The SF-6D classification system includes six multilevel dimensions (physical functioning, role of participation, social functioning, bodily pain, mental health, and vitality) and describes 18,000 health states [ 35].
The third strategy is to measure the health transition in all its complexity in its multifaceted, multilevel and life-course dimensions relevant to health policy.
While some authors acknowledge a correlation only for certain groups of patients others have reported an influence of spinal canal dimensions in multilevel foraminal narrowing [ 1, 3, 17, 22- 24].
With 2 testlets, the dimensions of numerical integration for a multilevel testlet model was 6, 3 dimensions at the within-cluster level and 3 dimensions at the between-cluster level.
We also use Principal Component Analysis to reduce the dimension of the multilevel feature set, and test the results on this new descriptor as well.
After a factorial analysis to identify the underlying responsibility dimensions, we performed a multilevel regression model in order to investigate individual and service covariates of responsibility attribution.
Others aimed to find more universal and non-normative concepts of integration such as multilevel approach (Scholten & Penninx, 2016), different dimensions of integration (Heckmann et al., 2001), and integration as an on-going process (Penninx & Martiniello, 2004).
The results of the multilevel regressions including separate models for body dimensions (body mass and fat-free mass), age, and interaction term of age and maturity status as explanatory factors of change in peak VO2 and its relation with body dimensions are summarized in Table 3 (for body mass) and Table 4 (for fat-free mass).
Multilevel regression models were used with two dimensions (physical and mental component scores) as continuous dependent variables and general practice and patient characteristics, including the hypothesized interaction between gender and employment (based on the previous studies [ 15, 27, 28]), as the independent variables.
