Exploratory Factor Analysis (EFA)

What is Exploratory Factor Analysis? Exploratory Factor Analysis Explained

Exploratory Factor Analysis (EFA) is a statistical technique used to uncover the underlying structure or latent factors in a dataset. It is a dimensionality reduction technique that aims to explain the relationships among observed variables by identifying a smaller number of unobservable factors that account for the variation in the data. EFA is commonly used in fields such as psychology, social sciences, market research, and other disciplines where researchers want to understand the underlying constructs or dimensions behind their measured variables.

The main steps involved in performing EFA are as follows:

Define the Research Question: Clearly define the research question or the constructs that you want to explore. For example, if you are studying customer satisfaction, you might be interested in identifying the underlying factors that contribute to satisfaction.

Select Variables: Choose a set of variables that are believed to be related to the constructs of interest. These variables can be measured using different scales or questionnaires.

Determine Factorability: Assess whether the dataset is suitable for factor analysis. Check for sample size adequacy, the correlation matrix, and the presence of outliers or missing values. Ensure that the variables are reasonably correlated to justify applying EFA.

Choose Extraction Method: Select an extraction method to estimate the initial factor structure. The most commonly used methods include Principal Component Analysis (PCA) and Principal Axis Factoring (PAF). PCA focuses on accounting for maximum variance, while PAF aims to capture common factors.

Determine the Number of Factors: Decide on the number of factors to extract. This can be based on statistical criteria such as eigenvalues (i.e., factors with eigenvalues greater than 1) or scree plot examination. Researchers’ expertise and theoretical considerations also play a role in determining the number of factors.

Factor Rotation: Apply factor rotation techniques to improve the interpretability of the factor structure. Rotation helps to achieve simpler and more distinct factors. Orthogonal rotation methods (e.g., Varimax) assume that factors are uncorrelated, while oblique rotation methods (e.g., Promax) allow for correlations among factors.

Interpret and Name Factors: Interpret the rotated factor structure and assign meaningful labels or names to the identified factors based on the pattern of loadings (i.e., the relationships between the variables and the factors). This involves examining the strength and direction of the loadings to understand the underlying meaning of each factor.

Assess Model Fit: Evaluate the overall model fit to assess how well the factor model represents the observed data. Common fit indices include the Kaiser-Meyer-Olkin (KMO) measure of sampling adequacy and Bartlett’s test of sphericity.

EFA helps reduce the dimensionality of the data by identifying a smaller number of latent factors that explain the relationships among observed variables. It provides insights into the underlying constructs or dimensions present in the data, aiding in theory development, hypothesis testing, and further analysis.