Testing the structural and cross-cultural validity of the KIDSCREEN-27 quality of life questionnaire
- 1.2k Downloads
The aim of this study is to assess the structural and cross-cultural validity of the KIDSCREEN-27 questionnaire.
The 27-item version of the KIDSCREEN instrument was derived from a longer 52-item version and was administered to young people aged 8–18 years in 13 European countries in a cross-sectional survey. Structural and cross-cultural validity were tested using multitrait multi-item analysis, exploratory and confirmatory factor analysis, and Rasch analyses. Zumbo’s logistic regression method was applied to assess differential item functioning (DIF) across countries. Reliability was assessed using Cronbach’s alpha.
Responses were obtained from n = 22,827 respondents (response rate 68.9%). For the combined sample from all countries, exploratory factor analysis with procrustean rotations revealed a five-factor structure which explained 56.9% of the variance. Confirmatory factor analysis indicated an acceptable model fit (RMSEA = 0.068, CFI = 0.960). The unidimensionality of all dimensions was confirmed (INFIT: 0.81–1.15). Differential item functioning (DIF) results across the 13 countries showed that 5 items presented uniform DIF whereas 10 displayed non-uniform DIF. Reliability was acceptable (Cronbach’s α = 0.78–0.84 for individual dimensions).
There was substantial evidence for the cross-cultural equivalence of the KIDSCREEN-27 across the countries studied and the factor structure was highly replicable in individual countries. Further research is needed to correct scores based on DIF results. The KIDSCREEN-27 is a new short and promising tool for use in clinical and epidemiological studies.
KeywordsCross-cultural equivalence Health-related Quality of Life Item response theory Pediatric Questionnaire
Source of support: The KIDSCREEN project was financed by a grant from the European Commission (QLG-CT-2000-00751) within the EC 5th Framework-Programme “Quality of Life and Management of Living Resources”.
- 1.World Health Organization Regional Office for Europe (1996). The Ljubljana charter on reforming health care. Paper presented at the Ljubljana Conference, Ljubljana.Google Scholar
- 13.Ravens-Sieberer, U., Auquier, P., Erhart, M., et al. The KIDSCREEN-27 quality of life measure for children and adolescents – psychometric results from a cross-cultural survey in 13 European countries. Quality of Life Research. doi: 10.1007/s11136-007-9240-2.
- 14.Mokken, R. (1971). A theory and procedure of scale analysis. The Hague: Mouton, Berlin De Gruyter.Google Scholar
- 15.Mokken, R., & Lewis, C. (1982). A nonparametric approach to the analysis of dichotomous item responses. Applied Psychological Measurement, 6, 417–430.Google Scholar
- 16.Molenaar, I. (1982). Mokken sclaing revisited. Kwantitatieve Methoden, 3, 145–164.Google Scholar
- 17.Wright, B., & Masters, G. (1982). Rating scale analysis. Chicago: MESA Press.Google Scholar
- 18.Wright, B., & Stone, M. (1979). Best test design. Chicago: MESA Press.Google Scholar
- 19.Zumbo, B. (1999). A handbook on the theory and methods of differential item functioning (DIF): Logistic regression modeling as a unitary framework for binary and Likert-type (Ordinal). Item scores. Ottawa, ON: Directorate of Human Resources Research and Evaluation, Department of National Defense.Google Scholar
- 20.Ware, J., Harris, W., & Gandek, B. (1997). MAP-R for Windows: Multitrait:multi-item analysis program—Revised user’s guide. Boston, MA: Health Assessment Laboratory.Google Scholar
- 21.Thompson, B. (2004). Exploratory and confirmatory factor analysis: Understanding concepts and applications. American Psychological Association.Google Scholar
- 22.Rajmil, L., Berra, S., von-Rueden, U., Tebe, C., Erhart, M., Gosh, A., et al. (2004). Representativity of 12 national surveys of children and adolescents 8–18 years old included in the KIDSCREEN HRQOL study. Quality of Life Research, 13(9), 1576.Google Scholar
- 23.Veldman, D. (1978). Fortran programming for the behavioural sciences. New York: Holt, Rinehart, & Winston.Google Scholar
- 25.Maruyama, G. (1998). Basic of structural equation modeling. Thousand Oaks, CA: Sage.Google Scholar
- 26.Bollen, K., & Long, J. (1993). Alternative ways of assessing model fit. Testing structural equation models. Sage Publications.Google Scholar
- 32.Nunnally, J., & Bernstein, I. (1994). Psychometric theory. New York: McGraw-Hill, Inc.Google Scholar
- 36.Linacre, J. (2003). A user guide to Winsteps. Rasch model computer program. Chicago, IL: MESA edition.Google Scholar
- 37.Jöreskog, K., & Sörbom, D. (1996). LISREL 8:User’s reference guide. Chicago: Scientific Software International.Google Scholar
- 38.Jöreskog, K., & Sörbom, D. (1988). PRELIS – A program for multivariate data screening and data summarization. A preprocessor for LISREL (2nd ed.). Chicago, Il: Scientific Software, Inc.Google Scholar
- 42.Gorsuch, R. (1983). Factor analysis. Hillsdale, NJ: Erlbaum.Google Scholar
- 43.Hu, L., & Bentler, P. (1995). Evaluating model fit. In R. Hoyle (Ed.), Structural equation modeling: Concepts, Issues, and Applications (pp. 76—99). Thousand Oaks, CA: Sage.Google Scholar
- 47.Erhart, M., Ravens-Sieberer, U., Hagquist, C., Robitail, S., & van Buuren, S. (2006). Does correcting for differential item functioning (DIF) using two different techniques enhances the validity and diagnostic quality of adolescents HRQoL test scores? Quality of Life Research, 15, A113–A114.Google Scholar