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Sorting, Napping® &co
Let’s practice!
Holistic / global approaches
Analytical
Traditional descriptive analyses
Free Choice profiling
Flash profiling
Main
reasons
to try
Sorting, Napping®
Spontaneous
Less cost
Reduce time
Criteria important to the individual
Are you sure the goal is reached?
A lot of comments about the methods (various comments
heard among users):
POSITIVE ASPECTS
“It's easy to perform and easy for panelists to
understand”
“useful as initial screening of prototypes or
category samples.”
‘very intuitive... ’
“practical to screen sensory properties of a
large set of products”
…
Sorting
NEGATIVE ASPECTS
“needs a certain amount of products to make
it work, but this is not always possible”
“not a diagnostic tool”
“request simultaneous presentation of
products and not suitable for cosmetic
products”
“Sorting is a technique to discover what is
salient to consumers/judges not a substitute
for DA”
…
Are you sure the goal is reached?
A lot of comments about the methods (various comments
heard among users):
POSITIVE ASPECTS
“rapid, inexpensive, easy-to-use”
“compact, easy to visualize”
“highly flexible and easy to understand
for consumer”
“practical to screen sensory properties
of a large set of product”
Napping®
…
NEGATIVE ASPECTS
“hard to understand”
“challenge to analyse”
“hard to interprete, time consuming”
“difficult for consumers to position the
products”
“Very difficult task for consumers + data
analysis”
…
Application of the methods in real life
The idea of this tutorial is to have a look at the main difficulties of the
two methods and to see the way they have been used, modified,
transformed, adapted by the different users to suit their needs facing
the different difficulties.
We will just propose some solutions that we have seen working for
some users, we won’t discuss them. They may be suitable for you,
your products, your working area… or not.
It’s just a panel where you may pick ideas for your own work.
Calculation seems to be a key point: difficult for some, easy for
others… We will see this with the specialist Sebastien who will explain
what you can do with all this.
The example used in this presentation
The objects are 16 cards. Each card contains a geometric
shape combining 7 criteria (shape, color, size...).
Those cards have been presented several times by
AgroCampus Ouest in different publications.
The sorting Task
The simple sorting task
The subject is invited to group the different products according to
their resemblances.
For example, you can ask:
Please group the different products according to their resemblances.
You must put two products in the same group if they are similar.
You must assess the resemblances or the differences between these
products according to your own criteria.
The structure of the collected data set
Each subject gives his own partition of the 16 cards.
Group 1
Group 2
Group 1
Group 1
Group 1
…
Group 2
Group 3
…
Group 3
Group 2
…
Group 2
Group 3
…
How to code data
N subjects are sorting P products.
For a particular subject, K groups are built.
You can consider this as:
• N Partitions of P products
• N Individual dissimilarity matrix (distance matrix)
• Aggregated dissimilarity matrix
• N Individual disjunctive table
• Aggregated disjunctive table
Depending on the calculation method you will apply,
the data will be coded differently.
