MockJurors: Mock Jurors' Confidence in Their Verdicts

In bayesbr: Beta Regression on a Bayesian Model

Description

Answers from mock jurors. It presents the difference in the juror's confidence in a conventional two-option verdict (guilt x absolution) versus a three-option verdict (the new option is "unproven"), in the presence or absence of conflicting testimonial evidence.

Usage

data("MockJurors")

Format

A data frame containing 104 observations on 3 variables.

verdict

a variable indicating whether a two-option or three-option verdict is requested. If verdict is 0 is interpreted as two-option, if verdict is 1 is interpreted as three-option.

conflict

Is there conflicting testimonial evidence? If 0, yes. If 1, no.

confidence

jurors degree of confidence in his/her verdict, scaled to the open unit interval.

Details

The data were collected by Professor Daily at the Australian National University among first-year psychology students. Smithson and Verkuilen (2006) used the original confidence data and transformed it to a scale of 0 to 1, using the following calculation: ((original_confidence/100) * 103 - 0.5) / 104.

The verdict and conflict variables that was a qualitative variable was transformed into a quantitative variable to be used by the package functions.

Source

Example 1 from Smithson and Verkuilen (2006) supplements.

References

doi: 10.1037/1082-989X.11.1.54 Smithson, M., and Verkuilen, J. (2006). A Better Lemon Squeezer? Maximum-Likelihood Regression with Beta-Distributed Dependent Variables. Psychological Methods, 11(7), 54–71.

doi: 10.1080/10888430709336633 Pammer, K., and Kevan, A. (2004). The Contribution of Visual Sensitivity, Phonological Processing and Non-Verbal IQ to Children's Reading. Unpublished manuscript, The Australian National University, Canberra.

Examples

data("MockJurors", package = "bayesbr")


bbr = bayesbr(confidence~verdict+conflict, iter=1000,
             data = MockJurors)

bayesbr documentation built on July 17, 2021, 1:07 a.m.