Factor Analysis: A Data Reduction Technique
Vaibhav Chittora*
Dr. YSPUHF, Nauni, Solan, Himachal Pradesh (173 230), India
Heerendra Prasad
Dr. YSPUHF, Nauni, Solan, Himachal Pradesh (173 230), India
Prashant Vasishth
ICAR-Indian Agricultural Research Institute, Pusa, New Delhi, Delhi (110 012), India
Mohit Sharma
ICAR-Indian Agricultural Research Institute, Pusa, New Delhi, Delhi (110 012), India
DOI: NIL
Keywords: EFA, Factor analysis, Identity matrix, PCA
Abstract
In many studies we observe large number of variables and in these variables many of them gives common information that is why it is not worthy to study all the variables as it complicates analysis and interpretation. There it comes the use of data reduction techniques. These techniques extract some most influencing variables from the large variables. Factor analysis (FA) is a multivariate technique that is used to describe the relationships between different variables under study (observable variables) with new variables called factors, where the number of factors is less than the number of original variables. FA works efficiently and produces fewer factors to describe the relationship if the variables under study are highly correlated. For instance, if all of the variables in one group are highly correlated among themselves and have little correlation with the variables in the remaining groups, each group can represent a factor. FA is considered an extension of principal component analysis since the ultimate objective for both techniques is a data reduction.
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Reference
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