Exploratory factor analysis (EFA) is a statistical method used to uncover the relationships among a large set of measured variables, often applied in research for scale development. It identifies latent constructs without pre-existing hypotheses and requires careful consideration of the number of variables included. Various EFA examples include analyses with continuous and categorical indicators and different factor structures.

1. Explain exploratory factor analysis and give real life examples
Solution
In multivariate statistics, exploratory factor analysis (EFA) is a statistical method used to
uncover the underlying structure of a relatively large set of variables. EFA is a technique within
factor analysis whose overarching goal is to identify the underlying relationships between
measured variables.[1] It is commonly used by researchers when developing a scale (a scale is a
collection of questions used to measure a particular research topic) and serves to identify a set of
latent constructs underlying a battery of measured variables.[2] It should be used when the
researcher has no a priori hypothesis about factors or patterns of measured variables.[3]Measured
variables are any one of several attributes of people that may be observed and measured. An
example of a measured variable would be the physical height of a human being. Researchers
must carefully consider the number of measured variables to include in the analysis.[2] EFA
procedures are more accurate when each factor is represented by multiple measured variables in
the analysis.
Following is the set of EFA examples,
. • 4.1: Exploratory factor analysis with continuous factor indicators
• 4.2: Exploratory factor analysis with categorical factor indicators
• 4.3: Exploratory factor analysis with continuous, censored, categorical, and count factor
indicators*
• 4.4: Exploratory factor mixture analysis with continuous latent class indicators
• 4.5: Two-level exploratory factor analysis with continuous factor indicators
• 4.6: Two-level exploratory factor analysis with both individual- and cluster-level factor
indicators
• 4.7: Bi-factor exploratory factor analysis with continuous factor indicators
* Example uses numerical integration in the estimation of the model. This can be
computationally demanding depending on the size of the problem.