Case Studies

  1. How to Handle Hard-to-Change Factors or Components in a Designed Experiment

    February 2018
    Because interactions abound in the coatings industry, the multifactor and multicomponent test matrices provided by the design of experiments (DOE) approach is very appealing. However, carrying out DOE correctly requires that runs be randomized whenever possible to counteract the bias that may be introduced by time-related trends, such as aging of materials, increasing humidity, and the like. But what if complete randomization proves to be inconvenient or impossible? In this case, a specialized form of design called “split plot” becomes attractive, because of its ability to effectively group hard-to-change (HTC) factors. A split plot accommodates both HTC factors and those factors that are easy to change (ETC).
    Authors: Mark J. Anderson
    Publication: CoatingsTech
  2. How to Handle Hard-to-Change Factors Using a Split Plot

    September 2016
    Carrying out a DOE correctly requires that runs be randomized whenever possible to counteract the bias that may be introduced by time-related trends. If complete randomization proves to be impossible, however, a specialized form of design—called a split plot—is useful because of its ability to effectively group hard-to-change (HTC) factors. It accommodates both HTC and easy-to-change factors in the design.
    Authors: Mark J. Anderson
    Publication: Chemical Engineering
  3. PCR Process Optimized via Split-Plot DOE

    May 2005

    This paper illustrates the use of design of experiments (DOE) and split-plot design to quickly and effectively determine the factor settings that maximize amplification in a polymerase chain reaction (PCR) experiment. As presented at the 2005 ASQ World Congress.

    Authors: Whitcomb, Pat & Kraber, Shari
    Publication: Conference