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) ?
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.
In situations where path analysis does not yield model fit indices how can we compare different models. Can we use an average of the R-squares? Is it too crude? Please can you suggests some ways around this.
Why does the model have no fit indices? Does it have zero degrees of freedom? R-square is not a fit measure. It is a measure of variance explained. Fit compares the observed covariance matrix to the model estimated covariance matrix.