psych (version 2.1.9)

# fa.extension: Apply Dwyer's factor extension to find factor loadings for extended variables

## Description

Dwyer (1937) introduced a method for finding factor loadings for variables not included in the original analysis. This is basically finding the unattenuated correlation of the extension variables with the factor scores. An alternative, which does not correct for factor reliability was proposed by Gorsuch (1997). Both options are an application of exploratory factor analysis with extensions to new variables. Also useful for finding the validities of variables in the factor space.

## Usage

```fa.extension(Roe,fo,correct=TRUE)
fa.extend(r,nfactors=1,ov=NULL,ev=NULL,n.obs = NA, np.obs=NULL,
correct=TRUE,rotate="oblimin",SMC=TRUE,   warnings=TRUE, fm="minres",
alpha=.1,omega=FALSE,cor="cor",use="pairwise",cor.correct=.5,weight=NULL, ...)```

## Arguments

Roe

The correlations of the original variables with the extended variables

fo

The output from the `fa` or `omega` functions applied to the original variables.

correct

correct=TRUE produces Dwyer's solution, correct=FALSE produces Gorsuch's solution

r

A correlation or data matrix with all of the variables to be analyzed by fa.extend

ov

The original variables to factor

ev

The extension variables

nfactors

Number of factors to extract, default is 1

n.obs

Number of observations used to find the correlation matrix if using a correlation matrix. Used for finding the goodness of fit statistics. Must be specified if using a correlaton matrix and finding confidence intervals.

np.obs

Pairwise number of observations. Required if using fm="minchi", suggested in other cases to estimate the empirical goodness of fit.

rotate

"none", "varimax", "quartimax", "bentlerT", "geominT" and "bifactor" are orthogonal rotations. "promax", "oblimin", "simplimax", "bentlerQ, "geominQ" and "biquartimin" and "cluster" are possible rotations or transformations of the solution. The default is to do a oblimin transformation, although versions prior to 2009 defaulted to varimax.

SMC

Use squared multiple correlations (SMC=TRUE) or use 1 as initial communality estimate. Try using 1 if imaginary eigen values are reported. If SMC is a vector of length the number of variables, then these values are used as starting values in the case of fm='pa'.

warnings

warnings=TRUE => warn if number of factors is too many

fm

factoring method fm="minres" will do a minimum residual (OLS), fm="wls" will do a weighted least squares (WLS) solution, fm="gls" does a generalized weighted least squares (GLS), fm="pa" will do the principal factor solution, fm="ml" will do a maximum likelihood factor analysis. fm="minchi" will minimize the sample size weighted chi square when treating pairwise correlations with different number of subjects per pair.

alpha

alpha level for the confidence intervals for RMSEA

omega

Do the extension analysis for an omega type analysis

cor

Pass the kind of correlation to fa (defaults to Pearson, can use mixed)

use

Option for the cor function on how to handle missing data.

cor.correct

The correction to be passed to mixed, tet, or polycor (defaults to .5)

weight

Should we weight the variables? (see `fa`)

...

Additional parameters, specifically, keys may be passed if using the target rotation, or delta if using geominQ, or whether to normalize if using Varimax

## Details

It is sometimes the case that factors are derived from a set of variables (the Fo factor loadings) and we want to see what the loadings of an extended set of variables (Fe) would be. Given the original correlation matrix Ro and the correlation of these original variables with the extension variables of Roe, it is a straight forward calculation to find the loadings Fe of the extended variables on the original factors. This technique was developed by Dwyer (1937) for the case of adding new variables to a factor analysis without doing all the work over again. But, as discussed by Horn (1973) factor extension is also appropriate when one does not want to include the extension variables in the original factor analysis, but does want to see what the loadings would be anyway.

This could be done by estimating the factor scores and then finding the covariances of the extension variables with the factor scores. But if the original data are not available, but just the covariance or correlation matrix is, then the use of `fa.extension` is most appropriate.

The factor analysis results from either `fa` or `omega` functions applied to the original correlation matrix is extended to the extended variables given the correlations (Roe) of the extended variables with the original variables.

`fa.extension` assumes that the original factor solution was found by the `fa` function.

For a very nice discussion of the relationship between factor scores, correlation matrices, and the factor loadings in a factor extension, see Horn (1973).

The `fa.extend` function may be thought of as a "seeded" factor analysis. That is, the variables in the original set are factored, this solution is then extended to the extension set, and the resulting output is presented as if both the original and extended variables were factored together. This may also be done for an omega analysis.

The example of codefa.extend compares the extended solution to a direct solution of all of the variables using `factor.congruence`.

## References

Paul S. Dwyer (1937) The determination of the factor loadings of a given test from the known factor loadings of other tests. Psychometrika, 3, 173-178

Gorsuch, Richard L. (1997) New procedure for extension analysis in exploratory factor analysis, Educational and Psychological Measurement, 57, 725-740

Horn, John L. (1973) On extension analysis and its relation to correlations between variables and factor scores. Multivariate Behavioral Research, 8, (4), 477-489.

See Also as `fa`, `principal`, `Dwyer`

## Examples

Run this code
``````# NOT RUN {
#The Dwyer Example
Ro <- Dwyer[1:7,1:7]
Roe <- Dwyer[1:7,8]
fo <- fa(Ro,2,rotate="none")
fe <- fa.extension(Roe,fo)

