What does exploratory factor analysis tell you?

What does exploratory factor analysis tell you?

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.

What is the difference between CFA and EFA?

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). Confirmatory factor analysis (CFA) is a statistical technique used to verify the factor structure of a set of observed variables.

How do you report exploratory factor analysis results?

Usually, you summarize the results of the EFA into one table which contains all items used for the EFA, their factor loadings and the names of the factors. Then you indicate in the notes of the table the method of extraction, the method of rotation and the cutting value of extracting factors.

How many participants are needed for exploratory factor analysis?

Exploratory factor analysis (EFA) is generally regarded as a technique for large sample sizes (N), with N = 50 as a reasonable absolute minimum.

Why exploratory factor analysis is important?

EFA is essential to determine underlying factors/constructs for a set of measured variables; while CFA allows the researcher to test the hypothesis that a relationship between the observed variables and their underlying latent factor(s)/construct(s) exists.

Is exploratory factor analysis enough?

EFA could be used to establish a preliminary construct validity but if you have the theory that support the factor structure, it is better to use CFA. if you are intending to use EFA, choose common factor method (in SPSS) not PCA.

Should I use exploratory or confirmatory factor analysis?

Cut-offs of factor loadings can be much lower for exploratory factor analyses. When you are developing scales, you can use an exploratory factor analysis to test a new scale, and then move on to confirmatory factor analysis to validate the factor structure in a new sample.


EFA is a data-driven approach which is generally used as an investigative technique to identify relationships among variables. SEM is an a priori theory approach which is most often used to determine the extent to which an already established theory about relationships among variables is supported by empirical data.

Is PCA exploratory factor analysis?

Principal Component Analysis (PCA) and Exploratory Factor Analysis (EFA) are both variable reduction techniques and sometimes mistaken as the same statistical method. However, there are distinct differences between PCA and EFA. Examples of PCA and EFA with PRINCOMP and FACTOR will be illustrated and discussed.