Factor Analysis

Definition:

Factor Analysis is a statistical method used to uncover underlying factors or latent variables that explain the pattern of correlations among a set of observed variables. It aims to identify the unobservable variables that contribute to the observed data.

How it works:

Factor Analysis involves reducing a large number of variables into a smaller set of factors, which are linear combinations of the original variables. These factors are derived based on the shared variance among the observed variables. The factors are considered to be representative of the underlying latent variables or constructs.

Applications:

Factor Analysis is commonly used in various fields, including psychology, social sciences, market research, and finance. It can help in identifying the underlying dimensions, such as personality traits, consumer behavior factors, or financial risk factors, that influence the observed data.

Benefits:

Factor Analysis provides a way to simplify complex data structures and extract meaningful information. It helps in reducing the dimensionality of data, uncovering hidden relationships, and gaining insights into the underlying factors that drive the observed patterns.

Limitations:

Factor Analysis relies heavily on the assumptions of linearity, normality, and homoscedasticity. Violation of these assumptions can affect the accuracy of the results. Additionally, interpretation of the factors is subjective and requires expert judgment.