Missing data are a common problem in a variety of measurement settings, including
responses to items on both cognitive and affective assessments. Researchers have shown …

The most user-friendly and authoritative resource on missing data has been completely
revised to make room for the latest developments that make handling missing data more …

This book provides practical guidance for statisticians, clinicians, and researchers involved
in clinical trials in the biopharmaceutical industry, medical and public health organisations …

This article compares a variety of imputation strategies for ordinal missing data on Likert
scale variables (number of categories= 2, 3, 5, or 7) in recovering reliability coefficients …

Missing data are a common problem for researchers working with surveys and other types of
questionnaires. Often, respondents do not respond to one or more items, making the …

Incomplete data are ubiquitous in social sciences; as a consequence, available data are
inefficient (ineffective) and often biased. In the literature, multiple imputation is known to be …

Objectives Previous research has shown that while missing data are common in
bioarchaeological studies, they are seldom handled using statistically rigorous methods …

Muscle dysmorphia (MD) is a type of body dysmorphic disorder related to body image
disturbances associated with musculature and leanness. This study surveyed the presence …

Multivariate analysis of variance (MANOVA) is widely used in educational research to
compare means on multiple dependent variables across groups. Researchers faced with the …

Methods of uniform differential item functioning (DIF) detection have been extensively
studied in the complete data case. However, less work has been done examining the …