Evaluation of multi-parameter test statistics for multiple imputation
Y Liu, CK Enders - Multivariate Behavioral Research, 2017 - Taylor & Francis
ABSTRACT In Ordinary Least Square regression, researchers often are interested in
knowing whether a set of parameters is different from zero. With complete data, this could be …
knowing whether a set of parameters is different from zero. With complete data, this could be …
Investigation of multiple imputation in low-quality questionnaire data
JR Van Ginkel - Multivariate Behavioral Research, 2010 - Taylor & Francis
The performance of multiple imputation in questionnaire data has been studied in various
simulation studies. However, in practice, questionnaire data are usually more complex than …
simulation studies. However, in practice, questionnaire data are usually more complex than …
[HTML][HTML] Multiple Imputation When Variables Exceed Observations: An Overview of Challenges and Solutions
Missing data are a prevalent problem in psychological research that can reduce statistical
power and bias parameter estimates. These problems can be mostly resolved with multiple …
power and bias parameter estimates. These problems can be mostly resolved with multiple …
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 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 …
[图书][B] Multilevel multiple imputation: An examination of competing methods
SA Mistler - 2015 - search.proquest.com
Missing data are common in psychology research and can lead to bias and reduced power if
not properly handled. Multiple imputation is a state-of-the-art missing data method …
not properly handled. Multiple imputation is a state-of-the-art missing data method …
[PDF][PDF] Multiple overimputation: A unified approach to measurement error and missing data
Social scientists typically devote considerable effort to mitigating measurement error during
data collection but then ignore the issue during data analysis. Although many statistical …
data collection but then ignore the issue during data analysis. Although many statistical …
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 …
[PDF][PDF] Missing data and multiple imputation decision tree
Adequately addressing missing data is a pervasive issue in the social sciences. Failure to
correctly address missing data can lead to biased or inefficient estimation of parameters …
correctly address missing data can lead to biased or inefficient estimation of parameters …
Multiple imputation with principal components for non-normal categorical data
Multiple Imputation (MI) is the most efficient way of estimating missing values for non-normal
categorical variables (Enders, 2017). A challenge related to MI is the choice of appropriate …
categorical variables (Enders, 2017). A challenge related to MI is the choice of appropriate …