Best practices for addressing missing data through multiple imputation
Authors: Woods, A.D. et al.
Journal: Infant and Child Development
Volume: 33
Issue: 1
eISSN: 1522-7219
ISSN: 1522-7227
DOI: 10.1002/icd.2407
Abstract:A common challenge in developmental research is the amount of incomplete and missing data that occurs from respondents failing to complete tasks or questionnaires, as well as from disengaging from the study (i.e., attrition). This missingness can lead to biases in parameter estimates and, hence, in the interpretation of findings. These biases can be addressed through statistical techniques that adjust for missing data, such as multiple imputation. Although multiple imputation is highly effective, it has not been widely adopted by developmental scientists given barriers such as lack of training or misconceptions about imputation methods. Utilizing default methods within statistical software programs like listwise deletion is common but may introduce additional bias. This manuscript is intended to provide practical guidelines for developmental researchers to follow when examining their data for missingness, making decisions about how to handle that missingness and reporting the extent of missing data biases and specific multiple imputation procedures in publications.
https://eprints.bournemouth.ac.uk/38295/
Source: Scopus
Best practices for addressing missing data through multiple imputation
Authors: Woods, A.D. et al.
Journal: INFANT AND CHILD DEVELOPMENT
Volume: 33
Issue: 1
eISSN: 1522-7219
ISSN: 1522-7227
DOI: 10.1002/icd.2407
https://eprints.bournemouth.ac.uk/38295/
Source: Web of Science (Lite)
Best practices for addressing missing data through multiple imputation
Authors: Woods, A.D. et al.
Journal: Infant and Child Development
Publisher: Wiley-Blackwell
ISSN: 1522-7219
DOI: 10.1002/icd.2407
Abstract:A common challenge in developmental research is the amount of incomplete and missing data that occurs from respondents failing to complete tasks or questionnaires, as well as from disengaging from the study (i.e., attrition). This missingness can lead to biases in parameter estimates and, hence, in the interpretation of findings. These biases can be addressed through statistical techniques that adjust for missing data, such as multiple imputation. Although multiple imputation is highly effective, it has not been widely adopted by developmental scientists given barriers such as lack of training or misconceptions about imputation methods. Utilizing default methods within statistical software programs like listwise deletion is common but may introduce additional bias. This manuscript is intended to provide practical guidelines for developmental researchers to follow when examining their data for missingness, making decisions about how to handle that missingness and reporting the extent of missing data biases and specific multiple imputation procedures in publications.
https://eprints.bournemouth.ac.uk/38295/
Source: Manual
Best practices for addressing missing data through multiple imputation
Authors: Woods, A.D. et al.
Journal: Infant and Child Development
Pages: 1-37
Publisher: Wiley-Blackwell
ISSN: 1522-7219
Abstract:A common challenge in developmental research is the amount of incomplete and missing data that occurs from respondents failing to complete tasks or questionnaires, as well as from disengaging from the study (i.e., attrition). This missingness can lead to biases in parameter estimates and, hence, in the interpretation of findings. These biases can be addressed through statistical techniques that adjust for missing data, such as multiple imputation. Although multiple imputation is highly effective, it has not been widely adopted by developmental scientists given barriers such as lack of training or misconceptions about imputation methods. Utilizing default methods within statistical software programs like listwise deletion is common but may introduce additional bias. This manuscript is intended to provide practical guidelines for developmental researchers to follow when examining their data for missingness, making decisions about how to handle that missingness and reporting the extent of missing data biases and specific multiple imputation procedures in publications.
https://eprints.bournemouth.ac.uk/38295/
Source: BURO EPrints