Opens a connection to an ePIRLS data file and
returns an edsurvey.data.frame
with
information about the file and data.
read_ePIRLS(path, countries, forceReread = FALSE, verbose = TRUE)
a character value to the full directory to the ePIRLS extracted SPSS (.sav) set of data
a character vector of the country/countries to include using
the three-digit ISO country code.
A list of country codes can be found on Wikipedia at
https://en.wikipedia.org/wiki/ISO_3166-1#Current_codes,
or other online sources. Consult the ePIRLS User Guide to help determine what countries
are included within a specific testing year of ePIRLS.
To select all countries, use a wildcard value of *
.
a logical value to force rereading of all processed data.
The default value of FALSE
will speed up the read_ePIRLS
function by
using existing read-in data already processed.
a logical value to either print or suppress status message output.
The default value is TRUE
.
an edsurvey.data.frame
for a single specified country or an edsurvey.data.frame.list
if multiple countries specified
Reads in the unzipped files downloaded from the ePIRLS international database(s) using the IEA Study Data Repository. Data files require the SPSS data file (.sav) format using the default filenames.
An ePIRLS edsurvey.data.frame
includes three distinct data levels:
student
school
teacher
When the getData
function is called using a ePIRLS edsurvey.data.frame
,
the requested data variables are inspected, and it handles any necessary data merges automatically.
Note that the school
data will always be returned merged to the student
data, even if only school
variables are requested.
Only if teacher
variables are requested by the getData
call, will cause teacher
data to be merged.
A student
can be linked to many teachers
, which varies widely between countries.
Please note that calling the dim
function for an ePIRLS edsurvey.data.frame
will result in
the row count as if the teacher
dataset was merged.
This row count will be considered the full data N
of the edsurvey.data.frame
, even if no teacher
data were included in an analysis.
The column count returned by dim
will be the count of unique column variables across all three data levels.
readNAEP
, readTIMSS
, getData
, and download_ePIRLS
# NOT RUN {
usa <- read_ePIRLS("C:/ePIRLS/2016", countries = c("usa"))
gg <- getData(usa, c("itsex", "totwgt", "erea"))
head(gg)
edsurveyTable(erea ~ itsex, usa)
# }
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