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We fit the resulting multilevel model with RJMCMC (Richardson and Green 1997) to separate the sources and obtain posterior distributions for the number of sources and their individual parameters.
A statistical method is presented for attributing explained variation in patient care to different levels of aggregation in a multilevel model with the aim of prioritizing and targeting quality improvement interventions.
Using this multivariate multilevel model with latent variables, we examined the relationship between growth in mathematics and science achievement during middle and high school among students and schools, and we demonstrated that such a model was more sensitive to this relationship.
Coradi Vellacott et al. (2003) have analyzed the PISA 2000 data for Switzerland using a hierarchical multilevel model with dummy variables for the share of students who do not speak the test language in the second level of their model.
We first fitted a multilevel model with respondents nested within neighbourhoods, and age group and sex as the predictor variables (model 1 in table 2).
Data were analyzed in a binomial multilevel model with tolla as a random effect.
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Therefore, we must consider multilevel models with area effects.
We use linear multilevel models with random intercept and we account for sample selection.
Aim #2: assess the association between the social network characteristics and health-related outcomes (education, income-generation) and between the sexual network characteristics and HIV-risk outcomes using multilevel modeling with network data.
Growth was estimated using multilevel models with child-specific intercepts and trajectories.
The data were analysed using continuation ratio ordinal multilevel models with multiple imputation for missing covariates.
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