liss: Internet use in a panel data set.

In lavaan.survey: Complex Survey Structural Equation Modeling (SEM)

Description

The longitudinal internet studies for the social sciences (LISS) panel is a web survey panel recruited by probability sampling of households. All household members participate in the survey. This dataset contains four waves of data (2008-2011) on panel participants' internet usage.

Usage

data(liss)

Format

A data frame with 7369 observations on the following 6 variables.

nohouse_encr

Household identifier.

nomem_encr

Person identifier.

cs08a247

Natural logarithm of number of hours internet usage at home per week in 2008.

cs09b247

Natural logarithm of number of hours internet usage at home per week in 2009.

cs10c247

Natural logarithm of number of hours internet usage at home per week in 2010.

cs11d247

Natural logarithm of number of hours internet usage at home per week in 2011.

Author(s)

Daniel Oberski - http://daob.nl/ - [e-mail]

Source

Data were obtained from http://www.lissdata.nl/dataarchive/. See also http://www.lissdata.nl/dataarchive/data_variables/view/795.

References

Scherpenzeel A.C. (2011). "Data Collection in a Probability-Based Internet Panel: How the LISS Panel was Built and How it Can be Used." Bulletin of Sociological Methodology, 109(1), 56-61.

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/.

Examples

data(liss)

# Estimating the reliability of internet usage with the "quasi-simplex" 
#   (a.k.a. Gaussian latent Markov) model. 

# A quasi-simplex model for four time points, setting the error variance 
#   to be estimated equal over time.
model.liss <- "
   cs08 =~ 1 * cs08a247
   cs09 =~ 1 * cs09b247
   cs10 =~ 1 * cs10c247
   cs11 =~ 1 * cs11d247
 
   cs09 ~ cs08
   cs10 ~ cs09
   cs11 ~ cs10
 
   cs08a247 ~~ vare * cs08a247
   cs09b247 ~~ vare * cs09b247
   cs10c247 ~~ vare * cs10c247
   cs11d247 ~~ vare * cs11d247
 
   cs08 ~~ vart08 * cs08
 
   reliab.ratio := vart08 / (vart08 + vare)
"

# Fit the model using listwise deletion
fit.liss <- lavaan(model.liss, auto.var = TRUE, meanstructure = TRUE, 
  int.ov.free = TRUE, data = liss)
  
# Fit the model accounting for nesting of respondents within households
des.liss <- svydesign(ids = ~nohouse_encr, prob = ~1, data = liss)
fit.liss.surv <- lavaan.survey(fit.liss, des.liss)
fit.liss.surv

# Complex survey inference on the reliability of interest:
parameterEstimates(fit.liss.surv)[24, ]

## To deal with missing data (including attrition), multiple imputation can be used.
## For example using the mice library (although any MI software is suitable)

## Uncomment below to run this time-intensive analysis
## NOT RUN:

# set.seed(20140221)
# library("mice") 
# liss.imp <- mice(liss, m = 100, method = "norm", maxit = 100)

## Turn the mice object into a list() of imputed datasets
# liss.implist <- lapply(seq(liss.imp$m), function(im) complete(liss.imp, im))

## After obtaining the list of imputed datasets, 
##  use the mitools package to turn it into an imputation list
# library("mitools")
# liss.implist <- imputationList(liss.implist)

## Give the imputation list as data to a svydesign object
# des.liss.imp <- svydesign(ids = ~nohouse_encr, prob = ~1, data = liss.implist)

## lavaan.survey can be used as usual, using the 
##    svydesign object that has an imputation list as data
## Standard errors and chi-square tests will account for both the clustering and the 
##   imputation uncertainty applying Rubin's rules. 
# fit.liss.surv.mi <- lavaan.survey(fit.liss, des.liss.imp)
# fit.liss.surv.mi

## After this analysis, we can again perform inference on the reliability of interest:
# parameterEstimates(fit.liss.surv.mi)[24, ]

lavaan.survey documentation built on May 2, 2019, 3:41 p.m.