library("betareg")
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))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
CarandSalespersonindicating 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.