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On neighbourhood level, several multilevel analyses have been conducted.
MCMC was used because quasi-likelihood methods used to model multilevel analyses have been reported to be biased [ 32, 33].
Multilevel analyses have found that the overall social and economic characteristics of residential areas are associated with a broad range of health outcomes independent of individual indicators of SES (Pickett and Pearl 2001).
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Similarly, the multilevel analyses had lower values for both the Akaike Information Criterion and the Schwarz's Bayesian Information Criterion for each beneficiary group.
Multilevel analyses showed that the school/home-based intervention had significant effects on reading enjoyment and reading curiosity.
Covariate analyses using multilevel analyses were conducted to uncover factors that had the most influence on the number of COTiD referrals.
Determination of suitable sample sizes for multilevel analyses is complex, in part because explicit formulas have not yet been derived for general models.
There is a dearth of studies that have explored this problem using multilevel analyses of neighbourhood effects on individual health outcomes.
The results of multilevel analyses revealed that both shared leadership and high commonality had positive effects on team members' intended performance and predicted satisfaction.
We would have preferred to use alternative methods, such as multilevel analyses, as we lose information and the analyses become less efficient when we aggregate.
However, previous multilevel analyses (including the individual participants and their neighbourhoods as separate levels) have shown that no cluster effects were found.
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