We propose a bi-objective algorithm based on the k-nearest neighbors (biokNN) method to
perform imputation of missing values for data with multilevel structures with continuous …

The treatment of missing data can be difficult in multilevel research because state-of-the-art
procedures such as multiple imputation (MI) may require advanced statistical knowledge or …

Multiple imputation (MI) can be used to address missing data at Level 2 in multilevel
research. In this article, we compare joint modeling (JM) and the fully conditional …

During the past decades, the explosive increase in the population in urban areas has
presented a great challenge to control the impact of the generation of municipal solid waste …

Missing data due to non-response is pervasive in large-scale survey research. Failing to
appropriately account for these missing values can lead to erroneous findings and false …

This dissertation explores the changes in knowledge and attitudes about HIV/AIDS in sub-
Saharan Africa (SSA) over time. Specifically, this work estimates the trends of 16 key …

This thesis evaluates the performance of different multiple imputation methods in imputing
country-level proportions of key indicators that are missing in time-series-cross-sectional …

Multilevel models have become one of the most frequently used statistical models for
analyzing multilevel data. These types of data occur in many fields of psychology when …