Design and analysis for constrained order-of-addition experiments

TL;DR

This paper develops models and optimal design strategies for order-of-addition experiments with constraints, enabling efficient analysis of component sequences in applications like surveys and scheduling.

Contribution

It introduces new models for constrained order-of-addition experiments and systematic methods for constructing optimal experimental designs under these constraints.

Findings

Full designs are proven to be D- and G-optimal under the models.

Systematic construction methods for optimal fractional designs are developed.

Application to survey data demonstrates efficient assessment of question order effects.

Abstract

In an order-of-addition (OofA) experiment, the sequence of m different components can significantly impact the experiment's response. In many OofA experiments, the components are subject to constraints, where certain orders are impossible. For example, in survey design and job scheduling, the components are often arranged into groups, and these groups of components must be placed in a fixed order. If two components are in different groups, their pairwise order is determined by the fixed order of their groups. Design and analysis are needed for these pairwise-group constrained OofA experiments. A new model is proposed to accommodate pairwise-group constraints. This paper also introduces a model for mixed-pairwise constrained OofA experiments, which allows one pair of components within each group to have a pre-determined pairwise order. It is proven that the full design, which uses all feasible orders exactly once, is D- and G-optimal under the proposed models. Systematic construction methods are used to find optimal fractional designs for pairwise-group and mixed-pairwise constrained OofA experiments. The proposed methods efficiently assess the impact of question order in a survey dataset, where participants answered generalized intelligence questions in a randomly assigned order under mixed-pairwise constraints.