Factor Analysis | What is Factor Analysis? | Factor Analysis Explained | Machine Learning | Edureka

Summary

This video provides a comprehensive overview of factor analysis, a statistical technique used for data reduction and discovering latent variables. It explains the importance of clean data, the assumptions required for effective analysis, and the purpose of simplifying complex datasets. The video covers the two main types of factor analysis—Exploratory Factor Analysis (EFA) and Confirmatory Factor Analysis (CFA)—and addresses key issues such as PCA vs. Factor Analysis, interpreting results with factor loadings, and selecting the right number of factors. Overall, it is a great introduction to mastering factor analysis for business and research applications.

Takeaways

• 😀 Factor analysis is a statistical method used to reduce the number of variables by identifying underlying factors that explain the variation in data.
• 😀 Latent variables are unobservable factors inferred from observed variables, like job satisfaction being derived from various aspects such as pay, role, and co-worker satisfaction.
• 😀 Factor analysis helps simplify complex datasets by grouping related variables into fewer, more meaningful factors.
• 😀 One key assumption in factor analysis is that the data should be clean, meaning no missing values or outliers.
• 😀 A general rule for sample size in factor analysis is to have at least 5 observations per variable, with larger sample sizes improving reliability.
• 😀 Factor analysis is based on the correlation between variables, so variables must be interrelated for the technique to work effectively.
• 😀 The primary purpose of factor analysis is data reduction, allowing analysts to work with fewer, more meaningful variables.
• 😀 Exploratory Factor Analysis (EFA) is used to discover underlying structures in the data, while Confirmatory Factor Analysis (CFA) is used to test hypotheses based on those structures.
• 😀 The most common methods used in EFA include Principal Component Analysis (PCA) and Common Factor Analysis.
• 😀 Factor loadings help interpret how much variance in a variable is explained by a specific factor, helping identify important factors in the data.
• 😀 When determining how many factors to keep, methods like the scree plot or the eigenvalue greater than 1 criterion can be used to identify the optimal number of factors.

Q & A

What is the purpose of factor analysis in data analysis?

Factor analysis is used to reduce the number of observed variables into fewer latent variables or factors, simplifying complex datasets, uncovering hidden patterns, and supporting data-driven decisions in business and research.

What are latent variables in factor analysis?

Latent variables are unobservable variables inferred from observed ones. They cannot be directly measured but are essential for understanding patterns in data, such as job satisfaction or business confidence.

What assumptions are made when performing factor analysis?

Factor analysis assumes clean data (no outliers or missing values), a sufficient sample size (minimum 5 observations per variable), interrelated variables, numeric data, and ideally normalized data.

How does factor analysis support data reduction?

Factor analysis helps reduce the number of variables in a dataset by grouping related variables into fewer latent factors, thus simplifying the analysis and focusing on the most significant patterns.

What is the difference between exploratory factor analysis (EFA) and confirmatory factor analysis (CFA)?

EFA is used to discover the underlying structure of the data and gain insights, while CFA is used to test the hypotheses formed during EFA using modeling and equations to validate the structure.

How can the results of factor analysis be interpreted?

Factor loadings are used to interpret the results. These represent the correlation between each observed variable and its corresponding latent factor, showing how much of the variance is explained by each factor.

What is cross loading in factor analysis, and how is it addressed?

Cross loading occurs when a variable shows significant correlation with more than one factor. This is addressed by rotating the factors to better define the relationship between variables and factors.

How do you decide how many factors to select in factor analysis?

The number of factors to select can be determined by using a scree plot or applying the eigenvalue greater-than-1 rule, which suggests including factors with an eigenvalue above 1.

What is the role of eigenvalues in factor analysis?

Eigenvalues represent the amount of variance explained by a factor. Factors with eigenvalues greater than 1 are generally considered significant and are included in the analysis.

When should principal component analysis (PCA) be used instead of factor analysis?

PCA should be used when the goal is to eliminate variables with high variance and focus on reducing dimensionality, while factor analysis is used when discovering latent variables that explain the common variance among observed variables.