Bayesian Item Response Modeling

Fox, Jean-Paul

doi:10.1007/978-1-4419-0742-4

Cited by 407 publications

(210 citation statements)

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“…Another possible extension of the MAPR model would be to combine it with Monte Carlo simulation and then to use this technology in estimating response models within a full Bayesian framework [73,74]. In principle, the MAPR model may be suitable to measure other unidimensional, subjective phenomena such as well-being, capabilities, and happiness; under certain conditions it might be used in social value judgments (e.g., reimbursement decisions).…”

Section: Discussionmentioning

confidence: 99%

A generalized measurement model to quantify health: The multiattribute preference response model

Krabbe¹

2015

Industrial Innovation

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After 40 years of deriving metric values for health status or health-related quality of life, the effective quantification of subjective health outcomes is still a challenge. Here, two of the best measurement tools, the discrete choice and the Rasch model, are combined to create a new model for deriving health values. First, existing techniques to value health states are briefly discussed followed by a reflection on the recent revival of interest in patients' experience with regard to their possible role in health measurement. Subsequently, three basic principles for valid health measurement are reviewed, namely unidimensionality, interval level, and invariance. In the main section, the basic operation of measurement is then discussed in the framework of probabilistic discrete choice analysis (random utility model) and the psychometric Rasch model. It is then shown how combining the main features of these two models yields an integrated measurement model, called the multi-attribute preference response (MAPR) model, which is introduced here. This new model transforms subjective individual rank data into a metric scale using responses from patients who have experienced certain health states. Its measurement mechanism largely prevents biases such as adaptation and coping. Several extensions of the MAPR model are presented. The MAPR model can be applied to a wide range of research problems. If extended with the selfselection of relevant health domains for the individual patient, this model will be more valid than existing valuation techniques.

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Section: Discussionmentioning

confidence: 99%

A generalized measurement model to quantify health: The multiattribute preference response model

Krabbe¹

2015

Industrial Innovation

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“…In order to evaluate the parameter recovery by using different prior distributions for item parameters, a vague prior distribution for the item parameters, represented by the product of an indicator function ensuring positive discrimination parameters, i.e. ALBERT, 1992;FOX;BAKER;KIM, 2004), was compared to the empirical prior distributions elicited using regression trees.…”

Section: Example 1 Item Parameter Estimationmentioning

confidence: 99%

Bayesian computerized adaptive testing

Veldkamp

Matteucci

2013

Ensaio: aval.pol.públ.Educ.

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Computerized adaptive testing (CAT) comes with many advantages. Unfortunately, it still is quite expensive to develop and maintain an operational CAT. In this paper, various steps involved in developing an operational CAT are described and literature on these topics is reviewed. Bayesian CAT is introduced as an alternative, and the use of empirical priors is proposed for estimating item and person parameters to reduce the costs of CAT. Methods to elicit empirical priors are presented and a two small examples are presented that illustrate the advantages of Bayesian CAT. Implications of the use of empirical priors are discussed, limitations are mentioned and some suggestions for further research are formulated.

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“…A Gaussian link function is used here but other link functions, such as the logit [4], can be employed. For each item there exists a vector of threshold parameters γ j = (γ j,0 , γ j,1 , .…”

Section: Item Response Models For Ordinal Datamentioning

confidence: 99%

“…Item response modelling [4] is an established method for analysing ordinal response data. It is assumed that the observed ordinal response to an item will be level k, say, if the underlying latent variable lies within a specified interval.…”

Section: Introductionmentioning

confidence: 99%