NDLI: A cautionary note on the analysis of randomized block designs with a few missing values

Content Provider	Springer Nature Link
Author	Mehrotra, Devan V.
Copyright Year	2004
Abstract	The randomized block design is routinely employed in the social and biopharmaceutical sciences. With no missing values, analysis of variance (AOV) can be used to analyze such experiments. However, if some data are missing, the AOV formulae are no longer applicable, and iterative methods such as restricted maximum likelihood (REML) are recommended, assuming block effects are treated as random. Despite the well-known advantages of REML, methods like AOV based on complete cases (blocks) only (CC-AOV) continue to be used by researchers, particularly in situations where routinely only a few missing values are encountered. Reasons for this appear to include a natural proclivity for non-iterative, summary-statistic-based methods, and a presumption that CC-AOV is only trivially less efficient than REML with only a few missing values (say≤10%). The purpose of this note is two-fold. First, to caution that CC-AOV can be considerably less powerful than REML even with only a few missing values. Second, to offer a summary-statistic-based, pairwise-available-case-estimation (PACE) alternative to CC-AOV. PACE, which is identical to AOV (and REML) with no missing values, outperforms CC-AOV in terms of statistical power. However, it is recommended in lieu of REMLonly if software to implement the latter is unavailable, or the use of a “transparent” formula-based approach is deemed necessary. An example using real data is provided for illustration.
Starting Page	51
Ending Page	66
Page Count	16
File Format	PDF
ISSN	09325026
Journal	Statistical Papers
Volume Number	45
Issue Number	1
e-ISSN	16139798
Language	English
Publisher	Springer-Verlag
Publisher Date	2004-01-01
Publisher Place	Berlin, Heidelberg
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	analysis of variance linear mixed model restricted maximum likelihood Satterthwaite approximation Statistics for Business/Economics/Mathematical Finance/Insurance Probability Theory and Stochastic Processes Economic Theory Operations Research/Decision Theory
Content Type	Text
Resource Type	Article
Subject	Statistics and Probability Statistics, Probability and Uncertainty

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