nach oben

Quality of Life Research

Erschienen in:

01.11.2013

Factorial invariance of pediatric patient self-reported fatigue across age and gender: a multigroup confirmatory factor analysis approach utilizing the PedsQL™ Multidimensional Fatigue Scale

verfasst von: James W. Varni, A. Alexander Beaujean, Christine A. Limbers

Erschienen in: Quality of Life Research | Ausgabe 9/2013

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config

KI-gestützte Suche

Aus

Abstract

Objectives

In order to compare multidimensional fatigue research findings across age and gender subpopulations, it is important to demonstrate measurement invariance, that is, that the items from an instrument have equivalent meaning across the groups studied. This study examined the factorial invariance of the 18-item PedsQL™ Multidimensional Fatigue Scale items across age and gender and tested a bifactor model.

Methods

Multigroup confirmatory factor analysis (MG-CFA) was performed specifying a three-factor model across three age groups (5–7, 8–12, and 13–18 years) and gender. MG-CFA models were proposed in order to compare the factor structure, metric, scalar, and error variance across age groups and gender. The analyses were based on 837 children and adolescents recruited from general pediatric clinics, subspecialty clinics, and hospitals in which children were being seen for well-child checks, mild acute illness, or chronic illness care.

Results

A bifactor model of the items with one general factor influencing all the items and three domain-specific factors representing the General, Sleep/Rest, and Cognitive Fatigue domains fit the data better than oblique factor models. Based on the multiple measures of model fit, configural, metric, and scalar invariance were found for almost all items across the age and gender groups, as was invariance in the factor covariances. The PedsQL™ Multidimensional Fatigue Scale demonstrated strict factorial invariance for child and adolescent self-report across gender and strong factorial invariance across age subpopulations.

Conclusions

The findings support an equivalent three-factor structure across the age and gender groups studied. Based on these data, it can be concluded that pediatric patients across the groups interpreted the items in a similar manner regardless of their age or gender, supporting the multidimensional factor structure interpretation of the PedsQL™ Multidimensional Fatigue Scale.

Vorheriger Artikel Development and validation of the positive affect and well-being scale for the neurology quality of life (Neuro-QOL) measurement system

Nächster Artikel Strengthening the assessment of factorial invariance across population subgroups: a commentary on Varni et al. (2013)

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Varni, J. W., & Limbers, C. A. (2009). The Pediatric Quality of Life Inventory™: Measuring pediatric health-related quality of life from the perspective of children and their parents. Pediatric Clinics of North America, 56, 843–863.PubMedCrossRef

Eiser, C. (2004). Use of quality of life measures in clinical trials. Ambulatory Pediatrics, 4, 395–399.PubMedCrossRef

Varni, J. W., Handen, B. L., Corey-Lisle, P. K., Guo, Z., Manos, G., Ammerman, D. K., et al. (2012). Effect of Aripiprazole 2 to 15 mg/d on health-related quality of life in the treatment of irritability associated with Autistic Disorder in children: A post hoc analysis of two controlled trials. Clinical Therapeutics, 34, 980–992.PubMedCrossRef

FDA. (2009). Guidance for industry: Patient-reported outcome measures: Use in medical product development to support labeling claims: Food and Drug Administration, U.S. Department of Health and Human Services, Rockville, MD.

Varni, J. W., Seid, M., & Rode, C. A. (1999). The PedsQL™: Measurement model for the Pediatric Quality of Life Inventory. Medical Care, 37, 126–139.PubMedCrossRef

Varni, J. W., Seid, M., & Kurtin, P. S. (2001). PedsQL™ 4.0: Reliability and validity of the Pediatric Quality of Life Inventory™ version 4.0 Generic Core Scales in healthy and patient populations. Medical Care, 39, 800–812.PubMedCrossRef

Varni, J. W., Limbers, C. A., & Burwinkle, T. M. (2007). How young can children reliably and validly self-report their health-related quality of life?: An analysis of 8,591 children across age subgroups with the PedsQL™ 4.0 Generic Core Scales. Health and Quality of Life Outcomes, 5(1), 1–13.PubMedCrossRef

Sweeting, H. (1995). Reversals of fortune?: Sex differences in health in childhood and adolescence. Social Science and Medicine, 40, 77–90.PubMedCrossRef

Arrington-Sanders, R., Yi, M. S., Tsevat, J., Wilmott, R. W., Mrus, J. M., & Britto, M. T. (2006). Gender differences in health-related quality of life of adolescents with cystic fibrosis. Health and Quality of Life Outcomes, 4(5), 1–8.

