Explain exploratory factor analysis and give real life examp

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.