Are there any guide lines on determining which test to use?
For example, given 100 subjects with both Exam A and Exam B, and some student observations for those exams, i want to compare which exam is harder.
More specifically, I have 100 different subjects (ex. math, french, english, ...etc), and each subjects consists 2 exams, exam A & exam B. Now, I distribute these exams to a class of 100 and if a student score above 50%, then the observation is set as 1 and 0 otherwise.
An example table:
Subject Exam Class Score
MATH A 90
MATH B 10
FRENCH A 51
FRENCH B 49
... ... ...
Which statistical test should i perform if I want to compare which exam is harder?
Summary of the question (by @user2974951):
- We have 100 students, each student takes test A and B for 100 different subjects. The results of the tests are independent, so the results of test A does not affect the result of test B, and a student score in subject 1 will have no effect on the score of subject 2. The data is already aggregated on the subject and exam level, that is we only have the frequency of students which passed. Our goal is to determine whether test A is harder than test B, based on the aggregated data.
fit <- lmer(score ~ exam + subject + (exam | class))
. See benwhalley.github.io/just-enough-r/fitting-models.html, for examples $\endgroup$