Dr. Santhosh Kumar A.V.
Factor Analysis, Principle Component Analysis (PCA), Rotation, Variance, Orthogonal rotation
Factors represent the underlying concepts that cannot be adequately measured by a single variable. Factor analysis is carried with an objective to reduce a large number of variables into manageable smaller factors for further analysis. Several extraction methods are available, but principle component analysis (PCA) is used most commonly. PCA starts extracting the maximum variance and puts them into the first factor. After that, it removes that variance explained by the first factors and then starts extracting maximum variance for the second factor. This process goes on to the last factor. Rotations that allow for correlation are called oblique rotations; rotations that assume the factors are not correlated are called orthogonal rotations. It should, however, be emphasized that right rotation must be selected for making sense of the results of factor analysis. The main purpose of this study was to understand the conceptual background, application of factor analysis in social science research and to reduce a large number of variables into manageable smaller factors for further analysis of the impact of soft skills training on employability among B-School Graduates in Bangalore.
Article Details
Unique Paper ID: 152033

Publication Volume & Issue: Volume 8, Issue 2

Page(s): 215 - 219
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