10.

Jorngarden, A., Wettergen, L., & von Essen, L. (2006). Measuring health-related quality of life in adolescents and young adults: Swedish normative data for the SF-36 and the HADS, and the influence of age, gender, and method of administration. Health and Quality of Life Outcomes, 4(91), 1–10.

11.

Reinfjell, T., Diseth, T. H., Veenstra, M., & Vikan, A. (2006). Measuring health-related quality of life in young adolescents: Reliability and validity in the Norwegian version of the Pediatric Quality of Life Inventory™ 4.0 (PedsQL™) Generic Core Scales. Health and Quality of Life Outcomes, 4(61), 1–9.

12.

Upton, P., Eiser, C., Cheung, I., Hutchings, H. A., Jenney, M., Maddocks, A., et al. (2005). Measurement properties of the UK-English version of the Pediatric Quality of Life Inventory™ 4.0 (PedsQL™) Generic Core Scales. Health and Quality of Life Outcomes, 3(22), 1–7.

13.

Vaez, M., & Laflamme, L. (2003). Health behaviors, self-rated health, and quality of life: A study among first-year Swedish university students. Journal of American College Health, 51, 156–162.PubMedCrossRef

14.

Meredith, W., & Teresi, J. A. (2006). An essay on measurement and factorial invariance. Medical Care, 44, S69–S77.PubMedCrossRef

15.

Gregorich, S. E. (2006). Do self-report instruments allow meaningful comparisons across diverse population groups?: Testing measurement invariance using the confirmatory factor analysis framework. Medical Care, 44, S78–S94.PubMedCrossRef

16.

Varni, J. W., Stucky, B. D., Thissen, D., DeWitt, E. M., Irwin, D. E., Lai, J. S., et al. (2010). PROMIS Pediatric Pain Interference Scale: An item response theory analysis of the pediatric pain item bank. Journal of Pain, 11, 1109–1119.PubMedCrossRef

17.

French, B., & Finch, W. (2006). Confirmatory factor analytic procedures for the determination of measurement invariance. Structural Equation Modeling, 13, 378–402.CrossRef

18.

Teresi, J. A. (2006). Overview of quantitative measurement methods: Equivalence, invariance, and differential item functioning in health applications. Medical Care, 44, S39–S49.PubMedCrossRef

19.

Vandenberg, R., & Lance, C. (2000). A review and synthesis of the measurement invariance literature: Suggestions, practices, and recommendations for organizational research. Organizational Research Methods, 3, 4–70.CrossRef

20.

Little, T. D. (1997). Mean and covariance structures (MACS) analyses of cross-cultural data: Practical and theoretical issues. Multivariate Behavioral Research, 32, 53–76.CrossRef

21.

Limbers, C. A., Newman, D. A., & Varni, J. W. (2008). Factorial invariance of child self-report across healthy and chronic health condition groups: A confirmatory factor analysis utilizing the PedsQL™ 4.0 Generic Core Scales. Journal of Pediatric Psychology, 33, 630–639.PubMedCrossRef

22.

Limbers, C. A., Newman, D. A., & Varni, J. W. (2008). Factorial invariance of child self-report across socioeconomic status groups: A multigroup confirmatory factor analysis utilizing the PedsQL™ 4.0 Generic Core Scales. Journal of Behavioral Medicine, 31, 401–411.PubMedCrossRef

23.

Limbers, C. A., Newman, D. A., & Varni, J. W. (2009). Factorial invariance of child self-report across race/ethnicity groups: A multigroup confirmatory factor analysis approach utilizing the PedsQL™ 4.0 Generic Core Scales. Annals of Epidemiology, 19, 575–581.PubMedCrossRef

24.

