In eldafani/intsvy: International Assessment Data Manager

knitr::opts_chunk$set(echo = TRUE)
library("intsvy")
dir <- "/home/eldani/eldani/International LSA/PISA/2022"

library("devtools")
install_github("eldafani/intsvy")
library("intsvy")

Select data

Check names of variables in student and school files and names of participating countries for data selection. Datasets are stored in directory, dir.

pisa.var.label(folder = dir,
               student.file="CY08MSP_STU_QQQ.sav",
               school.file="CY08MSP_SCH_QQQ.sav", output = dir)

Select data for three countries, student's SES, sex, and school's location

pisa <- pisa.select.merge(folder = dir,
                          student.file="CY08MSP_STU_QQQ.sav",
                          school.file="CY08MSP_SCH_QQQ.sav",
                          student= c("ESCS", "ST004D01T"),
                          school = c("SC001Q01TA"), 
                          countries = c("ARG", "FRA", "PER"))

Calculate average mathematics score by country

The average mathematics score and its associated standard error are presented, first for Argentina, second for France and third for Peru.

pisa.mean.pv(pvlabel = paste0("PV", 1:10, "MATH"), by = "CNT", data = pisa)

Calculate average mathematics score by country and sex

pisa.mean.pv(pvlabel = paste0("PV", 1:10, "MATH"), by = c("CNT", "ST004D01T"), data = pisa)

Calculate proficiency levels by country

Cutoff scores for mathematics performance

mathcut <- c(357.77, 420.07, 482.38, 544.68, 606.99, 669.3)

Produce table with results

pisa.ben.pv(pvlabel= paste0("PV", 1:10, "MATH"), cutoff= mathcut, by="CNT", data=pisa)

Estimate regression of reading on sex by country

pisa.reg.pv(pvlabel= paste0("PV", 1:10, "READ"), x="ST004D01T", by = "CNT", data=pisa)

Estimate regression of mathematics on SES by country

pisa.reg.pv(pvlabel= paste0("PV", 1:10, "MATH"), x="ESCS", by = "CNT", data=pisa)

eldafani/intsvy documentation built on March 18, 2024, 7:54 p.m.