The AFE allows to generate structures of theoretical models and hypotheses that can be empirically tested (Gorsuch, 1983), without having previous specifications of the model or considering both the number of factors and the relationship between them (Gerbin & Hamilton, 1996).

The technique used by the AFE is to automatically extract the factors with a certain statistical criterion, obtaining the simplest factor structure in terms of its easiest and most significant interpretation (Thurstone, 1931 and 1947).

Confirmatory Factor Analysis (AFC)

The AFC allows to correct or corroborate, if any, the deficiencies of the AFE leading to a greater contrast of the specified hypotheses (Bollen, 1989), in the same way it analyzes the matrix of covariances instead of correlations, which helps establish whether the indicators are equivalent (Batista and Coenders, 1998).

The AFC is represented by flowcharts (path diagram), according to their particular specifications. The rectangles represent the items and ellipses, the common factors. Unidirectional arrows between common factors and items express saturation and bidirectional arrows indicate the correlation between common or unique factors (Jöreskog, 1969).

In conclusion, these models provide the appropriate statistical framework to assess the validity and reliability of each item instead of performing a global analysis (Batista, Coenders and Alonso, 2004), helping the researcher to optimize both the construction of a measuring instrument and The analysis of results.

In the AFC it is necessary to observe the factor loads that allow establishing the correlation between the variables and the factors (Garson, 2012). The closer they get to one, the greater the correlation will be. An empirical rule in the AFC states that the charges must be> = a 0.07. Although it is high and some factors and variables may be left out of the model, these should be taken at the discretion of the researcher, (Raubenheimer, 2004).

Taking the criteria of Garson (2012), Raubenheimer (2004) and Widaman (1993) in which they establish that, in order to eliminate collinearity between variables to the maximum, it is necessary to take the variables whose loads are> = 0.07, especially in the case of a Model proposal, in which case, its construction is based on the existing theory.

Thank you so much! CFA is the second step but I wonder about the difference between EFA and Principal Component Analysis in my case

Roxane Corbel hi. Hope the below link helps

https://support.sas.com/resources/papers/proceedings/proceedings/sugi30/203-30.pdf

Dear Roxane, PCA is actually a type if EFA, the other type is factor analysis. The main difference is whether to include total variance and whether the solution always converges. PCA is useful to validate a single scales, whereas factor analysis is useful for suggesting multiple scales. Please see my study guide for more information.

Can you please send the "components of index construct", if possible.

regards

Karthikeyan