate techniques. Experienced instructors who have more than 15 years experience in the design and analysis of sensory and consumer test data will assist participants. They are very knowledgeable and proficient with most statistical packages. |
3.0 |
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Design and Analysis of Discrimination Tests |
Discriminative tests are used to determine if sensory differences exist between two or more than two products. Several tests exist. It is therefore important to know what they are used for and what statistical tools are available for the data analysis. |
2.0 |
 |
Panel Performance Assessment |
The assessment of the performance of a panel deals with several notions : repeatability, agreement, usage of scales and discriminating power. Differents statistical tools can be used to quantify these measures of panel performance. |
1.0 |
 |
Statistical Analysis of Consumer Test Data |
This course covers the statistical methods available in most statistical software to perform the most frequently used tests in consumer studies. It will allow participants to understand the different statistical tests possible, choose the most appropriate tests for the questionnaire used and select the most appropriate graphical representations to extract and visualize the salient features of the collected data. |
1.0 |
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Preference Mapping in Practice |
This applied training session in statistics is intended for all the people collecting preference data and wishing to determine consumer segments of product preferences in order to identify market opportunities.
This course covers powerful preference mapping techniques to explore and understand the preference structure of consumers. |
1.0 |
