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Assessing measurement invariance of three depression scales between neurologic samples and community samples

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Abstract

Purpose

Measurement invariance is necessary for meaningful group comparisons. The purpose of this study was to test measurement invariance of three patient-reported measures of depressive symptoms between neurologic and community samples.

Methods

The instruments tested included the center for epidemiologic studies depression scale (CESD-20), the patient health questionnaire-9 (PHQ-9), and the patient-reported outcome measurement information system depression short form (PROMIS-D-8). Responses from a community sample were compared to responses from samples with two neurologic conditions: multiple sclerosis and spinal cord injury. Multi-group confirmatory factor analysis was used to evaluate successive levels of measurement invariance: (a) configural invariance, i.e., equivalent item factor structure between groups; (b) metric invariance, i.e., equivalent unstandardized factor loadings between groups; and (c) scalar invariance, i.e., equivalent item intercepts between groups.

Results

Results of this study supported metric invariance for the CESD-20, PHQ-9, and PROMIS-D-8 scores between the community sample and the samples with neurologic conditions. The most rigorous form of invariance (i.e., scalar) also holds for the CESD-20 and the PROMIS-D-8.

Conclusions

The current study suggests that depressive symptoms as measured by three different outcome measures have the same meaning across clinical and community samples. Thus, the use of these measures for group comparisons is supported.

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Abbreviations

CESD:

Center for epidemiologic studies depression scale

CFA:

Confirmatory factor analysis

DIF:

Differential item functioning

MG-CFA:

Multi-group confirmatory factor analysis

MS:

Multiple sclerosis

PHQ:

Patient health questionnaire

PROMIS:

Patient-reported outcome measurement information system

SCI:

Spinal cord injury

References

  1. Cole, J. C., Rabin, A. S., Smith, T. L., & Kaufman, A. S. (2004). Development and validation of a Rasch-derived CES-D short form. Psychological Assessment, 16(4), 360–372. doi:10.1037/1040-3590.16.4.360.

    Article  PubMed  Google Scholar 

  2. Kroenke, K., Spitzer, R. L., Williams, J. B., & Löwe, B. (2010). The patient health questionnaire somatic, anxiety, and depressive symptom scales: a systematic review. General Hospital Psychiatry, 32(4), 345–359. doi:10.1016/j.genhosppsych.2010.03.006.

    Article  PubMed  Google Scholar 

  3. Radloff, L. S. (1977). The CES-D scale a self-report depression scale for research in the general population. Applied Psychological Measurement, 1(3), 385–401. doi:10.1177/014662167700100306.

    Article  Google Scholar 

  4. Cheung, G. W., & Rensvold, R. B. (2002). Evaluating goodness-of-fit indexes for testing measurement invariance. Structural Equation Modeling, 9(2), 233–255. doi:10.1207/S15328007SEM0902_5.

    Article  Google Scholar 

  5. King-Kallimanis, B. L., ter Hoeven, C. L., de Haes, H. C., Smets, E. M., Koning, C. C., & Oort, F. J. (2012). Assessing measurement invariance of a health-related quality-of-life questionnaire in radiotherapy patients. Quality of Life Research, 21(10), 1745–1753. doi:10.1007/s11136-011-0094-2.

    Article  PubMed Central  PubMed  Google Scholar 

  6. Millsap, R. E. (2012). Statistical approaches to measurement invariance. New York: Routledge.

    Google Scholar 

  7. Spitzer, R. L., Kroenke, K., Williams, J. B., & Patient Health Questionnaire Primary Care Study Group. (1999). Validation and utility of a self-report version of PRIME-MD: The PHQ primary care study. The Journal of the American Medical Association, 282(18), 1737–1744. doi:10.1001/jama.282.18.1737.

    Article  CAS  Google Scholar 

  8. Cella, D., Riley, W., Stone, A., Rothrock, N., Reeve, B., Yount, S., et al. (2010). The Patient-Reported Outcomes Measurement Information System (PROMIS) developed and tested its first wave of adult self-reported health outcome item banks: 2005–2008. Journal of Clinical Epidemiology, 63(11), 1179–1194. doi:10.1016/j.jclinepi.2010.04.011.

    Article  PubMed Central  PubMed  Google Scholar 

  9. Pilkonis, P. A., Choi, S. W., Reise, S. P., Stover, A. M., Riley, W. T., & Cella, D. (2011). Item banks for measuring emotional distress from the Patient-Reported Outcomes Measurement Information System (PROMIS®): Depression, anxiety, and anger. Assessment, 18(3), 263–283. doi:10.1177/1073191111411667.

