Dear Robrecht,
You want to see the link between a qualitative variable and several
continuous variables. This objective can be attend by a model such
as a logistic model for instance.
With principal component methods such as PCA, the objective is to
have a multidimensional approach to better visualize the
individuals, their resemblance and differences, and to see the
relationships between variables.
So I think you can use MFA or PCA to see your data set, and then you
should use a model such as a logistic one.
FH
Le 07/04/2023 à 17:31, Robrecht Bollen
a écrit :
Dear François,
I have a follow-up question concerning sensory variables in
MFA. In the Wine example of the MFA video your colleague uses
the "Origin" of the wine as a supplementary variable
(which I understand). But what if you want to use this variable
as an active variable?
In my research I combine descriptive sensory notes (frequency
table) with quality scores of coffees that have been processed
differently (e.g. fermented, non-fermented, ...). Using MFA, I
want to assess which variables correlate the most with the
Processing method (qualitative variable). When I run the
analysis with Processing variable as active I get a
significant correlation of groups with the dimensions (Dim1,
Dim2,...) but when I set the Processing variable to supplementary
the automatic description of the axis is not correlated with
this variable.
I find no clear explanation of when to choose if a variable
should be active or supplementary. If my main objective is to
study the relationship of the processing variable with
descriptive and qualitative data I just set this variable to
active?
Thank you very much for your time,
Robrecht
Op woensdag 5 oktober 2022 om
20:57:22 UTC+2 schreef François Husson: