Multiple Imputation to Balance Unbalanced Designs for Two-Way Analysis of Variance

Abstract: A balanced ANOVA design provides an unambiguous interpretation of the F-tests, and has more power than an unbalanced design. In earlier literature, multiple imputation was proposed to create balance in unbalanced designs, as an alternative to Type-III sum of squares. In the current simulation study we studied four pooled statistics for multiple imputation, namely D₀, D₁, D₂, and D₃ in unbalanced data, and compared them with Type-III sum of squares. Statistics D₁ and D₂ generally performed best regarding Type-I error rates, and had power rates closest to that of Type-III sum of squares. Additionally, for the interaction, D₁ produced power rates higher than Type-III sum of squares. For multiply imputed datasets D₁ and D₂ may be the best methods for pooling the results in multiply imputed datasets, and for unbalanced data, D₁ might be a good alternative to Type-III sum of squares regarding the interaction. https://meth.psychopen.eu/index.php/meth/article/view/6085

Object Details

• Location: Deutsche Nationalbibliothek Frankfurt am Main
• Extent: Online-Ressource
• Language: Englisch

References and Relationships

• Bibliographic citation: Multiple Imputation to Balance Unbalanced Designs for Two-Way Analysis of Variance ; volume:17 ; number:1 ; day:31 ; month:03 ; year:2021 ; Methodology ; 17, Heft 1 (31.03.2021)

Contributors, Places and Time

• Creator: van Ginkel, Joost R. ; Kroonenberg, Pieter M.

Further information

• DOI: 10.5964/meth.6085
• URN: urn:nbn:de:101:1-2021050105090749963236
• Rights: Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
• Last update: 14.08.2025, 10:55 AM CEST