Partition-primed Probability Judgement Task for Car Dealership

Description

In this study participants were asked to judge how likely it is that a customer trades in a coupe or that a customer buys a car form a specific salesperson out of four possible salespersons.

Usage

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

Format

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

task

a factor with levels Car and Salesperson indicating the condition.

probability

a numeric vector of the estimated probability.

NFCCscale

a numeric vector of the NFCC scale.

Details

All participants in the study were undergraduate students at The Australian National University, some of whom obtained course credit in first-year Psychology for their participation in the study.

The NFCC scale is a combined scale of the Need for Closure and Need for Certainty scales which are strongly correlated.

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?

Source

Taken from Smithson et al. (2011) supplements.

References

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:10.3102/1076998610396893

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 = "betareg")
library("flexmix")
car_betamix <- betamix(probability ~ 1, data = CarTask, k = 3,
  extra_components = list(extraComponent(type = "uniform", coef = 1/2,
  delta = 0.01), extraComponent(type = "uniform", coef = 1/4, delta = 0.01)),
  FLXconcomitant = FLXPmultinom(~ task))