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The testlet model cannot estimate DIF parameter as accurately as the multilevel testlet model under most conditions, but was slightly more efficient than the multilevel testlet model.
The multilevel testlet model flags 9 items as DIF-present items.
The multilevel testlet model flags 13 out of 21 items as DIF-present items.
The testlet model and the multilevel testlet model perform equivalently across all conditions in terms of DIF detection rate.
The testlet model and the multilevel testlet model, however, outperform LR and HLR when there is DIF contamination.
The multilevel testlet model assumes that the person and item clustering effects are independent of each other.
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In summary, LR and HLR outperform the testlet and the multilevel testlet models when there is no impact between groups.
When there is no impact between groups, LR and HLR outperform the testlet and multilevel testlet models, and their average bias are −0.06, −0.06, 0.21, and 0.38, respectively.
Generally speaking, LR and HLR underestimate the DIF parameter, whereas the testlet and the multilevel testlet models overestimate the DIF parameters under most conditions.
Jiao et al. (2012) developed a four-level multilevel testlet IRT model to account for the dual dependency.
The testlet model can be considered as the bifactor Multiple Indicators and Multiple Causes (MIMIC) model.
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