Title
Unidimensional calibrations and interpretations of composite traits for multidimensional tests
Abstract
A two-stage process that considers the multidimensionality
of tests under the framework of
unidimensional item response theory (IRT) is
described and evaluated. In the first stage, items
are clustered in a multidimensional latent space
with respect to their direction of maximum discrimination.
The separate item clusters are
subsequently calibrated using a unidimensional IRT
model to provide item parameter and trait
estimates for composite traits in the context of the
multidimensional trait space. This application is
proposed as a workable compromise to some of
the estimation, indeterminacy, and interpretation
problems that affect the direct use of multidimensional
IRT procedures for item calibration
and trait estimation. The findings of a study based
on simulated multidimensional data indicate that
there are identifiable gains in estimation robustness
and score interpretation with almost no sacrifice in
goodness-of-fit using this two-stage approach to
modeling composite latent traits. Index terms:
item response theory, model fit, multidimensionality,
parameter estimation; model fit; multidimensionality
in IRT; parameter estimation; person fit; reference
composites; trait estimation.
Identifiers
other: doi:10.1177/014662169201600308
Previously Published Citation
Leucht, Richard M & Miller, Timothy R. (1992). Unidimensional calibrations and interpretations of composite traits for multidimensional tests. Applied Psychological Measurement, 16, 279-293. doi:10.1177/014662169201600308
Suggested Citation
Leucht, Richard M.; Miller, Timothy R..
(1992).
Unidimensional calibrations and interpretations of composite traits for multidimensional tests.
Retrieved from the University of Minnesota Digital Conservancy,
https://hdl.handle.net/11299/115720.