socsupport {DAAG}

Social Support Data

Description

Data from a survey on social and other kinds of support.

Usage

socsupport

Format

This data frame contains the following columns:

gender

a factor with levels female, male

age

age, in years, with levels 18-20, 21-24, 25-30, 31-40,40+

country

a factor with levels australia, other

marital

a factor with levels married, other, single

livewith

a factor with levels alone, friends, other, parents, partner, residences

employment

a factor with levels employed fulltime, employed part-time, govt assistance, other, parental support

firstyr

a factor with levels first year, other

enrolment

a factor with levels full-time, part-time, <NA>

emotional

summary of 5 questions on emotional support availability

emotionalsat

summary of 5 questions on emotional support satisfaction

tangible

summary of 4 questions on availability of tangible support

tangiblesat

summary of 4 questions on satisfaction with tangible support

affect

summary of 3 questions on availability of affectionate support sources

affectsat

summary of 3 questions on satisfaction with affectionate support sources

psi

summary of 3 questions on availability of positive social interaction

psisat

summary of 3 questions on satisfaction with positive social interaction

esupport

summary of 4 questions on extent of emotional support sources

psupport

summary of 4 questions on extent of practical support sources

supsources

summary of 4 questions on extent of social support sources (formerly, socsupport)

BDI

Score on the Beck depression index (summary of 21 questions)

Source

Melissa Manning, Psychology, Australian National University

Examples

attach(socsupport)

not.na <- apply(socsupport[,9:19], 1, function(x)!any(is.na(x)))
ss.pr1 <- princomp(as.matrix(socsupport[not.na, 9:19]), cor=TRUE)  
pairs(ss.pr1$scores[,1:3])
sort(-ss.pr1$scores[,1])        # Minus the largest value appears first
pause()

not.na[36] <- FALSE
ss.pr <- princomp(as.matrix(socsupport[not.na, 9:19]), cor=TRUE)  
summary(ss.pr)          # Examine the contribution of the components
pause()

# We now regress BDI on the first six principal components:
ss.lm <- lm(BDI[not.na] ~ ss.pr$scores[, 1:6], data=socsupport)
summary(ss.lm)$coef
pause()

ss.pr$loadings[,1]
plot(BDI[not.na] ~  ss.pr$scores[ ,1], col=as.numeric(gender), 
pch=as.numeric(gender),  xlab ="1st principal component", ylab="BDI")
topleft <- par()$usr[c(1,4)]
legend(topleft[1], topleft[2], col=1:2, pch=1:2, legend=levels(gender))