The effectiveness of several iterative methods of
item response theory (IRT) item bias detection was
examined in a simulation study. The situations employed
were based on biased items created using a
two-dimensional IRT model. Previous research
demonstrated that the non-iterative application of
some IRT parameter linking procedures produced
unsatisfactory results in a simulation study involving
unidirectional item bias. A modified form of
Drasgow’s iterative item parameter linking method
and an adaptation of Lord’s test purification
procedure were examined in conditions that simulated
unidirectional and mixed-directional forms of
item bias. The results illustrate that iterative linking
holds promise for differentiating biased from
unbiased items under several item bias conditions.
In addition, a combination of methods, involving
cycles of iterative linking followed by ability scale
purification, was found to be even more effective
than iterative linking alone. This combination of
procedures totally eliminated false positive misidentifications
for the most pervasive item bias condition,
and false negative misidentifications were also
reduced. Combining iterative linking with ability
scale purification appears to be a viable method
for analyzing multidimensional IRT data with
unidimensional IRT item-bias methods. Index
terms: ability scale purification, item bias, item
response theory, iterative linking, iterative methods,
metric linking, multidimensional IRT model.
Park, Dong-gun & Lautenschlager, Gary J. (1990). Improving IRT item bias detection with iterative linking and ability scale purification. Applied Psychological Measurement, 14, 163-173. doi:10.1177/014662169001400205
Park, Dong-gun; Lautenschlager, Gary J..
Improving IRT item bias detection with iterative linking and ability scale purification.
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