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Latent variable mixture models to test for differential item functioning: a population-based analysis

Overview of attention for article published in Health and Quality of Life Outcomes, May 2017
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2 tweeters
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1 Redditor

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Title
Latent variable mixture models to test for differential item functioning: a population-based analysis
Published in
Health and Quality of Life Outcomes, May 2017
DOI 10.1186/s12955-017-0674-0
Pubmed ID
Authors

Xiuyun Wu, Richard Sawatzky, Wilma Hopman, Nancy Mayo, Tolulope T. Sajobi, Juxin Liu, Jerilynn Prior, Alexandra Papaioannou, Robert G. Josse, Tanveer Towheed, K. Shawn Davison, Lisa M. Lix

Abstract

Comparisons of population health status using self-report measures such as the SF-36 rest on the assumption that the measured items have a common interpretation across sub-groups. However, self-report measures may be sensitive to differential item functioning (DIF), which occurs when sub-groups with the same underlying health status have a different probability of item response. This study tested for DIF on the SF-36 physical functioning (PF) and mental health (MH) sub-scales in population-based data using latent variable mixture models (LVMMs). Data were from the Canadian Multicentre Osteoporosis Study (CaMos), a prospective national cohort study. LVMMs were applied to the ten PF and five MH SF-36 items. A standard two-parameter graded response model with one latent class was compared to multi-class LVMMs. Multivariable logistic regression models with pseudo-class random draws characterized the latent classes on demographic and health variables. The CaMos cohort consisted of 9423 respondents. A three-class LVMM fit the PF sub-scale, with class proportions of 0.59, 0.24, and 0.17. For the MH sub-scale, a two-class model fit the data, with class proportions of 0.69 and 0.31. For PF items, the probabilities of reporting greater limitations were consistently higher in classes 2 and 3 than class 1. For MH items, respondents in class 2 reported more health problems than in class 1. Differences in item thresholds and factor loadings between one-class and multi-class models were observed for both sub-scales. Demographic and health variables were associated with class membership. This study revealed DIF in population-based SF-36 data; the results suggest that PF and MH sub-scale scores may not be comparable across sub-groups defined by demographic and health status variables, although effects were frequently small to moderate in size. Evaluation of DIF should be a routine step when analysing population-based self-report data to ensure valid comparisons amongst sub-groups.

Twitter Demographics

The data shown below were collected from the profiles of 2 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 41 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 41 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 8 20%
Student > Ph. D. Student 7 17%
Student > Master 6 15%
Student > Doctoral Student 5 12%
Professor 2 5%
Other 6 15%
Unknown 7 17%
Readers by discipline Count As %
Psychology 7 17%
Medicine and Dentistry 7 17%
Nursing and Health Professions 4 10%
Mathematics 3 7%
Social Sciences 2 5%
Other 7 17%
Unknown 11 27%

Attention Score in Context

This research output has an Altmetric Attention Score of 2. 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 17 May 2017.
All research outputs
#9,450,625
of 15,442,255 outputs
Outputs from Health and Quality of Life Outcomes
#860
of 1,660 outputs
Outputs of similar age
#145,605
of 267,955 outputs
Outputs of similar age from Health and Quality of Life Outcomes
#1
of 1 outputs
Altmetric has tracked 15,442,255 research outputs across all sources so far. This one is in the 36th percentile – i.e., 36% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,660 research outputs from this source. They receive a mean Attention Score of 4.0. This one is in the 44th percentile – i.e., 44% of its peers scored the same or lower than it.
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 267,955 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 42nd percentile – i.e., 42% of its contemporaries scored the same or lower than it.
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