Is CFA necessary after EFA?
In exploratory factor analysis, all measured variables are related to every latent variable. But in confirmatory factor analysis (CFA), researchers can specify the number of factors required in the data and which measured variable is related to which latent variable. CFA will notify you of such cases, EFA will not.
What is CFA and EFA?
Confirmatory factor analysis (CFA) and exploratory factor analysis (EFA) are similar techniques, but in exploratory factor analysis (EFA), data is simply explored and provides information about the numbers of factors required to represent the data.
What comes first EFA or CFA?
Usually EFA is conducted before CFA.
What is EFA used for?
Exploratory factor analysis (EFA) is generally used to discover the factor structure of a measure and to examine its internal reliability. EFA is often recommended when researchers have no hypotheses about the nature of the underlying factor structure of their measure.
Can you run EFA and CFA on the same data?
It is generally a bad idea to do an EFA and a CFA on the same data for the exact reason you mention: A factor structure derived from an EFA will almost always fit very well in a CFA using the same data. EFA and CFA are closely related, so it is no surprise that this is the case.
When should we use EFA?
EFA is often recommended when researchers have no hypotheses about the nature of the underlying factor structure of their measure. Exploratory factor analysis has three basic decision points: (1) decide the number of factors, (2) choosing an extraction method, (3) choosing a rotation method.
When should I take CFA?
In statistics, confirmatory factor analysis (CFA) is a special form of factor analysis, most commonly used in social research. It is used to test whether measures of a construct are consistent with a researcher’s understanding of the nature of that construct (or factor).
What does Rmsea measure?
RMSEA is an absolute fit index, in that it assesses how far a hypothesized model is from a perfect model. On the contrary, CFI and TLI are incremental fit indices that compare the fit of a hypothesized model with that of a baseline model (i.e., a model with the worst fit).
What is a CFA model?
What is eigenvalue EFA?
The eigenvalue is a measure of how much of the variance of the observed variables a factor explains. Any factor with an eigenvalue ≥1 explains more variance than a single observed variable.