Skip to main content
Top

2018 | OriginalPaper | Chapter

9. Two-Level Factorial Designs

Authors : Paul D. Berger, Robert E. Maurer, Giovana B. Celli

Published in: Experimental Design

Publisher: Springer International Publishing

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

Abstract

We now change our focus from the number of factors in the experiment to the number of levels those factors have. Specifically, in this and the next several chapters, we consider designs in which all factors have two levels. Many experiments are of this type. This is because two is the minimum number of levels a factor can have and still be studied, and by having the minimum number of levels (2), an experiment of a certain size can include the maximum number of factors. After all, an experiment with five factors at two levels each contains 32 combinations of levels of factors (25), whereas an experiment with these same five factors at just one more level, three levels, contains 243 combinations of levels of factors (35) – about eight times as many combinations! Indeed, studying five factors at three levels each (35 = 243 combinations) requires about the same number of combinations as are needed to study eight factors at two levels each (28 = 256). As we shall see in subsequent chapters, however, one does not always carry out (that is, “run”) each possible combination; nevertheless, the principle that fewer levels per factor allows a larger number of factors to be studied still holds.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

Appendix
Available only for authorised users
Footnotes
1
Of course, the observed difference in response rate between any of the two treatment combinations is not necessarily the true difference, because the data are a sample that estimates the true values.
 
2
One reviewer of the first edition of this book was adamant that using the same notation for the factor, the true effect, and the estimate of the effect was “very sloppy and will be confusing to the student.” We respectfully disagree. Virtually all treatises on this subject use this notation; what would be confusing would be to change a notation that is so universal. But we, once again, thank the reviewer for reinforcing our interest in alerting the reader to what we and other experimental designers are doing.
 
3
Because the dependent variable is a proportion, the normality assumption traditionally made when performing and interpreting an ANOVA may not be strictly true. However, this is not the primary issue here, and the robustness of the normality assumption is likely to keep this consideration from having a material effect on the results of an ANOVA. If necessary, we can come closer to normality by transforming the data using the “standard” transformation for proportion data for this purpose, \( {Y}_{\mathrm{transformed}}=\arcsin \sqrt{Y} \). See, for example, G. E. P. Box and D. R. Cox (1964), “An Analysis of Transformations.” Journal of the Royal Statistical Society. Series B (Methodological), vol. 26, pp. 211–252. 
 
4
It would be reasonable to assume that the response as a function of price is monotonically [i.e., continuously and consistently] decreasing.
 
5
In situations where replication is relatively inexpensive, and most of the cost of a data point is driven by changes in setup, the cost of 32 different treatment combinations may not be less than the cost of 16 replicates of six treatment combinations.
 
6
For more information, see C. A. Hawkins and R. J. Halonen (1973), “Profitability in Buying Puts and Calls.” Decision Sciences, vol. 4, pp. 109–118.
 
7
If the interest is on other statistical properties of the factors (e.g., confidence interval, median, variance, etc.), one can click on Analyze > Explore and fill the dependent and factor lists.
 
Metadata
Title
Two-Level Factorial Designs
Authors
Paul D. Berger
Robert E. Maurer
Giovana B. Celli
Copyright Year
2018
DOI
https://doi.org/10.1007/978-3-319-64583-4_9

Premium Partner