Factor Analysis and PCA YouTube Lecture Handouts: Factor Analysis and PCA Analysis

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Factor Analysis and Principal Component Analysis

Factor Analysis

  • Reduce large number of variables into fewer number of factors

  • Co-variation is due to latent variable that exert casual influence on observed variables

  • Communalities – each variable’s variance that can be explained by factors

Principal Component Analysis

  • Variable reduction process – smaller number of components that account for most variance in set of observed variables

  • Explain maximum variance with fewest number of principal components

Table Showing of Principal Component Analysis

PCA

Factor Analysis

Observed variance is analyzed

Shared variance is analyzed

1.00’s are put in diagonal – all variance in variables

Communalities in diagonal – only variance shared with other variables are included – exclude error variance and variance unique to each variable

Analyze variance

Analyze covariance