R: Apply the Kaiser normalization when rotating factors

kaiser {psych} | R Documentation

## Apply the Kaiser normalization when rotating factors

### Description

Kaiser (1958) suggested normalizing factor loadings before rotating them, and then denormalizing them after rotation. The GPArotation package does not (by default) normalize, nor does the `fa` function. Then, to make it more confusing, varimax in stats does,Varimax in GPArotation does not. `kaiser` will take the output of a non-normalized solution and report the normalized solution.

### Usage

    kaiser(f, rotate = "oblimin",m=4,pro.m=4)

### Arguments

`f` | A factor analysis output from `fa` or a factor loading matrix.
`rotate` | Any of the standard rotations avaialable in the GPArotation package.
`m` | a parameter to pass to `Promax`
`pro.m` | A redundant parameter, which is used to replace m in calls to Promax

### Details

Best results if called from an unrotated solution. Repeated calls using a rotated solution will produce incorrect estimates of the correlations between the factors.

### Value

See the values returned by GPArotation functions

### Note

Prepared in response to a question about why `fa` oblimin results are different from SPSS.

### Author(s)

William Revelle

### References

Kaiser, H. F. (1958) The varimax criterion for analytic rotation in factor analysis. Psychometrika 23, 187-200.

### See Also

`fa`, `Promax`

### Examples

    f3 <- fa(Thurstone,3)
    f3n <- kaiser(fa(Thurstone,3,rotate="none"))
    f3p <- kaiser(fa(Thurstone,3,rotate="none"),rotate="Promax",m=3)
    factor.congruence(list(f3,f3n,f3p))

[Package psych version 1.7.8 Index]