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The advantage of using multilevel analyses is that missing data are handled by using all available data, including cases with partially missing data (Snijders and Bosker 2012).
First, the deviance of multilevel analyses is significantly lower than that of standard linear regression analyses for each beneficiary group (p < 0.001).
Determination of suitable sample sizes for multilevel analyses is complex, in part because explicit formulas have not yet been derived for general models.
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Given the nested nature of the data, multilevel analyses were used to assess consultant effects on student outcomes.
Cross-sectional and longitudinal multilevel analyses were conducted with over 28,000 diabetic primary care patients of the Geisinger Clinic.
Multilevel analyses were used to estimate the changes in physiological measures during the task based on personal characteristics and problem-solving activities.
Sophisticated approaches like multilevel analyses are needed to discover subtle differences between healthy people and people at the onset of disease in these types of studies.
Multilevel analyses were conducted.
Multilevel analyses were performed using STATA XTmixed.
Multilevel analyses were used to analyse the data.
Multilevel analyses were conducted with MLwiN 2.20 software.
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