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In addition to the limited evidence of models of MDT and impacts of MDT management on processes and outcomes of interest, the review found no evidence of what an optimal MDT model should include.
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Curiously, the evidence of model shrinkage was there all along, easy to track.
This provides evidence of model fits as the hypothesized model can represent adequately the observed data.
So, if the model satisfies this assumption, positive evidence of model fit is available.
In terms of goodness of fit and tracking the data city by city, our model performs well and there is no evidence of model misspecification.
Standard 4.10 of the Standards for Educational and Psychological Testing (American Educational Research Association 2014) recommends obtaining evidence of model fit when an IRT model is used to make inferences from a data set.
These indices of model parsimony identify relative evidence of model(s) from a set of candidate models.
None of these plots showed any evidence of model misspecification (data not shown).
Residual diagnostics for non-constant variance and non-normality revealed no evidence of model inadequacy.
Practicality simply asks: Does the current data provide evidence of model problems?
But χ is the strongest available test and hence provides the most trustworthy evidence of model misspecification or other problems.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com