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2014 | OriginalPaper | Chapter

Estimating a Rasch Model via Fuzzy Empirical Probability Functions

Authors : Lucio Bertoli-Barsotti, Tommaso Lando, Antonio Punzo

Published in: Analysis and Modeling of Complex Data in Behavioral and Social Sciences

Publisher: Springer International Publishing

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Abstract

The joint maximum likelihood estimation of the parameters of the Rasch model is hampered by several drawbacks, the most relevant of which are that: (1) the estimates are not available for item or person with perfect scores; (2) the item parameter estimates are severely biased, especially for short tests. To overcome both these problems, in this paper a new method is proposed, based on a fuzzy extension of the empirical probability function and the minimum Kullback–Leibler divergence estimation approach. The new method warrants the existence of finite estimates for both person and item parameters and results very effective in reducing the bias of joint maximum likelihood estimates.

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previous chapter Clustering of Stratified Aggregated Data Using the Aggregate Association Index: Analysis of New Zealand Voter Turnout (1893–1919)
next chapter Scale Reliability Evaluation for A-Priori Clustered Data
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Metadata
Title
Estimating a Rasch Model via Fuzzy Empirical Probability Functions
Authors
Lucio Bertoli-Barsotti
Tommaso Lando
Antonio Punzo
Copyright Year
2014
Book
Analysis and Modeling of Complex Data in Behavioral and Social Sciences

Print ISBN: 978-3-319-06691-2

Electronic ISBN: 978-3-319-06692-9

Copyright Year: 2014

DOI
https://doi.org/10.1007/978-3-319-06692-9_4

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