Factor analysis method-linear index dimensionality reduction

to sum up

Like principal component analysis, we can use factor scores f1 and f2 as two new variables for subsequent modeling (such as clustering, regression, etc.)

Note: The factor analysis model cannot be used for comprehensive evaluation. Although many papers are written in this way, this is a big problem. For example, the type of variable, the method of selecting factors, and the effect of rotation on the final effect are difficult to clarify.

Suggest:

1. Linear dimensionality reduction first factor analysis
2. Using SPSS software will be much faster
3. The data to be multiplied by the factor score must be standardized first (it can be obtained with one click in the descriptive statistics of spss)
4. Factor analysis is often better than principal component analysis, because factor loading rotation can be performed, so more solutions can be obtained to carry out the connotation of new factors