Factor Analysis (FA) is an exploratory data analysis method used to search influential underlying factors or latent variables from a set of observed variables.

In this tutorial, you’ll learn the basics of factor analysis and how to implement it in python.

Factor Analysis (FA) is an exploratory data analysis method used to search influential underlying factors or latent variables from a set of observed variables. It helps in data interpretations by reducing the number of variables. It extracts maximum common variance from all variables and puts them into a common score.

Factor analysis is widely utilised in market research, advertising, psychology, finance, and operation research. Market researchers use factor analysis to identify price-sensitive customers, identify brand features that influence consumer choice, and helps in understanding channel selection criteria for the distribution channel.

In this tutorial, you are going to cover the following topics:

- Factor Analysis
- Types of Factor Analysis
- Determine Number of Factors
- Factor Analysis Vs. Principle Component Analysis
- Factor Analysis in python
- Adequacy Test
- Interpreting the results
- Pros and Cons of Factor Analysis
- Conclusion

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Factor analysis is a linear statistical model. It is used to explain the variance among the observed variable and condense a set of the observed variable into the unobserved variable called factors. Observed variables are modeled as a linear combination of factors and error terms (Source). Factor or latent variable is associated with multiple observed variables, who have common patterns of responses. Each factor explains a particular amount of variance in the observed variables. It helps in data interpretations by reducing the number of variables.

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