Item-level fit analysis not only serves as a complementary check to global fit analysis, it is also essential in scale development because the fit results will guide item revision and/or deletion (Liu & Maydeu-Olivares, 2014). During data collection, missing response data may likely happen due to various reasons. Chi-square-based item fit…

This study analysed polytomous items' psychometric properties according to nonparametric item response theory (NIRT) models. Thus, simulated datasets--three different test lengths (10, 20 and 30 items), three sample distributions (normal, right and left skewed) and three samples sizes (100, 250 and 500)--were generated by conducting 20…

Type I error rates for PARSCALE's fit statistic were examined. Data were generated to fit the partial credit or graded response model, with test lengths of 10 or 20 items. The ability distribution was simulated to be either normal or uniform. Type I error rates were inflated for the shorter test length and, for the graded-response model, also for…