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Rasch analysis for binary data with nonignorable nonresponses

Authors: BERTOLI BARSOTTI L; PUNZO, ANTONIO;

Rasch analysis for binary data with nonignorable nonresponses

Abstract

This paper introduces a two-dimensional Item Response Theory (IRT) model to deal with nonignorable nonresponses in tests with dichotomous items. One dimension provides information about the omitting behavior, while the other dimension is related to the person’s “ability”. The idea of embedding an IRT model for missingness into the measurement model is not new but, differently from the existing literature, the model presented in this paper belongs to the Rasch family of models. As a member of the exponential family, the model offers several advantages, such as existence of non trivial sufficient statistics and possibility of specific objective parameter estimation; feasibility of conditional inference; goodness of fit analysis via conditional likelihood ratio tests. Maximum likelihood estimation is discussed, and the applicability of the proposed model is illustrated by using a real data set.

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Italy
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Keywords

Rasch model; missing data; item nonresponse; multidimensional item response model; conditional maximum likelihood estimate;, Rasch model, missing data, item nonresponse, multidimensional item response model, conditional maximum likelihood estimate

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
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