A multidimensionality-based differential item functioning
(DIF) analysis paradigm is presented that unifies
the substantive and statistical DIF analysis approaches by
linking both to a theoretically sound and mathematically
rigorous multidimensional conceptualization of DIF.
This paradigm has the potential (1) to improve understanding
of the causes of DIF by formulating and testing
substantive dimensionality-based DIF hypotheses; (2) to
reduce Type 1 error through a better understanding of
the possible multidimensionality of an appropriate
matching criterion; and (3) to increase power through
the testing of bundles of items measuring similar dimensions.
Using this approach, DIF analysis is shown to
have the potential for greater integration in the overall
test development process. Index terms: bias, bundle
DIF, cluster analysis, DIF estimation, DIF hypothesis
testing, differential item functioning, dimensionality,
DIMTEST, item response theory, multidimensionality,
sensitivity review, SIBTEST.