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This chapter walks the reader through the process of generating an experimental design in R. We generate an experimental design by combining attributes and levels into alternatives and choice tasks, which allow us to estimate model parameters and understand preferences through trade-offs. In this chapter, we cover three different types of designs: orthogonal, random, and efficient designs, highlighting their respective advantages. We delve deeper into efficient designs, covering important concepts such as attribute level balance, utility balance, and priors. Using the spdesign package in R, we demonstrate how to use different efficiency criteria and estimation algorithms to generate efficient designs, and review important checks and considerations for your design.

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Experimental Design

  • 章节

  • 出版模式：

    开放获取

  • 发表日期：

    2025年

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  • Petr Mariel,
  • Danny Campbell,
  • Erlend Dancke Sandorf,
  • Jürgen Meyerhoff,
  • Ainhoa Vega-Bayo,
  • Rebecca Blevins

摘要

This chapter walks the reader through the process of generating an experimental design in R. We generate an experimental design by combining attributes and levels into alternatives and choice tasks, which allow us to estimate model parameters and understand preferences through trade-offs. In this chapter, we cover three different types of designs: orthogonal, random, and efficient designs, highlighting their respective advantages. We delve deeper into efficient designs, covering important concepts such as attribute level balance, utility balance, and priors. Using the spdesign package in R, we demonstrate how to use different efficiency criteria and estimation algorithms to generate efficient designs, and review important checks and considerations for your design.