I’m using R’s mirt package to fit some (unidimensional) GPC models to a set of Likert-style items with five levels in each. I’m looking for some advice on how to test for the standard IRT assumptions.
Testing unidimensionality with a scree plot using
fa.parallel() works fine. However, regarding local independence and item fit: since the sample is large (around 4,000) and the number of items in each scale is small (3-4) the correlations given by Yen’s Q3 (using
residuals()) inevitably come out strongly negative (around -0.6), and the p-values for
itemfit() come our very small.
Do people happen to know of any alternative (perhaps graphical) ways to evaluate local independence and item fit under these circumstances, preferably in ways that are well-integrated with mirt?
I don’t think switching to ltm will help me here: it would mean I could run a DIMTEST using sirt’s
conf.detect(), but ltm’s
item fit() and sirt’s
Q3() are for dichotomous models only - and would likely have the same issues with large samples and small scales anyway.
Any advice would be greatly appreciated!