MockJurors | R Documentation |

Data with responses of naive mock jurors to the conventional conventional two-option verdict (guilt vs. acquittal) versus a three-option verdict setup (the third option was the Scottish 'not proven' alternative), in the presence/absence of conflicting testimonial evidence.

`data("MockJurors")`

A data frame containing 104 observations on 3 variables.

- verdict
factor indicating whether a two-option or three-option verdict is requested. (A sum contrast rather than treatment contrast is employed.)

- conflict
factor. Is there conflicting testimonial evidence? (A sum contrast rather than treatment contrast is employed.)

- confidence
jurors degree of confidence in his/her verdict, scaled to the open unit interval (see below).

The data were collected by Daily (2004) among first-year psychology
students at Australian National University. Smithson and Verkuilen (2006)
employed the data scaling the original confidence (on a scale 0–100)
to the open unit interval: `((original_confidence/100) * 103 - 0.5) / 104`

.

The original coding of `conflict`

in the data provided from Smithson's
homepage is -1/1 which Smithson and Verkuilen (2006) describe to mean
no/yes. However, all their results (sample statistics, histograms, etc.)
suggest that it actually means yes/no which was employed in `MockJurors`

.

Example 1 from Smithson and Verkuilen (2006) supplements.

Deady, S. (2004).
The Psychological Third Verdict: 'Not Proven' or 'Not Willing to Make a Decision'?
*Unpublished honors thesis*, The Australian National University, Canberra.

Smithson, M., and Verkuilen, J. (2006).
A Better Lemon Squeezer? Maximum-Likelihood Regression with
Beta-Distributed Dependent Variables.
*Psychological Methods*, **11**(7), 54–71.

`betareg`

, `ReadingSkills`

, `StressAnxiety`

```
data("MockJurors", package = "betareg")
library("lmtest")
## Smithson & Verkuilen (2006, Table 1)
## variable dispersion model
## (NOTE: numerical rather than analytical Hessian is used for replication,
## Smithson & Verkuilen erroneously compute one-sided p-values)
mj_vd <- betareg(confidence ~ verdict * conflict | verdict * conflict,
data = MockJurors, hessian = TRUE)
summary(mj_vd)
## model selection for beta regression: null model, fixed dispersion model (p. 61)
mj_null <- betareg(confidence ~ 1 | 1, data = MockJurors)
mj_fd <- betareg(confidence ~ verdict * conflict | 1, data = MockJurors)
lrtest(mj_null, mj_fd)
lrtest(mj_null, mj_vd)
## McFadden's pseudo-R-squared
1 - as.vector(logLik(mj_null)/logLik(mj_vd))
## visualization
if(require("lattice")) {
histogram(~ confidence | conflict + verdict, data = MockJurors,
col = "lightgray", breaks = 0:10/10, type = "density")
}
## see demo("SmithsonVerkuilen2006", package = "betareg") for more details
```