Assessing measurement invariance of three depression scales between neurologic samples and community samples
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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.
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 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.
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.
KeywordsMeasurement invariance Multi-group confirmatory factor analysis Depression
Center for epidemiologic studies depression scale
Confirmatory factor analysis
Differential item functioning
Multi-group confirmatory factor analysis
Patient health questionnaire
Patient-reported outcome measurement information system
Spinal cord injury
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.
- 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.PubMedCentralPubMedCrossRefGoogle 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.CrossRefGoogle 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.PubMedCentralPubMedCrossRefGoogle 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.PubMedCentralPubMedCrossRefGoogle 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.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.PubMedCrossRefGoogle 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.PubMedCrossRefGoogle Scholar
- 17.Muthén, L. K., & Muthén, B. O. (1998–2013). Mplus User’s Guide (7th ed.). Los Angeles, CA: Muthén & Muthén.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.Google Scholar
- 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
- 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.PubMedCentralPubMedCrossRefGoogle Scholar