If there is local dependency between items (items should only be related through the latent variable), you will see inflated change scores (if there is change). 4/6
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If response category thresholds are disordered (almost always seen when using too many response categories in combination with no labels or only labels on endpoints), you will have a lot of noise in your data, making comparisons even harder. Also, you are likely to have issues with invariance. 5/6
Ooops, forgot to paste the final point... If you have any of the issues listed (except local dependency issues), you will not be able to make reasonable comparisons to other research results at all, even if everyone uses the same questionnaire.
6/6
After all, I think students and researchers (especially those who are code-phobic) are more likely to use these advanced methods if they are presented in an easy-to-use way,and the developers evidently prioritise things the users want. A good way of increasing uptake and getting the word out? 2/2
I think there is a lack of accessible resources. I like the book "Rasch models in health" (2013) and https://openpublishing.library.umass.edu/pare/article/id/1585/ I have an old preprint that tries to simplify and pinpoint key issues to look for in a paper but cannot recommend it in its current state...
...since it needs revising (ongoing) to incorporate correct information about critical values for GOF metrics: https://osf.io/preprints/osf/3htzc_v1 We basically use the 4 criteria as presented by Kreiner (2007), who refers to Rosenbaum (1989).
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6/6
McNeish, D. (2023). https://doi.org/10.1037/amp0001213
McNeish, D. (2023). https://doi.org/10.1080/00273171.2022.2163477
McNeish, D., & Wolf, M. G. (2021). https://doi.org/10.1037/met0000425