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The change in goodness of fit from DIF model as compared to the 1PL model was evaluated by means of likelihood ratio tests [ 13, 14].
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One strategy is to observe the change in goodness-of-fit by adding one more parameter and testing the significance of that change.
The baseline model contained all the covariates; a series of submodels were then fitted in which one of the covariates was removed and the change in goodness-of-fit calculated to determine the significance of their independent contribution.
In this, the initial or baseline model contained all the covariates; a series of submodels were then fitted in which one and only one of the covariates was removed and the change in goodness-of-fit calculated to determine the significance of their independent contribution.
But, the changes in goodness of fit were significant in the last three restricted models (P < 0.01).
Compared with the previous model separately, it was found that there were no significant changes in goodness of fit for the measurement weights invariance model (⊿χ2 = 21.528, ⊿df = 16, P > 0.10) and structural weights invariance model (⊿χ2 = 11.207, ⊿df = 6, P > 0.05).
The authors proposed a heuristic procedure based on the relationship between the number of components in the model and the observed changes in goodness-of-fit to determine the optimal number of components [ 51].
Early life variables could be omitted from Model 3 without significant change in the goodness of fit for CRP, vWF and ICAM.
The statistical significance of the variance at organization levels was judged by the change in the goodness-of-fit of the model to the data, as measured by the change in the model's log likelihood ratio produced by eliminating that level from the model.
Changes in the goodness of fit produced by including these periodic components were measured by the Akaike's information criterion (AIC).
Table 3-wrap> shows the change in deviance, or goodness-of-fit, in the sequential building of the models.
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