Reaction Time Data

Data from Maxwell and Delaney (1990, p. 497) representing the reaction times of 10 subjects in some task where visual stimuli are tilted at 0, 4, and 8 degrees; with noise absent or present. Each subject responded to 3 tilt x 2 noise = 6 conditions. The data thus comprise a repeated measure design with two within-S factors.

Format

A data frame with 10 observations giving the reaction time for the 6 conditions.

deg0NA

a numeric vector

deg4NA

a numeric vector

deg8NA

a numeric vector

deg0NP

a numeric vector

deg4NP

a numeric vector

deg8NP

a numeric vector

Source

Baron, J. and Li, Y. (2003). Notes on the use of R for psychology experiments and questionnaires, https://cran.r-project.org/doc/contrib/Baron-rpsych.pdf

References

Michael Friendly (2010). HE Plots for Repeated Measures Designs. Journal of Statistical Software, 37(4), 1-40. doi:10.18637/jss.v037.i04 .

Maxwell, S. E. & Delaney, H. D. (1990). Designing Experiments and Analyzing Data: A model comparison perspective. Pacific Grove, CA: Brooks/Cole.

Examples

data(ReactTime)
(RT.mod <- lm(as.matrix(ReactTime)~1))
#> 
#> Call:
#> lm(formula = as.matrix(ReactTime) ~ 1)
#> 
#> Coefficients:
#>              deg0NA  deg4NA  deg8NA  deg0NP  deg4NP  deg8NP
#> (Intercept)  462     510     528     492     660     762   
#> 

# within-S factors
within <- expand.grid(tilt=ordered(c(0,4,8)), noise=c("NA", "NP"))
car::Anova(RT.mod, idata=within, idesign=~tilt * noise)
#> Note: model has only an intercept; equivalent type-III tests substituted.
#> 
#> Type III Repeated Measures MANOVA Tests: Pillai test statistic
#>             Df test stat approx F num Df den Df    Pr(>F)    
#> (Intercept)  1   0.98518   598.45      1      9 1.527e-09 ***
#> tilt         1   0.88760    31.59      2      8 0.0001596 ***
#> noise        1   0.78955    33.77      1      9 0.0002560 ***
#> tilt:noise   1   0.91822    44.91      2      8 4.472e-05 ***
#> ---
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

heplot(RT.mod, idata=within, idesign=~tilt * noise, iterm="tilt")
#> Note: model has only an intercept; equivalent type-III tests substituted.


# plotting means and std errors directly

levels <- expand.grid(Tilt=c(0,4,8), noise=c("NA", "NP"))
(means.df <- data.frame(levels, mean=colMeans(ReactTime), se=sqrt(diag(var(ReactTime)))/9))
#>        Tilt noise mean        se
#> deg0NA    0    NA  462  6.324555
#> deg4NA    4    NA  510  9.558139
#> deg8NA    8    NA  528  8.777075
#> deg0NP    0    NP  492  9.838197
#> deg4NP    4    NP  660 12.171612
#> deg8NP    8    NP  762 12.976712

with(means.df, {
  plot(Tilt, mean, type="n", main="Reaction Time data", xlab="Tilt", ylab="Reaction time")
  colors <- rep(c("red", "blue"), each=3)
  pts <- rep(c(15, 16), each=3)

  lines(Tilt[1:3], mean[1:3], col="red", lwd=2)
  lines(Tilt[4:6], mean[4:6], col="blue", lwd=2)
  points(Tilt, mean, pch=pts, col=colors, cex=1.2)
  arrows(Tilt, mean-se, Tilt, mean+se, angle=90, code=3, 
      col=colors, len=.05, lwd=2)
  # labels at last point, in lieu of legend
  text(Tilt[3], mean[3]-10, labels="NA", col="red", pos=1)
  text(Tilt[6], mean[6]-10, labels="NP", col="blue", pos=1)
  }
  )