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Guo, G. & Zhao, H. Multilevel modeling for binary data.
Regression analyses for trait data and multilevel modeling for daily diary data both revealed a pattern of two competing alexithymic mechanisms: difficulty in identifying feelings was related to increased GSEs, whereas externally-oriented thinking was related to decreased GSEs.
Multilevel modeling for binary data was performed to estimate the associations between adolescent and parental values and adolescent problem behaviors.
Unfortunately, none of these studies used multilevel modeling for the statistical analysis, which could have been beneficial in the evaluation of dynamic changes in proinflammatory markers.
The robustness issue and the choice of sample size and power in multilevel modeling for continuous dependent variables has been studied by several authors [ 4- 13].
The application of multilevel modeling for the analysis of longitudinal data, in this case repeated measurements on the same individuals, is strongly recommended in the literature [ 41, 42].
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Data were analyzed using the Multilevel Model for Change framework.
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.
For the dependent variable, self-rated health, we used multilevel models for binary responses.
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