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

Methodology

Pieter M. Kroonenberg¹  Joost R. van Ginkel² 

[1] Department of Child and Family Studies, Leiden University, Leiden, The Netherlands;Department of Psychology, Leiden University, Leiden, The Netherlands;

关键词: unbalanced designs;    multiple imputation;    two-way analysis of variance;    missing data;    type-iii sum of squares;   

DOI  :  10.5964/meth.6085

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown