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Recent advances in analysis of differential item functioning in health research using the Rasch model

Overview of attention for article published in Health and Quality of Life Outcomes, September 2017
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  • Good Attention Score compared to outputs of the same age (65th percentile)

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5 tweeters
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1 Facebook page
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1 Redditor

Citations

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63 Mendeley
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Title
Recent advances in analysis of differential item functioning in health research using the Rasch model
Published in
Health and Quality of Life Outcomes, September 2017
DOI 10.1186/s12955-017-0755-0
Pubmed ID
Authors

Curt Hagquist, David Andrich

Abstract

Rasch analysis with a focus on Differential Item Functioning (DIF) is increasingly used for examination of psychometric properties of health outcome measures. To take account of DIF in order to retain precision of measurement, split of DIF-items into separate sample specific items has become a frequently used technique. The purpose of the paper is to present and summarise recent advances of analysis of DIF in a unified methodology. In particular, the paper focuses on the use of analysis of variance (ANOVA) as a method to simultaneously detect uniform and non-uniform DIF, the need to distinguish between real and artificial DIF and the trade-off between reliability and validity. An illustrative example from health research is used to demonstrate how DIF, in this case between genders, can be identified, quantified and under specific circumstances accounted for using the Rasch model. Rasch analyses of DIF were conducted of a composite measure of psychosomatic problems using Swedish data from the Health Behaviour in School-aged Children study for grade 9 students collected during the 1985-2014 time periods. The procedures demonstrate how DIF can be identified efficiently by ANOVA of residuals, and how the magnitude of DIF can be quantified and potentially accounted for by resolving items according to identifiable groups and using principles of test equating on the resolved items. The results of the analysis also show that the real DIF in some items does affect person measurement estimates. Firstly, in order to distinguish between real and artificial DIF, the items showing DIF initially should not be resolved simultaneously but sequentially. Secondly, while resolving instead of deleting a DIF item may retain reliability, both options may affect the content validity negatively. Resolving items with DIF is not justified if the source of the DIF is relevant for the content of the variable; then resolving DIF may deteriorate the validity of the instrument. Generally, decisions on resolving items to deal with DIF should also rely on external information.

Twitter Demographics

The data shown below were collected from the profiles of 5 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 63 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 63 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 13 21%
Student > Master 8 13%
Other 7 11%
Student > Doctoral Student 5 8%
Student > Bachelor 4 6%
Other 15 24%
Unknown 11 17%
Readers by discipline Count As %
Psychology 10 16%
Social Sciences 10 16%
Medicine and Dentistry 7 11%
Nursing and Health Professions 4 6%
Mathematics 3 5%
Other 16 25%
Unknown 13 21%

Attention Score in Context

This research output has an Altmetric Attention Score of 4. 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 20 January 2020.
All research outputs
#5,068,852
of 16,641,846 outputs
Outputs from Health and Quality of Life Outcomes
#624
of 1,827 outputs
Outputs of similar age
#96,170
of 279,868 outputs
Outputs of similar age from Health and Quality of Life Outcomes
#1
of 1 outputs
Altmetric has tracked 16,641,846 research outputs across all sources so far. This one has received more attention than most of these and is in the 69th percentile.
So far Altmetric has tracked 1,827 research outputs from this source. They receive a mean Attention Score of 4.4. This one has gotten more attention than average, scoring higher than 65% 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 279,868 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 65% of its contemporaries.
We're also able to compare this research output to 1 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them