Quality of Life Research

, Volume 16, Issue 6, pp 981–990 | Cite as

Differential item functioning impact in a modified version of the Roland–Morris Disability Questionnaire

  • Paul K. Crane
  • Karynsa Cetin
  • Karon F. Cook
  • Kurt Johnson
  • Richard Deyo
  • Dagmar Amtmann
Original Paper

Abstract

Objective

To evaluate a modified version of the Roland–Morris Disability Questionnaire for differential item functioning (DIF) related to several covariates.

Background

DIF occurs in an item when, after controlling for the underlying trait measured by the test, the probability of endorsing the item varies across groups.

Methods

Secondary data analysis of two studies of participants with back pain (total n = 875). We used a hybrid item response theory/ logistic regression approach for detecting DIF. We obtained scores that accounted for DIF. We evaluated the impact of DIF on individual and group scores, and compared scores that ignored or accounted for DIF in terms of the strength of association with SF-36 subscale scores.

Results

DIF was found in 18/23 items. Salient scale-level differential functioning was found related to age, education, and employment. Overall 24 participants (3%) had salient scale-level differential functioning. Mean scores across demographic groups differed minimally when accounting for DIF. The strength of association of scores with SF-36 scores was similar for scores that ignored and scores that accounted for DIF.

Conclusions

The modified version of the Roland–Morris Disability Questionnaire appears to have largely negligible DIF related to the covariates assessed here.

Keywords

Differential item functioning Item response theory Logistic regression Test bias 

Abbreviations

2PL

2-parameter logistic model. In this parametric item response theory model, two parameters are modeled for each item: item difficulty and item discrimination

DIF

Differential item functioning. DIF occurs when an item has different statistical properties in different groups when controlling for the underlying trait or ability measured by the test

IRT

Item response theory. This is a technique for analyzing item-level test data based on the premise that item responses are a function of the relationship between an underlying latent trait and characteristics of the item

SIP

Sickness Impact Profile. This is a patient-reported outcome measure of the impact of illnesses

SLIP

Seattle Lumbar Imaging Project, one of the two datasets of low back pain subjects analyzed in this study

Notes

Acknowledgements

Data were collected under the auspices of grants P60 AR48093 from the National Institutes of Health, National Institute for Arthritis, Musculoskeletal, and Skin Diseases, and HS-09499 from the Agency for Healthcare Research and Quality. Data were analyzed under the auspices of U01AR52171-01 from the National Institutes of Health, National Institute of Arthritis and Musculoskeletal and Skin Diseases. Data collection and analyses were reviewed by the University of Washington’s Institutional Review Board.

