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However, it means that some analyses – e.g. multilevel analyses with the use of the university level – are not applicable.
The results of multilevel analyses with this measurement model are comparable to results using all the plausible values and averaging the coefficients.
Therefore, we only included respondent's occupation in the multilevel analyses with the largest group (nurse) as the reference category.
MLwiN software package 2.0.2 [ 30] was used for the multilevel analyses, with Binomial, Penalized Quasi-Likelihood (PQL) procedures [ 31].
However, the subsequent use of multilevel analyses, with the completion per assessment of both parents as a binary variable, strengthened the statistical analyses.
As the SDQ and the PSAI were administered to one parent only, it was not possible to conduct multilevel analyses with these variables.
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Multilevel analyses deals with data that are hierarchically structured [ 40] with one dependent variable measured at the lowest level and exploratory variables at the same and higher levels.
Based on univariate Poisson regression models and likelihood ratio tests (p < 0.2), seven candidate village-level variables were selected for inclusion as candidate variables in initial multilevel analyses together with household-level variables.
Cross-sectional and longitudinal multilevel analyses were conducted with over 28,000 diabetic primary care patients of the Geisinger Clinic.
Multilevel analyses were conducted with MLwiN 2.20 software.
The multilevel analyses were performed with MLWin version 2.24.
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