Skip to contents

Prepare ILSA Data

prepILSA.Rd

Modifies ILSA data to meet official participation cases, selects columns and transforms data into simple data frames converting missing values to NAs.

Usage

prepILSA(
  df,
  study = NULL,
  year = NULL,
  specification = NULL,
  fixN = TRUE,
  columns = NULL
)

Arguments

df

a data frame.

study

an optional character vector indicating the ILSA name, for a list of available ILSA, check autoILSA. If NULL, the ILSA name will be determined by the column names in the data frame.

year

a numeric vector indicating the ILSA name, for a list of available cycles, check autoILSA.

specification

a character value indicating extra specification like grade (e.g., "G8" for TIMSS) or subject (e.g., "Math" for TIMSSADVANCED).

fixN

a logical value indicating if data should be "fixed" to meet official criteria. For example, reducing the sample for certain countries in TIMSS 1995. Default is TRUE.

columns

a character vector indicating which columns should be selected. If NULL, all columns will be selected.

Examples


data(timss99)

head(timss99)
#>      IDCNTRY CNTRY IDCNTRY_STR    TOTWGT JKZONE JKREP BSMMAT01 BSMMAT02
#> 5047     152   CHL       Chile  30.45785      6     0   386.66   364.03
#> 2642     152   CHL       Chile  27.59028     41     0   432.69   452.06
#> 1414     152   CHL       Chile  40.13670     21     1   342.83   366.80
#> 8586     392   JPN       Japan 279.79560     41     1   446.48   472.37
#> 2818     152   CHL       Chile  36.95128     45     1   247.62   250.38
#> 4707     152   CHL       Chile  32.72866     75     1   498.33   514.51
#>      BSMMAT03 BSMMAT04 BSMMAT05 BSSSCI01 BSSSCI02 BSSSCI03 BSSSCI04 BSSSCI05
#> 5047   383.67   412.24   353.79   442.62   446.64   343.96   385.70   426.69
#> 2642   451.47   456.83   440.25   516.88   469.45   486.77   568.88   501.23
#> 1414   419.76   410.11   352.05   464.16   245.48   423.77   390.25   240.32
#> 8586   429.80   443.71   463.09   497.88   473.06   350.56   452.29   544.69
#> 2818   284.34   176.84   224.88   395.63   341.74   271.16   351.62   262.55
#> 4707   497.91   481.09   480.25   521.61   531.39   542.79   511.49   500.97

newdata <- prepILSA(df = timss99, columns = paste0("BSMMAT0",1:5),fixN = FALSE)

head(newdata)
#>   BSMMAT01 BSMMAT02 BSMMAT03 BSMMAT04 BSMMAT05
#> 1   386.66   364.03   383.67   412.24   353.79
#> 2   432.69   452.06   451.47   456.83   440.25
#> 3   342.83   366.80   419.76   410.11   352.05
#> 4   446.48   472.37   429.80   443.71   463.09
#> 5   247.62   250.38   284.34   176.84   224.88
#> 6   498.33   514.51   497.91   481.09   480.25