The data set SocGrades contains four outcome measures on student performance
in an introductory sociology course together with six potential predictors.
These data were used by Marascuilo and Levin (1983) for an example of
canonical correlation analysis, but are also suitable as examples of
multivariate multiple regression, MANOVA, MANCOVA and step-down analysis
in multivariate linear models.
Usage
data(SocGrades)
Format
A data frame with 40 observations on the following 10 variables.
class
Social class, an ordered factor with levels 1 > 2 > 3
sex
sex, a factor with levels FM
gpa
grade point average
boards
College Board test scores
hssoc
previous high school unit in sociology, a factor with 2 no, yes
pretest
score on course pretest
midterm1
score on first midterm exam
midterm2
score on second midterm exam
final
score on final exam
eval
course evaluation
Details
midterm1, midterm2, final, and possibly eval are the response variables.
All other variables are potential predictors.
The factors class, sex, and hssoc can be used with
as.numeric in correlational analyses.
Source
Marascuilo, L. A. and Levin, J. R. (1983).
Multivariate Statistics in the Social Sciences
Monterey, CA: Brooks/Cole, Table 5-1, p. 192.
Examples
data(SocGrades)
# basic MLM
grades.mod <- lm(cbind(midterm1, midterm2, final, eval) ~
class + sex + gpa + boards + hssoc + pretest, data=SocGrades)
Anova(grades.mod, test="Roy")
clr <- c("red", "blue", "darkgreen", "magenta", "brown", "black", "darkgray")
heplot(grades.mod, col=clr)
pairs(grades.mod, col=clr)
## Not run:
heplot3d(grades.mod, col=clr, wire=FALSE)
## End(Not run)
if (require(candisc)) {
# calculate canonical results for all terms
grades.can <- candiscList(grades.mod)
# extract canonical R^2s
unlist(lapply(grades.can, function(x) x$canrsq))
# plot class effect in canonical space
heplot(grades.can, term="class", scale=4)
# 1 df terms: show canonical scores and weights for responses
plot(grades.can, term="sex")
plot(grades.can, term="gpa")
plot(grades.can, term="boards")
}