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Measuring global physical health in children with cerebral palsy: illustration of a multidimensional bi-factor model
Stephen M Haley1, Pengsheng Ni, Helene M Dumas
1Health and Disability Research Institute, Boston University School of Public Health, 580 Harrison Ave, Boston, MA, 02218, USA. smhaley@bu.edu
Insights
A new computer adaptive testing (CAT) model accurately measures physical health in children with cerebral palsy (CP). This efficient method distinguishes between severity levels and types of CP, correlating well with other health measures.
Area of Science:
- Psychometrics
- Pediatric Health Assessment
- Childhood Disability Research
Background:
- Children with cerebral palsy (CP) require accurate physical health assessments.
- Existing measures may not fully capture the complexity of physical health in this population.
- Multidimensional item response theory (MIRT) and computer adaptive testing (CAT) offer potential for improved assessment.
Purpose of the Study:
- To apply a bi-factor model to determine test dimensionality for a new global physical health measure.
- To assess a multidimensional CAT using computer simulations for children with CP.
- To evaluate the accuracy and efficiency of the CAT for physical health assessment in children with CP.
Main Methods:
- Recruited 306 parent respondents of children with CP.
- Compared four confirmatory factor analysis models, including unidimensional, two-factor MIRT, and bi-factor MIRT (fixed and varied slopes).
- Evaluated score estimates from simulated CATs against total item bank scores and external measures.
Main Results:
- Confirmatory factor analysis supported separate pain and fatigue sub-factors within the bi-factor MIRT model.
- The bi-factor MIRT model with fixed slopes demonstrated that item bank scores discriminated across CP severity and types.
- Simulated CAT scores from 10- and 15-item versions accurately reflected global physical health scores.
Conclusions:
- The bi-factor MIRT CAT, particularly 10- and 15-item versions, provides accurate global physical health scores for children with CP.
- The CAT effectively discriminates across known severity groups and CP types, correlating with concurrent measures.
- CATs offer an efficient approach for collecting complex physical health data in children with CP.
Purpose:
The purposes of this study were to apply a bi-factor model for the determination of test dimensionality and a multidimensional CAT using computer simulations of real data for the assessment of a new global physical health measure for children with cerebral palsy (CP).
Methods:
Parent respondents of 306 children with cerebral palsy were recruited from four pediatric rehabilitation hospitals and outpatient clinics. We compared confirmatory factor analysis results across four models: (1) one-factor unidimensional; (2) two-factor multidimensional (MIRT); (3) bi-factor MIRT with fixed slopes; and (4) bi-factor MIRT with varied slopes. We tested whether the general and content (fatigue and pain) person score estimates could discriminate across severity and types of CP, and whether score estimates from a simulated CAT were similar to estimates based on the total item bank, and whether they correlated as expected with external measures.
Results:
Confirmatory factor analysis suggested separate pain and fatigue sub-factors; all 37 items were retained in the analyses. From the bi-factor MIRT model with fixed slopes, the full item bank scores discriminated across levels of severity and types of CP, and compared favorably to external instruments. CAT scores based on 10- and 15-item versions accurately captured the global physical health scores.
Conclusions:
The bi-factor MIRT CAT application, especially the 10- and 15-item versions, yielded accurate global physical health scores that discriminated across known severity groups and types of CP, and correlated as expected with concurrent measures. The CATs have potential for collecting complex data on the physical health of children with CP in an efficient manner.