Varni, J. W., Limbers, C. A., & Newman, D. A. (2009). Using factor analysis to confirm the validity of children’s self-reported health-related quality of life across different modes of administration. Clinical Trials, 6, 185–195.PubMedCrossRef

25.

Varni, J. W., Limbers, C. A., Newman, D. A., & Seid, M. (2008). Longitudinal factorial invariance of the PedsQL™ 4.0 Generic Core Scales child self-report version: One year prospective evidence from the California State Children’s Health Insurance Program (SCHIP). Quality of Life Research, 17, 1153–1162.PubMedCrossRef

26.

Newman, D. A., Limbers, C. A., & Varni, J. W. (2010). Factorial invariance of child self-report across English and Spanish language groups in a Hispanic population utilizing the PedsQL™ 4.0 Generic Core Scales. European Journal of Psychological Assessment, 26, 194–202.CrossRef

27.

Limbers, C. A., Newman, D. A., & Varni, J. W. (2008). Factorial invariance of child self-report across age subgroups: A confirmatory factor analysis of ages 5 to 16 years utilizing the PedsQL™ 4.0 Generic Core Scales. Value in Health, 11, 659–668.PubMedCrossRef

28.

Varni, J. W., Limbers, C. A., & Newman, D. A. (2008). Factorial invariance of the PedsQL™ 4.0 Generic Core Scales child self-report across gender: A multigroup confirmatory factor analysis with 11,356 children ages 5 to 18. Applied Research in Quality of Life, 3, 137–148.CrossRef

29.

Varni, J. W., Burwinkle, T. M., Katz, E. R., Meeske, K., & Dickinson, P. (2002). The PedsQL™ in pediatric cancer: Reliability and validity of the Pediatric Quality of Life Inventory™ Generic Core Scales, Multidimensional Fatigue Scale, and cancer module. Cancer, 94, 2090–2106.PubMedCrossRef

30.

Varni, J. W., Burwinkle, T. M., & Szer, I. S. (2004). The PedsQL™ Multidimensional Fatigue Scale in pediatric rheumatology: Reliability and validity. Journal of Rheumatology, 31, 2494–2500.PubMed

31.

Varni, J. W., Burwinkle, T. M., Limbers, C. A., & Szer, I. S. (2007). The PedsQL™ as a patient-reported outcome in children and adolescents with fibromyalgia: An analysis of OMERACT domains. Health and Quality of Life Outcomes, 5(9), 1–12.PubMedCrossRef

32.

Palmer, S. N., Meeske, K. A., Katz, E. R., Burwinkle, T. M., & Varni, J. W. (2007). The PedsQL™ Brain Tumor Module: Initial reliability and validity. Pediatric Blood & Cancer, 49, 287–293.CrossRef

33.

Marcus, S. B., Strople, J. A., Neighbors, K., Weissberg-Benchell, J., Nelson, S. P., Limbers, C. A., et al. (2009). Fatigue and health-related quality of life in pediatric inflammatory bowel disease. Clinical Gastroenterology and Hepatology, 7, 554–561.PubMedCrossRef

34.

Gold, J. I., Mahrer, N. E., Yee, J., & Palermo, T. M. (2009). Pain, fatigue, and health-related quality of life in children and adolescents with chronic pain. Clinical Journal of Pain, 25, 407–412.PubMedCrossRef

35.

Varni, J. W., Limbers, C. A., Bryant, W. P., & Wilson, D. P. (2009). The PedsQL™ Multidimensional Fatigue Scale in type 1 diabetes: Feasibility, reliability, and validity. Pediatric Diabetes, 10, 321–328.PubMedCrossRef

36.

Varni, J. W., Limbers, C. A., Bryant, W. P., & Wilson, D. P. (2010). The PedsQL™ Multidimensional Fatigue Scale in pediatric obesity: Feasibility, reliability, and validity. International Journal of Pediatric Obesity, 5, 34–42.PubMedCrossRef

37.