    Article  PubMed Central  PubMed  Google Scholar 

  10. American Psychiatric Association (2000). Diagnostic and statistical manual of mental disorders: DSM-IV-TR (4th ed., text revision ed.). Washington, DC: American Psychiatric Association.

  11. Kroenke, K., Spitzer, R. L., & Williams, J. B. (2001). The Phq-9. Journal of General Internal Medicine, 16(9), 606–613. doi:10.1046/j.1525-1497.2001.016009606.x.

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  12. Narrow, W. E., Clarke, D. E., Kuramoto, S. J., Kraemer, H. C., Kupfer, D. J., Greiner, L., & Regier, D. A. (2013). DSM-5 Field Trials in the United States and Canada, part III: Development and reliability testing of a cross-cutting symptom assessment for DSM-5. American Journal of Psychiatry, 170(1), 71–82. doi:10.1176/appi.ajp.2012.12071000.

    Article  PubMed  Google Scholar 

  13. Miller, W. C., Anton, H. A., & Townson, A. F. (2007). Measurement properties of the CESD scale among individuals with spinal cord injury. Spinal cord, 46(4), 287–292. doi:10.1038/sj.sc.3102127.

    Article  PubMed  Google Scholar 

  14. Amtmann, D., Bamer, A. M., Cook, K. F., Askew, R. L., Noonan, V. K., & Brockway, J. A. (2012). University of Washington self-efficacy scale: A new self-efficacy scale for people with disabilities. Archives of Physical Medicine and Rehabilitation, 93(10), 1757–1765. doi:10.1016/j.apmr.2012.05.001.

    Article  PubMed  Google Scholar 

  15. Cook, K. F., Molton, I. R., & Jensen, M. P. (2011). Fatigue and aging with a disability. Archives of Physical Medicine and Rehabilitation, 92(7), 1126–1133. doi:10.1016/j.apmr.2011.02.017.

    Article  PubMed  Google Scholar 

  16. Krause, J. S., Saunders, L. L., Bombardier, C., & Kalpakjian, C. (2011). Confirmatory factor analysis of the Patient Health Questionnaire-9: A study of the participants from the spinal cord injury model systems. PM&R, 3(6), 533–540. doi:10.1016/j.pmrj.2011.03.003.

    Article  Google Scholar 

  17. Muthén, L. K., & Muthén, B. O. (1998–2013). Mplus User’s Guide (7th ed.). Los Angeles, CA: Muthén & Muthén.

  18. Bentler, P. M. (1980). Multivariate analysis with latent variables: Causal modeling. Annual Review of Psychology, 31(1), 419–456. doi:10.1146/annurev.ps.31.020180.002223.

    Article  Google Scholar 

  19. Tucker, L. R., & Lewis, C. (1973). A reliability coefficient for maximum likelihood factor analysis. Psychometrika, 38(1), 1–10. doi:10.1007/BF02291170.

    Article  Google Scholar 

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

  21. Kline, R. B. (2005). Principles and practice of structural equation modeling. New York: Guilford press.

    Google Scholar 

  22. Browne, M., & Cudeck, R. (1993). Alternative ways of assessing model fit. London: Sage.

    Google Scholar 

  23. Muthén, B., & Asparouhov, T. (2012). Bayesian structural equation modeling: A more flexible representation of substantive theory. Psychological Methods, 17(3), 313–335. doi:10.1037/a0026802.

    Article  PubMed  Google Scholar 

  24. Fong, T. C., & Ho, R. T. (2014). Testing gender invariance of the Hospital Anxiety and Depression Scale using the classical approach and Bayesian approach. Quality of Life Research, 23(5), 1421–1426. doi:10.1007/s11136-013-0594-3.

    Article  PubMed  Google Scholar 

  25. Cook, K. F., Bombardier, C. H., Bamer, A. M., Choi, S. W., Kroenke, K., & Fann, J. R. (2011). Do somatic and cognitive symptoms of traumatic brain injury confound depression screening? Archives of Physical Medicine and Rehabilitation, 92(5), 818–823. doi:10.1016/j.apmr.2010.12.008.

    Article  PubMed Central  PubMed  Google Scholar 

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Acknowledgments

Research reported in this paper was supported by the Agency for Healthcare Research and Quality (AHRQ) under award number R03HS020700. The content is solely the responsibility of the authors and does not necessarily represent the official views of the AHRQ.

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Correspondence to Hyewon Chung.

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Chung, H., Kim, J., Askew, R.L. et al. Assessing measurement invariance of three depression scales between neurologic samples and community samples. Qual Life Res 24, 1829–1834 (2015). https://doi.org/10.1007/s11136-015-0927-5

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  • DOI: https://doi.org/10.1007/s11136-015-0927-5

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