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Data were analyzed using multilevel modelling for longitudinal data (piecewise growth curve models).
Changes in outcomes over time were investigated using multilevel modelling for longitudinal data.
In principle more sophisticated analyses could be carried out using multilevel modelling for each examiner, but since our intention here was to produce a straightforward method which is easy to implement, we have not considered such models further.
Advantages of using multilevel modelling for longitudinal data analysis are that it is not necessary that subjects are measured on the same time points nor do follow up times need to be uniform.
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Data were analyzed using the Multilevel Model for Change framework.
For the dependent variable, self-rated health, we used multilevel models for binary responses.
This abnormal phenomenon has been explained by a multilevel model for light emission as discussed before [18].
Several authors have suggested the use of multilevel models for the analysis of data from single case designs.
It also introduces structural equation models and provides a description of simple multilevel models for data from populations with a two-level hierarchical structure.
Accordingly, this study devises a multilevel model for examining the links among contextual variables and collects data via a multi-informant questionnaire design.
Table 3 Coefficient estimates and their standard errors (s.e)., odds ratios (OR), and their 95% confidence interval of the binary logistic multilevel model for the life satisfaction (probability of being satisfied) Est.
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