Verbal Aggression item responses

Description

These are the item responses to a questionaire on verbal aggression. These data are used throughout De Boeck and Wilson (2004) to illustrate various forms of item response models.

Format

A data frame with 7584 observations on the following 13 variables.

Anger

the subject's Trait Anger score as measured on the State-Trait Anger Expression Inventory (STAXI)

Gender

the subject's gender - a factor with levels M and F

item

the item on the questionaire, as a factor

resp

the subject's response to the item - an ordered factor with levels no < perhaps < yes

id

the subject identifier, as a factor

btype

behavior type - a factor with levels curse, scold and shout

situ

situation type - a factor with levels other and self indicating other-to-blame and self-to-blame

mode

behavior mode - a factor with levels want and do

r2

dichotomous version of the response - a factor with levels N and Y

Source

Data originally from the UC Berkeley BEAR Center; original link is available at https://web.archive.org/web/20221128003829/https://old.bear.berkeley.edu/page/materials-explanatory-item-response-models, but the data are no longer accessible there.

References

De Boeck and Wilson (2004), Explanatory Item Response Models, Springer.

Examples

str(VerbAgg)
## Show how  r2 := h(resp) is defined:
with(VerbAgg, stopifnot( identical(r2, {
     r <- factor(resp, ordered=FALSE); levels(r) <- c("N","Y","Y"); r})))

xtabs(~ item + resp, VerbAgg)
xtabs(~ btype + resp, VerbAgg)
round(100 * ftable(prop.table(xtabs(~ situ + mode + resp, VerbAgg), 1:2), 1))
person <- unique(subset(VerbAgg, select = c(id, Gender, Anger)))
require(lattice)
densityplot(~ Anger, person, groups = Gender, auto.key = list(columns = 2),
            xlab = "Trait Anger score (STAXI)")

if(lme4:::testLevel() >= 3) { ## takes about 15 sec
    print(fmVA <- glmer(r2 ~ (Anger + Gender + btype + situ)^2 +
 		   (1|id) + (1|item), family = binomial, data =
		   VerbAgg), corr=FALSE)
} ## testLevel() >= 3
if (interactive()) {
## much faster but less accurate
    print(fmVA0 <- glmer(r2 ~ (Anger + Gender + btype + situ)^2 +
                             (1|id) + (1|item), family = binomial,
                         data = VerbAgg, nAGQ=0L), corr=FALSE)
} ## interactive()