Nothing
## ---- echo=FALSE, warning=FALSE, message=FALSE, error=FALSE--------------
knitr::opts_chunk$set(collapse = TRUE, comment = "#>", echo=TRUE, warning=FALSE, message=FALSE, error=FALSE)
## ------------------------------------------------------------------------
# Load the library
library(rcrtan)
# Some data
test_data <- tibble::tribble(
~ID, ~Q1, ~Q2, ~Q3, ~Q4, ~Q5, ~Q6, ~Q7, ~Q8, ~Q9, ~Q10, ~Total,
1441L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 10L,
1387L, 1L, 1L, 1L, 1L, 1L, 0L, 0L, 1L, 1L, 1L, 8L,
1994L, 1L, 1L, 1L, 1L, 1L, 0L, 0L, 1L, 1L, 1L, 8L,
1453L, 1L, 1L, 1L, 1L, 0L, 1L, 0L, 1L, 1L, 1L, 8L,
1679L, 1L, 0L, 1L, 0L, 0L, 1L, 1L, 1L, 1L, 1L, 7L,
1899L, 1L, 1L, 1L, 1L, 0L, 1L, 0L, 1L, 1L, 1L, 8L,
1631L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 1L, 1L, 1L, 9L,
1894L, 1L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 9L,
1206L, 0L, 0L, 1L, 1L, 0L, 1L, 0L, 1L, 0L, 1L, 5L,
1163L, 1L, 1L, 1L, 1L, 1L, 0L, 0L, 1L, 1L, 1L, 8L,
1964L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 10L,
1050L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 10L,
1034L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 10L,
1826L, 1L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 9L,
1973L, 1L, 1L, 1L, 1L, 0L, 1L, 0L, 1L, 1L, 1L, 8L,
1936L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 8L,
1114L, 1L, 0L, 0L, 1L, 1L, 0L, 0L, 0L, 1L, 1L, 5L,
1181L, 0L, 1L, 1L, 1L, 0L, 0L, 1L, 1L, 1L, 1L, 7L,
1917L, 1L, 1L, 1L, 1L, 0L, 1L, 0L, 1L, 1L, 1L, 8L
)
# Analyze the dichomous data. Use look_up = TRUE if you want to see the look up tables from Subkoviak (1988).
sub_ex_one <- subkoviak(data = test_data, items = 2:11, raw_cut_score = 8)
## ------------------------------------------------------------------------
sub_ex_one
## ------------------------------------------------------------------------
sub_ex_two <- subkoviak(data = test_data, items = 10, raw_cut_score = 8, total = "Total")
sub_ex_two
## ------------------------------------------------------------------------
# When item level information is known
phi_d_one <- phi_domain(data = test_data, items = 2:11)
# When only total scores and number of items on the test are known
phi_d_two <- phi_domain(data = test_data, items = 10, total = "Total")
phi_d_one
phi_d_two
## ------------------------------------------------------------------------
phi_l_one <- rcrtan::phi_lambda(test_data, 2:11, cut_score = 0.80)
phi_l_two <- phi_lambda(test_data, 10, cut_score = 0.80, total = 'Total')
phi_l_one
phi_l_two
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