library("devtools")
install_github("eldafani/intsvy")
library("intsvy")
## ── Attaching core tidyverse packages ──────────────────────────────────────────────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr 1.1.4 ✔ readr 2.1.5
## ✔ forcats 1.0.0 ✔ stringr 1.5.1
## ✔ ggplot2 3.5.1 ✔ tibble 3.2.1
## ✔ lubridate 1.9.2 ✔ tidyr 1.3.0
## ✔ purrr 1.0.1
## ── Conflicts ────────────────────────────────────────────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors
The object dir specifies the directory path where the TIMSS 2023 data is located (eg. “/home/data”). Variable selection can be done with aid of timssg8.var.label(dir)
timss <- timssg8.select.merge(folder= dir,
student= c("ITSEX", "BSDGSLM", "BSDGSVM", "BSDGEDUP"),
school= c("BCBG05A", "BCDGSBC"),
countries = c("ARE", "CHL", "GEO", "SGP"))
timss.mean.pv(pvlabel=paste0("BSMMAT0", 1:5), by= "IDCNTRYL", data=timss)
## IDCNTRYL Freq Mean s.e. SD s.e
## 1 Chile 4478 415.97 3.21 83.71 2.11
## 2 Georgia 4872 466.75 3.22 92.08 2.13
## 3 Singapore 4772 605.32 6.05 96.98 3.55
## 4 United Arab Emirates 32740 488.51 1.73 103.86 1.39
timss.mean.pv(pvlabel= paste0("BSMMAT0", 1:5), by= c("IDCNTRYL", "ITSEX"),
data=timss %>% drop_na(ITSEX))
## IDCNTRYL ITSEX Freq Mean s.e. SD s.e
## 1 Chile 1 2218 404.71 3.47 82.06 1.95
## 2 Chile 2 2260 426.85 3.67 83.84 3.00
## 3 Georgia 1 2389 462.61 3.47 89.88 2.25
## 4 Georgia 2 2483 470.82 3.86 93.99 2.60
## 5 Singapore 1 2305 601.87 6.91 93.51 3.74
## 6 Singapore 2 2467 608.45 8.26 99.92 4.10
## 7 United Arab Emirates 1 16662 482.13 1.89 99.46 1.42
## 8 United Arab Emirates 2 16077 494.70 2.18 107.61 1.75
intsvy.ben.pv(pvnames= paste0("BSMMAT0", 1:5), cutoff = c(400, 475, 550, 625),
by= c("IDCNTRYL", "ITSEX"), data=timss %>% drop_na(ITSEX),
config = timss8_conf)
## IDCNTRYL ITSEX Benchmark Percentage Std. err.
## 1 Chile 1 At or above 400 50.93 1.96
## 2 Chile 1 At or above 475 19.22 1.21
## 3 Chile 1 At or above 550 4.32 0.61
## 4 Chile 1 At or above 625 0.60 0.25
## 5 Chile 2 At or above 400 62.43 1.90
## 6 Chile 2 At or above 475 28.15 1.52
## 7 Chile 2 At or above 550 7.20 0.84
## 8 Chile 2 At or above 625 0.85 0.25
## 9 Georgia 1 At or above 400 73.86 1.58
## 10 Georgia 1 At or above 475 45.62 1.96
## 11 Georgia 1 At or above 550 17.14 1.42
## 12 Georgia 1 At or above 625 2.89 0.53
## 13 Georgia 2 At or above 400 76.28 1.90
## 14 Georgia 2 At or above 475 47.67 1.90
## 15 Georgia 2 At or above 550 21.39 1.45
## 16 Georgia 2 At or above 625 4.73 0.93
## 17 Singapore 1 At or above 400 96.68 0.80
## 18 Singapore 1 At or above 475 89.37 1.70
## 19 Singapore 1 At or above 550 74.20 2.50
## 20 Singapore 1 At or above 625 44.84 3.20
## 21 Singapore 2 At or above 400 96.88 0.82
## 22 Singapore 2 At or above 475 89.31 1.83
## 23 Singapore 2 At or above 550 74.08 2.89
## 24 Singapore 2 At or above 625 47.70 3.45
## 25 United Arab Emirates 1 At or above 400 78.02 0.76
## 26 United Arab Emirates 1 At or above 475 52.71 0.88
## 27 United Arab Emirates 1 At or above 550 25.47 0.75
## 28 United Arab Emirates 1 At or above 625 7.74 0.47
## 29 United Arab Emirates 2 At or above 400 79.13 0.85
## 30 United Arab Emirates 2 At or above 475 57.25 0.91
## 31 United Arab Emirates 2 At or above 550 32.12 0.91
## 32 United Arab Emirates 2 At or above 625 11.67 0.67
intsvy.ben.pv(pvnames= paste0("BSMMAT0", 1:5), cutoff = c(400, 475, 550, 625),
by= c("IDCNTRYL", "ITSEX"), data=timss %>% drop_na(ITSEX),
config = timss8_conf)
## IDCNTRYL ITSEX Benchmark Percentage Std. err.
## 1 Chile 1 At or above 400 50.93 1.96
## 2 Chile 1 At or above 475 19.22 1.21
## 3 Chile 1 At or above 550 4.32 0.61
## 4 Chile 1 At or above 625 0.60 0.25
## 5 Chile 2 At or above 400 62.43 1.90
## 6 Chile 2 At or above 475 28.15 1.52
## 7 Chile 2 At or above 550 7.20 0.84
## 8 Chile 2 At or above 625 0.85 0.25
## 9 Georgia 1 At or above 400 73.86 1.58
## 10 Georgia 1 At or above 475 45.62 1.96
## 11 Georgia 1 At or above 550 17.14 1.42
## 12 Georgia 1 At or above 625 2.89 0.53
## 13 Georgia 2 At or above 400 76.28 1.90
## 14 Georgia 2 At or above 475 47.67 1.90
## 15 Georgia 2 At or above 550 21.39 1.45
## 16 Georgia 2 At or above 625 4.73 0.93
## 17 Singapore 1 At or above 400 96.68 0.80
## 18 Singapore 1 At or above 475 89.37 1.70
## 19 Singapore 1 At or above 550 74.20 2.50
## 20 Singapore 1 At or above 625 44.84 3.20
## 21 Singapore 2 At or above 400 96.88 0.82
## 22 Singapore 2 At or above 475 89.31 1.83
## 23 Singapore 2 At or above 550 74.08 2.89
## 24 Singapore 2 At or above 625 47.70 3.45
## 25 United Arab Emirates 1 At or above 400 78.02 0.76
## 26 United Arab Emirates 1 At or above 475 52.71 0.88
## 27 United Arab Emirates 1 At or above 550 25.47 0.75
## 28 United Arab Emirates 1 At or above 625 7.74 0.47
## 29 United Arab Emirates 2 At or above 400 79.13 0.85
## 30 United Arab Emirates 2 At or above 475 57.25 0.91
## 31 United Arab Emirates 2 At or above 550 32.12 0.91
## 32 United Arab Emirates 2 At or above 625 11.67 0.67