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
Abstract :
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.
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.
