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Partial credit model and pivot anchoring.

Authors: R K, Bode;

Partial credit model and pivot anchoring.

Abstract

This article contains information on the Rasch measurement partial credit model: what it is, how it differs from other Rasch models, when to use it, and how to use it. The calibration of instruments with increasingly complex items is described, starting with dichotomous items and moving on to polychotomous items using a single rating scale, and mixed polychotomous items using multiple rating scales, and instruments in which each item has its own rating scale. It also introduces a procedure for aligning rating scale categories to be used when more than one rating scale is used in a single instrument. Pivot anchoring is defined and an illustration of its use with the mental health scale of the SF-36 that contains positive and negative worded items is provided. It finally describes the effect of pivot anchoring on step calibrations, the item hierarchy, and person measures.

Related Organizations
Keywords

Psychiatric Status Rating Scales, Models, Statistical, Mental Disorders, Calibration, Decision Making, Humans

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Powered by OpenAIRE graph
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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!
7
Average
Top 10%
Average
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