Factor Analysis
- Reduces a set of observed variables to a smaller number of underlying factors or dimensions.
- Commonly applied in psychology, social sciences, and market research to reveal latent structure.
- Helps researchers simplify complex datasets and identify important patterns or trends.
Definition
Section titled “Definition”Factor analysis is a statistical method used to identify underlying patterns or relationships among a set of variables.
Explanation
Section titled “Explanation”Factor analysis examines correlations among observed variables to discover a smaller number of latent factors or dimensions that explain those relationships. By analyzing a dataset of measurements, the method seeks underlying factors that account for shared variance across variables, making complex data easier to interpret. It is commonly used in psychology and social sciences and can also be applied in areas such as market research.
Examples
Section titled “Examples”Psychology example
Section titled “Psychology example”A researcher uses factor analysis on responses from participants across a variety of psychological tests and may identify three underlying factors that explain relationships among traits such as personality, intelligence, and motivation: openness, agreeableness, and conscientiousness.
Market research example
Section titled “Market research example”A researcher analyzes data on consumers’ purchasing habits, demographics, and attitudes and may identify three underlying factors that drive consumer behavior: price, convenience, and quality.
Use cases
Section titled “Use cases”- Understanding latent structure in psychological and social-science measurements.
- Identifying key dimensions that influence consumer behavior in market research.
Related terms
Section titled “Related terms”- Underlying factors or dimensions
- Variables
- Latent variables