syn.res1, syn.res2, syn.res3: Synthetic respondent data sets

Description

Data sets artificially created for the "Examples" in this package.

Usage

data(syn.res1)
data(syn.res2)
data(syn.res3)

Arguments

Details

The "Examples" for each function in this package is based on three hypothetical cases in which choice experiments are applied to measure consumers' valuation of an agricultural product.

The agricultural product has three attributes: (1) the region of origin: this attribute has three levels---"Region A," "Region B," and "Region C."; (2) the eco-friendly label: this describe the three types of cultivation method---"Conv. (conventional cultivation method)," "More (more eco-friendly cultivation method)," and "Most (most eco-friendly cultivation method); and (3) the price per piece of the product---"$1," "$1.1," "$1.2."

syn.res1(Unlabeled choice experiments)

The data set syn.res1 is based on a case in which an unlabeled choice experiment design created by the mix-and-match method is used in a questionnaire survey. A total of 100 respondents were assumed to have been requested to select their most preferred from among two agricultural products and the option "none of these."

c{ Q1. Please select your most preferred alternative from the following: }

lll{ Product 1 Product 2 Region of origin Region B Region C Eco-friendly More Most Price $1.1 $1.2 } ll{ 1. I select product 1. 2. I select product 2. 3. I select none of these. }

A total of 9 choice experiment questions are created by the function rotation.design . Each respondent had to respond to a total of 9 choice experiment questions, implying that the sample size of the analysis based on their responses was 900 (= 9 choice experiment questions per respondent * 100 respondents). In the example, the effect of the respondents' gender on their valuations of the cultivation methods was also examined (see "Examples" for the function make.dataset). See the first case in "Example" for the function make.dataset.

syn.res2 (Labeled choice experiments)

The data set syn.res2 is based on a case in which a labeled choice experiment design created by the L^MA method is used in a questionnaire survey. A total of 100 respondents were assumed to have been requested to select their most preferred from among three agricultural products and the option "none of these."

c{ Q1. Please select your most preferred alternative from the following: }

llll{ Region A Region B Region C Eco-friendly Most Conv. More Price $1.1 $1 $1.1 }

ll{ 1. I select the Region A product. 2. I select the Region B product. 3. I select the Region C product. 4. I select none of these. }

Although the agricultural products have also three attributes and their levels mentioned above, the region of origin attribute is treated as an alternative specific attribute: the first, second, and third alternatives in a choice set always read as "Region A," "Region B," and "Region C," respectively.

A total of 18 choice experiment questions were created by the function Lma.design and divided into two blocks: this means that two types of questionnaire were created, each of which was randomly assigned to the respondents. Therefore, the sample size of the analysis based on their responses was 900 (= 9 choice experiment questions per respondent * 100 respondents). See the second case in "Examples" for the function make.dataset.

syn.res3 (Binary choice experiments with an opt-out option)

The data set syn.res3 is based on a case in which a binary choice experiment design created by the L^MA method is used in a questionnaire survey. A total of 100 respondents were assumed to have been requested to decide whether they select an agricultural product or not.

c{ Q1. Would you like to purchase the following? }

ll{ Product Region of origin Region A Eco-friendly Conv. Price $1 }

ll{ 1. Yes. 2. No. }

A total of 9 binary choice experiment questions are created by the function Lma.design. Each respondent had to respond to a total of 9 binary choice experiment questions, implying that the sample size of the analysis based on their responses was 900 (= 9 binary choice experiment questions per respondent * 100 respondents). See the last case in "Examples" for the function make.dataset.

References

Aizaki, H. (2012) Basic Functions for Supporting an Implementation of Choice Experiments in R. Journal of Statistical Software, Code Snippets, 50(2), 1--24. http://www.jstatsoft.org/v50/c02/

Examples

Run this code

# See "Examples" for the function make.dataset.

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