Precise and Imprecise Probabilities and Priming for Weather Task

Description

In this experiment, participants judged the likelihood of Sunday being the hottest day of week

Usage

```data(WeatherTask)
```

Format

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

`priming`

a variable. If 0, `two-fold` (case prime); If 1, `seven-fold` (class prime).

`eliciting`

a variable. If 0, `precise`;If 1, `imprecise` (lower and upper limit).

`agreement`

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

Details

All study participants were from the first or second year, none of the participants had an in-depth knowledge of probability.

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.

The `priming` and `eliciting` variables that was a qualitative variable was transformed into a quantitative variable to be used by the package functions.

Source

Taken from Smithson et al. (2011) supplements.

References

doi: 10.3102/1076998610396893 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 = "bayesbr")

bbr <- bayesbr(agreement~eliciting+priming, data = WeatherTask,
iter = 200)
```

[Package bayesbr version 0.0.1.0 Index]