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Quality of Life Research
Erschienen in: Quality of Life Research 1/2015

01.01.2015 | Quantitative Methods Special Section

Energy, fatigue, or both? A bifactor modeling approach to the conceptualization and measurement of vitality

verfasst von: Nina Deng, Rick Guyer, John E. Ware Jr.

Erschienen in: Quality of Life Research | Ausgabe 1/2015

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Abstract

Purpose

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.

Methods

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.

Results

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.

Conclusions

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.
Vorheriger Artikel The St George’s Respiratory Questionnaire revisited: a psychometric evaluation
Nächster Artikel Quantifying ‘problematic’ DIF within an IRT framework: application to a cancer stigma index

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Literatur
1.
Oxford desk dictionary and thesaurus, American edition, New York: Oxford University Press, 1997.
2.
Hunt, S. M., & McEwen, J. (1989). The development of a subjective health indicator. Sociology of Health & Illness, 2, 231–246.CrossRef
3.
Kaplan, R. M., Bush, J. W., & Berry, C. C. (1976). Health status: types of validity and the index of well-being. Health Services Research, 11(4), 478–507.PubMedCentralPubMed
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.
5.
Parkerson, G. R, Jr, Broadhead, W. E., & Tse, C. K. (1990). The Duke Health Profile. A 17-item measure of health and dysfunction. Medical Care, 28(11), 1056–1072.PubMedCrossRef
6.
Ware, J. E., & Sherbourne, C. D. (1992). The MOS 36-item short-form health survey (SF-36): I. Conceptual framework and item selection. Medical Care, 30, 473–483.PubMedCrossRef
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.CrossRef
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.PubMedCentralPubMedCrossRef
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.PubMedCentralPubMedCrossRef
10.
Ricci, J. A., Chee, E., Lorandeau, A. L., & Berger, J. (2007). Fatigue in the U.S. workforce: Prevalence and implications for lost productive work time. Journal of Occupational and Environmental Medicine, 49, 1–10.PubMedCrossRef
11.
Croog, S. H., Levine, S., Testa, M. A., Brown, B., Bulpitt, C. J., Jenkins, C. D., et al. (1986). The effects of antihypertensive therapy on the quality of life. New England Journal of Medicine, 314(26), 1657–1664.PubMedCrossRef
12.
Levine, S., Croog, S. H., Sudilovsky, A., & Testa, M. A. (1987). Effects of antihypertensive medications on vitality and well-being. Journal of Family Practice, 25(4), 357–363.PubMed
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.
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.
15.
Stewart, A. L., & Ware, J. E. (Eds.). (1992). Measuring functioning and well-Being: the medical outcomes study approach. Durham: Duke University Press.
16.
McNair, D., Lorr, M., & Dropplemen, L. (1971). Edits manual: Profile of mood states. San Diego: Educational and Industrial Testing Services.
17.
Webster, K., Cella, D., & Yost, K. (2003). The functional assessment of chronic illness therapy (FACIT) measurement system: Properties, applications and interpretation. Health and Quality of Life Outcomes, 1, 1–7.CrossRef
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.
19.
Holzinger, K. J., & Swineford, F. (1937). The bi-factor method. Psychometrika, 2, 41–54.CrossRef
20.
Reise, S. P., Morizot, J., & Hays, R. D. (2007). The role of the bifactor model in resolving dimensionality issues in health outcomes measures. Quality of Life Research, 16(Supplement 1), 19–31.PubMedCrossRef
21.
Cella, D., Lai, J. S., & Stone, A. (2011). Self-reported fatigue: One dimension or more? Lessons from the functional assessment of chronic illness therapy-fatigue (FACIT-F) questionnaire. Supportive Care in Cancer, 19, 1441–1450.PubMedCrossRef
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.
23.
Carle, A. C., & Weech-Maldonado, R. (2012). Validly interpreting patients’ reports: Using bifactor and multidimensional models to determine whether surveys and scales measure one or more constructs. Medical Care, 50(9 Suppl 2), S42–S48.PubMedCrossRef
24.
Lai, J., Crane, P. K., & Cella, D. (2006). Factor analysis techniques for assessing sufficient unidimensionality of cancer related fatigue. Quality of Life Research, 15, 1179–1190.PubMedCrossRef
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.
26.
Stein, K. D., Martin, S. C., Hann, D. M., & Jacobsen, P. B. (1998). A multidimensional measure of fatigue for use with cancer patients. Cancer Practice, 6, 143–152.PubMedCrossRef
27.
Smets, E. M., Garssen, B., Bonke, B., & De Haes, J. C. (1995). The multidimensional fatigue inventory (MFI) psychometric qualities of an instrument to assess fatigue. Journal of Psychosomatic Research, 39, 315–325.PubMedCrossRef
