Dataset TIMSS 2011

Description

Example dataset TIMSS 2011

Usage

data(data.timss1)
data(data.timss1.ind)
data(data.timss2)
data(data.timssrep)
data(data.timss3)
data(data.timss4)

Format

The dataset data.timss1 is a list containing 5 imputed datasets. The dataset data.timss1.ind contains response indicators of these 5 imputed datatsets in data.timss1.

The dataset data.timss2 is a list containing 5 datasets in which only plausible values are imputed, but student covariates are missing.

The dataset data.timssrep contains replicate weights of students.

The dataset data.timss3 is a TIMSS dataset with some missing student covariates and all 5 plausible values contained in one file.

The dataset data.timss4 is a list containing nested multiply imputed datasets, with 5 between-nest and 4 within-nest imputations.

Examples

## Not run: 	
library(survey)
library(lavaan.survey)
library(intsvy)
library(mitools)
    
#############################################################################
# EXAMPLE 1: TIMSS dataset data.timss3 (one dataset including all PVs)
#############################################################################   

data(data.timss2)
data(data.timss3)
data(data.timssrep)

# Analysis based on official 'single' datasets (data.timss3)
# There are 5 plausible values, but student covariates are not imputed.

#--- create object of class BIFIE data
bdat3 <- BIFIE.data(data.timss3 , wgt = data.timss3$TOTWGT , 
              wgtrep = data.timssrep[,-1] , fayfac = 1)
summary(bdat3)              
# This BIFIEdata object contains one dataset in which all
# plausible values are included. This object can be used
# in analysis without plausible values.
# Equivalently, one can define bdat3 much simpler by
bdat3 <- BIFIE.data.jack(data.timss3 , jktype="JK_TIMSS")
summary(bdat3)              

#--- In the following, the object bdat4 is defined with 5 datasets 
# referring to 5 plausible values.
bdat4 <- BIFIE.data.jack(data.timss3 , pv_vars = c("ASMMAT","ASSSCI") ,
               jktype="JK_TIMSS")
summary(bdat4)              

#--- create object in survey package
dat3a <- as.data.frame( cbind( data.timss2[[1]] , data.timssrep ) )
RR <- ncol(data.timssrep) - 1       # number of jackknife zones
svydes3 <- survey::svrepdesign(data=dat3a,weights=~TOTWGT ,type="JKn",
                 repweights='w_fstr[0-9]', scale=1,  rscales=rep(1,RR))
summary(svydes3)

#--- create object with imputed datasets in survey
datL <- data.timss2
# include replicate weights in each dataset
for (ii in 1:5){
    dat1 <- datL[[ii]]
    dat1 <- cbind(  dat1 , data.timssrep[,-1] ) 
    datL[[ii]] <- dat1 
                }
datL <- mitools::imputationList(list( datL[[1]],datL[[2]],datL[[3]],datL[[4]],datL[[5]]) )
svydes4 <- survey::svrepdesign(data=datL,weights=~TOTWGT ,type="JKn",
                   repweights='w_fstr[0-9]', scale=1,  rscales=rep(1,RR))
summary(svydes4)

#--- reconstruct data.timss3 for intsvy package. Plausible values must be labeled
# as PV01, PV02, ... and NOT PV1, PV2, ...
data.timss3a <- data.timss3
colnames(data.timss3a) <- gsub( "ASMMAT" , "ASMMAT0" , colnames(data.timss3a) )
colnames(data.timss3a) <- gsub( "ASSSCI" , "ASSSCI0" , colnames(data.timss3a) )

#***************************
# Model 1: Linear regression (no grouping variable)

#--- linear regression in survey
mod1a <-  survey::svyglm( scsci ~ migrant + books , design = svydes3) 
summary(mod1a)

#--- regression with pirls.reg (intsvy)
mod1b <- intsvy::pirls.reg( y = "scsci" , x = c("migrant" , "books" ) , data= data.timss3)
mod1b

