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Factor analysis is commonly used
in data reduction, scale development, psychometric quality of a measure and the
assessment of the dimensionality of a set of variables. Although it can be used
for a variety of purposes it is used to determine a small number of factors
based on a particular number of inter-related quantitative variables. Unlike
directly measured variables, some variables are not a single measurable entity.
They are constructs that are derived from the measurement of other, directly
observable variables. Factor analysis measures not directly observable
constructs by measuring several of its underlying dimensions. The identification
of such underlying factors simplifies the understanding and description of complex
constructs.
From
this we can infer that factor analysis reduces the multitude of observable factors
into a smaller set of factors and then these factors can be used to explain
complex phenomenon using tools like permap etc.From the use of the various
tools later we can conclude or observe relationships between the various
factors which in turn are relationships between the directly observable factors
and then it can be turned to strategy.
Then
we proceed to discriminant analysis. It is a feature of SPSS which allows us to
build predictive models for grouping .It is composed of a discriminant function
based on linear combination of predictor variables. The purpose of this is to
group variables and to discard variables which are little related to the group distinctions.
We would like to know the relationship between a group of independent variables
and one category variable. Using this relationship we can predict a classification
based on the independent variables or we can also evaluate how well the independent
variables separate the categories in the classification.
It is similar to regression analysis. A
discriminant score can be calculated from the weighted combination of the independent
variables. We then use a maximum likelihood technique to assign a case to a
group from a specified cut off score. We
used this method to predict the defaulters of a bank loan and tested the theory
with the SPSS software. By using discriminant analysis we could develop a model
to find the number of people who would be defaulters and decide whether to
provide the loan to them or not.
By Rohith Emmanuel
Group B
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