Some PISA Datasets

Some PISA datasets.

data(data.pisaNLD)

Format

The dataset data.pisaNLD is a data frame with 3992 observations on 405 variables which is a part of the Dutch PISA 2006 data.

Source

Downloaded from doi:10.18637/jss.v020.i05 (Fox, 2007).

References

Fox, J.-P. (2007). Multilevel IRT Modeling in practice with the package mlirt. Journal of Statistical Software, 20(5), 1-16. doi:10.18637/jss.v020.i05

Examples

if (FALSE) {
library(mitools)
library(survey)
library(intsvy)

#############################################################################
# EXAMPLE 1: Dutch PISA 2006 dataset
#############################################################################

data(data.pisaNLD)
data <- data.pisaNLD

#--- Create object of class BIFIEdata

# list variables with plausible values: These must be named
# as pv1math, pv2math, ..., pv5math, ...
pv_vars <- toupper( c("math", "math1", "math2", "math3", "math4",
             "read", "scie", "prob") )
# create 5 datasets including different sets of plausible values
dfr <- NULL
VV <- length(pv_vars)
Nimp <- 5           # number of plausible values
for (vv in 1:VV){
      vv1 <- pv_vars[vv]
      ind.vv1 <- which( colnames(data) %in% paste0("PV", 1:Nimp, vv1) )
      dfr2 <- data.frame( "variable"=paste0("PV", vv1), "var_index"=vv,
          "data_index"=ind.vv1, "impdata_index"=1:Nimp )
      dfr <- rbind( dfr, dfr2 )
}

sel_ind <- setdiff( 1:( ncol(data) ), dfr$data_index )
data0 <- data[, sel_ind ]
V0 <- ncol(data0)
newvars <- seq( V0+1, V0+VV )
datalist <- as.list( 1:Nimp )
for (ii in 1:Nimp ){
    dat1 <- data.frame( data0, data[, dfr[ dfr$impdata_index==ii, "data_index" ]])
    colnames(dat1)[ newvars ] <- paste0("PV",pv_vars)
    datalist[[ii]] <- dat1
}

# dataset with replicate weights
datarep <- data[, grep( "W_FSTR", colnames(data) ) ]
RR <- ncol(datarep)     # number of replicate weights

# create BIFIE object
bifieobj <- BIFIEsurvey::BIFIE.data( datalist, wgt=data[, "W_FSTUWT"],
                 wgtrep=datarep, fayfac=1 / RR / ( 1 - .5 )^2 )
# For PISA: RR=80 and therefore fayfac=1/20=.05
summary(bifieobj)

#--- Create BIFIEdata object immediately using BIFIE.data.jack function
bifieobj1 <- BIFIEsurvey::BIFIE.data.jack( data.pisaNLD, jktype="RW_PISA", cdata=TRUE)
summary(bifieobj1)

#--- Create object in survey package
datL <- mitools::imputationList(list( datalist[[1]],datalist[[2]],
                  datalist[[3]],datalist[[4]],datalist[[5]]) )
pisades <- survey::svrepdesign(ids=~ 1, weights=~W_FSTUWT, data=datL,
                    repweights="W_FSTR[0-9]+", type="Fay", rho=0.5, mse=TRUE)
print(pisades)

#++++++++++++++ some comparisons with other packages +++++++++++++++++++++++++++++++

#**** Model 1: Means for mathematics and reading
# BIFIEsurvey package
mod1a <- BIFIEsurvey::BIFIE.univar( bifieobj, vars=c("PVMATH", "PVREAD") )
summary(mod1a)

# intsvy package
mod1b <- intsvy::pisa.mean.pv(pvlabel="MATH", data=data.pisaNLD )
mod1b

# survey package
mod1c <- with( pisades, survey::svymean(PVMATH~1, design=pisades) )
res1c <- mitools::MIcombine(mod1c)
summary(res1c)

#**** Model 2: Linear regression
# BIFIEsurvey package
mod2a <- BIFIEsurvey::BIFIE.linreg( bifieobj, dep="PVMATH",
              pre=c("one","ANXMAT","HISEI"))
summary(mod2a)

# intsvy package
mod2b <- intsvy::pisa.reg.pv(pvlabel="MATH", x=c("ANXMAT","HISEI"), data=data.pisaNLD)
mod2b

# survey package
mod2c <- with( pisades, survey::svyglm(PVMATH~ANXMAT+HISEI, design=pisades) )
res2c <- mitools::MIcombine(mod2c)
summary(res2c)
}