The Factor analysis is one of a statistical method which is used to make an observation of a dataset to checks its variability and durability. It is consider as data reduction activity. However, factor analysis purpose is to find an independent latent variables. The factor analysis are a part of machine which links and it is relatable to mining of data. CFA is used for factor analysis and different variables are measured on pre-established theory. However, it helps user to assume the variables each subset. There are two approaches used by CFA. A construction of validity is essential of CFA to construct validity.

The first is traditional method which is not similar like common factor analysis but it is standard and based on the principle factor analysis. It helps the research to get more detailed information regarding insight factor loading. The second approach is SEM, which will allow the user to observe the variable through representation of covariance between each and every pair of latent. In SEM method, if you get standardized error term then consider it less the absolute “value 2”. It is known as good factor and if more than 2 than there is might be some unexplained variance. And the most importantly, factor analysis will help you to explain it. Factor analysis are done in order to make a data that can be simplify. It helps to reduce the variables in numbers which are used in regression models. However, the data become understandable and manageable. It takes huge data into account and make changes in it.