#an example from simulated data
set.seed(42)
d <- sim.item(12)    #two orthogonal factors
R <- cor(d)
Ro <- R[c(1,2,4,5,7,8,10,11),c(1,2,4,5,7,8,10,11)]
Roe <- R[c(1,2,4,5,7,8,10,11),c(3,6,9,12)]
fo <- fa(Ro,2)
fe <- fa.extension(Roe,fo)
fa.diagram(fo,fe=fe)

#alternatively just specify the original variables and the extension variables
fe = fa.extend(R, 2, ov =c(1,2,4,5,7,8,10,11), ev=c(3,6,9,12))
fa.diagram(fe\$fo, fe = fe\$fe)

#create two correlated factors
fx <- matrix(c(.9,.8,.7,.85,.75,.65,rep(0,12),.9,.8,.7,.85,.75,.65),ncol=2)
Phi <- matrix(c(1,.6,.6,1),2)
sim.data <- sim.structure(fx,Phi,n=1000,raw=TRUE)
R <- cor(sim.data\$observed)
Ro <- R[c(1,2,4,5,7,8,10,11),c(1,2,4,5,7,8,10,11)]
Roe <- R[c(1,2,4,5,7,8,10,11),c(3,6,9,12)]
fo <- fa(Ro,2)
fe <- fa.extension(Roe,fo)
fa.diagram(fo,fe=fe)

#now show how fa.extend works with the same data set
#note that we have to make sure that the variables are in the order to do the factor congruence
fe2 <- fa.extend(sim.data\$observed,2,ov=c(1,2,4,5,7,8,10,11),ev=c(3,6,9,12))
fa.diagram(fe2,main="factor analysis with extension variables")
fa2 <- fa(sim.data\$observed[,c(1,2,4,5,7,8,10,11,3,6,9,12)],2)
factor.congruence(fe2,fa2)
summary(fe2)

#an example of extending an omega analysis

rep(0,20)),c(c(c(.9,.8,.7,.6),rep(0,20)),c(.9,.8,.7,.6))),ncol=5)
ss <- c(1,2,3,5,6,7,9,10,11,13,14,15,17,18,19)
Ro <- cor(five.factor\$observed[,ss])
Re <- cor(five.factor\$observed[,ss],five.factor\$observed[,-ss])
om5 <-omega(Ro,5)   #the omega analysis
om.extend <- fa.extension(Re,om5) #the extension analysis
om.extend #show it
#now, include it in an omega diagram