Binary or graded disagree-agree responses to attitude
items are often collected for the purpose of attitude
measurement. Although such data are sometimes analyzed
with cumulative measurement models, recent
studies suggest that unfolding models are more appropriate
(Roberts, 1995; van Schuur & Kiers, 1994). Advances
in item response theory (IRT) have led to the
development of several parametric unfolding models for
binary data (Andrich, 1988; Andrich & Luo, 1993;
Hoijtink, 1991); however, IRT models for unfolding
graded responses have not been proposed. A parametric
IRT model for unfolding either binary or graded responses
is developed here. The graded unfolding model
(GUM) is a generalization of Andrich & Luo’s hyperbolic
cosine model for binary data. A joint maximum likelihood
procedure was implemented to estimate GUM parameters,
and a subsequent recovery simulation showed
that reasonably accurate estimates could be obtained
with minimal data demands (e.g., as few as 100 respondents
and 15 to 20 six-category items). The applicability
of the GUM to common attitude testing situations is illustrated
with real data on student attitudes toward capital
punishment. Index terms: attitude measurement,
graded unfolding model, hyperbolic cosine model, ideal
point process, item response theory, Likert scale, Thurstone
scale, unfolding model, unidimensional scaling.
Roberts, James S & Laughlin, James E. (1996). A unidimensional item response model for unfolding responses from a graded disagree-agree response scale. Applied Psychological Measurement, 20, 231-255. doi:10.1177/014662169602000305
Roberts, James S.; Laughlin, James E..
A unidimensional item response model for unfolding responses from a graded disagree-agree response scale.
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