Can I both scale (z-score) and use standardized = TRUE in summary function?

[maryam]

Hi,

I just stumbled upon a post where someone recommended to use the natural (original raw) dataset and then use the (standardized = TRUE) in the summary() function. My question is that is it wrong to first scale the data (-z-score standardization) and also use the above argument in summary()?? Would the results' values be wrong/distorted if I have done so?

EX:

data<- read.csv("data.csv")     # this is the raw original data but many variables have different scales/metric

scaled.data <- scale(data)

fit<-  sem(model, scaled.data, sample.nobs = 210, estimator = "MLM")

summary (fit, ci = TRUE, fit.measures = TRUE, standardized = TRUE)

Many thanks,

Maryam

[PD]

In most situations you do not want to use z scores as input. Use the raw data to get the variance-covariance matrix and then interpret the standardized coefficients in the output. When using z scores as input you can get incorrect standard errors.

[Terrence Jorgensen]

is it wrong to first scale the data (-z-score standardization) and also use the above argument in summary()?? Would the results' values be wrong/distorted if I have done so?

Your SEs and tests would be wrong.

Cudeck, R. (1989). Analysis of correlation matrices using covariance structure models. Psychological Bulletin, 105(2), 317-327.

Terrence D. Jorgensen

Assistant Professor, Methods and Statistics

Research Institute for Child Development and Education, the University of Amsterdam

http://www.uva.nl/profile/t.d.jorgensen

[maryam nasseri]

Thank you so much to both Terrence and PD for their helpful answers.

Best

Maryam

--
You received this message because you are subscribed to the Google Groups "lavaan" group.
To unsubscribe from this group and stop receiving emails from it, send an email to lavaan+un...@googlegroups.com.
To post to this group, send email to lav...@googlegroups.com.
Visit this group at https://groups.google.com/group/lavaan.
For more options, visit https://groups.google.com/d/optout.