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Multilevel analysis revealed positive effects on student achievement and motivation.
Then, a multilevel analysis revealed that students' self-regulation affects students' emotional states.
Furthermore, multilevel analysis revealed that physical demands were negatively related to the intention to continue to work for the pre-retirement employer after retirement entry, whereas social support at work strengthened the relationship between outcome expectations and same-employer-post-retirement work intention.
Multilevel analysis revealed a significant reduction in prescription days between 2007/2008 and 2009/2010.
Multilevel analysis revealed that contextual HDI remained associated with dental pain regardless individual-level characteristics.
Multilevel analysis revealed that performing below standard history taking was consistent at the GP level between the two ISP visits.
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The multilevel model analysis revealed no significant clustering between veterinarians or herds (data not shown).
The multilevel model analysis revealed that health seeking behavior was significantly associated with insurance coverage.
Multivariate multilevel regression analysis revealed that survey time was significantly and negatively related to three of six patient-reported experience scales: doctor services (Beta = -0.424, p< 0.05), information about examinations (Beta = -0.566, p < 0.05) and organization (Beta = -0.528, p < 0.05).
* p < 0.05; ns, not significant ** p < 0.01; * p < 0.05; ns, not significant Multilevel regression analysis revealed that several background variables were significantly associated with patient-reported experiences (Table 3).
Multivariate multilevel binary logistic regression analysis revealed that the following factors were significantly associated with EBF after controlling for confounders: (a) decreased child age in months; (b) geopolitical region; (c) antenatal clinic visits; (d) household wealth, and (f) gender.
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CEO of Professional Science Editing for Scientists @ prosciediting.com