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 …
[图书][B] Flexible imputation of missing data
S Van Buuren - 2018 - books.google.com
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 …
mean imputation, only work under highly restrictive conditions, which are often not met in …
Tutorial: Assessing the impact of nonignorable missingness on regression analysis using Index of Local Sensitivity to Nonignorability.
Data sets with missing observations are common in psychology research. One typically
analyzes such data by applying statistical methods that rely on the assumption that the …
analyzes such data by applying statistical methods that rely on the assumption that 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) …
Multiple imputation of missing data for multilevel models: Simulations and recommendations
S Grund, O Lüdtke, A Robitzsch - Organizational Research …, 2018 - journals.sagepub.com
Multiple imputation (MI) is one of the principled methods for dealing with missing data. In
addition, multilevel models have become a standard tool for analyzing the nested data …
addition, multilevel models have become a standard tool for analyzing the nested data …
Regression models involving nonlinear effects with missing data: A sequential modeling approach using Bayesian estimation.
When estimating multiple regression models with incomplete predictor variables, it is
necessary to specify a joint distribution for the predictor variables. A convenient assumption …
necessary to specify a joint distribution for the predictor variables. A convenient assumption …
Maximum likelihood and multiple imputation missing data handling: How they work, and how to make them work in practice.
This chapter provides an overview of maximum likelihood estimation and multiple
imputation, two major missing data handling strategies with strong support from the …
imputation, two major missing data handling strategies with strong support from the …
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 …
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 …