Description Usage Format Author(s) Source References See Also Examples
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.
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A data frame with 2273 observations of 13 variables.
idnoRespondent identifier.
psuPrimary sampling unit (PSU).
dweightESS design weights.
stratvalStratification variable (UK regions).
gvjbevnJob for everyone, governments' responsibility (0-10).
gvhlthcHealth care for the sick, governments' responsibility (0-10).
gvslvolStandard of living for the old, governments' responsibility (0-10).
gvslvueStandard of living for the unemployed, governments' responsibility (0-10).
gvcldcrChild care services for working parents, governments' responsibility (0-10).
gvpdlwkPaid leave from work to care for sick family, governments' responsibility (0-10).
sbprvpvSocial benefits/services prevent widespread poverty (1-5).
sbeqsocSocial benefits/services lead to a more equal society (1-5).
sbcwkfmSocial benefits/services make it easier to combine work and family (1-5).
Daniel Oberski - http://daob.nl/ - daniel.oberski@gmail.com
This dataset was retrieved from http://www.europeansocialsurvey.org/data/download.html?r=4 and converted to an R dataset.
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.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 | 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
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