Pages that link to "Item:Q2103281"

The following pages link to An evaluation of methods to handle missing data in the context of latent variable interaction analysis: multiple imputation, maximum likelihood, and random forest algorithm (Q2103281):

Displaying 5 items.

• Recursive partitioning for missing data imputation in the presence of interaction effects (Q1623390) (← links)
• Should we impute or should we weight? Examining the performance of two CART-based techniques for addressing missing data in small sample research with nonnormal variables (Q1658370) (← links)
• Navigating choices when applying multiple imputation in the presence of multi-level categorical interaction effects (Q1731416) (← links)
• Evaluation of four multiple imputation methods for handling missing binary outcome data in the presence of an interaction between a dummy and a continuous variable (Q2039155) (← links)
• Evaluation of latent construct correlations in the presence of missing data: a note on a latent variable modelling approach (Q2905123) (← links)