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A General Strategy for Analyzing Data From Split-Plot and Multistratum Experimental Designs
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Journal Article
A General Strategy for Analyzing Data From Split-Plot and Multistratum Experimental Designs
2012
Overview
Increasingly, industrial experiments use multistratum designs, such as split-plot and strip-plot designs. Often, these experiments span more than one processing stage. The challenge is to identify an appropriate multistratum design, along with an appropriate statistical model. In this article, we introduce Hasse diagrams in the response surface context as a tool to visualize the unit structure of the experimental design, the randomization and sampling approaches used, the stratum in which each experimental factor is applied, and the degrees of freedom available in each stratum to estimate main effects, interactions, and variance components. We illustrate their use on several responses measured in a large study of the adhesion properties of coatings to polypropylene. We discuss quantitative, binary, and ordered categorical responses, for designs ranging from a simple split-plot to a strip-plot that involves repeated measurements of the response. The datasets discussed in this article are available online as supplementary materials, along with sample SAS programs.
Publisher
Taylor & Francis Group,The American Society for Quality and The American Statistical Association,American Society for Quality and the American Statistical Association,American Society for Quality
Subject
/ Coatings
/ Computer science; control theory; systems
/ Data processing. List processing. Character string processing
/ Exact sciences and technology
/ Generalized linear mixed model
/ Memory organisation. Data processing
/ Modeling
/ Ovens
/ Sampling
/ Sciences and techniques of general use
/ Software
