Skip to main content

Advertisement

Log in

Measurement invariance of the PROMIS pain interference item bank across community and clinical samples

  • Published:
Quality of Life Research Aims and scope Submit manuscript

Abstract

Purpose

This study examined the measurement invariance of responses to the patient-reported outcomes measurement information system (PROMIS) pain interference (PI) item bank. The original PROMIS calibration sample (Wave I) was augmented with a sample of persons recruited from the American Chronic Pain Association (ACPA) to increase the number of participants reporting higher levels of pain. Establishing measurement invariance of an item bank is essential for the valid interpretation of group differences in the latent concept being measured.

Methods

Multi-group confirmatory factor analysis (MG-CFA) was used to evaluate successive levels of measurement invariance: configural, metric, and scalar invariance.

Results

Support was found for configural and metric invariance of the PROMIS-PI, but not for scalar invariance.

Conclusions and recommendations

Based on our results of MG-CFA, we recommend retaining the original parameter estimates obtained by combining the community sample of Wave I and ACPA participants. Future studies should extend this study by examining measurement equivalence in an item response theory framework such as differential item functioning analysis.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1

Similar content being viewed by others

Abbreviations

ACPA:

American Chronic Pain Association

CFA:

Confirmatory factor analysis

IRT:

Item response theory

MG-CFA:

Multi-group confirmatory factor analysis

PI:

Pain interference

PROMIS:

Patient-Reported Outcomes Measurement Information System

References

  1. Dworkin, R. H., Turk, D. C., Farrar, J. T., Haythornthwaite, J. A., Jensen, M. P., & Katz, N. P. (2005). Core outcome measures for chronic pain clinical trials: IMMPACT recommendations. Pain, 113(1–2), 9–19.

    Article  PubMed  Google Scholar 

  2. Amtmann, D., Cook, K., Jensen, M. P., Chen, W.-H., Choi, S., Revicki, D., et al. (2010). Development of a PROMIS item bank to measure pain interference. Pain, 150(1), 173–182.

    Article  PubMed  Google Scholar 

  3. Riley, W., Rothrock, N., Bruce, B., Christodolou, C., Cook, K., & Hahn, E. A. (2010). Patient-reported outcomes measurement information system (PROMIS) domain names and definitions revisions: further assessment of content validity in IRT-derived item banks. Quality of Life Research, 19(9), 1311–1321.

    Article  PubMed  Google Scholar 

  4. Choi, S. W., Cook, K. F., & Dodd, B. G. (1997). Parameter recovery for the partial credit model using MULTILOG. Journal of Outcome Measurement, 1(2), 114–142.

    PubMed  CAS  Google Scholar 

  5. Samejima, F. (1969). Estimation of latent ability using a response pattern of graded scores. Psychometrika Monograph Supplement, 34 (4, Pt. 2, No 17).

  6. Cella, D., Riley, W., Stone, A., Rothrock, N., Reeve, B., Yount, S., et al. (2010). Initial item banks and first wave testing of the patient–reported outcomes measurement information system (PROMIS) network: 2005–2008. Journal of Clinical Epidemiology, 63(11), 1179–1194.

    Article  PubMed  Google Scholar 

  7. Liu, H. H., Cella, D., Gershon, R., Shen, J., Morales, L. S., Riley, W., et al. (2010). Representativeness of the PROMIS Internet panel. Journal of Clinical Epidemiology, 63(11), 1169–1178.

    Article  PubMed  Google Scholar 

  8. Rothrock, N. E., Hays, R. D., Spritzer, K., Yount, S. E., Riley, W., & Cella, D. (2010). Relative to the general US population, chronic diseases are associated with poorer health–related quality of life as measured by the patient–reported outcomes measurement information system (PROMIS). Journal of Clinical Epidemiology, 63(11), 1195–1204.

    Article  PubMed  Google Scholar 

  9. Meredith, W. (1993). Measurement invariance, factor analysis, and factorial invariance. Psychometrika, 58(4), 525–543.

    Article  Google Scholar 

  10. Horn, J. L., & McArdle, J. J. (1992). A practical and theoretical guide to measurement invariance in aging research. Experimental Aging Research, 18(3), 117–144.

    Article  PubMed  CAS  Google Scholar 

  11. Steenkamp, E. M. J., & Baumgartner, H. (1998). Assessing measurement invariance in cross-national consumer research. Journal of Consumer Research, 25(1), 78–90.

    Article  Google Scholar 

  12. Muthén, L. K., & Muthén, B. O. (1998–2010). Mplus user’s guide. 6th ed. Los Angeles, CA: Muthén & Muthén.

  13. Bentler, P. M. (1980). Multivariate analysis with latent variables: Causal modeling. Annual Review of Psychology, 31(1), 419–456.

