What is Factor Analytics?

Factor analysis is a statistical method that simplifies complex data by identifying underlying factors, or latent variables, that explain correlations among a set of variables. It helps summarize information, detect patterns, and reduce redundancy.

Purposes of Factor Analysis

1. Data Reduction: Simplifies large datasets by grouping correlated variables into fewer factors, making analysis easier.
2. Identifying Latent Constructs: Uncovers unobserved variables (e.g., brand perception) that drive correlations among observed variables.
3. Handling Multicollinearity: Reduces multicollinearity in regression models by creating uncorrelated factors.
4. Variable Selection for Modeling: Selects key variables for modeling, ensuring more manageable and interpretable results, especially in structural equation modeling (SEM).

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