What is Discriminant Analysis?
- Discriminant Analysis may be
used for two objectives: either we
want to assess the adequacy of classification, given the group
memberships of the objects under study; or we wish to assign objects
to one of a number of (known) groups of objects. Discriminant Analysis may thus
have a descriptive or a predictive objective.
The aim of all descriptive techniques is to generate
quantitative data which describes the similarities and differences among a set
of products. Each method has a different approach; however the basic framework
of all the techniques is the same:
- selection of panel members
- term generation (or selection of appropriate lexicon)
- concept formation
- testing of panel consonance
- evaluation of products
Descriptive techniques include Free Choice Profiling (FCP),
the Spectrum™ method, Quantitative Descriptive Analysis™ (QDA), Flavor Profile
Method, Texture Profile Method, Flash Profiling and generic descriptive
analysis. Generic descriptive analysis generally takes pieces from QDA and
Spectrum™ methods, but is modified to suit the goals of the project and
limitations of the product being tested. Of the methods mentioned here, FCP and
Flash Profiling involved the use of untrained consumers rather than a trained
panel (although a trained panel can be used). This main point of
differentiation makes these techniques faster and cheaper to conduct as there
is no training involved
DA is used when:
The dependent is categorical with
the predictor IV’s at interval level such as age, income, attitudes,
perceptions, and years of education, although dummy variables can be used as
predictors as in multiple regression. Logistic regression IV’s can be of any
level of measurement.
There are several purposes of DA:
- To investigate differences between groups on the basis of the attributes of the cases,
- Indicating which attributes contribute most to group separation. The descriptive technique successively identifies the linear combination of attributes known as canonical
- Discriminant functions (equations) which contribute maximally to group separation.
- Predictive DA addresses the question of how to assign new cases to groups.
- The DA function uses a person’s scores on the predictor variables to predict the category to which the individual belongs.
- To determine the most parsimonious way to distinguish between groups.
- To classify cases into groups. Statistical significance tests using chi square enable you
- To see how well the function separates the groups.
- To test theory whether cases are classified as predicted
Abishek Machama
Team -D
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