Simulated Latin Square data set with subjects and items

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Description

Simulated lexical decision latencies with SOA as treatment, using a Latin Square design with subjects and items, as available in Raaijmakers et al. (1999).

Usage

1

Format

A data frame with 144 observations on the following 6 variables.

Group

a factor with levels G1, G2 and G3, for groups of subjects

Subject

a factor with subjects labelled S1, ... S12.

Word

a factor with words labelled W1 ... W12.

RT

a numeric vector for reaction times.

SOA

a factor with levels long, medium, and short.

List

a factor with levels L1, L2, and L3 for lists of words.

Source

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.

Examples

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## 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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