## Weather Task With Priming and Precise and Imprecise Probabilities

### Description

In this study participants were asked to judge how likely Sunday is to be the hottest day of the week.

### Usage

data(WeatherTask)

### Format

A data frame with 345 observations on the following 3 variables.

priming

a factor with levels two-fold (case prime) and seven-fold (class prime).

eliciting

a factor with levels precise and imprecise (lower and upper limit).

agreement

a numeric vector, probability indicated by participants or the average between minimum and maximum probability indicated.

### Details

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:

two-fold

[What is the probability that] the temperature at Canberra airport on Sunday will be higher than every other day next week?

seven-fold

[What is the probability that] the highest temperature of the week at Canberra airport will occur on Sunday?

For eliciting the instructions were if

precise

to assign a probability estimate,

imprecise

to assign a lower and upper probability estimate.

### 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("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))