School Science Survey Data
The science data frame has 1385 rows and 7 columns.
The data are on attitudes to science, from a survey where there were results from 20 classes in private schools and 46 classes in public schools.
science
This data frame contains the following columns:
a factor with levels
ACT Australian Capital Territory,
NSW New South Wales
a factor with levels
private school,
public school
a factor, coded to identify the school
a factor, coded to identify the class
a factor with levels
f, m
a summary score based on two of the questions, on a scale from 1 (dislike) to 12 (like)
a factor with levels corresponding to each class
Francine Adams, Rosemary Martin and Murali Nayadu, Australian National University
classmeans <- with(science, aggregate(like, by=list(PrivPub, Class), mean))
names(classmeans) <- c("PrivPub","Class","like")
dim(classmeans)
attach(classmeans)
boxplot(split(like, PrivPub), ylab = "Class average of attitude to science score", boxwex = 0.4)
rug(like[PrivPub == "private"], side = 2)
rug(like[PrivPub == "public"], side = 4)
detach(classmeans)
if(require(lme4, quietly=TRUE)) {
science.lmer <- lmer(like ~ sex + PrivPub + (1 | school) +
(1 | school:class), data = science,
na.action=na.exclude)
summary(science.lmer)
science1.lmer <- lmer(like ~ sex + PrivPub + (1 | school:class),
data = science, na.action=na.exclude)
summary(science1.lmer)
ranf <- ranef(obj = science1.lmer, drop=TRUE)[["school:class"]]
flist <- science1.lmer@flist[["school:class"]]
privpub <- science[match(names(ranf), flist), "PrivPub"]
num <- unclass(table(flist)); numlabs <- pretty(num)
## Plot effect estimates vs numbers
plot(sqrt(num), ranf, xaxt="n", pch=c(1,3)[as.numeric(privpub)],
xlab="# in class (square root scale)",
ylab="Estimate of class effect")
lines(lowess(sqrt(num[privpub=="private"]),
ranf[privpub=="private"], f=1.1), lty=2)
lines(lowess(sqrt(num[privpub=="public"]),
ranf[privpub=="public"], f=1.1), lty=3)
axis(1, at=sqrt(numlabs), labels=paste(numlabs))
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