In many industrial experiments, complete randomization of the runs is impossible as, often, they involve factors whose levels are hard or costly to change. In such cases, the split-plot design is a cost-efficient alternative that reduces the number of independent settings of the hard-to-change...

In industrial experimentation there is growing interest in studies that span more than one processing step. Convenience often dictates restrictions in randomization in passing from one processing step to another. When the study encompasses three processing steps, this leads to split-split-plot...

Split-plot and other multi-stratum structures are widely used in factorial and response surface experiments and residual maximum likelihood (REML) and generalized least squares (GLS) estimation is seen as the state-of-the-art method of data analysis for nonorthogonal designs. We analyze data...

Despite the fact that many conjoint choice experiments offer respondents a no-choice option in every choice set, the optimal design of conjoint choice experiments involving no-choice options has received only a limited amount of attention in the literature. In this article, we present an...

Response surface experiments often involve only quantitative factors, and the response is fit using a full quadratic model in these factors. The term response surface implies that interest in these studies is more on prediction than parameter estimation since the points on the fitted surface are...

In a discrete choice experiment, each respondent chooses the best product or service sequentially from many groups or choice sets of alternative goods. The alternatives, called profiles, are described by level combinations from a set of predefined attributes. Respondents sometimes make their...