Multigroup SEM with std.lv=TRUE

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Anran

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Jul 10, 2019, 8:05:20 PM7/10/19
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Hi everyone,

I am running multiple SEM in lavaan. When I set std.lv=TRUE, the model will converge. However, when I leave it as default, the model does not converge. I am relatively new in SEM, could someone please tell me why this should happen?

The codes are as follows, 

 ALL.model5<-'
External =~ sf1+sf2+sf3+sf4+sq1+sq2
Improvement =~ si1+si2+si3+si4+si5+ti1+ti2+ti3+ti4+ti5+ti6
Irrelevance =~ ig1+ig2+ig3+bd1+bd2+bd3+bd4+bd5
affect=~pe1+pe2+ce1+ce2+ce3+ce4+ce5+ce6
General =~ sf1+sf2+sf3+sf4+sq1+sq2+si1+si2+si3+si4+si5+ti1+ti2+ti3+ti4+ti5+ti6+ig1+ig2+ig3+bd1+bd2+bd3+bd4+bd5+pe1+pe2+ce1+ce2+ce3+ce4+ce5+ce6
General ~~0*External
General ~~0*Improvement
General ~~0*Irrelevance
General ~~0*affect
External ~~0*Improvement
External ~~0*Irrelevance
External ~~0*affect
Improvement ~~0*Irrelevance
Improvement ~~0*affect
Irrelevance ~~0*affect
TM_EFFORT =~ tm_ef1+ tm_ef4 + tm_ef5
TM_Importance =~  tm_ip3 + tm_ip4 + tm_ip5
TM_TA =~ tm_ta1 + tm_ta2
TM_EFFORT ~ TM_Importance
TM_EFFORT ~ TM_TA
TM_EFFORT ~ General
TM_Importance ~ External
TM_Importance ~ General
TM_TA ~ General
TM_TA ~~ TM_Importance
'

fit.All.model5.config <- sem(ALL.model5, estimator="MLM", data=OriData, 
                             group="TM_Conditions")
and lavaan gives me warning:
Warning message:
In lavaan::lavaan(model = ALL.model5, data = OriData, group = "TM_Conditions",  :
  lavaan WARNING: the optimizer warns that a solution has NOT been found!

However, if I do
fit.All.model5.config <- sem(ALL.model5, estimator="MLM", data=OriData, std.lv=TRUE,
+                              group="TM_Conditions")

lavaan would process normally. 

Am I not supposed to use std.lv=TRUE in sem? 


Thank you in advance!

Anran

Terrence Jorgensen

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Jul 13, 2019, 12:23:46 PM7/13/19
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When I set std.lv=TRUE, the model will converge. However, when I leave it as default, the model does not converge. I am relatively new in SEM, could someone please tell me why this should happen?

When it converges with std.lv=TRUE, do you have any negative factor loadings that would be unexpected?  Or any negative residual variances?  It is a bifactor model, so I wonder whether the first factor loading for the same item of 2 different factors being fixed to 1 would cause any problems in case the model doesn't actually fit the items that well.  Providing your data might help troubleshoot.

Terrence D. Jorgensen
Assistant Professor, Methods and Statistics
Research Institute for Child Development and Education, the University of Amsterdam

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