Best practices for addressing missing data through multiple imputation

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...

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Veröffentlicht in:	Infant and child development 2024-01, Vol.33 (1), p.n/a
Hauptverfasser:	Woods, Adrienne D., Gerasimova, Daria, Van Dusen, Ben, Nissen, Jayson, Bainter, Sierra, Uzdavines, Alex, Davis‐Kean, Pamela E., Halvorson, Max, King, Kevin M., Logan, Jessica A. R., Xu, Menglin, Vasilev, Martin R., Clay, James M., Moreau, David, Joyal‐Desmarais, Keven, Cruz, Rick A., Brown, Denver M. Y., Schmidt, Kathleen, Elsherif, Mahmoud M.
Format:	Artikel
Sprache:	eng
Schlagworte:	Attrition Best practice Bias Decision making development Guidelines Misconceptions Missing data missingness mechanisms Multiple imputation open scholarship
Online-Zugang:	Volltext
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