Types of Factor Analysis
There are two main types of Factor Analysis used in data science:
1. Exploratory Factor Analysis (EFA)
Exploratory Factor Analysis (EFA) is used to uncover the underlying structure of a set of observed variables without imposing preconceived notions about how many factors there are or how the variables are related to each factor. It explores complex interrelationships among items and aims to group items that are part of unified concepts or constructs.
- Researchers do not make a priori assumptions about the relationships among factors, allowing the data to reveal the structure organically.
- Exploratory Factor Analysis (EFA) helps in identifying the number of factors needed to account for the variance in the observed variables and understanding the relationships between variables and factors.
2. Confirmatory Factor Analysis (CFA)
Confirmatory Factor Analysis (CFA) is a more structured approach that tests specific hypotheses about the relationships between observed variables and latent factors based on prior theoretical knowledge or expectations. It uses structural equation modeling techniques to test a measurement model, wherein the observed variables are assumed to load onto specific factors.
- Confirmatory Factor Analysis (CFA) assesses the fit of the hypothesized model to the actual data, examining how well the observed variables align with the proposed factor structure.
- This method allows for the evaluation of relationships between observed variables and unobserved factors, and it can accommodate measurement error.
- Researchers hypothesize the relationships between variables and factors before conducting the analysis, and the model is tested against empirical data to determine its validity.
In summary, while Exploratory Factor Analysis (EFA) is more exploratory and flexible, allowing the data to dictate the factor structure, Confirmatory Factor Analysis (CFA) is more confirmatory, testing specific hypotheses about how the observed variables are related to latent factors. Both methods are valuable tools in understanding the underlying structure of data and have their respective strengths and applications.
Factor Analysis | Data Analysis
Factor analysis is a statistical method used to analyze the relationships among a set of observed variables by explaining the correlations or covariances between them in terms of a smaller number of unobserved variables called factors.
Table of Content
- What is Factor Analysis?
- What does Factor mean in Factor Analysis?
- How to do Factor Analysis (Factor Analysis Steps)?
- Factor Analysis Example (Factor Analyzer):
- Why do we need factor analysis?
- Most Commonly used Terms in Factor Analysis
- Types of Factor Analysis
- Types of Factor Extraction Methods
- Assumptions of Factor Analysis
- FAQs : Factor analysis