Weather Task With Priming and Precise and Imprecise Probabilities
In this study participants were asked to judge how likely Sunday is to be the hottest day of the week.
data(WeatherTask)
A data frame with 345 observations on the following 3 variables.
priminga factor with levels two-fold (case
prime) and seven-fold (class prime).
elicitinga factor with levels precise and
imprecise (lower and upper limit).
agreementa numeric vector, probability indicated by participants or the average between minimum and maximum probability indicated.
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 priming the questions were:
[What is the probability that] the temperature at Canberra airport on Sunday will be higher than every other day next week?
[What is the probability that] the highest temperature of the week at Canberra airport will occur on Sunday?
For eliciting the instructions were if
to assign a probability estimate,
to assign a lower and upper probability estimate.
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("WeatherTask", package = "betareg")
library("flexmix")
wt_betamix <- betamix(agreement ~ 1, data = WeatherTask, k = 2,
extra_components = extraComponent(type = "betareg", coef =
list(mean = 0, precision = 2)),
FLXconcomitant = FLXPmultinom(~ priming + eliciting))Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.