Session 15/16
Factor Analysis:- Part 2
Scree Test Criterion:- The scree test is used to
identify the optimum number of factors accounts for 60 percent of the total
variance (and in some instances even less) as satisfactory. that can be
extracted before the amount of unique variance begins to dominate the common
variance structure. The scree test is derived by plotting the latent roots
against the number of factors in their order of extraction, and the shape of
the resulting curve is used to evaluate the cutoff point. The point at which
the curve first begins to straighten out is considered to indicate the maximum
number of factors to extract.
Interpreting the factors
- The initial unrotated factor matrix is computed to assist in obtaining a preliminary indication of the number of factors to extract. The factor matrix contains factor loadings for each variable on each factor.
- A rotational method to achieve simpler and theoretically more meaningful factor solutions. In most cases rotation of the factors improves the interpretation by reducing some of the ambiguities that often accompany initial unrotated factor solutions. These achieve the objective of data reduction, in most instances will not provide information that offers the most adequate interpretation of the variables under examination.
Factor Rotation
An
important tool in interpreting factors is factor rotation. The unrotated output
maximises the variance accounted for by the first(general factor) and
subsequent factors( based on residual amount of variance), and forcing the
factors to be orthogonal This data-compression comes at the cost of having most
items load on the early factors, and usually, of having many items load
substantially on more than one factor. Rotation serves to make the output more
understandable, by seeking so-called "Simple Structure": A pattern of
loadings where items load most strongly on one factor, and much more weakly on
the other factors. Rotations can be orthogonal or oblique (allowing the factors
to correlate)
Three
major orthogonal approaches have been developed:
- QUARTIMAX focuses on rotating the initial factor so that a variable loads high on one factor and as low as possible on all other factors. In these rotations, many variables can load high or near on the same factor because the technique centers on simplifying the rows
- VARIMAX this method maximizes the sum of variances of required loadings of the factor matrix centers on simplifying the columns of the factor matrix.
- EQUIMAX approach is a compromise between the QUARTIMAX and VARIMAX approaches and is used infrequently.
Practical Applications of Factor
Analysis
Factor
analysis plays a role in most industries. Through statistical planning,
companies can make better choices for everything from multi-channel marketing
to inventory control. By breaking down
the key factors, you can tweak processes to create the most effective channels
and strategies. Put simply, factor analysis takes the guesswork out of
budgeting, advertising and even staffing.
Human Resources
Many
factors influence the staffing of a company. Through statistical
interpretation, human resource professionals can create a balanced environment.
A staffer might combine different variables together to determine if a company
can benefit from fewer contractors and more in-house talent. Testing allows
proper screening of employees using factor analysis. Market research and
analysis can be the key to getting the best fit in graduates each year.
Submitted
By,
Saraniya
Subramaniam
14047
HR-Group
F
References
http://www.networkedcranfield.com/cell/Knowledgebase/Quants%20Material/Factor%20Analysis.pdf
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