Varni, J. W., Limbers, C. A., Bryant, W. P., & Wilson, D. P. (2012). Assessment of fatigue in pediatric patients with short stature utilizing the PedsQL™ Multidimensional Fatigue Scale. Children’s Health Care, 41, 162–181.CrossRef

38.

Pohl, J. F., Limbers, C. A., Kay, M. T., Harman, A., Rollins, M., & Varni, J. W. (2012). Health-related quality of life in pediatric patients with long-standing pancreatitis. Journal of Pediatric Gastroenterology and Nutrition, 54, 657–663.PubMedCrossRef

39.

MacAllister, W. S., Christodoulou, C., Troxell, R., Milazzo, M., Block, P., Preston, T. E., et al. (2009). Fatigue and quality of life in pediatric multiple sclerosis. Multiple Sclerosis, 15, 1502–1508.PubMedCrossRef

40.

Varni, J. W., & Limbers, C. A. (2008). The PedsQL™ Multidimensional Fatigue Scale in young adults: Feasibility, reliability and validity in a university student population. Quality of Life Research, 17, 105–114.PubMedCrossRef

41.

Robert, R. S., Paxton, R. J., Palla, S. L., Yang, G., Askins, M. A., Joy, S. E., et al. (2012). Feasibility, reliability, and validity of the Pediatric Quality of Life Inventory™ Generic Core Scales, Cancer Module, and Multidimensional Fatigue Scale in long-term adult survivors of pediatric cancer. Pediatric Blood & Cancer, 59, 703–707.CrossRef

42.

Gordijn, M. S., Cremers, E. M., Kaspers, G. J., & Gemke, R. J. (2011). Fatigue in children: Reliability and validity of the Dutch PedsQL™ Multidimensional Fatigue Scale. Quality of Life Research, 20, 1103–1108.PubMedCrossRef

43.

Kobayashi, K., Okano, Y., & Hohashi, N. (2011). Reliability and validity of the PedsQL™ Multidimensional Fatigue Scale in Japan. Quality of Life Research, 20, 1091–1102.PubMedCrossRef

44.

Enders, C. K., & Bandalos, D. L. (2001). The relative performance of full information maximum likelihood estimation for missing data in structural equation models. Structural Equation Modeling, 8, 430–457.CrossRef

45.

Aday, L. A. (1996). Designing and conducting health surveys: A comprehensive guide (2nd ed.). San Francisco: Jossey-Bass.

46.

Fowler, F. J. (1995). Improving survey questions: Design and evaluation. Thousand Oaks, CA: Sage.

47.

Schwarz, N., & Sudman, N. (Eds.). (1996). Answering questions: Methodology for determining cognitive and communicative processes in survey research. San Francisco: Jossey-Bass.

48.

Ringold, S., Wallace, C. A., & Rivara, F. P. (2009). Health-related quality of life, physical function, fatigue, and disease activity in children with established polyarticular juvenile idiopathic arthritis. Journal of Rheumatology, 36, 1330–1336.PubMedCrossRef

49.

Dampier, C., Lieff, S., Lebeau, P., Rhee, S., McMurray, M., Rogers, Z., et al. (2010). Health-related quality of life in children with sickle cell disease: A report from the Comprehensive Sickle Cell Centers Clinical Trial Consortium. Pediatric Blood & Cancer, 55, 485–494.CrossRef

50.

Fairclough, D. L. (2002). Design and analysis of quality of life studies in clinical trials: Interdisciplinary statistics. New York: Chapman & Hall/CRC.

51.

Millsap, R. E. (2011). Statistical approaches to measurement invariance. New York: Routledge.

52.

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

53.

Reise, S., Morizot, J., & Hays, R. (2007). The role of the bifactor model in resolving dimensionality issues in health outcomes measures. Quality of Life Research, 16, 19–31.PubMedCrossRef

54.

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.PubMedCrossRef

55.

Byrne, B. M., & Stewart, S. M. (2006). The MACS approach to testing for multigroup invariance of a second-order structure: A walk through the process. Structural Equation Modeling, 13(2), 287–321.CrossRef

56.

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

57.

