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donata posted on Wednesday, August 25, 2004  3:12 pm



I have 10 variables for a two factor CFA. Two of the variables are negative skewed (1.8). Is it possible to tranform this variable using LN and then use them for the CFA? I know that the covarianca matrix would change. Is there any other solution? thanks in advance! 

bmuthen posted on Wednesday, August 25, 2004  3:26 pm



You can transform the variable using Define. But you can also leave it as is and simply use nonnormality robust estimation using the estimator MLR. The choice is affected by what your assumption is regarding the relationship between the factor and this indicator  is it linear or not? 

donata posted on Thursday, August 26, 2004  8:59 am



My assumption is that the relationship between the factor and the indicator is linear. But tranforming the variable the variance will change and as a consequence also the covariance matrix. Does it make any problem? Thanks a lot. Donata 

bmuthen posted on Thursday, August 26, 2004  11:59 pm



If you assume that you have a linear relationship without the transformation, I would not transform. Instead, I would either use a nonnormality robust analysis (MLR or MLM), or use censored or twopart modeling. If you assume that you get closer to a linear relationship after transforming, then choose that approach. Do the transform in Define. Yes the covariance matrix will change but that should not be a problem. 

ywang posted on Friday, April 23, 2010  12:52 pm



Dear Dr. Muthens: "Centering" command is to ask Mplus to do the transformation: newx=xmean(x). Is there a similar simple command to request Mplus to generate standardized scores for the indicator variables automatically? I mean, do we have to use "define" function like the following, e.g. define: newx1=(x1mean(x1))/std(x1). Thanks! 


You would need to use DEFINE to create standardized scores. 

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