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Sampling weights were scaled for multilevel analysis in Stata following Chantala, Blanchette, & Suchindran, 2011.
Overall, this study represents a multilevel analysis in examining the relationships between the student and school level affective variables and mathematics performance in PISA 2012 across Indonesia, Malaysia, and Thailand.
The study demonstrates the importance of multilevel analysis in improving the understanding of CSR drivers, relative to single level models, even if the significance of specific drivers and levels may vary by context.
It is important to highlight that the ignorance of using multilevel analysis in dealing with the PISA dataset could jeopardize the important empirical evidence such as a decomposition of variance of student performance by school and student level in line with the nested structure of the dataset (Raudenbush and Bryk 2002).
This was confirmed in a multilevel analysis in which we estimated the 95% -confidence interval (CI-95%) for the proportion of non-adherent patients.
This multilevel analysis in which adjustment was made for possible effects of clustering explained nearly 50% (46.35%) of the variance in contacts with the outpatient mental health services.
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In the multilevel analysis applied in this paper, countries constitute the highest (third) level (n = 20), while regions within countries constitute the second level.
The multilevel analysis performed in this study was adjusted for baseline PPD.
The multilevel analysis presented in this paper is able to determine the isolated effect of the EAP.
Multilevel analysis resulted in initial variables at the start of treatment that predict completion of questionnaires by both parents.
The multilevel analysis introduced in the Cox model (shared frailties Cox regression model), showed that there is a correlation between patients recovered in the same hospital (theta < 0.05), therefore we have a heterogeneity between the hospital with a latent common group effect.
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