Chi-square model fit of 0.000 PreviousNext
Mplus Discussion > Structural Equation Modeling >
 Sarah Strand posted on Friday, March 30, 2007 - 12:19 am
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 1.000
TLI 1.000

H0 Value -21243.164
H1 Value -21243.164

Information Criteria

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

file is "R:\Users\Sarah Strand\public\Data\MPlus Data\subequalgroups2.txt";
format is free;
type is individual;

NAMES ARE AID FEMALE ... etc [omitted to preserve space];

IDVAR = aid;
USEVARIABLES ARE par_app perc_map perc_dap;


approval by par_app perc_map perc_dap;

standardized h1se;
 Linda K. Muthen posted on Friday, March 30, 2007 - 6:03 am
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
 Linda K. Muthen posted on Wednesday, October 31, 2012 - 12:28 pm
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
Dear Linda,

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) ?

Thank you for your help,
 Linda K. Muthen posted on Thursday, November 01, 2012 - 7:29 am
These results can be reported. This is not an uncommon situation in path analysis.
 Melissa MacLeod posted on Thursday, January 16, 2014 - 8:12 pm
Hi, I am wondering why df=0 is not uncommon in path analysis. My model should not be just-identified because I have a sample size of 390 and am only trying to estimate 4 free parameters but my fit indices come up with a df=0. I'm wondering why this is and if there is any way to have positive df using path analysis. Even in the output examples you have on the website for linear regression they are 0 but this limits the usefulness of the model.

Thank you,
 Linda K. Muthen posted on Friday, January 17, 2014 - 5:55 am
In path analysis, degrees of freedom are computed as the number of parameters in the H1 model minus the number of parameters in the H0 model. They are not in this case related to the sample size.
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