Exploratory factor analysis is a statistical method that examines the correlations among a set of observed variables to discover how many underlying latent factors they represent and which items belong to each. You use it when you do not yet have a theory about your measurement structure and you want the data to suggest one. It is the discovery stage of scale development, the step that comes before you ever try to confirm a structure.
The contrast with confirmatory factor analysis defines both methods. Confirmatory analysis tests a structure you specified in advance. Exploratory factor analysis imposes nothing: it lets the pattern of correlations reveal how the items cluster, how many dimensions exist, and where items load weakly or on more than one factor. When you have a brand-new pool of questionnaire items and no prior evidence about their structure, exploration comes first.
When to use exploratory factor analysis
Reach for exploratory factor analysis when you are developing a new scale, when you are adapting an existing instrument so heavily that its original structure may no longer hold, or when you have a large set of items and suspect they reduce to a smaller number of underlying dimensions. It answers questions such as how many factors my items represent, which items hang together, and which items are redundant or do not belong.
It is the wrong tool once you already have a hypothesized structure. If theory or a previous study tells you the factors and their items, you should be confirming that structure, not rediscovering it. Running exploration when you already know the answer wastes the opportunity to test it formally.
The key decisions
Several choices shape the result, and reviewers scrutinize each one.
The first is how many factors to retain. The old habit of keeping every factor with an eigenvalue above one is now considered unreliable. Better approaches include parallel analysis, which compares your eigenvalues to those from random data, and the scree plot, read alongside interpretability and theory. Retaining too many factors splinters the structure; retaining too few merges distinct constructs.
The second is the rotation. Rotation makes the loadings easier to interpret. An oblique rotation, which allows factors to correlate, is usually more realistic for psychological and social constructs than an orthogonal rotation that forces them to be independent, because real constructs are rarely uncorrelated.
The third is the extraction method, such as principal axis factoring or maximum likelihood, chosen partly on whether your data meet distributional assumptions. These decisions interact, and documenting them transparently is part of a defensible analysis our survey data analysis team handles routinely.