Factorial Invariance of Child Self-Report Across Race/Ethnicity Groups: A Multigroup Confirmatory Factor Analysis Approach Utilizing the PedsQL™∗ 4.0 Generic Core Scales
Introduction
Children are uniquely positioned to report their perspectives on their health and well-being through their perceptions of their health-related quality of life (HRQOL) outcomes. The last decade has witnessed a significant growth in the measurement of pediatric HRQOL in pediatric medicine and health services research. A HRQOL instrument must be multidimensional, consisting at the minimum of the physical, psychological (including emotional and cognitive), and social health dimensions delineated by the World Health Organization 1, 2. Although the measurement of pediatric self-reported HRQOL in clinical trials and school and community populations has been advocated for a number of years 3, 4, 5, the emerging paradigm shift toward patient-reported outcomes (PROs) (2) has provided the opportunity to further emphasize the value for child self-report HRQOL measurement as efficacy outcomes in clinical trials 6, 7, 8, 9, 10 and population-based interventions (11).
Racial and ethnic disparities in healthcare quality and comparative public health have received increasing scrutiny in efforts to ensure equity in the provision and receipt of health services to minority populations 11, 12. Federal regulations and guidelines support the inclusion of racial and ethnic minorities in clinical research, including pharmaceutical trials, in an effort to ameliorate health disparities among racial and ethnic groups (13). Measuring HRQOL is an essential component in addressing health disparities for minority populations, including children, to document the comparative public health outcomes among different race/ethnicity subpopulations from the perspective of minority families 4, 11, 14. However, in order to have greater confidence that a HRQOL instrument is measuring the same constructs across different race/ethnicity subpopulations (i.e., that the items have the same meaning for all participants regardless of race/ethnicity), it is essential to demonstrate factorial invariance across different race/ethnicity groups 15, 16, 17.
Generic HRQOL instruments enable comparisons across diverse pediatric populations, including chronic health conditions, as well as benchmarking with healthy populations 18, 19. In order for these comparisons to be valid, items on an HRQOL measure must have equivalent meaning across the subpopulations being compared (20); that is, they must demonstrate factorial invariance 21, 22, 23. Multigroup confirmatory factor analysis (CFA) is a method used to examine these levels of factorial invariance across groups 20, 21.
The use of multigroup CFA for invariance testing has witnessed a considerable growth in recent years 20, 21. However, there has been a relative absence of studies that have evaluated the factorial invariance of HRQOL measures for pediatric subpopulations 24, 25. Furthermore, the majority of studies to date have focused on establishing configural and metric invariance, ignoring higher levels of factorial invariance that examine group differences in item-specific intercepts (21). Without establishing stricter levels of factorial invariance, race/ethnicity differences in observed health outcomes may be confounded by differences in item-specific intercepts.
The PedsQL™ 4.0 Generic Core Scales instrument has been previously demonstrated to be a feasible, reliable, and valid 23-item HRQOL measure for pediatric patients with chronic health conditions and healthy school and community populations 4, 5, 26, including its use in an expanding number of countries in Europe and worldwide 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38. A number of international studies have tested the factor structure of the PedsQL™ using exploratory factor analysis or CFA within a designated country, typically with a homogeneous population sample 30, 31, 32, 36. However, we are not aware of a study which has used the multigroup CFA framework to compare the factorial invariance of the PedsQL™ simultaneously across different race/ethnicity subpopulations, which may have significant utility as a statistical method for international cross-cultural assessment research in which different race/ethnicity subpopulations are compared within and across countries.
As such, the objective of the present study was to examine the factorial invariance of child self-reported HRQOL across four race/ethnicity groups utilizing the multigroup CFA framework with the PedsQL™ 4.0 Generic Core Scales.
Section snippets
Participants and Settings
The sample contains child self-report race/ethnicity data on 5,490 children ages 5 to 18 years from the PedsQL™ 4.0 Generic Core Scales database (previously published data, n = 5,388, 98.1%; unpublished data, n = 102, 1.9%). Participants were 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 (n = 1,945, 35.4%), from a State Children's Health Insurance Program (SCHIP) in
Participant Characteristics
The age, gender, and health status of the participants across the race/ethnicity subpopulations are presented in Table 1. With the exception of health status, the demographic characteristics do not differ substantially between race/ethnicity groups in the current sample. As noted previously, all child surveys were completed in English. Mean socioeconomic status was unavailable for these samples.
Factorial Invariance: Goodness of Fit Indices
The chi-square values (with degrees of freedom), fit indices, and change in CFI values for the
Discussion
The present findings demonstrate that when self-reporting their HRQOL, children who completed the PedsQL™ 4.0 Generic Core Scales across the four race/ethnicity groups studied had a similar five-factor HRQOL model structure. As a result, it can be concluded that children in this study had a similar interpretation of the items regardless of race/ethnicity.
Raju, Laffitte, and Byrne (51) succinctly describe the importance of measurement equivalence by stating: “When measurement equivalence is
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The PedsQL™ is available at http://www.pedsql.org.