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By design, large-scale educational testing programs often have a large proportion of missing data. Since the effect of missing data on differential item functioning (DIF) assessment has been investigated in recent years and it has been found that Type I error rates tend to be inflated, it is of great importance to adapt existing DIF assessment methods to the inflation. The DIF-free-then-DIF (DFTD) strategy, which originally involved one single-scale purification procedure to identify DIF-free items, has been extended to involve another scale purification procedure for the DIF assessment in this study, and this new method is called the dual-scale purification (DSP) procedure. The performance of the DSP procedure in assessing DIF in large-scale programs, such as Program for International Student Assessment (PISA), was compared with the DFTD strategy through a series of simulation studies. Results showed the superiority of the DSP procedure over the DFTD strategy when tests consisted of many DIF items and when data were missing by design as in large-scale programs. Moreover, an empirical study of the PISA 2009 Taiwan sample was provided to show the implications of the DSP procedure. The applications as well as further studies of DSP procedure are also discussed.

Improving the Assessment of Differential Item Functioning in Large-Scale Programs With Dual-Scale Purification of Rasch Models: The PISA Example