Missing data pose challenges to real-life data analysis. Simple ad-hoc fixes, like deletion or
mean imputation, only work under highly restrictive conditions, which are often not met in …

Developing new imputation methodology has become a very active field. Unfortunately,
there is no consensus on how to perform simulation studies to evaluate the properties of …

The use of multiple imputation (MI) is becoming increasingly popular for addressing missing
data. Although some conventional MI approaches have been well studied and have shown …

Ecologically distinct species-species with distinct trait combinations-are not directly
prioritized in current conservation frameworks. The consequence of this blind spot means …

Large scale assessment data often has a multilevel structure. When dealing with missing
values, such structures need to be taken into account to prevent underestimation of the …

Synthetic datasets simultaneously allow for the dissemination of research data while
protecting the privacy and confidentiality of respondents. Generating and analyzing synthetic …

IO psychologists often face the need to reduce the length of a data collection effort due to
logistical constraints or data quality concerns. Standard practice in the field has been either …

Both registers and surveys can contain classification errors. These errors can be estimated
by making use of a composite data set. We propose a new method based on latent class …

Detailed breakdowns on totals are often collected in surveys, such as a breakdown of total
product sales by product type. These multinomial data are often sparsely reported with wide …

There has been an extensive body of methodological literature supporting the effectiveness
of planned missingness (PM) designs for reducing survey length. However, in industrial …