… analysis, several studies have shown that it is particularly important for the imputation
model to take the multilevel structure into account if analyses based on the imputed data are to …

… Multiple imputation is increasingly used to address the presence of missing data in clinical
… imputation may be used rather than multiple imputation. We therefore also compared model …

… Under the structural equation modeling (SEM) framework, a better approach that takes into
… deletion and multiple imputation in the context of ordinal factor analysis models with missing …

… The aim of this article is to compare the performance of six multiple imputation methods
to the commonly used CCA, representing fundamentally different ways of approaching the …

… to outline a novel multiple imputation approach to multilevel … models with random intercepts),
classic multiple imputation … normal variables in structural equation modeling software such …

… multiple imputation to impute missing values when the substantive analysis is a weighted
model. … which define level-two, (ii) we include random intercepts, and (iii) we allow the variance …

… methods such as multiple imputation. This is particularly true when subsequent analyses
are complex (eg, a hierarchical structural equation model with a complex latent measurement …

… imputation models to parametric imputation models in MICE by specifying a compatible
parametric imputation model as … We compare performance of the MICE imputation methods via …

… imputation model needs to include this structure. Here we introduce our joint modelling
package for multiple imputation of multilevel data, jomo, which uses a multivariate normal model …

… analysis, it is clear that using the total N under FIML will produce values of \( {R}_{Adj}^2 \)
equivalent to those that would be obtained under the same model using multiple imputation. …