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The adequacy of fit is evaluated using multiple fit statistics and their ideal values are shown at the bottom of summary fit Table 1 [ 39].> Stochastic ordering of items is evaluated through the fit of data to the model.
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SEM allows for constructing and validating measurement models and provides multiple model fit statistics and modification indices, which facilitates item reduction and evaluation and comparison of the original and shortened APQ.
Additional file 2: Model fit statistics for multiple group (English/Sinhala) models.
As discussed previously, multiple items and persons demonstrated inadequate fit statistics, which is indicative that the persons and the items are not performing as expected by the Rasch model.
To avoid type I errors due to multiple testing, the p-values for fit statistics and DIF analyses were Bonferroni-adjusted to the alpha level (i.e. p = 0.05/number of tests carried out) [ 38].
The significance of all chi-square fit statistics are Bonferroni adjusted to account for multiple testing [ 27].
If multiple data set IDs are given, then the simultaneous Chi Squared fit statistics are calculated for those data sets.
List all Sherpa fit statistics.
Model values and fit statistics are updated each time a new fit is run.
Fit statistics are also indicators for unidimensionality.
Fit statistics for all items were within the acceptable range.
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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