No robust fit indices are provided

[ahmad]

  Hi, I’m trying to calculate robust fit indices in lavaan, but they are not provided for my model, even though no errors or warnings appear. Please see the results of the fit indices below:

  

lavaan.mi object fit to 100 imputed data sets using: - lavaan (0.6-19) - lavaan.mi (0.1-0) See class?lavaan.mi help page for available methods. Convergence information: The model converged on 100 imputed data sets. Standard errors were available for all imputations. Estimator DWLS Optimization method NLMINB Number of model parameters 77 Number of observations 10233 Model Test User Model: Standard Scaled Test statistic 145.733 194.695 Degrees of freedom 366 366 P-value 1.000 1.000 Average scaling correction factor 0.902 Average shift parameter 86.207 simple second-order correction Pooling method D2 Pooled statistic “scaled.shifted” “scaled.shifted” correction applied BEFORE pooling Model Test Baseline Model: Test statistic 4573.264 8767.386 Degrees of freedom 406 406 P-value 0.000 0.000 Scaling correction factor 1.946 User Model versus Baseline Model: Comparative Fit Index (CFI) 1.000 1.000 Tucker-Lewis Index (TLI) 1.059 1.023 Robust Comparative Fit Index (CFI) NA Robust Tucker-Lewis Index (TLI) NA Root Mean Square Error of Approximation: RMSEA 0.000 0.000 90 Percent confidence interval - lower 0.000 0.000 90 Percent confidence interval - upper 0.000 0.000 P-value H_0: RMSEA <= 0.050 1.000 1.000 P-value H_0: RMSEA >= 0.080 0.000 0.000 Robust RMSEA NA 90 Percent confidence interval - lower NA 90 Percent confidence interval - upper NA P-value H_0: Robust RMSEA <= 0.050 NA P-value H_0: Robust RMSEA >= 0.080 NA Standardized Root Mean Square Residual: SRMR 0.079 0.079 Parameter Estimates: Parameterization Delta Standard errors Robust.sem Information Expected Information saturated (h1) model Unstructured Pooled across imputations Rubin's (1987) rules Augment within-imputation variance Scale by average RIV Wald test for pooled parameters t(df) distribution Pooled t statistics with df >= 1000 are displayed with df = Inf(inity) to save space. Although the t distribution with large df closely approximates a standard normal distribution, exact df for reporting these t tests can be obtained from parameterEstimates.mi()

Best wishes,

Ahmad