Best practices for addressing missing data through multiple imputation

Adrienne D. Woods, Daria Gerasimova, Ben Van Dusen, Jayson Nissen, Sierra Bainter, Alex Uzdavines, Pamela E. Davis-Kean, Max Halvorson, Kevin M. King, Jessica A.R. Logan, Menglin Xu, Martin R. Vasilev, James M. Clay, David Moreau, Keven Joyal-Desmarais, Rick A. Cruz, Denver M.Y. Brown, Kathleen Schmidt, Mahmoud M. Elsherif

Research output: Contribution to journalArticlepeer-review

11 Scopus citations


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.

Original languageEnglish (US)
JournalInfant and Child Development
StateAccepted/In press - 2023


  • development
  • missing data
  • missingness mechanisms
  • multiple imputation
  • open scholarship

ASJC Scopus subject areas

  • Developmental and Educational Psychology


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