how to improve (poor) fit indices

Hi all, Hi Mike,

I want to test five rival conceptualizations of a construct (service quality) through path analysis with the two stage approach meta-analytic structural equation modelling (with the metaSEM in R). I am using a path analysis instead of a factor analysis since the dimensions' construct are discussed to be formative rather than reflective. In all five models, I have three or four exogeneous variables and one endogeneous variable (the endogeneous variable is the same for all models, that is, behavioral intention).

I was able to run all five models. However, the fit indices of the models was very poor. For example, the following fit indices pertain to the model 1 (four exogeneous variables and one endogeneous variable):

Model 1 - Fitting structural equation models (Stage 2)
Sample size 59832
Chi-squared of target model 1196.1572
DF of target model 6
p value of target model 0.000
Root Mean Square Error of Approximation (RMSEA) 0.0576
Standardised Root Mean Square Residual (SRMR) 0.4498
TLI -0.0343
CFI 0.3794
AIC 1184.1572
BIC 1130.1614

I suspect that it happened because I did not let the exogenous variables to covary in the models. However, when I let them to covary, the models became saturated and I do not have the fit indices anymore (and I need them to compare the five models). Attached, it is the diagram from the model 1 (with exogeneous variables not covarying), the data from model 1, and the script.

Do you have any suggestion what I could do to obtain acceptable fit indices? And should I not present results like those from model 1, right?!

Thank you very much for your time.

Best, Rafael.