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donata posted on Wednesday, August 25, 2004 - 9:12 am
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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! |
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bmuthen posted on Wednesday, August 25, 2004 - 9:26 am
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You can transform the variable using Define. But you can also leave it as is and simply use non-normality 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? |
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donata posted on Thursday, August 26, 2004 - 2:59 am
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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 |
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bmuthen posted on Thursday, August 26, 2004 - 5:59 pm
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If you assume that you have a linear relationship without the transformation, I would not transform. Instead, I would either use a non-normality robust analysis (MLR or MLM), or use censored or two-part 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. |
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ywang posted on Friday, April 23, 2010 - 6:52 am
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Dear Dr. Muthens: "Centering" command is to ask Mplus to do the transformation: newx=x-mean(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=(x1-mean(x1))/std(x1). Thanks! |
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You would need to use DEFINE to create standardized scores. |
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