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A Review of the NIOSH Roadmap for Research on Asbestos Fibers and Other Elongate Mineral Particles (2009) / Chapter Skim
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Appendix C: Experimental Design Strategies
Pages 109-114

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From page 109...
... The main goal of a factorial design is to study the main effects of factors involved in the design. An experimenter may also be interested in studying the two factor interaction effects or even higher-order interactions.
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From page 110...
... SSE / 8(n − 1) As we see from the previous discussion, if the number of factors k in a 2k factorial design increases, the total number of runs in a complete factorial design outgrows the resources of most experimenters.
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From page 111...
... 25 ½ fractional replicate designs can estimate unconfounded main effects and two-factor interactions, but three-factor interactions may be confounded
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From page 112...
... Resolution IV 2k fractional factorial designs include 24-1, 26-2, 27-2, 27-3, 28-3, 28-4, 29-3, 29-4 designs, where for example, a 29-4 design reduces the total number of experimental conditions (i.e., factor level combinations) from 29 = 512 to a far more manageable 25 = 32 and still permits estimates and tests of main effects that are unconfounded by two-factor interactions.
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From page 113...
... and are also provided online by the National Institute of Standards and Technology at http://www.itl.nist.gov/div898/ handbook/pri/section3/pri3347.htm. Response Surface Methodology The previous discussion of fractional factorial designs is based on discrete levels of each factor (e.g., high or low, experimental or control)
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From page 114...
... Once the parameters have been estimated, we can use the estimated response surface to evaluate the values of x1 and x2 for a specific targeted value of y0 -- for example, a carcinogenetic threshold. A contour plot may help in this regard to estimate levels of x1 and x2 corresponding to a particular level of carcinogenic risk.
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Key Terms

  • factorial design
  • fractional factorial
  • factorial designs
  • fractional replicate
  • level combinations

  • replicate designs
  • estimate main
  • response surface
  • main effect
  • mate main

  • resolution iii
  • factor level
  • factor interactions
  • previous discussion
  • surface methodology

  • rial design
  • maximum likelihood
  • marginal maximum
  • estimated response
  • six process

  • interaction effect
  • average effects
  • targeted value
  • complete experiment
  • ice cream

  • factors involved
  • permits estimates
  • estimate unconfounded
  • nonlinear relations
  • main goal


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