Factor
analysis is a type of statistical technique,
·
The aim of factor
analysis is to simplify a complex data set by representing the set of variables
in terms of a smaller number of underlying (hypothetical or unobservable)
variables, known as factors.
To
validate the Factor Analysis, it needs to be compared with Cluster Analysis.
o It was found if
Cluster Analysis did not challenge the result of Factor Analysis then it confirmed
the result of Factor Analysis.
o Thus, Factor Analysis can be used to understand the
financial position and performance of the various industries in a more
practical and time saving manner.
There
are many ways in which data can be analyzed for a reliable solution but here I have
selected only three.
è Assuming that Study is carried for an industry
1. Correlation Study
With the help of inter-correlation matrix, some
variables would be excluded if they showed a very weak correlation (i.e. <
±0.5) with the other variables in the study. However, before elimination domain
knowledge is exercised to ensure that no important variable (financial ratio)
is excluded.
2. Multiple Regression Analysis
Factor Analysis is conducted on remaining variables
after doing correlation analysis and it helps to create factors for analysis.
Multiple regression analysis is conducted taking the Factor Scores of different
factors as dependant variables and the constituent variables in the respective
factor as independent variables. It is found that R-square (coefficient of
determination) for each such regression analysis is very high. It signifies the
presence of strong regression relationship amongst the factors and their
constituent variables. However, presence of
variables with low t-value (i.e. < 2) and the corresponding high p-value
(i.e. > 0.05) are found in different factors.
3. Factor Analysis
Factor Analysis is conducted once again on the remaining
variables from previous analysis. The Rotated Component Matrix is then produced.
It is observed that remaining variables have been categorized in factors. These
factors account for about X% of the total variance, which can be considered for
decision making.
To check whether the analysis is up to mark for use, Cluster
analysis is further done.
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