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Roberts, James S.; Donoghue, John R.; Laughlin, James E. – Applied Psychological Measurement, 2002
Investigated the data demands associated with the marginal maximum likelihood (MML) expected a posterior (EAP) methodology and the precision of the resulting parameter estimates when data fit the underlying model through simulation. Also studied the extent to which a misspecified prior distribution would affect the item and person parameter…
Descriptors: Estimation (Mathematics), Maximum Likelihood Statistics, Models, Research Methodology
Roberts, James S.; Donoghue, John R.; Laughlin, James E. – 1999
The generalized graded unfolding model (GGUM) (J. Roberts, J. Donoghue, and J. Laughlin, 1998) is an item response theory model designed to analyze binary or graded responses that are based on a proximity relation. The purpose of this study was to assess conditions under which item parameter estimation accuracy increases or decreases, with special…
Descriptors: Estimation (Mathematics), Item Response Theory, Maximum Likelihood Statistics, Sample Size
Roberts, James S.; Laughlin, James E. – 1996
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 investigations suggest that unfolding models are more appropriate (J. S. Roberts, 1995; W. H. Van Schuur and H. A. L. Kiers, 1994). Advances in…
Descriptors: Attitude Measures, Estimation (Mathematics), Item Response Theory, Mathematical Models