CarTask: Probability Judgment for Car Dealership with Partition

In bayesbr: Beta Regression on a Bayesian Model

Description

Participants who responded to the study were expected to judge the likelihood of a customer trades in a coupe or that a customer buys a car from a specific seller among four possible sellers.

Usage

data(CarTask)

Format

A data frame with 155 observations on the following 3 variables.

task

A variable specified as conditions. When 0 the set value is Car, when 1 the set value is Salesperson.

probability

a numeric vector of the estimated probability.

NFCCscale

a numeric vector of the NFCC scale.

Details

Study participants were graduate students from The Australian National University, some students received credits in Psychology for participating in the study.

With the Needs for Closing and Needs for Certainty scales strongly correlated, the NFCCscale is a combined scale between the previous two.

For task the questions were:

Car

What is the probability that a customer trades in a coupe?

Salesperson

What is the probability that a customer buys a car from Carlos?

The task variable that was a qualitative variable was transformed into a quantitative variable to be used by the package functions.

References

doi: 10.3102/1076998610396893 Smithson, M., Merkle, E.C., and Verkuilen, J. (2011). Beta Regression Finite Mixture Models of Polarization and Priming. Journal of Educational and Behavioral Statistics, 36(6), 804–831.

doi: 10.1080/15598608.2009.10411918 Smithson, M., and Segale, C. (2009). Partition Priming in Judgments of Imprecise Probabilities. Journal of Statistical Theory and Practice, 3(1), 169–181.

Examples

data("CarTask", package = "bayesbr")

car_bayesbr <- bayesbr(probability ~ NFCCscale + task, data = CarTask,
                      iter =100)

bayesbr documentation built on July 17, 2021, 1:07 a.m.