# faStandardize: Standardize the Unrotated Factor Loadings In fungible: Psychometric Functions from the Waller Lab

## Description

This function standardizes the unrotated factor loadings using two methods: Kaiser's normalization and Cureton-Mulaik standardization.

## Usage

 `1` ```faStandardize(method, lambda) ```

## Arguments

 `method` (Character) The method used for standardization. There are three option: "none", "Kaiser", and "CM". "none": No standardization is conducted on the unrotated factor loadings matrix "Kaiser": The rows of the unrotated factor loadings matrix are rescaled to have unit-lengths. "CM": Apply the Cureton-Mulaik standardization to the unrotated factor loadings matrix. `lambda` (Matrix) The unrotated factor loadings matrix (or data frame).

## Value

The resulting output can be used to standardize the factor loadings as well as providing the inverse matrix used to unstandardize the factor loadings after rotating the factor solution.

• Dv: (Matrix) A diagonal weight matrix used to standardize the unrotated factor loadings. Pre-multiplying the loadings matrix by the diagonal weight matrix (i.e., Dv

• DvInv: (Matrix) The inverse of the diagonal weight matrix used to standardize. To unstandardize the ultimate rotated solution, pre-multiply the rotated factor loadings by the inverse of Dv (i.e., DvInv

## References

Browne, M. W. (2001). An overview of analytic rotation in exploratory factor analysis. Multivariate Behavioral Research, 36(1), 111-150.

Cureton, E. E., & Mulaik, S. A. (1975). The weighted varimax rotation and the promax rotation. Psychometrika, 40(2), 183-195.

Other Factor Analysis Routines: `BiFAD()`, `Box26`, `GenerateBoxData()`, `Ledermann()`, `SLi()`, `SchmidLeiman()`, `faAlign()`, `faEKC()`, `faIB()`, `faLocalMin()`, `faMB()`, `faMain()`, `faScores()`, `faSort()`, `faX()`, `fals()`, `fapa()`, `fareg()`, `fsIndeterminacy()`, `orderFactors()`, `print.faMB()`, `print.faMain()`, `promaxQ()`, `summary.faMB()`, `summary.faMain()`