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The Guttman errors as a tool for response shift detection at subgroup and item levels

Overview of attention for article published in Quality of Life Research, March 2016
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Title
The Guttman errors as a tool for response shift detection at subgroup and item levels
Published in
Quality of Life Research, March 2016
DOI 10.1007/s11136-016-1268-8
Pubmed ID
Authors

Myriam Blanchin, Véronique Sébille, Alice Guilleux, Jean-Benoit Hardouin

Abstract

Statistical methods for identifying response shift (RS) at the individual level could be of great practical value in interpreting change in PRO data. Guttman errors (GE) may help to identify discrepancies in respondent's answers to items compared to an expected response pattern and to identify subgroups of patients that are more likely to present response shift. This study explores the benefits of using a GE-based method for RS detection at the subgroup and item levels. The analysis was performed on the SatisQoL study. The number of GE was determined for each individual at each time of measurement (at baseline T0 and 6 months after discharge M6). Individuals showing discrepancies (with many GE) were suspected to interpret the items differently from the majority of the sample. Patients having a large number of GE at M6 only and not at T0 were assumed to present RS. Patients having a small number of GE at T0 and M6 were assumed to present no RS. The RespOnse Shift ALgorithm in Item response theory (ROSALI) was then applied on the whole sample and on both groups. Different types of RS (non-uniform recalibration, reprioritization) were more prevalent in the group composed of patients assumed to present RS based on GE. On the opposite, no RS was detected on patients having few GE. Guttman errors and item response theory models seem to be relevant tools to discriminate individuals affected by RS from the others at the item level.

Twitter Demographics

The data shown below were collected from the profile of 1 tweeter who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 9 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Netherlands 1 11%
Unknown 8 89%

Demographic breakdown

Readers by professional status Count As %
Professor > Associate Professor 3 33%
Student > Master 2 22%
Student > Bachelor 1 11%
Student > Ph. D. Student 1 11%
Student > Doctoral Student 1 11%
Other 1 11%
Readers by discipline Count As %
Psychology 3 33%
Unspecified 2 22%
Mathematics 1 11%
Social Sciences 1 11%
Medicine and Dentistry 1 11%
Other 1 11%

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 16 November 2017.
All research outputs
#9,858,213
of 12,342,011 outputs
Outputs from Quality of Life Research
#1,192
of 1,773 outputs
Outputs of similar age
#191,914
of 275,489 outputs
Outputs of similar age from Quality of Life Research
#76
of 122 outputs
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