Factorial Treatment Structure and Complete Factorial Designs

2016 ◽  
pp. 85-106
Keyword(s):  
Factorial Designs ◽  
Treatment Structure

Practical analysis of factorial experiments in forestry

1995 ◽  
Vol 25 (3) ◽  
pp. 446-461 ◽  
Author(s):  
S.V. Stehman ◽  
M.P. Meredith
Keyword(s):  
Full Advantage ◽  
Practical Approach ◽  
Standard Errors ◽  
Factorial Designs ◽  
Factorial Experiments ◽  
Default Options ◽  
Practical Analysis ◽  
Treatment Structure ◽  
Main Effects ◽  
Multiple Comparison Procedures

Factorial designs are among the most frequently employed for arranging treatments in forestry experiments. Yet researchers often fail either to recognize the factorial treatment structure or to take full advantage of the structure for interpreting treatment effects. The analysis of factorial experiments should focus on comparisons of means of research interest specified by the investigator. Reliance on default options of computing packages or routine application of multiple comparison procedures often fails to address research hypotheses directly. A two-step strategy for the analysis of factorial experiments entails a check for interaction followed by estimation of either main effects or simple effects. This strategy emphasizes sensible mean comparisons through estimation of contrasts and their standard errors. The strategy also applies to the analysis of factorial experiments in which unequal replication or empty cells complicate the analysis. We summarize a practical approach for use by forest scientists and applied statisticians consulting with such scientists so that they may analyze and interpret their experiments more effectively.

Identification of effects and confounding patterns in factorial designs

Biometrika ◽  
1977 ◽  
Vol 64 (2) ◽  
pp. 347-354 ◽  
Author(s):  
R. A. BAILEY ◽  
F. H. L. GILCHRIST ◽  
H. D. PATTERSON
Keyword(s):  
Factorial Designs
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