Data imputation

Data imputation involves representing missing values in a dataset. Missing data create a number of potential challenges for statistical analysis. Missing values can increase the chances of making Type I and Type II errors, reduce statistical power, and limit the reliability of confidence intervals....

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Main Author: ROSENTHAL, Sonny
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Language:English
Published: Institutional Knowledge at Singapore Management University 2017
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Online Access:https://ink.library.smu.edu.sg/cis_research/212
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Institution: Singapore Management University
Language: English
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spelling sg-smu-ink.cis_research-12112024-09-02T04:48:03Z Data imputation ROSENTHAL, Sonny Data imputation involves representing missing values in a dataset. Missing data create a number of potential challenges for statistical analysis. Missing values can increase the chances of making Type I and Type II errors, reduce statistical power, and limit the reliability of confidence intervals. There are a number of statistical procedures available for researchers to replace missing values with reasonable estimations. Basic methods, such as mean substitution, regression imputation, and hot deck imputation may bias imputed values depending on the mechanism of missingness. Advanced methods include expectation maximization, full information maximum likelihood, and multiple imputation. These methods produce more reliable estimations of missing values, particularly when missingness is not at random. 2017-11-07T08:00:00Z text https://ink.library.smu.edu.sg/cis_research/212 info:doi/10.1002/9781118901731.iecrm0058 Research Collection College of Integrative Studies eng Institutional Knowledge at Singapore Management University imputation linear regression maximum likelihood measurement error missing values Communication Critical and Cultural Studies
institution Singapore Management University
building SMU Libraries
continent Asia
country Singapore
Singapore
content_provider SMU Libraries
collection InK@SMU
language English
topic imputation
linear regression
maximum likelihood
measurement error
missing values
Communication
Critical and Cultural Studies
spellingShingle imputation
linear regression
maximum likelihood
measurement error
missing values
Communication
Critical and Cultural Studies
ROSENTHAL, Sonny
Data imputation
description Data imputation involves representing missing values in a dataset. Missing data create a number of potential challenges for statistical analysis. Missing values can increase the chances of making Type I and Type II errors, reduce statistical power, and limit the reliability of confidence intervals. There are a number of statistical procedures available for researchers to replace missing values with reasonable estimations. Basic methods, such as mean substitution, regression imputation, and hot deck imputation may bias imputed values depending on the mechanism of missingness. Advanced methods include expectation maximization, full information maximum likelihood, and multiple imputation. These methods produce more reliable estimations of missing values, particularly when missingness is not at random.
format text
author ROSENTHAL, Sonny
author_facet ROSENTHAL, Sonny
author_sort ROSENTHAL, Sonny
title Data imputation
title_short Data imputation
title_full Data imputation
title_fullStr Data imputation
title_full_unstemmed Data imputation
title_sort data imputation
publisher Institutional Knowledge at Singapore Management University
publishDate 2017
url https://ink.library.smu.edu.sg/cis_research/212
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