heplots (version 1.3-9)

MockJury: Effects Of Physical Attractiveness Upon Mock Jury Decisions

Description

Male participants were shown a picture of one of three young women. Pilot work had indicated that the one woman was beautiful, another of average physical attractiveness, and the third unattractive. Participants rated the woman they saw on each of twelve attributes. These measures were used to check on the manipulation by the photo.

Then the participants were told that the person in the photo had committed a Crime, and asked to rate the seriousness of the crime and recommend a prison sentence, in Years.

Does attractiveness of the "defendant" influence the sentence or perceived seriousness of the crime? Does attractiveness interact with the nature of the crime?

Usage

data(MockJury)

Format

A data frame with 114 observations on the following 17 variables.

Attr

Attractiveness of the photo, a factor with levels Beautiful Average Unattractive

Crime

Type of crime, a factor with levels Burglary (theft of items from victim's room) Swindle (conned a male victim)

Years

length of sentence given the defendant by the mock juror subject

Serious

a rating of how serious the subject thought the defendant's crime was

exciting

rating of the photo for 'exciting'

calm

rating of the photo for 'calm'

independent

rating of the photo for 'independent'

sincere

rating of the photo for 'sincere'

warm

rating of the photo for 'warm'

phyattr

rating of the photo for 'physical attractiveness'

sociable

rating of the photo for 'exciting'

kind

rating of the photo for 'kind'

intelligent

rating of the photo for 'intelligent'

strong

rating of the photo for 'strong'

sophisticated

rating of the photo for 'sophisticated'

happy

rating of the photo for 'happy'

ownPA

self-rating of the subject for 'physical attractiveness'

References

Data from the thesis by Plaster, M. E. (1989). Inmates as mock jurors: The effects of physical attractiveness upon juridic decisions. M.A. thesis, Greenville, NC: East Carolina University.

Examples

# manipulation check:  test ratings of the photos classified by Attractiveness
jury.mod1 <- lm( cbind(phyattr, happy, independent, sophisticated) ~ Attr, data=MockJury)
Anova(jury.mod1, test="Roy")
## 
## Type II MANOVA Tests: Roy test statistic
##      Df test stat approx F num Df den Df    Pr(>F)    
## Attr  2    1.7672   48.156      4    109 < 2.2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 

heplot(jury.mod1, main="HE plot for manipulation check")
pairs(jury.mod1)

if (require(candisc)) {
	jury.can <- candisc(jury.mod1)
	jury.can
	heplot(jury.can, main="Canonical HE plot")
}

# influence of Attr of photo and nature of crime on Serious and Years
jury.mod2 <- lm( cbind(Serious, Years) ~ Attr * Crime, data=MockJury)
Anova(jury.mod2, test="Roy")
heplot(jury.mod2)

# stepdown test (ANCOVA), controlling for Serious
jury.mod3 <- lm( Years ~ Serious + Attr * Crime, data=MockJury)
Anova(jury.mod3)

# need to consider heterogeneous slopes?
jury.mod4 <- lm( Years ~ Serious * Attr * Crime, data=MockJury)
anova(jury.mod3, jury.mod4)