Factor Analysis (FA) is a statistical technique used to identify underlying relationships between observed variables. It helps in reducing a large set of variables into a smaller set of latent (unobserved) factors, making data interpretation easier.

Key Objectives of Factor Analysis:

1. Data Reduction: Simplifies complex datasets by grouping correlated variables.
2. Identifying Latent Constructs: Helps in discovering hidden patterns that influence the observed data.
3. Enhancing Predictive Power: By focusing on key factors, it improves the efficiency of predictive models.
4. Removing Multicollinearity: Reduces redundancy among variables in regression models.

Types of Factor Analysis:

1. Exploratory Factor Analysis (EFA):

   • Used when the underlying structure is unknown.
   • Identifies patterns among variables without predefined hypotheses.
   • Commonly used in social sciences, psychology, and market research.

2. Confirmatory Factor Analysis (CFA):

   • Used when the factor structure is already known or hypothesized.
   • Tests whether data fits a predefined model.
   • Applied in hypothesis testing and model validation.

Applications of Factor Analysis:

• Marketing Research: Identifying customer preferences and market segments.
• Finance: Assessing risk factors and investment patterns.
• Psychology & HR: Measuring personality traits and employee satisfaction.
• Healthcare: Understanding patient symptoms and medical conditions.

Limitations of Factor Analysis:

• Requires large datasets for accuracy.
• Interpretation of factors is subjective.
• Sensitive to outliers and missing data.
• Assumes linear relationships between variables.

Why is Factor Analysis Used?

1. Data Reduction & Simplification:

   • Helps condense a large number of variables into a smaller set of meaningful factors.
   • Reduces complexity while retaining most of the essential information.

2. Identifying Underlying Constructs:

   • Detects hidden relationships between variables that may not be directly observable.
   • Common in psychology, social sciences, and marketing to identify latent traits.

3. Improving Predictive Modeling:

   • Reduces multicollinearity in regression models by grouping correlated variables into factors.
   • Leads to more stable and interpretable models.

4. Enhancing Data Interpretation:

   • Helps researchers and analysts make sense of large datasets by categorizing similar variables.
   • Provides clear insights into how variables relate to each other.

5. Developing Measurement Scales:

   • Used to create and validate scales for psychological, behavioral, and business studies.
   • Ensures the reliability and validity of survey instruments.

6. Market Segmentation & Consumer Behavior Analysis:

   • Helps businesses understand customer preferences by grouping similar traits.
   • Aids in product positioning and targeted marketing.

7. Identifying Risk Factors in Finance:

   • Used to analyze investment risks by grouping financial indicators.
   • Helps investors make better portfolio decisions.