Any information would be very helpful!

- Thread starter jastingo
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Any information would be very helpful!

I'm a researcher not a statistician (caveat) but I use:

vif(fit) # variance inflation factors

Code:

`sqrt(vif(fit)) > 2 # problem?`

It is widely held that converting categorical variables into a series of dummy variables avoids this problem. Since taking SEM I am less sure this is the case. But regardless you would have to create dummies not use the categorical variable itself.

noetsi said:

The problem with using correlations for categorical variables is that most software use pearson product moment and that is invalid for categorical data. You should look at polychoric correlations instead. Unfortunately that takes special software like Mplus (I dont know if R does this)

Robert I. Kabacoff said:

OTHER TYPES OF CORRELATIONS

The hetcor() function in the polycor package can compute a heterogeneous correlation

matrix containing Pearson product-moment correlations between numeric

variables, polyserial correlations between numeric and ordinal variables, polychoric

correlations between ordinal variables, and tetrachoric correlations between two dichotomous

variables. Polyserial, polychoric, and tetrachoric correlations assume that

the ordinal or dichotomous variables are derived from underlying normal distributions.

The hetcor() function in the polycor package can compute a heterogeneous correlation

matrix containing Pearson product-moment correlations between numeric

variables, polyserial correlations between numeric and ordinal variables, polychoric

correlations between ordinal variables, and tetrachoric correlations between two dichotomous

variables. Polyserial, polychoric, and tetrachoric correlations assume that

the ordinal or dichotomous variables are derived from underlying normal distributions.