Preface
1
An Illustrative Example of Contingent Valuation
1.1
Before you get started
1.2
Overview
1.3
Getting started with contingent valuation analysis
1.4
The single-bounded dichotomous choice question model
1.4.1
Data management
1.4.2
Data visualisation
1.4.3
Model estimation
1.4.4
A typical initial model for a single bounded question study
1.5
The double-bounded dichotomous choice or follow up question model
1.5.1
Data management for the double bounded question
1.5.2
An initial general double bounded model
1.6
Extentions to look at individuals
1.7
An overview of a contingent valuation research report
1.7.1
Introduction
1.7.2
Methods
1.7.3
Results
1.7.4
Discussion
2
A Brief Example of Discrete Choice Experiments using the
support.CEs
and
apollo
Packages
2.1
Before you get started
2.2
Outline of the example
2.3
Loading packages
2.4
Designing choice sets
2.5
Preparing a dataset for analysis
2.6
Preparing an analysis
2.7
Conducting the analysis using
apollo
3
An Illustrative Example of Case 1 Best–Worst Scaling
3.1
Before you get started
3.2
Overview
3.2.1
Three variants of BWS
3.2.2
Case 1 BWS
3.3
Packages for Case 1 BWS
3.4
Designing a choice situation
3.5
Generating a BIBD
3.6
Creating questions
3.7
Conducting a survey
3.8
Creating a dataset
3.8.1
Preparing a dataset including responses to the questions
3.8.2
Combining the design and the responses
3.9
Measuring preferences
3.9.1
The counting approach
3.9.2
The modeling approach
Appendix 3A: Creating alternative datasets for analysis
Appendix 3B: Incorporating individuals’ characteristics
4
An Illustrative Example of Case 2 Best–Worst Scaling
4.1
Before you get started
4.2
Overview of Case 2 BWS
4.3
Packages for Case 2 BWS
4.4
Designing a choice situation
4.5
Generating an OMED
4.6
Creating questions
4.7
Conducting a survey
4.8
Creating a dataset
4.8.1
Preparing a dataset including responses to the questions
4.8.2
Combining the design and the responses
4.9
Measuring preferences
4.9.1
The counting approach
4.9.2
The modeling approach
Appendix 4A: Using the
mlogit
package
5
An Illustrative Example of Case 3 Best–Worst Scaling
5.1
Before you get started
5.2
Overview of Case 3 BWS
5.3
Packages for Case 3 BWS
5.4
Designing a choice situation
5.5
Generating designs
5.6
Creating questions
5.7
Conducting a survey/Synthesizing responses to questions
5.8
Creating a dataset
5.9
Measuring preferences
6
An Illustrative Example of Hedonic Pricing
7
An Illustrative Example of Travel Cost Methods
Appendix
A
Logit Model Introduction
A.1
Before you get started
A.2
Background to logistic regression
A.2.1
Introduction
A.2.2
A linear model when
\(y_{i}\)
is dichotomous
A.2.3
Motivation for the dependent variable: option 1
A.2.4
Motivation for the dependent variable: option 2
A.2.5
Optional technical notes on estimation
A.3
Worked example of a logit model
A.3.1
Data description
A.3.2
The linear probablity model
A.3.3
The logit model: basic implementation
A.3.4
Predicted values: log odd, odds, and probabilities
A.3.5
Marginal effects
A.3.6
The 50 percent support point in logit models
A.3.7
Adding estimate standard errors to the estimate
References
Non-Market Valuation with R
Chapter 6
An Illustrative Example of Hedonic Pricing
Under development