I am running a SEM using lavaan package in R. The idea is to check different mediation effects of social class of origin, cognitive abilities, education and personality on the log-income of the offsprings.
Basically, this is the path-scheme I designed:
path_male <- '
#latent variable
ability =~ c_cgwri_dv + c_cgwrd_dv + c_cgs7ca_dv + c_cgvfc_dv + c_cgna_dv
#Paths
ability ~ origin_class
education ~ ability + agree + conscientiousness + extrov + neurotic + openness + origin_class
log_income ~ education + origin_class + ability + agree + conscientiousness + extrov + neurotic + openness + job_type
#correlations
c_cgwri_dv ~~ c_cgwrd_dv
c_cgs7ca_dv ~~ c_cgna_dv
ability ~~ origin_class
conscientiousness~~origin_class
agree~~origin_class
openness~~ origin_class
neurotic~~ origin_class
extrov~~ origin_class
agree~~conscientiousness
agree~~openness
agree~~neurotic
agree~~extrov
conscientiousness~~openness
conscientiousness ~~neurotic
conscientiousness ~~extrov
openness~~neurotic
openness~~extrov
neurotic~~extrov
'
The resulting estimates are controversial. Indeed, all the measures to check the fit are quite good:
CFI= 0.978;
TLI=0.96;
RMSEA=0.028;
SRMR=0.020;
but the p-value of the chi-squared is 0.000 suggesting that the model is a poor-fit.
I also used the modification indices to check what additional paths/correlations are necessary to improve the fit, but those with high values (around 20/30) are not theoretically meaningful. What could be the reason of this contradiction between chi-squared and the other measures of fit?
Thanks
