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Using data from the 2006 PISA survey, we performed cross-classified multilevel analysis on the science performance of 9.279 15-year-old 15-year-old 15-year-oldnating fromigrantfferent childrens, living in 16 Western coriginating destination.
Therefore, de Lange et al. (2014) applied a multilevel analysis on the basis of data on young people from 29 countries who participated in the European Social Survey 2002, 2004, 2006 and 2008.
We did multilevel analysis on each outcome by using the iterative generalised least squares method of estimation.
The results of the logistic multilevel analysis on the dichotomous information item are shown in Table 4.
We also performed a multilevel analysis on the relationship between QOL and marital status while accounting for the provincial differences.
In addition, we tested the interactions between outcome, AMI phenotype and gender, and then carried out a multilevel analysis on patients with STEMI and NSTEMI separately.
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A multilevel analysis of the data on core toolkit scores and all users from all countries will then be undertaken with the aim of delineating the main, independent predictors in the toolkit of patients' perceived autonomy, dignity, satisfaction with care, as well as potential markers for recovery.
A multilevel analysis of the pooled data on core toolkit scores for all users from all countries will then be undertaken.
Given the structure of our data, consisting of students nested in institutions of higher education, we used multilevel analysis to examine its influence on FK.
We use logistic multilevel analysis to study the effect on the dichotomous primary outcome (adherent yes or no).
Results of multilevel analysis for all patients hospitalised on the first day of the index period (n = 6,325), are presented in Table 4.
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