lavaan.survey (version 1.1.3.1)

pisa.be.2003: Belgian school childrens' math efficacy and measured ability in the PISA study.

Description

Data from the OECD's 2003 Programme for International Student Assessment (PISA) in Belgium.

Also included are 80 so-called "replicate weights" calculated by Westat which must be accounted for using complex survey procedures.

Usage

data(pisa.be.2003)

Format

A data frame with 8796 observations of 102 variables.

Source

This dataset was retrieved from http://www.oecd.org/pisa/ and converted to an R dataset. For more information regarding the variables and the model estimated below, please see Ferla et al. (2009) and/or the PISA manual (OECD, 2009).

References

Ferla J, Valcke M, Cai Y (2009). "Academic Self-Efficacy and Academic Self- Concept: Reconsidering Structural Relationships." Learning and Individual Differences, 19(4), 499-505.

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/.

OECD (2009). PISA Data Analysis Manual: SPSS and SAS. 2nd edition. OECD.

See Also

lavaan.survey

Examples

  data(pisa.be.2003)


  # Simplified version of Ferla et al. (2009) model.
  model.pisa <- "
      math =~ PV1MATH1 + PV1MATH2 + PV1MATH3 + PV1MATH4
      neg.efficacy =~ ST31Q01 + ST31Q02 + ST31Q03 + ST31Q04 + 
                      ST31Q05 + ST31Q06 + ST31Q07 + ST31Q08
      neg.selfconcept =~ ST32Q02 + ST32Q04 + ST32Q06 + ST32Q07 + ST32Q09
  
      neg.selfconcept ~ neg.efficacy + ESCS + male
      neg.efficacy ~ neg.selfconcept + school.type + ESCS + male
      math ~ neg.selfconcept + neg.efficacy + school.type + ESCS + male
  "
  
  # Fit the model using lavaan
  fit <- lavaan(model.pisa, data = pisa.be.2003, auto.var = TRUE, std.lv = TRUE,
    meanstructure = TRUE, int.ov.free = TRUE, estimator = "MLM")
  
  ## Not run due to CRAN policies, 
  ##    uncomment below to account for replicate weights:
  
  ## Define the survey design using the BRR replicate weights provided by PISA
  ##   Note that these settings will work for _any_ analysis of PISA data...
  #des.rep <- svrepdesign(ids = ~1, weights = ~W_FSTUWT, data = pisa.be.2003, 
  #  repweights = "W_FSTR[0-9]+", type = "Fay", rho = 0.5)
  
  ## Fit the SEM model accounting for replicate weights
  #fit.surv <- lavaan.survey(lavaan.fit = fit, survey.design =  des.rep)
  
  #fit # Show fitmeasures results
  #fit.surv # Show fitmeasures results