Factor analysis attempts to identify underlying variables, or factors, that explain the pattern of correlations within a set of observed variables.
Factor analysis is used in data reduction to identify a small number of factors that explain most of the variance observed in a much larger number of variables.
The variables should be quantitative at the interval or ratio level. Z-score is used as a method of standardization between the variables. Variables are reduced on the basis of correlation. The variables converted into Z-score have a Mean of zero (0) and standard deviation of one (1).
Communalities table in the output table shows the common variance which each component can extract from other components. The higher the correlation, the better it is. Variables having extraction value less than 0.5 are not considered for analysis.
Difference between Factor Analysis and Component Analysis:
Component Analysis takes in account all variability in the variables whereas factor analysis estimates how much of the variability is due to common factors (communality).
Scree plot: The Scree test plots the components as the X axis and the corresponding eigenvalues as the Y-axis. As one moves to the right, toward later components, the eigenvalues drop. When the drop ceases and the curve makes an elbow toward less steep decline, Scree test says to drop all further components after the one starting the elbow.
Author: Mohit Agrawal
Group: Marketing Group 5