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Agreement of Children and Parents Scores on Chinese Version of Pediatric Quality of Life Inventory Version 4.0: Further Psychometric Development

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Abstract

Quality of life (QoL) is an important index that allows health practitioners to understand the overall health status of an individual. One commonly used reliable and valid QoL instrument with parallel items on parent and child questionnaires, the Pediatric Quality of Life Inventory Version 4.0 (PedsQL), has been being developed since 1997. However, the use of parent- and child-reported PedsQL is still under development. Using multitrait-multimethod (MTMM) analyses and absolute agreement analyses across parent and child questionnaires can further help health practitioners understand the construct of PedsQL, and the feasibility of PedsQL in clinical. We analyzed the questionnaires of 254 parent–child dyads. MTMM through confirmatory factor analyses and percent of smallest real difference (SRD%) were used for analyzing. Our results supported the construct validity of the PedsQL. Four traits (physical, emotional, social, and school) and two methods (parent-proxy reports and child self-reports) were distinguished by MTMM. Moreover, the results of absolute agreements suggested that parent-rated and child-rated PedsQL are close (SRD% = 17.88–30.55 %); thus, a parent-rated PedsQL can be a secondary outcome representing a child’s health. We conclude that the PedsQL is useful for measuring children’s QoL, and has helpful clinical implications.

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Notes

  1. Please note that Model 2 also provides the information of agreement between parents and children. However, we used other statistical methods (including ICC, SEM, and SRD) to examine the agreement based on the reason that Model 2 can only provide the overall agreement. In other words, Model 2 provides the agreement of the entire QoL, while other statistical methods we used provide the agreement for each QoL domain (e.g., physical and psychosocial domains).

  2. The ICC shares the same terms of variances in the repeated measures analysis of variance (ANOVA): variance between participants and that within participants. Of the variance within participants, it can be separated as variance between methods and residual variance. Therefore, we could have the following model: xij = μ + αi + βj + εij, where β represents for participants, α for methods (i.e., parent- and child-rated PedsQL in this study), and ε for residual. Then, the ICC can be computed as β variance divided by the sum of α, β, and ε variances. Based on the formula, we could easily know that a high value of ICC shows a fair degree of agreement between the methods. In addition, ICC simultaneously accounts for bias (i.e., whether children rate the PedsQL lower or higher than parents do) and association (i.e., whether children and parents understand the meaning of the PedsQL in the same way).

  3. SEM equals to the square root of the error variance: SEM = √σ2 error = √σ2 methods + √σ2 residual, where σ2 methods represents for the variance of child- and parent-rated PedsQL. Because ICC is computed as σ2 methods divided by total σ2, 1−ICC = σ2 residual divided by total σ2. SEM then can be calculated as: SEM = σtotal × √(1−ICC). Hence, based on the formula of SEM, we could examine the differences between child- and parent-rated PedsQL.

  4. SRD = 1.96 × SEM × √2, where 1.96 represents the 95 % confidence interval from a normal distribution, and √2 is used to account for the additional uncertainty introduced by using difference scores from the 2 independent measurements with the same variances (in our study, they are child-rated and parent-rated PedsQL). The variance of the difference scores (SD2 diff) can be computed from three sources: variances of the child-rated (SD2 child) and parent-rated (SD2 parent) scores, and the covariance (covchild, parent) between them. Therefore, SD2 diff = SD2 child−2 × covchild, parent + SD2 parent. Because the Pearson correlation (r) can be defined as the ratio of the covariance divided by the product of the corresponding SDs (i.e., SDchild × SDparent), covchild, parent is substituted by r × SDchild × SDparent. And SD2 diff = SD2 child−2 × r × SDchild × SDparent + SD2 parent. Assuming child-rated and parent-rated PedsQL have equal variability in the population, we can get SD2 diff = 2 × SD2 child−2 × r × SD2 child = 2 × SD2 child × (1−r). Taken square root in both sides: SDdiff = SDchild × √2 (1−r). Assuming child-rated and parent-rated PedsQL are independent, the Pearson correlation r is zero, and the last term is reduced to SDdiff = SDchild × √2. Hence, using √2 to multiply SEM is to conservatively consider the possibly largest uncertainty.

  5. The primary benefit of using SRD% is that it is independent of the units of measurement, and readers can easily understand the magnitude of a bias is. Another benefit of using the total range is that total range is the same across samples, while standard deviation will be changed in different samples.

  6. Model 1 only accounts for QoL trait; Model 2 only accounts for different methods (i.e., child- and parent-rated PedsQL); Model 3 accounts for one general QoL trait and two methods; Models 4 and 5 account for both four QoL traits and two methods. Therefore, comparing Models 4 and 1 helps us understand the method effects; Models 4 and 2 helps us examine the trait effects, that is, whether child- and parent-rated QoL were satisfactory converged, and indicates convergent validity. The difference between Models 4 and 3 is that Model 4 used QoL as separated traits, while Model 3 used QoL as one general trait. Therefore, comparing the two models help we understand the discriminant between the four QoL traits, and discriminant validity can be tested.

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Acknowledgments

This research was, in part, supported by the Ministry of Education, Taiwan, R. O. C. The Aim for the Top University Project to the National Cheng Kung University (NCKU).

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Authors and Affiliations

  1. Department of Psychology, College of Social Science, National Cheng Kung University, Tainan, Taiwan

    Chung-Ping Cheng

  2. Institute of Education, College of Social Science, National Cheng Kung University, Tainan, Taiwan

    Wei-Ming Luh

  3. Graduate Institute of Exercise Science, University of Taipei, Taipei, Taiwan

    Ai-Lun Yang

  4. Department of Occupational Therapy, College of Medicine, Fu Jen Catholic University, New Taipei City, Taiwan

    Chia-Ting Su

  5. Department of Public Health, College of Medicine, National Cheng Kung University, 1 University Road, Tainan, 70101, Taiwan

    Chung-Ying Lin

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Cheng, CP., Luh, WM., Yang, AL. et al. Agreement of Children and Parents Scores on Chinese Version of Pediatric Quality of Life Inventory Version 4.0: Further Psychometric Development. Applied Research Quality Life 11, 891–906 (2016). https://doi.org/10.1007/s11482-015-9405-z

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