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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.
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.
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.
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.
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- Factorial invariance of pediatric patient self-reported fatigue across age and gender: a multigroup confirmatory factor analysis approach utilizing the PedsQL™ Multidimensional Fatigue Scale
James W. Varni
A. Alexander Beaujean
Christine A. Limbers
- Springer Netherlands