A simulated dataset for demonstrating the mcqAnalysis package. The test contains 30 four-option multiple-choice items administered to 200 students. The data are generated under a two-parameter logistic framework with a deliberately mixed mix of item quality:
Items 1-8 are easy items with strong discrimination.
Items 9-24 are medium-difficulty items, most discriminating well.
Items 25-28 are harder items with progressively weaker discrimination.
Items 29-30 are deliberately badly-written items with negative discrimination (high-ability students get them wrong more often).
Item 30 additionally has a "trap" distractor disproportionately chosen by high-ability students, useful for demonstrating distractor analysis.
Format
A list with two components:
- responses
A 200 x 30 character matrix of student responses (values in
{"A", "B", "C", "D"}).- key
A named character vector of length 30 giving the correct answer for each item.
Examples
data(mcq_example)
str(mcq_example, max.level = 1)
#> List of 2
#> $ responses: chr [1:200, 1:30] "D" "D" "D" "D" ...
#> ..- attr(*, "dimnames")=List of 2
#> $ key : Named chr [1:30] "D" "D" "D" "A" ...
#> ..- attr(*, "names")= chr [1:30] "item01" "item02" "item03" "item04" ...
mcq_example$key
#> item01 item02 item03 item04 item05 item06 item07 item08 item09 item10 item11
#> "D" "D" "D" "A" "D" "B" "D" "A" "D" "C" "B"
#> item12 item13 item14 item15 item16 item17 item18 item19 item20 item21 item22
#> "A" "C" "B" "D" "A" "A" "D" "A" "D" "B" "B"
#> item23 item24 item25 item26 item27 item28 item29 item30
#> "D" "D" "A" "C" "D" "A" "C" "D"
head(mcq_example$responses)
#> item01 item02 item03 item04 item05 item06 item07 item08 item09
#> student001 "D" "D" "D" "A" "D" "B" "D" "A" "D"
#> student002 "D" "D" "D" "A" "A" "B" "A" "B" "D"
#> student003 "D" "D" "C" "A" "D" "B" "D" "A" "B"
#> student004 "D" "D" "D" "A" "D" "B" "D" "A" "D"
#> student005 "D" "D" "D" "A" "D" "B" "D" "A" "B"
#> student006 "D" "D" "D" "A" "D" "B" "A" "A" "B"
#> item10 item11 item12 item13 item14 item15 item16 item17 item18
#> student001 "C" "D" "B" "C" "B" "D" "A" "A" "D"
#> student002 "C" "C" "D" "B" "C" "C" "D" "D" "B"
#> student003 "C" "B" "B" "C" "C" "D" "C" "A" "A"
#> student004 "C" "A" "A" "D" "B" "D" "A" "A" "A"
#> student005 "C" "B" "A" "A" "D" "D" "A" "B" "D"
#> student006 "B" "B" "A" "A" "C" "C" "D" "A" "D"
#> item19 item20 item21 item22 item23 item24 item25 item26 item27
#> student001 "A" "D" "C" "B" "D" "D" "A" "A" "B"
#> student002 "D" "A" "C" "D" "B" "A" "B" "B" "D"
#> student003 "B" "D" "B" "C" "D" "D" "A" "D" "C"
#> student004 "A" "B" "D" "B" "A" "B" "C" "C" "D"
#> student005 "A" "D" "A" "C" "D" "D" "D" "B" "D"
#> student006 "A" "A" "C" "B" "D" "A" "C" "C" "D"
#> item28 item29 item30
#> student001 "A" "B" "A"
#> student002 "D" "D" "A"
#> student003 "C" "C" "D"
#> student004 "C" "A" "D"
#> student005 "D" "D" "A"
#> student006 "A" "D" "C"