Research Article
Analysis of split‐plot designs: an overview and comparison of methods
Abstract
Split‐plot designs are frequently needed in practice because of practical limitations and issues related to cost. This imposes extra challenges on the experimenter, both when designing the experiment and when analysing the data, in particular for non‐replicated cases. This paper is an overview and discussion of some of the most important methods for analysing split‐plot data. The focus is on estimation, testing and model validation. Two examples from an industrial context are given to illustrate the most important techniques. Copyright © 2006 John Wiley & Sons, Ltd.
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- Sebastian Hoffmeister, Andrea Geistanger, Optimal Semi-Split-Plot Designs with R, Applications in Statistical Computing, 10.1007/978-3-030-25147-5_17, (271-287), (2019).
- Narinder Singh Sahni, Greg F. Piepel, Tormod Næs, Product and Process Improvement Using Mixture-Process Variable Methods and Robust Optimization Techniques, Journal of Quality Technology, 10.1080/00224065.2009.11917772, 41, 2, (181-197), (2017).
- Sigit Nugroho, undefined, , 10.1063/1.4940869, (080012), (2016).
- Nuno Ricardo Costa, João Lourenço, Gaussian Process Model – An Exploratory Study in the Response Surface Methodology, Quality and Reliability Engineering International, 10.1002/qre.1940, 32, 7, (2367-2380), (2015).
- Guilherme L. Alexandrino, Ronei J. Poppi, Study of the Homogeneity of Drug Loaded in Polymeric Films Using Near‐Infrared Chemical Imaging and Split‐Plot Design, Journal of Pharmaceutical Sciences, 10.1002/jps.24051, 103, 8, (2356-2365), (2014).
- Elena Menichelli, Margrethe Hersleth, Trygve Almøy, Tormod Næs, Alternative methods for combining information about products, consumers and consumers’ acceptance based on path modelling, Food Quality and Preference, 10.1016/j.foodqual.2013.08.011, 31, (142-155), (2014).
- Shu Ikeda, Shun Matsuura, Hideo Suzuki, Two-Step Residual-Based Estimation of Error Variances for Generalized Least Squares in Split-Plot Experiments, Communications in Statistics - Simulation and Computation, 10.1080/03610918.2012.703280, 43, 2, (342-358), (2013).
- Matthias H. Y. Tan, C. F. Jeff Wu, A Bayesian Approach for Model Selection in Fractionated Split Plot Experiments With Applications in Robust Parameter Design, Technometrics, 10.1080/00401706.2013.778790, 55, 3, (359-372), (2013).
- Livio Corain, Susanna Ragazzi, Luigi Salmaso, A Permutation Approach to Split-Plot Experiments, Communications in Statistics - Simulation and Computation, 10.1080/03610918.2012.625778, 42, 6, (1391-1408), (2013).
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- T. Næs, V. Lengard, S. Bølling Johansen, M. Hersleth, Alternative methods for combining design variables and consumer preference with information about attitudes and demographics in conjoint analysis, Food Quality and Preference, 10.1016/j.foodqual.2009.09.004, 21, 4, (368-378), (2010).
- Tormod Næs, Per B. Brockhoff, Oliver Tomic, Design of Experiments for Sensory and Consumer Data, Statistics for Sensory and Consumer Science, 10.1002/9780470669181, (181-192), (2010).
- Tormod Næs, Per B. Brockhoff, Oliver Tomic, ANOVA for Sensory and Consumer Data, Statistics for Sensory and Consumer Science, 10.1002/9780470669181, (193-207), (2010).
- Tormod Næs, Per B. Brockhoff, Oliver Tomic, Investigating Important Factors Influencing Food Acceptance and Choice (Conjoint Analysis), Statistics for Sensory and Consumer Science, 10.1002/9780470669181, (95-125), (2010).
- Marie Gaudard, Philip Ramsey, Mia Stephens, Interactive data mining informs designed experiments, Quality and Reliability Engineering International, 10.1002/qre.971, 25, 3, (299-315), (2008).
- Douglas C. Montgomery, A Retrospective on Volume 23 of Quality and Reliability Engineering International, Quality and Reliability Engineering International, 10.1002/qre.904, 24, 1, (1-2), (2008).
- Frøydis Bjerke, Øyvind Langsrud, Are Halvor Aastveit, Restricted randomization and multiple responses in industrial experiments, Quality and Reliability Engineering International, 10.1002/qre.873, 24, 2, (167-181), (2007).
