In this study participants were asked to estimate upper and lower probabilities for event to occur and not to occur.
A data frame with 242 observations on the following 3 variables.
a factor with levels
Boeing stock and
a numeric vector of the average of the lower estimate for the event not to occur and the upper estimate for the event to occur.
a numeric vector of the differences of the lower and upper estimate for the event to occur.
All participants in the study were either first- or second-year undergraduate students in psychology, none of whom had a strong background in probability or were familiar with imprecise probability theories.
For the sunday weather task see
WeatherTask. For the Boeing
stock task participants were asked to estimate the probability that
Boeing's stock would rise more than those in a list of 30 companies.
For each task participants were asked to provide lower and upper estimates for the event to occur and not to occur.
Taken from Smithson et al. (2011) supplements.
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.
data("ImpreciseTask", package = "betareg") library("flexmix") wt_betamix <- betamix(location ~ difference * task, data = ImpreciseTask, k = 2, extra_components = extraComponent(type = "betareg", coef = list(mean = 0, precision = 8)), FLXconcomitant = FLXPmultinom(~ task))