mounting that alternative free-baseline approaches are not only logically and statistically justified but more effective (Lopez-Rivas et al., 2009; Stark et al., 2006; Woods & Grimm, 2011)
Free-baseline approaches to DIF detection begin by forming a baseline model that has only the necessary constraints for identification. In MGCFA DIF analysis, this might involve constraining the loadings and thresholds for one item to be equal across comparison groups. Reduced models are then formed by constraining the loading and threshold parameters simultaneously for one additional item at a time and examining the change in goodness of fit for each reduced model relative to the baseline. If the fit worsens significantly, then the item under investigation is flagged as DIF. Stark et al. (2006) showed that this method yielded high power and low Type I error for IRT and mean and covariance structure (MACS; Sörbom, 1974) DIF detection, and Woods and Grimm (2011) showed that this general approach is effective with MIMIC.