Lecture 2 Flashcards

1
Q

What is the random utility theory

A

People do not always choose the best option -> Some randomness involved

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2
Q

Name a few random components

A

Tiredness
Uncertainty
Distraction
Context effect (starbucks cups)

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3
Q

Direct questionnaire +/-

A
\+ Simple: # attributes large
\+ No high involvement assumed
- 'Everything is important'
- Not realistic: isolated attributes
- Explicit: social answers
- Subjective range of levels
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4
Q

Conjoint +/-

A
  • Complex: # attributes small
  • High involvement assumed
    + Forced to make trade off
    + More realistic: hypothetical products
    + Implicit: less social answers
    + Predefined range of levels
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5
Q

Attributes should…

A
  • be relevant for management
  • have varying levels (be realistic (car with 3 tires))
  • be expected to influence preferences
  • be clearly defined and communicable (no vague descriptions)
  • not exhibit strong correlations (tesla & electric)
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6
Q

Techniques for large numbers of attributes

A
  • Direct Survey
  • Partial-profile conjoint (only subsets of attributes)
  • Hybrid conjoint (survey + small conjoint)
  • Adaptive conjoint (survey + dynamic paired comparisons)
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7
Q

Levels should be….

A
  • interesting for management
  • Unambiguous (not “low” or “high”)
  • Separated enough (otherwise too little weight)
  • Realistic
  • Have no clear winner
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8
Q

Number of levels

A
  • Two is minimum
  • In case of linearity -> 2 will do
  • In case of nonlinearity -> more than 2
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9
Q

More levels than necessary is….. why?

A

inefficient - more parameters need to be estimated

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10
Q

Why equal numbers of levels?

A

Attributes with more levels are perceived as more important

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11
Q

Advantage of large questionnaire

A
  • More observations per respondent

- Increase in quality, as respondents learn how to answer

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12
Q

Disadvantage of large questionnaire

A
  • Decrease in quality as respondents get fatigued or bored
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13
Q

Respondents can complete how many tasks?

A

Up to 20, but in some case 12 may be maximum

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14
Q

Always add fixed holdout choice sets? True of False?

A

True

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15
Q

Full factorial design =

A
  • all possible combinations
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16
Q

Fractional factorial design =

A
  • Subset of all possible combinations
17
Q

Optimal design …

A
  • provides as much info as possible for given number of tasks
  • minimizes standard errors of part-worth estimates