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Future research should evaluate convergent validity using other validation measures such as accelerometers and more detailed recall questionnaires.
Validation measures were taken from CA2 since these measurements were completed closest in time to the PA questions.
These results were further validated by clValid [ 32], using internal and stability cluster validation measures to design a threshold line to generate 2 clusters.
The hierarchical clustering analyses were validated using c1Valid R Package [ 32], by internal and stability cluster validation measures.
In this study, we have taken an initial step in evaluating convergent validity of a relative PA question using indicators of physical functioning as validation measures.
Potential strengths of our study are the number of external validation measures for physical impairment and disease severity, and the approach to investigate validity.
In the following, we present more recent clustering validation measures.
Validation measures included global motor tasks and a neurological examination.
In this section, we evaluate the result of stream clustering algorithms with internal validation measures.
In USA, summed scores correlated well and in the expected directions with external validation measures.
We explained fundamental theories of internal validation measures and its examples.
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