Multivariate analysis and clustering on JAR (Just-About-Right) data in Excel
Jar data to analyze
The dataset provides valuable information on 72 assessors evaluating 8 different cheeses based on 9 JAR attributes.
Setting up the JAR Multivariate analysis and clustering in XLSTAT
Select the XLSTAT / Sensory data analysis / JAR Multivariate analysis and clustering. The dialog box pops up.
In the XLSTAT interface, select the just-about-right data corresponding to the descriptors.
Identify the scale corresponding to the JAR data.
Select the data corresponding to the product identifiers.
Select the data corresponding to the assessor identifiers.
Choose between the Explanatory analysis (CATATIS) and clustering analysis (CLUSCATA).
In the options tab, enter beta, the parameter for agreement between JAR and other answers.
Click on OK.
Interpret multivariate analysis of JAR data
The products/assessors table is displayed first, then all the results for the CATATIS method adapted to JAR data.
For example, we can see that product R is characterized by too little texture and consistency, and is too creamy.
For a better understanding of the results, please see Analysis of CATA data with CATATIS in Excel.
Interpret JAR data clustering
The products/assessors table is displayed first, then all the results for the CLUSCATA method adapted to JAR data.
Three clusters of assessors could be considered here.
For a better understanding of the results, please see Clustering subjects in a CATA task by means of CLUSCATA.
Was this article useful?