Contents
Should I use factor scores in regression?
To solve the multicollinearity problem and for a reliable regression analysis, the factor scores obtained from the factor analysis for multiple regression analysis should be used as independent variables.
Can you do EFA and CFA on the same data?
Conducting both EFA and CFA on the same dataset is just confirming the data rather than the model. FYI, it is rarely the case that a model obtained from EFA has a good model fit in CFA, particularly in the presence of more items with high cross-loadings.
Do you need EFA or PCA for factor analysis?
If you are still not exactly sure whether you should do EFA or PCA (then I bet you most likely need PCA), so launch your Factor Analysis program and select the factoring method “Principle Components” and you will be on your way to explain all the variance in your variables and extract your factors.
How are EFA and CFA used in SEM?
This can be done using Confirmatory Factor Analysis (CFA) (as opposed to Exploratory FA), which bridges factor analysis with Structural Equation Modelling (SEM). However, in order to do that cleanly, EFA should be independent from CFA: the factor structure should be explored in a “training” set, and then tested (or “confirmed”) in a “testing” set.
How are EFA and CFA used in machine learning?
However, in order to do that cleanly, EFA should be independent from CFA: the factor structure should be explored in a “training” set, and then tested (or “confirmed”) in a “testing” set. In other words, the dataset used for exploration and confirmation should not be the same, a standard widely adopted in the field of machine learning.
When to use exploratory or confirmatory factor analysis?
Exploratory factor analysis (EFA) could be described as orderly simplification of interrelated measures. EFA, traditionally, has been used to explore the possible underlying factor structure of a set of observed variables without imposing a preconceived structure on the outcome (Child, 1990).