Simulated data set with subjects and items requiring quasi-F ratios
Simulated lexical decision latencies with SOA as treatment, traditionally requiring an analysis using quasi-F ratios, as available in Raaijmakers et al. (1999).
data(quasif)
A data frame with 64 observations on the following 4 variables.
Subjecta factor coding subjects.
RTa numeric vector for simulated reaction times in lexical decision.
Itema factor coding items.
SOAa factor coding SOA treatment with levels long
and short.
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(quasif) items.quasif.fnc(quasif) ## End(Not run)
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