Principal Components Analysis a method Useful in Identification of Clusters of Variables


Authors : Dr. S.V.Kakade, Dr. Mrs. J.A.Salunkhe, Dr V.B.Jagadale, T.S.Bhosale.

Volume/Issue : Volume 3 - 2018, Issue 5 - May

Google Scholar : https://goo.gl/DF9R4u

Scribd : https://goo.gl/6L3SaB

Thomson Reuters ResearcherID : https://goo.gl/3bkzwv

There are many different methods that can be used to conduct a factor analysis which is a data reduction or structure detection method. The commonly used method for factor analysis is ‘Principal Components Analysis (PCA)’. The principal components account for most of the variance in the original variables. The data on some baseline variables and a 15 questions about ‘Attitude towards female feticide’ measured on Likert scale was collected from women admitted for delivery in KH&MRC, Karad; a teaching hospital. Principal components were extracted by using varimax rotation method. Components with eigenvalue ≥ 1.00 were identified as new (latent) variables. The PCA derived six components. It revealed that original variables in each component were inter-related with each other.

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