Abstract
We introduce the special section on nonparametric item response theory (IRT) in Quality of Life Research. Starting from the well-known Rasch model, we provide a brief overview of nonparametric IRT models and discuss the assumptions, the properties, and the investigation of goodness of fit. We provide references to more detailed texts to help readers getting acquainted with nonparametric IRT models. In addition, we show how the rather diverse papers in the special section fit into the nonparametric IRT framework. Finally, we illustrate the application of nonparametric IRT models using data from a questionnaire measuring activity limitations in walking. The real-data example shows the quality of the scale and its constituent items with respect to dimensionality, local independence, monotonicity, and invariant item ordering.
Highlights
Keywords Goodness of fit · Measurement of health-related attributes · Nonparametric item response theory · Rasch model. This special section of Quality of Life Research is devoted to nonparametric item response theory (IRT) models [1]
We review nonparametric IRT models, provide references to more detailed texts, and show how the diverse set of papers in the special section fits into the nonparametric IRT framework
Unreliability causes different orderings of X+ and in this particular draw estimated difficulty, discrimination and perhaps pseudoguessing parameters, but the fact that nonparametric IRT models do not commit to specific parametric item response functions (IRFs), instead estimating the whole function for each item from the data allows a complete picture of item response behavior for each item
Summary
This special section of Quality of Life Research is devoted to nonparametric item response theory (IRT) models [1]. Unreliability causes different orderings of X+ and in this particular draw estimated difficulty, discrimination and perhaps pseudoguessing parameters, but the fact that nonparametric IRT models do not commit to specific parametric IRFs, instead estimating the whole function for each item from the data allows a complete picture of item response behavior for each item This allows the researcher to see that the item only works well for people high on the scale of, for example, physical functioning, but not for the majority (Fig. 4, solid curve), or that the IRF only has a weak and irregular relation with physical functioning and is a candidate for replacement (Fig. 4, dashed curve).
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