#---- regression with BIFIEsurvey
mod1c <- BIFIE.linreg( bdat3 , dep="scsci" , pre = c("one" , "migrant" , "books" ) )
summary(mod1c)

#--- regression with lavaan.survey package
lavmodel <- "
    scsci ~ migrant + books
    scsci ~ 1
    scsci ~~ scsci
        "
# fit in lavaan
lavaan.fit <- lavaan::lavaan( lavmodel, data= data.timss3, estimator="MLM")
summary(lavaan.fit)
# using all replicated weights
mod1d <- lavaan.survey::lavaan.survey(lavaan.fit=lavaan.fit, survey.design=svydes3 )
summary(mod1d)

#***************************
# Model 2: Linear regression (grouped by female)

#--- linear regression in survey
mod2a <- survey::svyglm( scsci ~ 0 + as.factor(female) + as.factor(female):migrant 
               + as.factor(female):books , design = svydes3) 
summary(mod2a)

#--- regression with pirls.reg (intsvy)
mod2b <- intsvy::pirls.reg( y = "scsci" , x = c("migrant" , "books" ) , 
                 by = "female" , data= data.timss3)
mod2b[["0"]]   # regression coefficients female = 0
mod2b[["1"]]   # regression coefficients female = 1 

#--- regression with BIFIEsurvey
mod2c <- BIFIE.linreg( bdat3 , dep="scsci" , pre = c("one" , "migrant" , "books" ) , 
                 group="female")
summary(mod2c)   

#--- regression with lavaan.survey package
lavmodel <- "
    scsci ~ migrant + books
    scsci ~ 1
    scsci ~~ scsci
        "
# fit in lavaan
lavaan.fit <- lavaan::lavaan( lavmodel, data= data.timss3, group="female", estimator="MLM")
summary(lavaan.fit)
mod2d <- lavaan.survey::lavaan.survey(lavaan.fit=lavaan.fit, survey.design=svydes3 )
summary(mod2d)

#***************************
# Model 3: Linear regression with mathematics PVs
library(mitools)

#--- linear regression in survey
mod3a <- with(svydes4,  survey::svyglm( ASMMAT ~ migrant + books , design = svydes4 ) )
res3a <- mitools::MIcombine(mod3a)
summary(res3a)

#--- regression with pirls.reg.pv (intsvy)
mod3b <- intsvy::pirls.reg.pv( pvlabel = "ASMMAT" , x = c("migrant" , "books" ) , 
             data= data.timss3a)

#--- regression with BIFIEsurvey
mod3c <- BIFIE.linreg( bdat4 , dep="ASMMAT" , pre = c("one" , "migrant" , "books" ) )
summary(mod3c)

#--- regression with lavaan.survey package
lavmodel <- "
    ASMMAT ~ migrant + books
    ASMMAT ~ 1
    ASMMAT ~~ ASMMAT
        "
# fit in lavaan
lavaan.fit <- lavaan::lavaan( lavmodel, data= data.timss3a, group="female", estimator="MLM")
summary(lavaan.fit)
mod3d <- lavaan.survey::lavaan.survey(lavaan.fit=lavaan.fit, survey.design=svydes4 )
summary(mod3d)

#############################################################################
# EXAMPLE 2: TIMSS dataset data.timss4 | Nested multiply imputed dataset
#############################################################################  

data(data.timss4)
data(data.timssrep)

#**** create BIFIEdata object
bdat <- BIFIE.data( data.list=data.timss4 , wgt= data.timss4[[1]][[1]]$TOTWGT ,
            wgtrep=data.timssrep[, -1 ] , NMI=TRUE , cdata=TRUE )
summary(bdat)            

#**** Model 1: Linear regression for mathematics score
mod1 <- BIFIE.linreg( bdat , dep= "ASMMAT" , pre=c("one","books","migrant")  )
summary(mod1)

#*** Model 2: Univariate statistics ?BIFIE.univar
mod2 <- BIFIE.univar( bdat , vars = c("ASMMAT","ASSSCI","books") )
summary(mod2)

## End(Not run)

Results