scholarly journals Retraction of "Van Ginkel, J. R., & Kroonenberg, P. M. (2020). Much ado about nothing: Multiple imputation to balance unbalanced designs for two-way analysis of variance"

Methodology ◽  
2021 ◽  
Vol 16 (4) ◽  
pp. 354-355
Keyword(s):  
Multiple Imputation ◽  
Analysis Of Variance ◽  
Much Ado About Nothing ◽  
Unbalanced Designs

scholarly journals Much ado about nothing: Multiple imputation to balance unbalanced designs for two-way analysis of variance

Methodology ◽  
2020 ◽  
Vol 16 (4) ◽  
pp. 335-353
Author(s):  
Joost R. van Ginkel ◽  
Pieter M. Kroonenberg
Keyword(s):  
Multiple Imputation ◽  
Type I Error ◽  
Error Rates ◽  
Sum Of Squares ◽  
Unbalanced Data ◽  
Type I ◽  
Type Iii ◽  
Much Ado About Nothing ◽  
Unbalanced Designs ◽  
Multiply Imputed

In earlier literature, multiple imputation was proposed to create balance in unbalanced designs, as an alternative to Type III sum of squares in two-way ANOVA. In the current simulation study we studied four pooled statistics for multiple imputation, namely D₀, D₁, D₂, and D₃ in unbalanced data, and compared these statistics with Type III sum of squares. Statistics D₀ and D₂ generally performed best regarding Type-I error rates, and had power rates closest to that of Type III sum of squares. However, none of the statistics produced power rates higher than Type III sum of squares. The results lead to the conclusion that for multiply imputed datasets D₀ and D₂ may be the best methods for pooling the results of multiparameter estimates in multiply imputed datasets, and that for unbalanced data, Type III sum of square is to be preferred over using multiple imputation in obtaining ANOVA results.

Missing data in two-way analysis of variance

1990 ◽  
Vol 258 (2) ◽  
pp. R291-R297 ◽  
Author(s):  
B. K. Slinker ◽  
S. A. Glantz
Keyword(s):  
Missing Data ◽  
Analysis Of Variance ◽  
Testing Hypotheses ◽  
Unbalanced Designs ◽  
Regression Approach ◽  
Main Effects ◽  
F Statistics

We previously described a regression approach to analysis of variance computations that permitted analysis of unbalanced designs and experiments with missing data in two-way (or higher) analyses of variance Am. J. Physiol. 255 (Regulatory Integrative Comp. Physiol. 24): R353-R367, 1988. That approach can only be used correctly under a set of narrow, and relatively uninteresting, circumstances. In fact, in the example we worked, we extended that approach beyond its intended scope and incorrectly computed F statistics for testing hypotheses about the main effects of strain of rat and nephron site in a study of renal Na(+)-K(+)-adenosinetriphosphatase. This paper presents the correct approach, which can be generalized to most situations likely to be encountered when two-way, or higher, analyses of variance are used.

scholarly journals Multiple imputation to balance unbalanced designs for two-way analysis of variance

Methodology ◽  
2021 ◽  
Vol 17 (1) ◽  
pp. 39-57
Author(s):  
Joost R. van Ginkel ◽  
Pieter M. Kroonenberg
Keyword(s):  
Multiple Imputation ◽  
Type I Error ◽  
Good Alternative ◽  
Error Rates ◽  
Sum Of Squares ◽  
Unbalanced Data ◽  
Type I ◽  
Type Iii ◽  
Unbalanced Designs ◽  
Multiply Imputed

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.

Analysis of Variance and Covariance

2007 ◽  
Author(s):  
C. Patrick Doncaster ◽  
Andrew J. H. Davey
Keyword(s):  
Analysis Of Variance

Analysis of Variance Designs

2008 ◽  
Author(s):  
Glenn Gamst ◽  
Lawrence S. Meyers ◽  
A. J. Guarino
Keyword(s):  
Analysis Of Variance

Selflessness in Anorexia Nervosa as Reflected in the Rorschach Comprehensive System

2012 ◽  
Vol 33 (1) ◽  
pp. 78-93 ◽  
Author(s):  
Gili Curiel-Levy ◽  
Laura Canetti ◽  
Esti Galili-Weisstub ◽  
Myrna Milun ◽  
Eitan Gur ◽  
...  
Keyword(s):  
Multivariate Analysis ◽  
Anorexia Nervosa ◽  
Analysis Of Variance ◽  
Clinical Observation ◽  
Comparison Group ◽  
Comprehensive System ◽  
Multivariate Analysis Of Variance ◽  
Rorschach Comprehensive System ◽  
Group A

This study examines the expression of selflessness – the tendency to ignore one’s own needs and serve others’ needs – in Rorschach protocols of women suffering from anorexia nervosa. The protocols of 35 women suffering from anorexia nervosa were compared to 30 protocols of a psychiatric comparison group. A multivariate analysis of variance over five variables (AG, PER, PHR, COP, and GHR) was significant: Anorexic patients showed higher characteristics of selflessness compared to the psychiatric comparison group. These findings contribute to the validation of the Rorschach technique and to the clinical observation of selflessness in anorexic patients, and they emphasize specific characteristics in the treatment of anorexia nervosa patients.

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