How does factor analysis differ from principal component analysis (PCA)?
- Factor analysis does not involve rotation of variables, while PCA does
- Factor analysis looks for shared variance while PCA looks for total variance
- PCA focuses on unobservable variables, while factor analysis focuses on observable variables
- PCA is used for dimensionality reduction, while factor analysis is used for data cleaning
Factor analysis and PCA differ primarily in what they seek to model. Factor analysis models the shared variance among variables, focusing on the latent or unobservable variables, while PCA models the total variance and aims at reducing the dimensionality.
Loading...
Related Quiz
- What is the range of a discrete random variable?
- In hypothesis testing, a Type I error is committed when the null hypothesis is ______ but we ______ it.
- In which situation is Spearman's Rank Correlation preferable to Pearson's correlation?
- What is a Type I error in the context of hypothesis testing?
- ________ clustering is a density-based clustering method that can find arbitrary shaped clusters and is less affected by outliers.