European Social Survey (ESS) data from the 2008 (fourth) round in the United Kingdom. The data are from a questionnaire on "what the responsibilities of governments should or should not be". These were factor-analyzed by Roosma, Gelissen, and van Oorschot (2013). Also included are complex survey design variables.
Usage
data(ess4.gb)
Format
A data frame with 2273 observations of 13 variables.
idno
Respondent identifier.
psu
Primary sampling unit (PSU).
dweight
ESS design weights.
stratval
Stratification variable (UK regions).
gvjbevn
Job for everyone, governments' responsibility (0-10).
gvhlthc
Health care for the sick, governments' responsibility (0-10).
gvslvol
Standard of living for the old, governments' responsibility (0-10).
gvslvue
Standard of living for the unemployed, governments' responsibility (0-10).
gvcldcr
Child care services for working parents, governments' responsibility (0-10).
gvpdlwk
Paid leave from work to care for sick family, governments' responsibility (0-10).
sbprvpv
Social benefits/services prevent widespread poverty (1-5).
sbeqsoc
Social benefits/services lead to a more equal society (1-5).
sbcwkfm
Social benefits/services make it easier to combine work and family (1-5).
Jowell, R., Roberts, C., Fitzgerald, R., & Eva, G. (2007). Measuring attitudes
cross-nationally: Lessons from the european social survey. SAGE.
Oberski, D.L. (2014). lavaan.survey: An R Package for Complex Survey Analysis
of Structural Equation Models. Journal of Statistical Software, 57(1), 1-27.
http://www.jstatsoft.org/v57/i01/.
Roosma F., Gelissen J., van Oorschot W. (2013). "The Multidimensionality of
Welfare State Attitudes: A European Cross-National Study." Social Indicators
Research, 113(1), 235-255.
See Also
lavaan.survey
Examples
data(ess4.gb)
# Two-factor model based on Roosma et al (2013).
model.cfa <-
"range =~ gvjbevn + gvhlthc + gvslvol + gvslvue + gvcldcr + gvpdlwk
goals =~ sbprvpv + sbeqsoc + sbcwkfm"
# Fit the model using lavaan
fit.cfa.ml <- lavaan(model.cfa, data = ess4.gb, estimator = "MLM",
meanstructure = TRUE, int.ov.free = TRUE, auto.var = TRUE,
auto.fix.first = TRUE, auto.cov.lv.x = TRUE)
fit.cfa.ml
# Define the complex survey design for ESS 4 in the UK
des.gb <- svydesign(ids = ~psu, strata = ~stratval, weights = ~dweight,
data = ess4.gb)
# Fit the two-factor model while taking the survey design into account.
fit.cfa.surv <- lavaan.survey(fit.cfa.ml, survey.design = des.gb)
fit.cfa.surv