Stated Preference Methods Using R 1st Edition by Hideo Aizaki, Tomoaki Nakatani, Kazuo Sato 0367834286 9780367834289

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ISBN 10: 0367834286

ISBN 13: 9780367834289

Author: Hideo Aizaki, Tomoaki Nakatani, Kazuo Sato

Stated Preference Methods Using R explains how to use stated preference (SP) methods, which are a family of survey methods, to measure people’s preferences based on decision making in hypothetical choice situations. Along with giving introductory explanations of the methods, the book collates information on existing R functions and packages as well

Stated Preference Methods Using R 1st Table of contents:

1 Introduction

1.1 Stated preference methods and the role of R

1.2 Objective of this book

1.3 Overview of CV, DCEs, and BWS

1.3.1 Contingent valuation

1.3.2 Discrete choice experiments

1.3.3 Best–worst scaling

1.4 Random utility theory and discrete choice models

1.4.1 Random utility theory

1.4.2 Discrete choice models

1.5 Summary of the rest of this book

2 Contingent Valuation

2.1 Introduction

2.2 Overview of contingent valuation

2.2.1 Elicitation formats

Single-bounded dichotomous choice

Double-bounded dichotomous choice

2.2.2 Bid design

2.2.3 Parametric estimation method

Parametric estimation of SBDC data

Parametric estimation of DBDC data

2.2.4 Nonparametric estimation method

Nonparametric estimation of SBDC data

Nonparametric estimation of DBDC data

2.3 An R package for analyzing SBDC and DBDC CV data

2.3.1 Overview of package DCchoice

2.3.2 Installing the DCchoice package

2.3.3 Loading the package

2.3.4 Preparing datasets

2.3.5 Example datasets

Datasets included in DCchoice

The Albemarle–Pamlico sounds survey data

The Exxon Valdez Oil Spill data

Kriström’s data

The NaturalPark data

2.4 Parametric estimation of WTP

2.4.1 Estimating WTP with SBDC data

Examples

Example code: A log-logistic model of the Natural Park data

Example code: A log-normal model of the Natural Park data

2.4.2 Estimating WTP with DBDC data

Example code: Log-logistic and log-normal models for the Albemarle–Pamlico sounds data

2.4.3 Constructing confidence intervals

The Krinsky and Robb method

The bootstrap method

Example code

2.5 Nonparametric estimation of WTP

2.5.1 Kriström’s nonparametric estimation of SBDC data

Replicating Kriström’s results

2.5.2 Kaplan–Meier–Turnbull estimation of SBDC data

Example code using the Natural Park data

Example code using the Exxon Valdez Oil Spill data

2.5.3 Kaplan–Meier–Turnbull estimation of DBDC data

Example code using the Natural Park data

Example code using the double-bounded version of the Exxon Valdez Oil Spill data

2.6 Concluding remarks

2.A Appendix

The Exxon Valdez Oil Spill data

3 Discrete Choice Experiments

3.1 Introduction

3.2 Overview of DCEs

3.2.1 Basic terms used in DCEs

3.2.2 Steps for implementing DCEs

Step 1: Examining a hypothetical situation and creating a DCE design

Step 2: Preparing and conducting a questionnaire survey

Step 3: Preparing a dataset and conducting the analysis

3.3 R functions for DCEs

3.3.1 Overview

3.3.2 Creating a DCE design

Example code and output

3.3.3 Converting a DCE design into questionnaire format

Example code and output

3.3.4 Creating a design matrix

Example code and output

3.3.5 Creating a dataset

Example code and output

3.3.6 Conducting statistical analysis

Example code and output

3.3.7 Calculating goodness-of-fit measures

Example code and output

3.3.8 Calculating MWTPs

Example code and output

3.3.9 Testing the difference between two independent MWTP distributions

Example code and output

3.4 Example DCEs using R

3.4.1 Unlabeled DCE example

3.4.2 Labeled design example

3.4.3 BDCE example

3.5 Concluding remarks

4 Best–Worst Scaling

4.1 Introduction

4.2 Outline of BWS

4.2.1 BWS basics

4.2.2 Steps in BWS

Step 1: Constructing choice sets

Step 2: Preparing a questionnaire survey

Step 3: Analyzing responses to questions

4.3 R functions for BWS

4.3.1 Overview

4.3.2 Constructing choice sets

Example code and output

4.3.3 Preparing BWS questions

Example code and output

4.3.4 Creating the dataset

Example code and output

4.3.5 Analyzing responses using the counting approach

Example code and output

4.3.6 Analyzing responses using the modeling approach

Example code and output

4.4 Example BWS using R

4.4.1 BWS based on a two-level OMED

4.4.2 BWS based on a BIBD

4.5 Concluding remarks

4.A Appendix: Profile case BWS and multiprofile case BWS

4.A.1 Profile case BWS

4.A.2 Multiprofile case BWS

5 Basic Operations in R

5.1 Introduction

5.2 Getting started with R

5.2.1 Obtaining and installing R

5.2.2 Starting and quitting R

5.2.3 Using R as a calculator

5.2.4 Changing appearance

5.2.5 Accessing help

5.3 Enhancing R

5.3.1 Installing contributed add-on packages

5.3.2 Reading source code

5.3.3 Loading source code

5.4 Importing and exporting data

5.4.1 File formats

5.4.2 Importing data from a CSV file

5.4.3 Exporting R objects

5.5 Manipulating vectors and matrices

5.5.1 Manipulating vectors

5.5.2 Manipulating matrices

5.5.3 Operations on indexes

5.5.4 Random number generation

5.6 Data and object types

5.6.1 Data types

5.6.2 Object types

5.6.3 Examples

5.7 Implementing linear regression

5.7.1 Conducting the analysis

5.7.2 Displaying and summarizing output

5.7.3 Creating dummy variables

5.7.4 Updating the model

5.8 Drawing figures

Appendix A Other Packages Related to This Book

A.1 Introduction

A.2 Contingent valuation

A.3 Discrete choice models

A.4 Cluster, component, and factor analysis

A.5 Conjoint analysis

Appendix B Examples of Contrivance in Empirical Studies

B.1 Introduction

B.2 Providing information to respondents

B.3 Using product/service samples

B.4 Cost–benefit analysis and valuation

B.5 Using SP study results in simulations

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