Details
| Original language | English |
|---|---|
| Pages (from-to) | 188-195 |
| Number of pages | 8 |
| Journal | HortScience |
| Volume | 44 |
| Issue number | 1 |
| Publication status | Published - Feb 2009 |
Abstract
Experiments with complex treatment structures are not uncommon in horticultural research. For example, in augmented factorial designs, one control treatment is added to a full factorial arrangement, or an experiment might be arranged as a two-factorial design with some groups omitted because they are practically not of interest. Several statistical procedures have been proposed to analyze such designs. Suitable linear models followed by F-tests provide only global inference for main effects and their interactions. Orthogonal contrasts are demanding to formulate and cannot always reflect all experimental questions underlying the design. Finally, simple mean comparisons following global F-tests do not control the overall error rate of the experiment in the strong sense. In this article, we show how multiple contrast tests can be used as a tool to address the experimental questions underlying complex designs while preserving the overall error rate of the conclusions. Using simultaneous confidence intervals allows for displaying the direction, magnitude, and relevance of the mean comparisons of interest. Along with application in statistical software, shown by two examples, we discuss the possibilities and limitations of the proposed approach.
Keywords
- Augmented factorial, Fixed-dose combination, Linear model, Multiple comparisons, Simultaneous confidence intervals
ASJC Scopus subject areas
- Agricultural and Biological Sciences(all)
- Horticulture
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In: HortScience, Vol. 44, No. 1, 02.2009, p. 188-195.
Research output: Contribution to journal › Article › Research › peer review
}
TY - JOUR
T1 - Analysis of trials with complex treatment structure using multiple contrast tests
AU - Schaarschmidt, Frank
AU - Vaas, Lea
PY - 2009/2
Y1 - 2009/2
N2 - Experiments with complex treatment structures are not uncommon in horticultural research. For example, in augmented factorial designs, one control treatment is added to a full factorial arrangement, or an experiment might be arranged as a two-factorial design with some groups omitted because they are practically not of interest. Several statistical procedures have been proposed to analyze such designs. Suitable linear models followed by F-tests provide only global inference for main effects and their interactions. Orthogonal contrasts are demanding to formulate and cannot always reflect all experimental questions underlying the design. Finally, simple mean comparisons following global F-tests do not control the overall error rate of the experiment in the strong sense. In this article, we show how multiple contrast tests can be used as a tool to address the experimental questions underlying complex designs while preserving the overall error rate of the conclusions. Using simultaneous confidence intervals allows for displaying the direction, magnitude, and relevance of the mean comparisons of interest. Along with application in statistical software, shown by two examples, we discuss the possibilities and limitations of the proposed approach.
AB - Experiments with complex treatment structures are not uncommon in horticultural research. For example, in augmented factorial designs, one control treatment is added to a full factorial arrangement, or an experiment might be arranged as a two-factorial design with some groups omitted because they are practically not of interest. Several statistical procedures have been proposed to analyze such designs. Suitable linear models followed by F-tests provide only global inference for main effects and their interactions. Orthogonal contrasts are demanding to formulate and cannot always reflect all experimental questions underlying the design. Finally, simple mean comparisons following global F-tests do not control the overall error rate of the experiment in the strong sense. In this article, we show how multiple contrast tests can be used as a tool to address the experimental questions underlying complex designs while preserving the overall error rate of the conclusions. Using simultaneous confidence intervals allows for displaying the direction, magnitude, and relevance of the mean comparisons of interest. Along with application in statistical software, shown by two examples, we discuss the possibilities and limitations of the proposed approach.
KW - Augmented factorial
KW - Fixed-dose combination
KW - Linear model
KW - Multiple comparisons
KW - Simultaneous confidence intervals
UR - http://www.scopus.com/inward/record.url?scp=58849165338&partnerID=8YFLogxK
U2 - 10.21273/hortsci.44.1.188
DO - 10.21273/hortsci.44.1.188
M3 - Article
AN - SCOPUS:58849165338
VL - 44
SP - 188
EP - 195
JO - HortScience
JF - HortScience
SN - 0018-5345
IS - 1
ER -