Meade, A. W., Johnson, E. C., & Braddy, P. W. (2008). Power and sensitivity of alternative fit indices in tests of measurement invariance. Journal of Applied Psychology, 93(3), 568–592.PubMedCrossRef

58.

Bentler, P. M. (1990). Comparative fit indexes in structural models. Psychological Bulletin, 107(2), 238–246.PubMedCrossRef

59.

McDonald, R. P. (1989). An index of goodness-of-fit based on noncentrality. Journal of Classification, 6(1), 97–103.CrossRef

60.

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

61.

Browne, M. W., & Cudeck, R. (1993). Alternative ways of assessing model fit. In K. A. Bollen & J. S. Long (Eds.), Testing structural equation models (pp. 136–162). Newbury Park, CA: Sage.

62.

Bentler, P. (1990). Comparative fit indexes in structural models. Psychological Bulletin, 107, 238–246.PubMedCrossRef

63.

McDonald, R. P. (1989). An index of goodness-of-fit based on noncentrality. Journal of Classification, 6, 97–103.CrossRef

64.

Bentler, P. M. (1995). EQS structural equations program manual. Encino, CA: Multivariate Software.

65.

Marsh, H. W., Hau, K.-T., & Grayson, D. (2005). Goodness of fit in structural equation models. In A. Maydeu-Olivares & J. J. McArdle (Eds.), Contemporary psychometrics: A festschrift for Roderick P. McDonald (pp. 275–340). Mahwah, NJ: Lawrence Erlbaum.

66.

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

67.

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

68.

Yu, C.-Y. (2002). Evaluating cutoff criteria of model fit indices for latent variable models with binary and continuous outcomes. Unpublished Doctoral Dissertation, University of California, Los Angeles. Available from ProQuest Dissertations and Theses (AAT 3066425).

69.

Marsh, H. W., Hau, K.-T., & 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, 11(3), 320–341.CrossRef

70.

Team, R. D. C. (2011). R: A language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing.

71.

Rosseel, Y. (2012). lavaan: An R package for structural equation modeling. Journal of Statistical Software, 48, 1–36.

72.

Revelle, W. (2012). Psych: Procedures for psychological, psychometric, and personality research (Version 1.2.4) [Computer software]. Evanston, IL: Northwestern University.

73.

Green, S. B., Akey, T. M., Fleming, K. K., Hershberger, S. L., & Marquis, J. G. (1997). Effect of the number of scale points on Chi square fit indices in confirmatory factor analysis. Structural Equation Modeling, 4, 108–120.CrossRef

74.

West, S. G., Finch, J. F., & Curran, P. J. (1995). Structural equation models with non-normal variables: Problems and remedies. In R. A. Hoyle (Ed.), Structural equation modeling: Concepts, issues and applications (pp. 56–75). Newbury Park, CA: Sage.

75.

Bentler, P. M., & Chou, C. (1987). Practical issues in structural modeling. Sociological Methods & Research, 16, 78–117.CrossRef

76.

Mardia, K. V. (1970). Measures of multivariate skewness and kurtosis with applications. Biometrika, 57, 519–530.CrossRef

77.

Horn, J. (1965). A rationale and test for the number of factors in factor analysis. Psychometrika, 30, 179–185.PubMedCrossRef

78.

Velicer, W. F., Eaton, C. A., & Fava, J. L. (2000). Construct explication through factor or component analysis: A review and evaluation of alternative procedures for determining the number of factors or components. In R. D. Goffin & E. Helmes (Eds.), Problems and solutions in human assessment: Honoring Douglas Jackson at seventy (pp. 41–71). Boston: Kluwer.

79.

Osborne, J. W., Costello, A. B., & Kellow, T. J. (2008). Best practices in exploratory factor analysis. In J. W. Osborne (Ed.), Best practices in quantitative methods (pp. 86–99). Los Angeles: Sage.CrossRef

80.

Garrido, L. E., Abad, F. J., & Ponsoda, V. (2011). Performance of Velicer’s minimum average partial factor retention method with categorical variables. Educational and Psychological Measurement, 71, 551–570.CrossRef

81.

