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Best practices for addressing missing data through multiple imputation.

Woods, A. D., Gerasimova, D., Van Dusen, B., Nissen, J., Bainter, S., Uzdavines, A., Davis-Kean, P. E., Halvorson, M., King, K. M., Logan, J. A.R., Xu, M., Vasilev, M. R., Clay, J. M., Moreau, D., Joyal-Desmarais, K., Cruz, R. A., Brown, D. M.Y., Schmidt, K. and Elsherif, M. M., 2023. Best practices for addressing missing data through multiple imputation. Infant and Child Development, 1-37.

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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.

Item Type:Article
ISSN:1522-7219
Uncontrolled Keywords:development; missing data; missingness mechanisms; multiple imputation; open scholarship
Group:Faculty of Science & Technology
ID Code:38295
Deposited By: Symplectic RT2
Deposited On:08 Mar 2023 10:26
Last Modified:08 Mar 2023 10:26

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