Analyzing ordinal data from a split-plot design in the presence of a random block effect. Issue Volume 29:Issues 4(2017) (2nd October 2017)

• Record Type: Journal Article
• Authors: Arnouts, Heidi; Goos, Peter
• Is Part Of: Quality engineering. Volume 29:Issues 4(2017)
• Page Start: 553
• Page End: 562
• Publication Date: 2017-10-02
• Subjects: cumulative logit regression -- generalized linear mixed model -- random and fixed blocks -- split-plot design; Quality control -- Periodicals; Production management -- Quality control -- Periodicals; 658.5
• DOI: 10.1080/08982112.2017.1303069
• Languages: English
• ISSNs: 0898-2112

Abstract

ABSTRACT: Many industrial experiments involve some factors that are hard to change. In this situation, experimenters often choose to perform an experiment with restricted randomization, such as a split-plot or a strip-plot experiment. In this article, we discuss the analysis of an experiment concerning the adhesion between steel tire cords and rubber. Besides an ordinal response, the experiment also involves one hard-to-change factor. Therefore, the experimenters performed a split-plot experiment. An additional complication of the experiment is that there is also a blocking factor. A proper analysis of the experiment requires the inclusion of random effects in the model to account for its split-plot nature and its blocked nature. The need for random effects and the ordinal response necessitate the use of a mixed cumulative logit model.