References

  1. 1.
    Camilli, G., & Shepard, L. A. (1994). Methods for identifying biased test items. Thousand Oaks: Sage.Google Scholar
  2. 2.
    Holland, P. W., & Wainer, H. (Eds.) (1993). Differential item functioning. Hillsdale, NJ: Erlbaum.Google Scholar
  3. 3.
    Millsap, R. E., & Everson, H. T. (1993). Methodology review: Statistical approaches for assessing measurement bias. Applied Psychological Measurement, 17, 297–334.CrossRefGoogle Scholar
  4. 4.
    Roland, M., & Morris, R. (1983). A study of the natural history of back pain. Part I: Development of a reliable and sensitive measure of disability in low-back pain. Spine, 8, 141–144.PubMedCrossRefGoogle Scholar
  5. 5.
    Bergner, M., Bobbitt, R. A., Carter, W. B., & Gilson, B. S. (1981). The sickness impact profile: Development and final revision of a health status measure. Medical Care, 19, 787–805.PubMedCrossRefGoogle Scholar
  6. 6.
    Patrick, D. L., Deyo, R. A., Atlas, S. J., Singer, D. E., Chapin, A., & Keller, R. B. (1995). Assessing health-related quality of life in patients with sciatica. Spine, 20, 1899–1908; discussion 1909.PubMedCrossRefGoogle Scholar
  7. 7.
    Kucukdeveci, A. A., Tennant, A., Elhan, A. H., & Niyazoglu, H. (2001). Validation of the Turkish version of the Roland–Morris disability questionnaire for use in low back pain. Spine, 26, 2738–2743.PubMedCrossRefGoogle Scholar
  8. 8.
    Pietrobon, R., Taylor, M., Guller, U., Higgins, L. D., Jacobs, D. O., & Carey, T. (2004). Predicting gender differences as latent variables: Summed scores, and individual item responses: A methods case study. Health and Quality of Life Outcomes, 2, 59.PubMedCrossRefGoogle Scholar
  9. 9.
    Deyo, R. A., Mirza, S. K., Heagerty, P. J., Turner, J. A., & Martin, B. I. (2005). A prospective cohort study of surgical treatment for back pain with degenerated discs; study protocol. BMC Musculoskeletal Disorder, 6, 24.CrossRefGoogle Scholar
  10. 10.
    Jarvik, J. G., Hollingworth, W., Martin, B., Emerson, S. S., Gray, D. T, Overman, S., Robinson, D., Staiger, T., Wessbecher, F., Sullivan, S. D., Kreuter, W., & Deyo, R. A. (2003). Rapid magnetic resonance imaging vs radiographs for patients with low back pain: A randomized controlled trial. JAMA, 289, 2810–2818.PubMedCrossRefGoogle Scholar
  11. 11.
    Ware, J. E. Jr. (2000). SF-36 health survey update. Spine, 25, 3130–3139.PubMedCrossRefGoogle Scholar
  12. 12.
    StataCorp (2003). Stata statistical software: Release 8.0. College Station, TX: Stata Corporation.Google Scholar
  13. 13.
    Muraki, E., & Bock, D. (2003). PARSCALE for Windows version 4.1. Chicago: SSI.Google Scholar
  14. 14.
    Crane, P. K., Hart, D. L., Gibbons, L. E., & Cook, K. F. (2006). A 37-item shoulder functional status item pool had negligible differential item functioning. Journal of Clinical Epidemiology, 59, 478–484.PubMedCrossRefGoogle Scholar
  15. 15.
    Crane, P. K., Gibbons, L. E., Jolley, L., & van Belle, G. (2006). Differential item functioning analysis with ordinal logistic regression techniques: DIFdetect and difwithpar. Medical Care, 44, S115–S123.PubMedCrossRefGoogle Scholar
  16. 16.
    Crane, P. K., Gibbons, L. E., Narasimhalu, K., Lai, J. S., & Cella, D. (2007). Rapid detection of differential item functioning in assessments of health-related quality of life: The functional assessment of cancer therapy. Quality of Life Research, 16, 101–114.PubMedCrossRefGoogle Scholar
  17. 17.
    Crane, P. K., van Belle, G., & Larson, E. B. (2004). Test bias in a cognitive test: differential item functioning in the CASI. Statistics in Medicine, 23, 241–256.PubMedCrossRefGoogle Scholar
  18. 18.
    Crane, P. K., Gibbons, L. E., Ocepek-Welikson, K., Cook, K., Cella, D., Narasimhalu, K., Hays, R., & Teresi, J. (2007). A Comparison of three sets of criteria for determining the presence of differential item functioning using ordinal logistic regression. Quality of Life Research (in press).Google Scholar
  19. 19.
    Crane, P. K. (2006). Commentary on comparing translations of the EORTC QLQ-C30 using differential item functioning analyses. Quality of Life Research, 15, 1117–1118.CrossRefGoogle Scholar
  20. 20.
    Perkins, A. J., Stump, T. E., Monahan, P. O., & McHorney, C. A. (2006). Assessment of differential item functioning for demographic comparisons in the MOS SF-36 health survey. Quality of Life Research, 15, 331–348.PubMedCrossRefGoogle Scholar
  21. 21.
    Maldonado, G., & Greenland, S. (1993). Simulation study of confounder-selection strategies. American Journal of Epidemiology, 138, 923–936.PubMedGoogle Scholar

Copyright information

© Springer Science+Business Media B.V. 2007

Authors and Affiliations

  • Paul K. Crane
    • 1
  • Karynsa Cetin
    • 2
  • Karon F. Cook
    • 2
  • Kurt Johnson
    • 2
  • Richard Deyo
    • 1
  • Dagmar Amtmann
    • 2
  1. 1.Department of MedicineUniversity of Washington, Harborview Medical CenterSeattleUSA
  2. 2.Department of Rehabilitation MedicineUniversity of WashingtonSeattleUSA

Personalised recommendations