I am running measurement models for my data. One of my models runs, but I get a chi-square model value of 0.000. What does this mean? Is this a poor model? Below are my output and input; the data is secure so I cannot send it
Chi-Square Test of Model Fit Value 0.000* Degrees of Freedom 0 P-Value 0.0000 Scaling Correction Factor 1.000 for MLR
Chi-Square Test of Model Fit for the Baseline Model Value 45.180 Degrees of Freedom 3 P-Value 0.0000
CFI/TLI CFI 1.000 TLI 1.000
Loglikelihood H0 Value -21243.164 H1 Value -21243.164
Number of Free Parameters 9 Akaike (AIC) 42504.327 Bayesian (BIC) 42548.379 Sample-Size Adjusted BIC 42519.795 RMSEA 0.000 SRMR 0.000
DATA: file is "R:\Users\Sarah Strand\public\Data\MPlus Data\subequalgroups2.txt"; format is free; type is individual;
VARIABLE: NAMES ARE AID FEMALE ... etc [omitted to preserve space];
IDVAR = aid; USEVARIABLES ARE par_app perc_map perc_dap; MISSING ARE .; WEIGHT IS GSWGT3; CLUSTER IS PSUNUM;
Your model is just identified. It has zero degrees of freedom. In this case, model fit cannot be assessed.
Yellowdog posted on Wednesday, October 31, 2012 - 2:37 am
Dear Linda, we want to test a path model (N=189) with the following observed trait variables: - four predictors (IV1 to IV4) - two mediators (M1 and M2) that are, as expected a priori, strongly negatively correlated with each other (r = -.56) - with quality of life (QoL) as DV We specified the following model:
qol on iv1 iv2 iv3 iv3 m1 m2; m1 on iv1 iv2 iv3 iv4; m2 on iv1 iv2 iv3 iv4;
Our question refers to how to model the relationship between M1 and M2. There is equilibrium, but we cannot make assumptions on a direction of causality from one to the other. When we specify a nonrecursive feedback loop (m1 on m2 (p31); m2 on m1 (p32);), model estimation fails (see output below). If we only specify that M1 and M2 are correlated (m1 with m2 (p31);), model estimation fails too (see output below).
Chi-Square Test of Model Fit Value 0.000 Degrees of Freedom 0 P-Value 0.0000
RMSEA (Root Mean Square Error Of Approximation) Estimate 0.000 90 Percent C.I. 0.000 0.000 Probability RMSEA <= .05 0.000
How can we fix the problem? Many thanks for your help, Mario
The models are not failures. They are just-identified with zero degrees of freedom. Model fit cannot be assessed in this case. I would covary m1 with m2.
Yellowdog posted on Thursday, November 01, 2012 - 3:51 am
thank you for your reply.
We understand that with df=0, fit indices are not available. Refocusing our question, we are wondering whether the model outlined above and its results (with df=0) are valid, although we do not get information on how the model fits the data.
Can we go ahead and report path coefficients from this analysis as a final result? Or should we try to change the model specification until df>0 (e.g., using MODINDICES) ?