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Adjusting for cross-cultural differences in computer-adaptive tests of quality of life

Overview of attention for article published in Quality of Life Research, December 2017
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • Good Attention Score compared to outputs of the same age (76th percentile)
  • High Attention Score compared to outputs of the same age and source (84th percentile)

Mentioned by

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11 tweeters

Citations

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4 Dimensions

Readers on

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19 Mendeley
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Title
Adjusting for cross-cultural differences in computer-adaptive tests of quality of life
Published in
Quality of Life Research, December 2017
DOI 10.1007/s11136-017-1738-7
Pubmed ID
Authors

C. J. Gibbons, S. M. Skevington

Abstract

Previous studies using the WHOQOL measures have demonstrated that the relationship between individual items and the underlying quality of life (QoL) construct may differ between cultures. If unaccounted for, these differing relationships can lead to measurement bias which, in turn, can undermine the reliability of results. We used item response theory (IRT) to assess differential item functioning (DIF) in WHOQOL data from diverse language versions collected in UK, Zimbabwe, Russia, and India (total N = 1332). Data were fitted to the partial credit 'Rasch' model. We used four item banks previously derived from the WHOQOL-100 measure, which provided excellent measurement for physical, psychological, social, and environmental quality of life domains (40 items overall). Cross-cultural differential item functioning was assessed using analysis of variance for item residuals and post hoc Tukey tests. Simulated computer-adaptive tests (CATs) were conducted to assess the efficiency and precision of the four items banks. Splitting item parameters by DIF results in four linked item banks without DIF or other breaches of IRT model assumptions. Simulated CATs were more precise and efficient than longer paper-based alternatives. Assessing differential item functioning using item response theory can identify measurement invariance between cultures which, if uncontrolled, may undermine accurate comparisons in computer-adaptive testing assessments of QoL. We demonstrate how compensating for DIF using item anchoring allowed data from all four countries to be compared on a common metric, thus facilitating assessments which were both sensitive to cultural nuance and comparable between countries.

Twitter Demographics

The data shown below were collected from the profiles of 11 tweeters 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 19 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 19 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 3 16%
Student > Doctoral Student 3 16%
Student > Bachelor 3 16%
Unspecified 2 11%
Student > Ph. D. Student 2 11%
Other 6 32%
Readers by discipline Count As %
Psychology 4 21%
Medicine and Dentistry 4 21%
Nursing and Health Professions 3 16%
Arts and Humanities 2 11%
Engineering 2 11%
Other 4 21%

Attention Score in Context

This research output has an Altmetric Attention Score of 7. 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 April 2018.
All research outputs
#2,202,798
of 12,808,036 outputs
Outputs from Quality of Life Research
#205
of 1,880 outputs
Outputs of similar age
#89,556
of 386,387 outputs
Outputs of similar age from Quality of Life Research
#11
of 72 outputs
Altmetric has tracked 12,808,036 research outputs across all sources so far. Compared to these this one has done well and is in the 82nd percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,880 research outputs from this source. They receive a mean Attention Score of 4.0. This one has done well, scoring higher than 89% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 386,387 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 76% of its contemporaries.
We're also able to compare this research output to 72 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 84% of its contemporaries.