“…Thus, if we have and infit value of 1.40, then we can assert that there is 40% more data variability compared to the prediction of the model; while an outfit of 0.80 indicates that 20% less data variability is observed with respect to the model´s prediction. There are different criteria in selecting the cut-off values of MNSQ (e.g., Cadime, Ribeiro, Viana, Santos, & Prieto, 2014;Lee, Zhu, Ackley-Holbrook, Brower & Mcmurray, 2014;Linacre, 2012;Prieto & Delgado, 2003). In our case, we used an approximate range of 0.7-1.3 for MNSQ values to be an appropriate indication of good fit between data and model.…”