In OpenMx v. 2.11.5, the fit statistics output is the same whether type="cov" or type="cor". In a previous version of OpenMx, I recall that the manifest variable variances would be subtracted from the number of degrees of freedom and observed statistics, and the fit statistics were calculated using the reduced degrees of freedom. Now, the number of observed statistics number is reduced, but the degrees of freedom and fit statistics are not similarly adjusted.

Also, previously the warning about standard errors and fit statistics for type="cor" being potentially incorrect with the Steiger (1980) citation was printed when summary.MxModel() was called as when mxRun() was called. Now the warning only prints with mxRun()

For example, using the HS.ability.data example in help(mxModel), the output for type="cov" is:

Model Statistics: | Parameters | Degrees of Freedom | Fit (-2lnL units) Model: 21 24 10970.90 Saturated: 45 0 10928.42 Independence: 9 36 11882.01 Number of observations/statistics: 301/45 chi-square: χ² ( df=24 ) = 42.48505, p = 0.01138111 Information Criteria: | df Penalty | Parameters Penalty | Sample-Size Adjusted AIC: -5.514954 84.48505 87.79687 BIC: -94.485600 162.33436 95.73430 CFI: 0.9798547 TLI: 0.9697821 (also known as NNFI) RMSEA: 0.05058496 [95% CI (0.01638508, 0.07931089)] Prob(RMSEA <= 0.05): 0.4530891

The output for type="cor" is:

Model Statistics: | Parameters | Degrees of Freedom | Fit (-2lnL units) Model: 21 15 1788.898 Saturated: 45 -9 1746.413 Independence: 9 27 2700.000 Number of observations/statistics: 301/36 chi-square: χ² ( df=24 ) = 42.48505, p = 0.01138111 Information Criteria: | df Penalty | Parameters Penalty | Sample-Size Adjusted AIC: 12.48505 84.48505 87.79687 BIC: -43.12161 162.33436 95.73430 CFI: 0.9798547 TLI: 0.9697821 (also known as NNFI) RMSEA: 0.05058496 [95% CI (0.01638508, 0.07931089)] Prob(RMSEA <= 0.05): 0.4530891

I'm not sure which version this behavior changed in.

I'm not sure when the change occurred. Was this an intentional change? If so, was it determined that the unadjusted df/fit statistics are more likely to be correct (the problems with doing covariance structure analyses with correlations acknowledged)?