    Article  Google Scholar 

  14. Tucker, L. R., & Lewis, C. (1973). A reliability coefficient for maximum likelihood factor analysis. Psychometrika, 38, 1–10.

    Article  Google Scholar 

  15. Byrne, B. M. (1998). Structural equation modeling with LISREL, PRELIS, and SIMPLIS. Hillsdale, NJ: Lawrence Erlbaum.

    Google Scholar 

  16. Steiger, J. H., & Lind, J. C. (1980). Statistically-based tests for the number of common factors. In Paper presented at the annual spring meeting of the Psychometric Society, Iowa City, IA.

  17. Hu, L., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling: A Multidisciplinary Journal, 6(1), 1–55.

    Article  Google Scholar 

  18. Browne, M., & Cudeck, R. (1993). Alternative ways of assessing model fit. In K. Bollen & J. Long (Eds.), Testing structural equation models (pp. 136–162). London, England: Sage.

    Google Scholar 

  19. Marsh, H. W., Hau, K., & Wen, Z. (2004). In search of golden rules: Comment on hypothesis-testing approaches to setting cutoff values for fit indexes and dangers in overgeneralizing Hu and Bentler’s (1999) findings. Structural Equation Modeling: A Multidisciplinary Journal, 11(3), 320–341.

    Article  Google Scholar 

  20. Sivo, S. A., Fan, X., Witta, E. L., & Willse, J. T. (2006). The search for “optimal” cutoff properties: Fit index criteria in structural equation modeling. The Journal of Experimental Education, 74(3), 267–288.

    Article  Google Scholar 

  21. Cheung, G. W., & Rensvold, R. B. (2002). Evaluating goodness-of-fit indices for testing measurement invariance. Structural Equation Modeling: A Multidisciplinary Journal, 9(2), 233–255.

    Article  Google Scholar 

  22. French, B. F., & Finch, W. H. (2006). Confirmatory factor analytic procedures for the determination of measurement invariance. Structural Equation Modeling: A Multidisciplinary Journal, 13(3), 378–402.

    Article  Google Scholar 

  23. Mora, P. A., Contrada, R. J., Berkowitz, A., Musumeci-Szabo, T., Wisnivesky, J., & Halm, E. A. (2009). Measurement invariance of the mini asthma quality of life questionnaire across African–American and Latino adult asthma patients. Quality of Life Research, 18(3), 371–380.

    Article  PubMed  Google Scholar 

  24. Hill, C.D., Edwards, M.C., Thissen, D., Langer, M.M., Wirth, R.J., Burwinkle, T. M., et al. (2007). Practical issues in the application of item response theory: A demonstration using items from the Pediatric Quality of Life Inventory™ (PedsQL™) 4.0 Generic Core Scales. Medical Care, 45(5 Suppl 1), 39–47.

    Google Scholar 

  25. Reise, S. P., Widaman, K. F., & Pugh, R. H. (1993). Confirmatory factor analysis and item response theory: Two approaches for exploring measurement. Psychological Bulletin, 114(3), 552–567.

    Article  PubMed  CAS  Google Scholar 

  26. Chen, F., Sousa, K. H., & West, S. G. (2005). Teacher’s corner: Testing measurement invariance of second-order factor models. Structural Equation Modeling: A Multidisciplinary Journal, 12(3), 471–492.

    Article  Google Scholar 

  27. Yen, W. M. (1993). Scaling performance assessments: Strategies for managing local item dependence. Journal of Educational Measurement, 30, 187–213.

    Article  Google Scholar 

  28. Steinberg, L., & Thissen, D. (1996). Uses of item response theory and the testlet concept in the measurement of psychopathology. Psychological Methods, 1, 81–97.

    Article  Google Scholar 

  29. Stark, S., Chernshenko, O. S., & Drasgow, F. (2006). Detecting differential item functioning with confirmatory factor analysis and item response theory: Toward a unified strategy. Journal of Applied Psychology, 91(6), 1292–1306.

    Article  PubMed  Google Scholar 

Download references

Acknowledgments

The project described was supported by Award Number 3U01AR052177-06S1 from the National Institute of Arthritis and Musculoskeletal and Skin Diseases. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institute of Arthritis and Musculoskeletal and Skin Diseases or the National Institutes of Health.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Hyewon Chung.

Appendix

Appendix

See Table 4.

Table 4 47 PROMIS pain interference items administered to the ACPA sample

Rights and permissions

Reprints and permissions

About this article

Cite this article

Kim, J., Chung, H., Amtmann, D. et al. Measurement invariance of the PROMIS pain interference item bank across community and clinical samples. Qual Life Res 22, 501–507 (2013). https://doi.org/10.1007/s11136-012-0191-x

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11136-012-0191-x

Keywords

Navigation