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Based on prior expectations informed by content analyses, modeling results (model inter-item correlations and model residual correlations) and on model modification indices, the models were adjusted in order to reduce the overall misfit.
In order to ensure that the CFA produced the best fitting model, modification indices were examined.
Model modification indices were calculated for the configural invariance model to guide decisions about its specification.
To identify possible sources for this, we examined the model modification indices, and considered item loadings and content.
Model modification indices suggested that substantial improvement in fit could be obtained by including covariances among the residual errors of the SF-36 domains for RP and GH, VT and SF, and RP and RE.
The model modification indices of the factor variance for Tremor-at-rest were moderatelargerge to large (PSP: 70; PD-On: 19; PD-Off : 12) indicating that the common values were not very accurate for these variances, suggesting separate estimations of these quantities.
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Since the initial models did not fit the data very well, in the 3-factor models, modification indices were used to determine pairs of items which were highly correlated and the CFA was repeated allowing correlations between items.
Based on the fit of the different models and modification indices (not reported here), the final best-fitting and theoretically grounded three-factor model was the following.
Using a cutoff of 1.00 and above for model-generated modification indices, we identified six items showing evidence of noninvariance across gender, three items showing noninvariance across management status, and three items showing noninvariance across educational levels.
This model
Modification indices indicated that the model could not be improved by adding paths from items to other factors.
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CEO of Professional Science Editing for Scientists @ prosciediting.com