Multiple Imputation of Missing Values: Update

By Patrick Royston. 2005

838 citations recorded by the source corpus.

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Citations in the corpus

Decreasing the SES math achievement gap: Initial math proficiency and home learning environments (2015, methodology): “ICE handles complex data structures by fitting a sequence of chain equations to impute variables in order of increasing “missingness,” that is, the variable with the least missing values is imputed first and so on (Royston, 2005).”
Impulsivity, Offending, and the Neighborhood: Investigating the Person–Context Nexus (2012, methodology): “However, multiple imputation techniques in Stata (the ICE command) were used to examine potential bias resulting from missing impulsivity data ( Royston 2005 ).”
Export behaviour of SMEs in transition countries (2013, methodology): “For practical implementation of MI, we use the routines written for STATA (see Royston 2005a, b, 2007; Carlin et al. 2008).”
Socioeconomic Gaps in Early Childhood Experiences (2016, methodology): “The imputation model included all the variables described below and generated 20 imputed data sets through the MI command in Stata, which resulted in the expected relative efficiency ranging from 98.4% to 99.9% in the ECLS-K:98 and from 98.2% to 99.9% in the ECLS-K:10 (Royston, 2005; Rubin, 1987).”
How Supportive of Their Specific Purposes Do Youth Believe Their Family and Friends Are? (2013, methodology): “Multiple imputation procedures (ICE program in Stata 10.1; Royston, 2005) were conducted to handle the small number of missing data (<5%; Acock, 2005).”
Multiple Imputation by Chained Equations (MICE): Implementation in Stata (2011, background): “As described by Royston (2005b), to satisfy the normality assumption for a continuous variable, a transformation toward normality may be effective.”
Self-control, Victimization, and their Influence on Risky Lifestyles: A Longitudinal Analysis Using Panel Data (2006, methodology): “missing values ( Royston 2005a, b). This involved a three-step procedure in which we used the ICE function to generate 10 imputed data sets.”
Multiple Imputation of Missing Values: Update of Ice (2005, background): “In a second article, Royston (2005) described ice, an upgrade incorporating various improvements and changes to the software based on personal experience, discussion with colleagues, and user requests.”
The Causal Effect of Campus Residency on College Student Retention (2011, methodology): “Imputation using Chained Equations (ICE), an MI approach and program in Stata, mitigates this assumption by creating a small number, m, of completed copies of the data set in which missing observations are replaced by plausible values instead of assuming one “true” response model (Royston, 2005; van”
Why 'Good' Firms Do Bad Things: The Effects of High Aspirations, High Expectations and Prominence on the Incidence of Corporate Illegality (2010, methodology): “Following Jensen and Roy (2008), we employed multiple imputation using the “ice” command in Stata 9.2 (Royston, 2005a, 2005b) to impute values for governance variables with missing values.”
Working With Missing Values (2005, methodology): “Distinguishing what should and should not be imputed is usually impossible with a single code (e.g., 9) for every type of missing value.”
The Role of Teachers in Bullying: The Relation between Antibullying Attitudes, Efficacy, and Efforts to Reduce Bullying. (2014, methodology): “After multiple imputation (Royston, 2005), we were able to use all 2,776 cases; 49.”