Hypothetical Reaction Time Data for 2 x 3 Perceptual Experiment: Example data for chapter 12 of Maaxwell and Delaney (2004, Table 12.1, p. 574) in long format. Has two within.subjects factors: angle and noise.
md_12.1A data.frame with 60 rows and 4 variables.
Description from pp. 573:
Suppose that a perceptual psychologist studying the visual system was interested in determining the extent to which interfering visual stimuli slow the ability to recognize letters. Subjects are brought into a laboratory and seated in front of a tachistoscope. Subjects are told that they will see either the letter T or the letter I displayed on the screen. In some trials, the letter appears by itself, but in other trials, the target letter is embedded in a group of other letters. This variation in the display constitutes the first factor, which is referred to as noise. The noise factor has two levels?absent and present. The other factor varied by the experimenter is where in the display the target letter appears. This factor, which is called angle, has three levels. The target letter is either shown at the center of the screen (i.e., 0<U+00B0> off-center, where the subject has been instructed to fixate), 4<U+00B0> off-center or 8<U+00B0> off-center (in each case, the deviation from the center varies randomly between left and right). Table 12.1 presents hypothetical data for 10 subjects. As usual, the sample size is kept small to make the calculations easier to follow. The dependent measure is reaction time (latency), measured in milliseconds (ms), required by a subject to identify the correct target letter. Notice that each subject has six scores, one for each combination of the 2 x 3 design. In an actual perceptual experiment, each of these six scores would itself be the mean score for that subject across a number of trials in the particular condition. Although "trials" could be used as a third within-subjects factor in such a situation, more typically trials are simply averaged over to obtain a more stable measure of the individual's performance in each condition.
# NOT RUN {
data(md_12.1)
# Table 12.5 (p. 578):
aov_ez("id", "rt", md_12.1, within = c("angle", "noise"),
args.return=list(correction = "none", es = "none"))
# }
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