Example for Subject
(1)
0
0
1
1
1
0
0
1
1
1
1
1
0
1
0
1
1
1
0
1
1
1
0
1
0
and
in group 1 so distance()=0
and
in different group so distance()=1
Dissimilarity Matrix for Subject
Example for Subject
Group 1
Group 2
Group 3
1
0
0
1
0
0
0
1
0
0
0
1
0
1
0
Disjunctive table for Subject
(2)
Calculations : a lot of possibilities (1)
Family
Sub Family
Data used
Aggregated
dissimilarity
matrix
Methods
Metric MDS
Kruskal, 1964
Gygi et al, 2007
Non metric MDS
Schiffman, 1981; Lawless, 1989
Falahee et al, 1997
Faye et al, 2004
Metric INDSCAL
Distatis
Caroll et Chang, 1970
Abdi et al, 2007
Non Metric
INDSCAL
Takane et al, 1977
Methods closed to
Multidimensional
scaling
Individual
dissimilarity
matrix
Factorial Methods
Methods closed
to multiple
correspondence
analysis
Disjunctive table
Bibliography
ACM
MDSORT
FAST
IDSORT
CCSORT
Van der Kloot et Van Herk, 1991
Takane, 1981
Cadoret et al, 2009
Takane, 1982
Qannari et al, 2009
Calculations : a lot of possibilities (2)
Family
Clustering
Data used
Methods
Bibliography
Hierachical
clustering
Lebart et al, 2006
Coxon, 1999
Lawless, 1989
Giboreau et al, 2001
Additive trees
Sattah et Tversky, 1977
Barthélémy in Dubois, 1991
Poitevineau, 2002
Aggregated
dissimilarity matrix
Extract : MODULAD, 2011, Num43, P.Faye, P.Courcoux, E.M.Qannari, A.Giboreau
Calculations : a lot of possibilities (3)
You will see an example of what can be done easily with R
later with Sebastien.
Several topics of the conference will also cover this.
Used, modified, transformed, adapted…
Used with trained panel, consumers, specialists (panel not
formally trained but highly experienced) depending on the
objective
Verbalization task: the subjects are asked to describe the
groups
• Free comments
• Comments with a predefined list of terms
Hierarchical sorting task
• Descending: first the judges have to divide the set of products into
groups and he can subdivide these groups into finer groups, etc.,
until the final groups of products are homogeneous (Cadoret &Al,
2010)
• Ascending: make bigger groups of the groups previously made
(Qannari &Al, 2010)
Used to understand the process of a sorting task.
Example : Hierarchical sorting task
Napping®
Napping®
Projective mapping was first introduced by Risvik & al. 1994.
Napping® (The name derives from the French word ‘nappe’,
meaning ‘tablecloth' ) has been elaborated by Pages & al.
2003.
Each subject is given all the samples and a large table or
sheet of paper. They arrange their samples on the table,
using the distance between them to indicate how similar
(closely spaced) or different (more widely spaced) they
perceive them to be.
For example, you can ask:
Please position the different products on the rectangular map. The closer two
products are, the more similar they seem to be. Conversely, two products are
distant if you perceive much difference between them. You must assess the
resemblances or the differences between these products according to your own
criteria.
Napping®
Closed products
Very different products
The structure of the collected data set
Yj
Xj
The structure of the collected data set
Each subject gives 2 coordinates for the 16 cards.
X
Y
Xj
Yj
X
Y
X
Y
Calculations : a lot of possibilities
The final data frame is composed of P individual and 2*N
variables.
Several statistical methods have been developed to analyze this
kind of structure.
The most known (used?) are MFA (Multiple Factorial Analyses),
PMFA (Procustean Multiple Factor Analyses), INDSCAL
model, DISTATIS…
The aim of those algorithms is to find a common configuration
that will represent the product space (MFA, INDSCAL,
DISTATIS) and to compare the structure given by each
subject to the common configuration (PMFA).
Used, modified, transformed, adapted…
Used with trained panel, consumers, specialists (panel not formally trained
but highly experienced) depending on the objective
Verbalization task: the subjects are asked to describe the products to
explain the dimensions of the resulting perceptual map
• Free comments
• Comments with a predefined list of terms (it can be a very long list of terms
near 200-300 items)
Spontaneity vs Easier and faster interpretation… and as time is money…
Structure & size of the ‘tablecloth’
Sometimes you may need axes to be more structured or the
subject may need to materialize them.
Cr. P. Deneulin
Structure & size of the ‘tablecloth’
The subject may be able
to directly explain the
axis he chose.
On a square map, the subject may
be able to define the graduation of
the axes and modify them.
For example here, the subject could
want to precise that the origin of the
X axis is not null as a shape exists
on the card.
Napping® by modality
Idea first suggested by Pagès (2003), tested and presented by
Gilbert (2008) as a “happy medium” between the complete
holistic approach and the analytical approach of a profiling
by attribute.
