Energy, fatigue, or both? A bifactor modeling approach to the conceptualization and measurement of vitality
- 470 Downloads
Vitality is an important domain reflecting both the physical and emotional components of health-related quality of life. Because of its complexity, it has been defined and measured both broadly and narrowly. We explored the dimensionality of a very comprehensive item bank hypothesized to measure vitality and its related concepts.
Secondary analyses were conducted using the responses of 1,343 adults representative of the US general population to Internet-based surveys including 42 items compiled from multiple scales (e.g., SF-36 Vitality, PROMIS-Fatigue), covering a broad range of vitality-related content areas (energy, fatigue, and their interference with physical, mental, social activities, and quality of life). Exploratory and confirmatory factor models were evaluated independently using split-half samples. Bifactor model was used to assess the essential unidimensionality of the items, in comparison with traditional unidimensional, multidimensional, and hierarchical models. Method effects of a common scale or phrase were modeled via correlating errors.
The exploratory factor analysis identified one dominant factor. The confirmatory factor analysis identified a best-fitting (CFI = 0.964, RMSEA = 0.084) bifactor model with one general (vitality) and two group (energy and fatigue) factors, explaining 69, 3, and 4 % of total variance. Correlating errors accounting for the method effects were important in identifying the substantive dimensionality of the items.
The bifactor model proved to be useful for evaluating the dimensionality of a complex construct. Results supported conceptualizing and measuring vitality as a unidimensional energy-fatigue construct. We encourage future studies comparing practical implications of measures based on the broader and narrower conceptualizations of vitality.
KeywordsBifactor model Dimensionality Factor analyses Vitality Fatigue Quality of life
- 1.Oxford desk dictionary and thesaurus, American edition, New York: Oxford University Press, 1997.Google Scholar
- 4.Brook, R. H., Ware, J. E., Davies-Avery, A., et al. (1979). Overview of adult health status measures fielded in RAND’s Health Insurance Study. Medical Care, 17(7 Suppl), 1–131.Google Scholar
- 7.Cella, D., Riley, W., Stone, A., et al. (2010). PROMIS Cooperative Group. 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, 3(11), 1179–1194.CrossRefGoogle Scholar
- 8.Junghaenel, D. U., Christodoulou, C., Lai, J., & Stone, A. A. (2011). Demographic correlates of fatigue in the US general population: Results from the patient-reported outcomes measurement information system (PROMIS) initiative. Journal of Psychosomatic Research, 71, 117–123.PubMedCentralPubMedCrossRefGoogle Scholar
- 9.Lai, J. S., Cella, D., Choi, S., Junghaenel, D. U., Christodoulou, C., Gershon, R., et al. (2011). How item banks and their application can influence measurement practice in rehabilitation medicine: A PROMIS fatigue item bank example. Archives of Physical Medicine and Rehabilitation, 92(10 Suppl), S20–S27.PubMedCentralPubMedCrossRefGoogle Scholar
- 13.Dupuy, H. J. (1984). The psychological general well-being (PGWB) Index. In N. K. Wenger, M. E. Mattson, C. D. Furberg, & J. Elinson (Eds.), Assessment of quality of life in clinical trials of cardiovascular therapies. New York: Le Jacq.Google Scholar
- 14.Ware, J.E., Brook, R.H., Ross, D.A., Williams, K.N., Stewart, A.L., Rogers, W.H., et al. (1980). Conceptualization and measurement of health for adults in the Health Insurance Study: Vol. I: Model of health and methodology. Doc. no. R-1987/1-HEW. Santa Monica, CA: RAND Corporation.Google Scholar
- 15.Stewart, A. L., & Ware, J. E. (Eds.). (1992). Measuring functioning and well-Being: the medical outcomes study approach. Durham: Duke University Press.Google Scholar
- 16.McNair, D., Lorr, M., & Dropplemen, L. (1971). Edits manual: Profile of mood states. San Diego: Educational and Industrial Testing Services.Google Scholar
- 18.Dupuy, H.J. (1972). The psychological section of the current Health and nutrition Examination Survey (HANES). Proceedings of the public health conference on records and statistics meeting jointly with the national conference on mental health statistics. US Dept. of Healthy, Education and Welfare publication no. (HRAS) 74-12-14. Washington DC: US Govt. Printing Office.Google Scholar
- 22.Chen, F. F., Jing, Y., Hayes, A., & Lee, J. M. (2012). Two concepts or two approaches? A bifactor analysis of psychological and subjective well-being. Journal of Happiness Studies, 1, 1–36.Google Scholar
- 25.Ware, J. E, Jr, Kosinski, M., Dewey, J. E., & Gandek, B. (2001). How to score and interpret single-item health status measures: A manual for Users of the SF-8 health survey (with a Supplement on the SF-6 health survey). Lincoln, RI: QualityMetric Incorporated.Google Scholar
- 31.Kristensena, T.S., Borritza, M., Villadsena, E., Christensena, K.B. (2005). The Copenhagen Burnout Inventory: A new tool for the assessment of burnout. Work & Stress: An International Journal of Work, Health & Organisations, 19(3),192–207.Google Scholar
- 33.Ware, J. E., Harrington, M., Guyer, R., & Boulanger, R. (2012). A system for integrating generic and disease-specific patient-reported outcome (PRO) measures. Patient Reported Outcomes Newsletter, 48, 2–4.Google Scholar
- 34.Ware, J. E., & Guyer, R. (2014). Measuring physical and emotional health outcomes: How to use the general quality of life (QGEN ® ) measures in the QOLIX ® system. Worcester, MA: JWRG Incorporated.Google Scholar
- 37.Muthén, L.K., Muthén, B.O. (1998–2004). Mplus user’s guide (3rd ed.). Los Angeles, CA: Muthén & Muthén.Google Scholar
- 51.Reeve, B. B., Hays, R. D., Bjorner, J. B., Cook, K. F., Crane, P. K., Teresi, J. A., et al. (2007). PROMIS Cooperative Group. Psychometric evaluation and calibration of health-related quality of life item banks: Plans for the patient-reported outcomes measurement information system (PROMIS). Medical Care, 45((5 Suppl 1)), s22–s31.PubMedCrossRefGoogle Scholar
- 52.Rose, M., Bjorner, J. B., Becker, J., Fries, J. F., & Ware, J. E. (2008). Evaluation of a preliminary physical function item bank supported the expected advantages of the patient-reported outcomes measurement information system (PROMIS). Journal of Clinical Epidemiology, 61(1), 17–33.PubMedCrossRefGoogle Scholar
- 53.McLeod, L. D., Swygert, K. A., & Thissen, D. (2001). Factor analysis for items scored in two categories. In D. Thissen & H. Wainer (Eds.), Test Scoring (pp. 189–216). Hillsdale, NJ: Lawrence Erlbaum Associates.Google Scholar