Hi everyone, I have data from an experiment in which human participants were instructed to generate a random sequence of yes/no answers under 4 different conditions. I want to test how successful they were in doing this. More specifically, I want to test the null hypothesis that the 4 conditions come from a single population with a given level of randomness. Some searching turned up the runs.test() function in the tseries package. This looks promising, but I'm not sure how to proceed. A simplified version of the data structure is
Data <- data.frame(participant=c(rep(1,4),rep(2,4), rep(3,4), rep(4,4)), question = factor(rep(c("one", "two", "three", "four"), 4)), condition = factor(c(rep(1,8), rep(2,8))), answer=(factor(sign(rnorm(16)), labels=c("yes", "no"))))
Data
participant question condition answer 1 1 one 1 no 2 1 two 1 yes 3 1 three 1 yes 4 1 four 1 no 5 2 one 1 yes 6 2 two 1 yes 7 2 three 1 no 8 2 four 1 yes 9 3 one 2 yes 10 3 two 2 no 11 3 three 2 yes 12 3 four 2 yes 13 4 one 2 no 14 4 two 2 no 15 4 three 2 no 16 4 four 2 no My questions are: 1) Can I test my hypothesis using the runs.test() function? If no, is there a better approach? 2) Does it make sense to do a runs.test() for each condition, ignoring the participant variable? Or do I need to do a runs.test() separately for each participant? If the latter, how can I combine the information to test for differences across conditions? Thanks, Ista