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• W314471229 abstract "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 attention paid to the influence that a given item parameter value has on the estimation of another item parameter. This assessment was based on a recovery simulation in which the effects of sample size, item location, degree of item discrimination, and extremity of subjective category thresholds were varied. Results indicate that with 750 or more respondents, sample size has negligible effect on all but the estimation of subjective response category thresholds. The true extremity of both item location and item discrimination did affect the estimation of these parameters themselves, and also affected the estimation of other item parameters in the model. However, these effects were modest and had little impact on the estimation of the corresponding item response functions. These results suggest that marginal maximum likelihood estimates of item parameters will provide accurate results across a variety of item parameter configurations when the sample size is at or above the recommended levels. (Contains 1 table, 3 figures, and 14 references.) (Author/SLD) ******************************************************************************** Reproductions supplied by EDRS are the best that can be made from the original document. ******************************************************************************** 0 cr, Estimability of Parameters in the Generalized Graded Unfolding Model James S. Roberts Medical University of South Carolina John R. Donoghue Educational Testing Service James E. Laughlin University of South Carolina U.S. DEPARTMENT OF EDUCATION Office of Educational Research and Improvement EDUCATIONAL RESOURCES INFORMATION CENTER (ERIC) fEKis document has been reproduced as received from the person or organization originating it. CI Minor changes have been made to improve reproduction quality. Points of view or opinions stated in this document do not necessarily represent official OERI position or policy. PERMISSION TO REPRODUCE AND DISSEMINATE THIS MATERIAL HAS BEEN GRANTED BY 3aolyas__Arkrb TO THE EDUCATIONAL RESOURCES INFORMATION CENTER (ERIC) 1 Presented at the annual meeting of the American Educational Research Association, April 22, 1999, Montreal, Canada. Visit our web site devoted to item response theory models for unfolding: www.musc.edU/tdaPtr0l)e CNI 2 Correspondence should be directed to Dr. James S. Roberts, Medical University of South Carolina, Institute of Psychiatry 4 North, 67 President Street, P.O. Box 250861, Charleston, SC 29425; E-mail: roberjam@musc.edu. 2 BEST COPY AVAILABLE Abstract The generalized graded unfolding model (GGUM; Roberts, Donoghue & Laughlin, 1998, 1999) is an item response theory model designed to analyze binary or graded responses that are based on a proximity relation (Coombs, 1964). A typical application of the GGUM is in measurement situations where respondents are asked to indicate their level of agreement with a series of statements that span a bipolar attitude continuum (e.g., Thurstone or Likert attitude measurement scales).The generalized graded unfolding model (GGUM; Roberts, Donoghue & Laughlin, 1998, 1999) is an item response theory model designed to analyze binary or graded responses that are based on a proximity relation (Coombs, 1964). A typical application of the GGUM is in measurement situations where respondents are asked to indicate their level of agreement with a series of statements that span a bipolar attitude continuum (e.g., Thurstone or Likert attitude measurement scales). Roberts, Donoghue, and Laughlin (1998) have shown that when the data conform to the GGUM, then accurate item parameter estimates can be obtained with a marginal maximum likelihood procedure when the sample size is approximately 750 or more. Similarly, Roberts et al. have demonstrated that accurate expected a posteriori estimates of person parameters can be obtained with approximately 20 items with 6 response categories per item. Although the minimum data demands associated with these estimation procedures have been investigated, other characteristics about the robustness of parameter estimation accuracy remain unanswered. The purpose of this study was to assess conditions under which item parameter estimation accuracy increases or decreases, with special attention paid to the influence that a given item parameter value has on the estimation of another item parameter. This assessment was based on a recovery simulation in which the effects of sample size, item location, degree of item discrimination and extremity of subjective category thresholds were varied. The results indicated that with 750 or more respondents, sample size had negligible effects on all but the estimation of subjective response category thresholds. Additionally, the true extremity of both item location and item discrimination did affect the estimation of these parameters themselves, and also affected the estimation of other item parameters in the model. However, these effects were modest and had little impact on the estimation of the corresponding item response function. These results suggest that marginal maximum likelihood estimates of item parameters will provide accurate results across a variety of item parameter configurations when the sample size is at or above the recommended levels." @default.
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• W314471229 title "Estimability of Parameters in the Generalized Graded Unfolding Model." @default.
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