A Note on the Analysis of Designed Experiments with Complex Treatment Structure

By Hans-Peter Piepho, E. Williams, M. Fleck. 2006

151 citations recorded by the source corpus.

Many experiments involve a complex treatment structure, and it is not always immediately obvious how such experiments should be analysed. This paper shows by way of three examples how a suitable linear model can be formulated that provides a meaningful analysis of variance table and allows mean comparisons of interest to be obtained in a straightforward manner. Possible advantages of this approach compared to the use of linear contrasts are discussed. It is concluded that a well-chosen model can often considerably simplify the analysis and lead to useful statistical inferences. The approach advocated in this paper is going to be strongest when there is good design structure present.

Subfields: Management Science and Operations Research, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/d172702cf9824b4d58f8727925c65a432ff2d7d3

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Management Science and Operations Research

1. A. Büchse
2. A. Melchinger
3. Hans-Peter Piepho
4. Jens Möhring
5. Lea A. I. Vaas
6. Frank Schaarschmidt

In Statistics and Probability

1. Lea A. I. Vaas
2. Frank Schaarschmidt
3. Jens Möhring
4. Hans-Peter Piepho
5. A. Büchse
6. A. Melchinger

Citations in the corpus

• BLUP for phenotypic selection in plant breeding and variety testing (2008, methodology): “Unobserved levels will then be blocked out using dummy variables as suggested in Piepho et al. (2006).”
• Analysis of Trials with Complex Treatment Structure Using Multiple Contrast Tests (2009, methodology): “190 HORTSCIENCE VOL. 44(1) FEBRUARY 2009 JOBNAME: horts 44#1 2009 PAGE: 3 OUTPUT: December 29 19:00:51 2008 tsp/horts/180127/03047 F-tests after a suitable model reformulation in the analysis by Piepho et al. (2006).”