Towards the
fag end of course, we dealt with teo concepts
yesterday: Discriminant Analysis
&
Conjoint Analysis.
· Discriminant analysis is a statistical
procedure which allows us to classify cases in separate categories to which
they belong on the basis of a set of characteristic independent variables
called predictors or discriminant variables.
· The target variable (the one determining
allocation into groups) is a qualitative (nominal or ordinal)
one, while the characteristics are measured by quantitative variables.
Purpose:
The
main purpose of a discriminant function analysis is to predict group
membership based on a linear combination of the interval variables. The procedure
begins with a set of observations where both group membership and the values of
the interval variables are known. The end result of the procedure is a model
that allows prediction of group membership when only the interval variables are
known.
A
second purpose of discriminant function analysis is an understanding
of the data set, as a careful examination of the prediction model that results
from the procedure can give insight into the relationship between group
membership and the variables used to predict group membership.
Examples:
A
graduate admissions committee might divide a set of past graduate students into
two groups: students who finished the program in five years or less and those
who did not. Discriminant function analysis could be used to predict
successful completion of the graduate program based on GRE score and
undergraduate grade point average. Examination of the prediction model might
provide insights into how each predictor individually and in combination predicted
completion or non-completion of a graduate program.
Another
example might predict whether patients recovered from a coma or not based on
combinations of demographic and treatment variables. The predictor variables
might include age, sex, general health, time between incident and arrival at
hospital, various interventions, etc. In this case the creation of the
prediction model would allow a medical practitioner to assess the chance of
recovery based on observed variables. The prediction model might also give
insight into how the variables interact in predicting recovery.
Conjoint analysis attempts to determine the relative importance
consumers attach to salient attributes and the utilities they attach to the
levels of attributes.
The respondents are presented with stimuli that
consist of combinations of attribute levels and asked to evaluate these stimuli
in terms of their desirability.
Conjoint procedures attempt to assign values to the
levels of each attribute, so that the resulting values or utilities attached to
the stimuli match, as closely as possible, the input evaluations provided by
the respondents.
Advantages:
·
Estimates psychological tradeoffs that consumers make
when evaluating several attributes together
·
Measures preferences at the individual level
·
Uncovers real or hidden drivers which may not be apparent
to the respondent themselves
·
Realistic choice or shopping task
·
Able to use physical objects
·
If appropriately designed, the ability to model
interactions between attributes can be used to develop needs based segmentation
Disadvantages:
·
Designing conjoint studies can be complex
·
With too many options, respondents resort to
simplification strategies
·
Difficult to use for product positioning research because
there is no procedure for
converting perceptions about actual features to
perceptions about a reduced set of
underlying features
·
Respondents are unable to articulate attitudes toward new
categories, or may feel
forced to think about issues they would otherwise not
give much thought to
·
Poorly designed studies may over-value
emotional/preference variables and undervalue
concrete variables
· Does not take into account the number items per
purchase so it can give a poor
reading of market share
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