It consists in conducting a Napping® exercise separately for
each relevant sensory modality for example appearance,
odor, flavor, texture...
MFA can be used for each modality (to get a consensus map by
modality). HMFA (Hierarchical Multiple Factor Analysis) can
be used to get a global consensus map.
More time needed by subject
May be easier for the subject to have a more analytical approach
May be closer to the profiling results
Depends on you product category!
The sorted Napping®
The subjects are asked to provide a map but they have the
possibility to group them according to their similarity.
They are asked to describe the group.
Another happy medium between time and information gathered.
The structure of the collected data set
Each subject gives 2 coordinates and the group allocation by product.
Sorting Task
Napping
X
Y
Gr
Xj
Yj
Gj
X
Y
Gr
X
Y
Gr
Time to practice!
Practice
You will have to do two tests:
A Sorted Napping with categorization on 11 Gingerbreads
A Sorting with categorization on 10 Apple Juices
Practice: Sorted Napping
You will have to position and sort 11 samples.
Please position the different products on the rectangular map. The closer two products are, the more similar
they seem to be. Conversely, two products are distant if you perceive much difference between them.
You must assess the resemblances or the differences between these products according to your own
criteria.
In order to position the products, please click on the product code which is displayed in the right part and
drag it towards its position on the map. You can move it later by clicking on it and dragging it towards a
new position.
To allocate a sample into a group:
- If you drop the sample on a free area, a new group is created, except if the maximum number of groups
has been reached. In this case the sample is allocated to the closest group.
- If you drop the sample on another one, it will be allocated to the same group.
You may also click on a sample with the right button of the mouse in order to make appear the contextual
menu and define the group allocation.
To cancel the group allocation of a sample: you may click on the sample and drag it back to the start right
area, or allocate it to another group. You may also click with the right button and select Reset group in
the contextual menu.
To comment a group: please click on '>>' button.
Practice: Sorting
You will have to sort 10 samples.
Please group the different products according to their resemblances. You must put two products in the same
group if they are similar. You must assess the resemblances or the differences between these products
according to your own criteria.
To allocate a sample into a group, please click on the product code which is displayed in the right part and
drag it towards its position on the map.
- If you drop the sample on a free area, a new group is created, except if the maximum number of groups
has been reached. In this case the sample is allocated to the closest group.
- If you drop the sample on another one, it will be allocated to the same group.
You may also click on a sample with the right button of the mouse in order to make appear the contextual
menu and define the group allocation.
To cancel the group allocation of a sample: you may click on the sample and drag it back to the start right
area, or allocate it to another group. You may also click with the right button and select Reset group in
the contextual menu.
To change the group allocation of a product: you may click on the sample and move it on a sample of the
desired group. The sample will be then allocated to this group. You may also click on the sample with
the right button of the mouse to make appear the contextual menu and change the group allocation.
To comment a group: when you are happy with the groups presented, click on the >> button
to comment the different groups.
Questions
Did you find the task easy?
Did you remember the products’ characteristics easily?
Were you happy with your work?
Did you find easily when to stop separating the products?
Any comment about the number of products?
Any comment about the tasks?
Statistics break
What you have tested (Napping)
729
803
056
519
161
982
340
Craft biscuit factory
087
445
266
Organic
624
Use of the text file in R (example)
Example with the function fasnt (extract)
(do not forget to load the SensoMineR library first!)
Script to be used:
What you have tested (Sorting)
109
076
863
453
158
535
Local Handwork
994
240
Organic
699
371
Use of the text file in R (example)
Example with the function fast (extract)
(do not forget to load the SensoMineR library first!)
Script to be used:
Data used in this presentation
Fizz Users
The tested sessions will be downloadable from
our Web Site.
All participants
To use the results in R, the exported text files
from Fizz will also be accessible on our Web
site (in the Sensometrics News).
Contact us for any question!
Thank you!