Satorra, A., & Bentler, P. M. (1994). Corrections to test statistics and standard errors in covariance structure analysis. In A. von Eye & C. C. Clogg (Eds.), Latent variables analysis: Applications for developmental research (pp. 399–419). Thousands Oaks, CA: Sage.

82.

Curran, P. J., West, S. G., & Finch, J. F. (1996). The robustness of test statistics to nonnormality and specification error in confirmatory factor analysis. Psychological Methods, 1, 16–29.CrossRef

83.

Enders, C. K. (2001). The impact of nonnormality on full information maximum-likelihood estimation for structural equation models with missing data. Psychological Methods, 6, 352–370.PubMedCrossRef

84.

Zinbarg, R. E., Revelle, W., Yovel, I., & Li, W. (2005). Cronbach’s α, Revelle’s β, and Mcdonald’s ωh: Their relations with each other and two alternative conceptualizations of reliability. Psychometrika, 70, 123–133.CrossRef

85.

Raju, N. S., Laffitte, L. J., & Byrne, B. M. (2002). Measurement equivalence: A comparison of methods based on confirmatory factor analysis and item response theory. Journal of Applied Psychology, 87, 517–529.PubMedCrossRef

86.

American Educational Research Association, A. P. A., & National Council on Measurement in Education. (1999). American Educational Research Association, American Psychological Association, & National Council on Measurement in Education Standards for educational and psychological testing. Washington, DC.

87.

ter Wolbeek, M., van Doornen, L. J. P., Kavelaars, A., & Heijnen, C. J. (2006). Severe fatigue in adolescents: A common phenomenon? Pediatrics, 117, e1078–e1086.PubMedCrossRef

88.

Viner, R. M., Clark, C., Taylor, S. J. C., Bhui, K., Klineberg, E., Head, J., et al. (2008). Longitudinal risk factors for persistent fatigue in adolescents. Archives of Pediatrics and Adolescent Medicine, 162, 469–475.PubMedCrossRef

89.

ter Wolbeek, M., van Doornen, L. J. P., Kavelaars, A., & Heijnen, C. J. (2008). Predictors of persistent and new-onset fatigue in adolescent girls. Pediatrics, 121, e449–e457.PubMedCrossRef

90.

Reise, S. P., Bonifay, W. E., & Haviland, M. G. (2013). Scoring and modeling psychological measures in the presence of multidimensionality. Journal of Personality Assessment, 95, 129–140.

91.

Satorra, A., & Bentler, P. M. (2001). A scaled difference Chi square test statistic for moment structure analysis. Psychometrika, 66, 507–514.CrossRef

Titel: Factorial invariance of pediatric patient self-reported fatigue across age and gender: a multigroup confirmatory factor analysis approach utilizing the PedsQL™ Multidimensional Fatigue Scale
verfasst von: James W. Varni
A. Alexander Beaujean
Christine A. Limbers
Publikationsdatum: 01.11.2013
Verlag: Springer Netherlands
Erschienen in: Quality of Life Research / Ausgabe 9/2013
Print ISSN: 0962-9343
Elektronische ISSN: 1573-2649
DOI: https://doi.org/10.1007/s11136-013-0370-4

Springer Professional

Abstract

Objectives

Methods

Results

Conclusions

Bitte loggen Sie sich ein, um Zugang zu Ihrer Lizenz zu erhalten.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Wirtschaft"

Weitere Artikel der Ausgabe 9/2013

Psychometric validation of the physician global assessment scale for assessing severity of psoriasis disease activity

Physical activity and health-related quality of life among adult women in Cali, Colombia: a cross-sectional study

Deriving clinically meaningful cut-scores for fatigue in a cohort of breast cancer survivors: a Health, Eating, Activity, and Lifestyle (HEAL) Study

Quality-of-life loss of people admitted to burn centers, United States

The effect of weight status, lifestyle, and body image perception on health-related quality of life in children: a quantile approach

Changing values, changing outcomes: the influence of reprioritization response shift on outcome assessment after spine surgery

Premium Partner