# 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

1 |

### 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 http://dl.dropbox.com/u/1857674/betareg/betareg.html.

### References

Smithson, M., Merkle, E.C., and Verkuilen, J. (in press). Beta
Regression Finite Mixture Models of Polarization and
Priming. *Journal of Educational and Behavioral Statistics*.

Smithson, M., and Segale, C. (2009). Partition Priming in Judgments of
Imprecise Probabilities. *Journal of Statistical Theory and
Practice*, **3**(1), 169–181.

### Examples

1 2 3 4 5 6 | ```
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))
``` |