Nonparametric Estimation of Item and Respondent Locations from Unfolding-type Items |
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Authors: | Matthew S. Johnson |
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Affiliation: | (1) Baruch College, City University of New York, USA;(2) Department of Statistics & CIS, Baruch College, City University of New York, New York, NY 10010, USA |
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Abstract: | Unlike their monotone counterparts, nonparametric unfolding response models, which assume the item response function is unimodal, have seen little attention in the psychometric literature. This paper studies the nonparametric behavior of unfolding models by building on the work of Post (1992). The paper provides rigorous justification for a class of nonparametric estimators of respondents’ latent attitudes by proving that the estimators consistently rank order the respondents. The paper also suggests an algorithm for the rank ordering of items along the attitudes scale. Finally, the methods are evaluated using simulated data. This research was supported in part by an Educational Testing Service Gulliksen Fellowship, and by the National Science Foundation, Grant DMS-97.05032. The author would like to thank Brian Junker for his help and support on this paper and Paul Holland, Steve Fienberg, and Jay Kadane for their helpful comments. |
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Keywords: | unfolding response models ideal point models Thurstone scaling attitude scaling nonparametric item response theory |
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