Multiple imputation for multivariate missing-data problems: A data analyst's perspective
JL Schafer, MK Olsen - Multivariate behavioral research, 1998 - Taylor & Francis
Analyses of multivariate data are frequently hampered by missing values. Until recently, the
only missing-data methods available to most data analysts have been relatively ad1 hoc …
only missing-data methods available to most data analysts have been relatively ad1 hoc …
Missing data in multilevel research.
S Grund, O Lüdtke, A Robitzsch - 2019 - psycnet.apa.org
Multilevel data are often incomplete, for example, when participants refuse to answer some
items in a questionnaire or drop out of a study that involves multiple measurement …
items in a questionnaire or drop out of a study that involves multiple measurement …
Multiple imputation in multivariate research
Statistical analysis with missing data has always been a challenge. However, there have
been tremendous advances in statistical theory related to analysis with missing data. Of …
been tremendous advances in statistical theory related to analysis with missing data. Of …
[PDF][PDF] Multiple imputation for missing data: Concepts and new development (Version 9.0)
YC Yuan - SAS Institute Inc, Rockville, MD, 2010 - Citeseer
Multiple imputation provides a useful strategy for dealing with data sets with missing values.
Instead of filling in a single value for each missing value, Rubin's (1987) multiple imputation …
Instead of filling in a single value for each missing value, Rubin's (1987) multiple imputation …
The use of multiple imputation for the analysis of missing data.
This article provides a comprehensive review of multiple imputation (MI), a technique for
analyzing data sets with missing values. Formally, MI is the process of replacing each …
analyzing data sets with missing values. Formally, MI is the process of replacing each …
[PDF][PDF] An overview of multiple imputation
DB Rubin - Proceedings of the survey research methods section of …, 1988 - Citeseer
Multiple imputation for nonresponse in public-use files replaces each missing value by two
or more plausible values. The values can be chosen to represent both uncertainty about …
or more plausible values. The values can be chosen to represent both uncertainty about …
Multiple imputation in practice: comparison of software packages for regression models with missing variables
NJ Horton, SR Lipsitz - The American Statistician, 2001 - Taylor & Francis
Missing data frequently complicates data analysis for scientific investigations. The
development of statistical methods to address missing data has been an active area of …
development of statistical methods to address missing data has been an active area of …
Multiple imputation as a flexible tool for missing data handling in clinical research
CK Enders - Behaviour research and therapy, 2017 - Elsevier
The last 20 years has seen an uptick in research on missing data problems, and most
software applications now implement one or more sophisticated missing data handling …
software applications now implement one or more sophisticated missing data handling …
Multiple imputation using SAS software
Y Yuan - Journal of Statistical Software, 2011 - jstatsoft.org
Multiple imputation provides a useful strategy for dealing with data sets that have missing
values. Instead of filling in a single value for each missing value, a multiple imputation …
values. Instead of filling in a single value for each missing value, a multiple imputation …
Methods for handling missing data
JW Graham, PE Cumsille… - Handbook of psychology, 2003 - Wiley Online Library
This chapter describes a general approach to handling missing data in psychological
research. It provides a theoretical background in readable, nontechnical fashion. Our overall …
research. It provides a theoretical background in readable, nontechnical fashion. Our overall …