Simulated Latin Square data set with subjects and items
Simulated lexical decision latencies with SOA as treatment, using a Latin Square design with subjects and items, as available in Raaijmakers et al. (1999).
data(latinsquare)
A data frame with 144 observations on the following 6 variables.
Groupa factor with levels G1, G2 and
G3, for groups of subjects
Subjecta factor with subjects labelled
S1, ... S12.
Worda factor with words labelled W1 ... W12.
RTa numeric vector for reaction times.
SOAa factor with levels long, medium,
and short.
Lista factor with levels L1, L2, and L3
for lists of words.
Raaijmakers, J.G.W., Schrijnemakers, J.M.C. & Gremmen, F. (1999) How to deal with "The language as fixed effect fallacy": common misconceptions and alternative solutions, Journal of Memory and Language, 41, 416-426.
## Not run: data(latinsquare) library(lme4) latinsquare.with = simulateLatinsquare.fnc(latinsquare, nruns = 1000, with = TRUE) latinsquare.without = simulateLatinsquare.fnc(latinsquare, nruns = 1000, with = FALSE) latinsquare.with$alpha05 latinsquare.without$alpha05 ## End(Not run)
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