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. 2018 Jan 10;10:17–28. doi: 10.1016/j.conctc.2018.01.002

Table 1a.

Studies investigating the number of choice tasks, attributes, and attribute levels

Author, Year Outcome of interest Method to create design Design setting Distribution of Priors of parameter estimates Choice sets Alternatives Attributes Attribute levels Results
# Choice tasks
Vanniyasingam, 2016 [1] Relative
D-efficiency		 . . no priors 2–20 2–5 2–20 2–5
  • 1)

    Generally, as the number of choice tasks increases, relative D-efficiency increases
# Attributes
Vanniyasingam, 2016 [1] Relative
D-efficiency		 . . no priors 2–20 2–5 2–20 2–5
  • 1)

    Generally, increasing# attributes, decreases relative D-efficiency (not monotonically)

  • 2)

    designs with a small# of alternatives and large number of attributes could not be created.
# Attribute levels
Vanniyasingam, 2016 [1] Relative
D-efficiency		 . . no priors 2–20 2–5 2–20 2–5
  • 1)

    Generally, increasing# attribute levels, decreases relative D-efficiency

  • 2)

    designs yield higher D-efficiency measures when the# attribute levels match the number of alternatives

  • 3)

    Generally, binary attributes perform best across all other designs
Graβhoff, 2013 [32] Efficiency . . β1 = 0, β2 = 1 . 3 1–7 Unrestricted quantitative (continuous) and qualitative (binary) attributes
  • 1)

    Design-optimality was achieved when two alternatives were identical or differed only in the (unrestricted) quantitative variable, while the third alternative varies in all of the qualitative components.