28.
Neuberger, G. B. (2003). Measures of fatigue: The fatigue questionnaire, fatigue severity scale, multidimensional assessment of fatigue scale, and short form-36 vitality (energy/fatigue) subscale of the short form health survey. Arthritis Care & Research, 49, S175–S183.CrossRef
29.
Yellen, S. B., Cella, D. F., Webster, K., Blendowski, C., & Kaplan, E. (1997). Measuring fatigue and other anemia-related symptoms with the Functional Assessment of Cancer Therapy (FACT) measurement system. Journal of Pain and Symptom Management, 13(2), 63–74.PubMedCrossRef
30.
Schwartz, A. L. (1998). The Schwartz Cancer fatigue scale: Testing reliability and validity. Oncology Nursing Forum, 25(4), 711–717.PubMed
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.
32.
Appels, A., Höppener, P., & Mulder, P. (1987). A questionnaire to assess premonitory symptoms of myocardial infarction. International Journal of Cardiology, 17, 15–24.PubMedCrossRef
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.
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.
35.
Wu, H. S., & McSweeney, M. (2001). Measurement of fatigue in people with cancer. Oncology Nursing Forum, 28, 1371–1386.PubMed
36.
Jacobsen, P. B. (2004). Assessment of fatigue in cancer patients. Journal of the National Cancer Institute Monographs., 32, 93–97.PubMedCrossRef
37.
Muthén, L.K., Muthén, B.O. (1998–2004). Mplus user’s guide (3rd ed.). Los Angeles, CA: Muthén & Muthén.
38.
Reise, S. P., Moore, T. M., & Haviland, M. G. (2010). Bifactor models and rotations: Exploring the extent to which multidimensional data yield univocal scale scores. Journal of Personality Assessment, 92, 544–559.PubMedCentralPubMedCrossRef
39.
Chen, F. F., West, S. G., & Sousa, K. H. (2006). A comparison of bifactor and second-order models of quality-of-life. Multivariate Behavioral Research, 41, 189–225.CrossRef
40.
Chernyshenko, O. S., Stark, S., & Chan, K. Y. (2001). Investigating the hierarchical factor structure of the fifth edition of the 16PF: An application of the Schmid-Leiman orthogonalization procedure. Educational and Psychological Measurement, 61, 290–302.CrossRef
41.
Ebesutani, C., Drescher, C. F., Reise, S. P., Heiden, L., Hight, T. L., Damon, J. D., et al. (2012). The importance of modeling method effects: Resolving the (uni)dimensionality of the loneliness questionnaire. Journal of Personality Assessment, 94(2), 186–195.PubMedCrossRef
42.
Reddy, S. K. (1992). Effects of ignoring correlated measurement error in structural equation models. Educational and Psychological Measurement, 52, 549–570.CrossRef
43.
Cronbach, L. J. (1951). Coefficient alpha and the internal structure of tests. Psychometrika, 16, 297–334.CrossRef
44.
Brazier, J. E., Harper, R., Jones, N. M., O’Cathain, A., Thomas, K. J., Usherwood, T., et al. (1992). Validating the SF-36 health survey questionnaire: New outcome measure for primary care. BMJ, 305(6846), 160–164.PubMedCentralPubMedCrossRef
45.
Sireci, S. G., Wainer, H., & Thissen, D. (1991). On the reliability of testlet-based tests. Journal of Educational Measurement, 28, 237–247.CrossRef
46.
Tuerlinckx, F., & De Boeck, P. (2001). The effect of ignoring item interactions on the estimated discrimination parameters in item response theory. Psychological Methods, 6, 181–195.PubMedCrossRef
47.
DeMars, C. E. (2006). Application of the bi-factor multidimensional item response theory model to testlet-based tests. Journal of Educational Measurement, 43, 145–168.CrossRef
48.
Reise, S., Moore, T., & Maydeu-Olivares, A. (2011). Target rotations and assessing the impact of model violations on the parameters of unidimensional item response theory models. Educational and Psychological Measurement, 71, 684–711.CrossRef
49.
Lai, J., Cella, D., Dineen, K., Von Roenn, J., & Gershon, R. C. (2005). An item bank was created to improve measurement of cancer-related fatigue. Journal of Clinical Epidemiology, 58, 190–197.PubMedCrossRef
50.
Bode, R. K., Hahn, E. A., DeVellis, R., & Cella, D. (2010). Measuring participation: the patient-reported outcomes measurement information system experience. Archives of Physical Medicine and Rehabilitation, 91(9 Suppl), S60–S65.PubMedCentralPubMedCrossRef
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.PubMedCrossRef
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.PubMedCrossRef
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.
Metadaten
Titel
Energy, fatigue, or both? A bifactor modeling approach to the conceptualization and measurement of vitality
verfasst von
Nina Deng
Rick Guyer
John E. Ware Jr.
Publikationsdatum
01.01.2015
Verlag
Springer International Publishing
Erschienen in
Quality of Life Research / Ausgabe 1/2015
Print ISSN: 0962-9343
Elektronische ISSN: 1573-2649
DOI
https://doi.org/10.1007/s11136-014-0839-9

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