docs/pisa22.md

In eldafani/intsvy: International Assessment Data Manager

Examples with 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"))

## File character set is 'UTF-8'.

## Converting character set to UTF-8.

## Warning: 1 variables have duplicated labels:
##   LANGN

## File character set is 'UTF-8'.
## Converting character set to UTF-8.

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)

##   CNT  Freq   Mean s.e.    SD  s.e
## 1 ARG 12111 377.53 2.25 74.42 1.06
## 2 FRA  6770 473.94 2.49 91.06 1.07
## 3 PER  6968 391.24 2.34 77.91 1.17

Calculate average mathematics score by country and sex

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

##   CNT ST004D01T Freq   Mean s.e.    SD  s.e
## 1 ARG         1 6094 372.04 2.55 71.74 1.37
## 2 ARG         2 6017 383.03 2.42 76.63 1.29
## 3 FRA         1 3406 469.14 2.51 85.87 1.39
## 4 FRA         2 3364 478.93 3.44 95.90 1.30
## 5 PER         1 3474 383.65 2.48 74.58 1.50
## 6 PER         2 3494 398.82 2.82 80.38 1.37

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)

##    CNT          Benchmark Percentage Std. err.
## 1  ARG At or above 357.77      57.91      1.33
## 2  ARG At or above 420.07      27.07      1.18
## 3  ARG At or above 482.38       8.93      0.61
## 4  ARG At or above 544.68       2.01      0.27
## 5  ARG At or above 606.99       0.30      0.08
## 6  ARG  At or above 669.3       0.02      0.02
## 7  FRA At or above 357.77      89.04      0.69
## 8  FRA At or above 420.07      71.19      1.11
## 9  FRA At or above 482.38      46.97      1.21
## 10 FRA At or above 544.68      23.10      0.95
## 11 FRA At or above 606.99       7.39      0.51
## 12 FRA  At or above 669.3       1.14      0.20
## 13 PER At or above 357.77      64.31      1.24
## 14 PER At or above 420.07      33.83      1.18
## 15 PER At or above 482.38      13.06      0.78
## 16 PER At or above 544.68       3.31      0.39
## 17 PER At or above 606.99       0.49      0.12
## 18 PER  At or above 669.3       0.03      0.03

Estimate regression of reading on sex by country

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

## $ARG
##             Estimate Std. Error t value
## (Intercept)   422.10       4.43   95.35
## ST004D01T     -14.25       2.53   -5.63
## R-squared       0.01       0.00    2.82
## 
## $FRA
##             Estimate Std. Error t value
## (Intercept)   503.98       6.33   79.66
## ST004D01T     -20.21       4.26   -4.74
## R-squared       0.01       0.00    2.44
## 
## $PER
##             Estimate Std. Error t value
## (Intercept)   420.41       5.35   78.58
## ST004D01T      -8.11       3.10   -2.61
## R-squared       0.00       0.00    1.28

Estimate regression of mathematics on SES by country

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

## $ARG
##             Estimate Std. Error t value
## (Intercept)   398.46       2.03  196.74
## ESCS           25.69       1.20   21.45
## R-squared       0.15       0.01   11.56
## 
## $FRA
##             Estimate Std. Error t value
## (Intercept)   475.59       1.92  248.05
## ESCS           45.52       1.52   29.86
## R-squared       0.21       0.01   16.38
## 
## $PER
##             Estimate Std. Error t value
## (Intercept)   421.74       2.43  173.77
## ESCS           26.16       1.08   24.29
## R-squared       0.17       0.01   11.92

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