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Pooling ANOVA results from multiply imputed datasets : A simulation study. / Grund, Simon; Lüdtke, Oliver; Robitzsch, Alexander.

In: Methodology, Vol. 12, No. 3, 05.10.2016, p. 75-88.

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@article{d76bcf13333a435a9661d4fbc6d9fe11,
title = "Pooling ANOVA results from multiply imputed datasets: A simulation study",
abstract = "The analysis of variance (ANOVA) is frequently used to examine whether a number of groups differ on a variable of interest. The global hypothesis test of the ANOVA can be reformulated as a regression model in which all group differences are simultaneously tested against zero. Multiple imputation offers reliable and effective treatment of missing data; however, recommendations differ with regard to what procedures are suitable for pooling ANOVA results from multiply imputed datasets. In this article, we compared several procedures (known as D1, D2 and D3) using Monte Carlo simulations. Even though previous recommendations have advocated that D2 should be avoided in favor of D1 or D3, our results suggest that all procedures provide a suitable test of the ANOVA{\textquoteright}s global null hypothesis in many plausible research scenarios. In more extreme settings, D1 was most reliable, whereas D2 and D3 suffered from different limitations. We provide guidelines on how the different methods can be applied in one- and two-factorial ANOVA designs and information about the conditions under which some procedures may perform better than others. Computer code is supplied for each method to be used in freely available statistical software.",
keywords = "Educational assessment/measurements, multiple imputation, missing data, pooling, ANOVA",
author = "Simon Grund and Oliver L{\"u}dtke and Alexander Robitzsch",
year = "2016",
month = oct,
day = "5",
doi = "10.1027/1614-2241/a000111",
language = "English",
volume = "12",
pages = "75--88",
journal = " Methodology",
issn = "1614-1881",
publisher = "Hogrefe Publishing",
number = "3",

}

RIS

TY - JOUR

T1 - Pooling ANOVA results from multiply imputed datasets

T2 - A simulation study

AU - Grund, Simon

AU - Lüdtke, Oliver

AU - Robitzsch, Alexander

PY - 2016/10/5

Y1 - 2016/10/5

N2 - The analysis of variance (ANOVA) is frequently used to examine whether a number of groups differ on a variable of interest. The global hypothesis test of the ANOVA can be reformulated as a regression model in which all group differences are simultaneously tested against zero. Multiple imputation offers reliable and effective treatment of missing data; however, recommendations differ with regard to what procedures are suitable for pooling ANOVA results from multiply imputed datasets. In this article, we compared several procedures (known as D1, D2 and D3) using Monte Carlo simulations. Even though previous recommendations have advocated that D2 should be avoided in favor of D1 or D3, our results suggest that all procedures provide a suitable test of the ANOVA’s global null hypothesis in many plausible research scenarios. In more extreme settings, D1 was most reliable, whereas D2 and D3 suffered from different limitations. We provide guidelines on how the different methods can be applied in one- and two-factorial ANOVA designs and information about the conditions under which some procedures may perform better than others. Computer code is supplied for each method to be used in freely available statistical software.

AB - The analysis of variance (ANOVA) is frequently used to examine whether a number of groups differ on a variable of interest. The global hypothesis test of the ANOVA can be reformulated as a regression model in which all group differences are simultaneously tested against zero. Multiple imputation offers reliable and effective treatment of missing data; however, recommendations differ with regard to what procedures are suitable for pooling ANOVA results from multiply imputed datasets. In this article, we compared several procedures (known as D1, D2 and D3) using Monte Carlo simulations. Even though previous recommendations have advocated that D2 should be avoided in favor of D1 or D3, our results suggest that all procedures provide a suitable test of the ANOVA’s global null hypothesis in many plausible research scenarios. In more extreme settings, D1 was most reliable, whereas D2 and D3 suffered from different limitations. We provide guidelines on how the different methods can be applied in one- and two-factorial ANOVA designs and information about the conditions under which some procedures may perform better than others. Computer code is supplied for each method to be used in freely available statistical software.

KW - Educational assessment/measurements

KW - multiple imputation

KW - missing data

KW - pooling

KW - ANOVA

U2 - 10.1027/1614-2241/a000111

DO - 10.1027/1614-2241/a000111

M3 - Journal article

VL - 12

SP - 75

EP - 88

JO - Methodology

JF - Methodology

SN - 1614-1881

IS - 3

ER -

ID: 622754