Missing data: An update on the state of the art.
CK Enders - Psychological Methods, 2023 - psycnet.apa.org
The year 2022 is the 20th anniversary of Joseph Schafer and John Graham's paper titled
“Missing data: Our view of the state of the art,” currently the most highly cited paper in the …
“Missing data: Our view of the state of the art,” currently the most highly cited paper in the …
A model-based imputation procedure for multilevel regression models with random coefficients, interaction effects, and nonlinear terms.
Despite the broad appeal of missing data handling approaches that assume a missing at
random (MAR) mechanism (eg, multiple imputation and maximum likelihood estimation) …
random (MAR) mechanism (eg, multiple imputation and maximum likelihood estimation) …
Jomo: a flexible package for two-level joint modelling multiple imputation
M Quartagno, S Grund, J Carpenter - R Journal, 2019 - discovery.ucl.ac.uk
Multiple imputation is a tool for parameter estimation and inference with partially observed
data, which is used increasingly widely in medical and social research. When the data to be …
data, which is used increasingly widely in medical and social research. When the data to be …
Multiple imputation of missing data in multilevel models with the R package mdmb: a flexible sequential modeling approach
S Grund, O Lüdtke, A Robitzsch - Behavior Research Methods, 2021 - Springer
Multilevel models often include nonlinear effects, such as random slopes or interaction
effects. The estimation of these models can be difficult when the underlying variables …
effects. The estimation of these models can be difficult when the underlying variables …
Comparing DIC and WAIC for multilevel models with missing data
In Bayesian statistics, the most widely used criteria of Bayesian model assessment and
comparison are Deviance Information Criterion (DIC) and Watanabe–Akaike Information …
comparison are Deviance Information Criterion (DIC) and Watanabe–Akaike Information …
An investigation of factored regression missing data methods for multilevel models with cross-level interactions
A growing body of literature has focused on missing data methods that factorize the joint
distribution into a part representing the analysis model of interest and a part representing the …
distribution into a part representing the analysis model of interest and a part representing the …
Handling missing data in partially clustered randomized controlled trials.
Partially clustered designs are widely used in psychological research, especially in
randomized controlled trials that examine the effectiveness of prevention or intervention …
randomized controlled trials that examine the effectiveness of prevention or intervention …
Validity of data collected from randomized behavioral clinical trials during the COVID-19 pandemic
CA Mara, JL Peugh - Journal of Pediatric Psychology, 2020 - academic.oup.com
The COVID-19 pandemic and efforts to mitigate the spread and impact of the virus have
drastically altered day-to-day operations of research trials. In the United States, widespread …
drastically altered day-to-day operations of research trials. In the United States, widespread …
A Bayesian latent variable selection model for nonignorable missingness
Missing data are exceedingly common across a variety of disciplines, such as educational,
social, and behavioral science areas. Missing not at random (MNAR) mechanism where …
social, and behavioral science areas. Missing not at random (MNAR) mechanism where …
Evaluation of approaches for multiple imputation of three-level data
Background Three-level data arising from repeated measures on individuals who are
clustered within larger units are common in health research studies. Missing data are …
clustered within larger units are common in health research studies. Missing data are …