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Response Speed in a Probe Experiment

Source: R/datasets.R
Probe.Rd

Data from a probe experiment testing whether immediate memory for sentences is influenced by the phrase structure of the sentence. The data sets come from Timm (1975), Ex. 3.14 and Ex. 3.16 (p.244)

Format

Probe1: A data frame with 11 observations on the following 5 variables.

p1

speed at position 1

p2

speed at position 2

p3

speed at position 3

p4

speed at position 4

p5

speed at position 5

Probe2: A data frame with 20 observations on the following 6 variables.

stm

Short term memory capacity: a factor with levels High Low

p1

speed at position 1

p2

speed at position 2

p3

speed at position 3

p4

speed at position 4

p5

speed at position 5

Source

Timm, N. (1975) Multivariate analysis, with applications in education and psychology Brooks/Cole.

Details

Procedure: Subjects listened to tape-recorded sentences. Each sentence was followed by a "probe word" from one of 5 positions within the sentence. The subject had to respond with the word which immediately followed the probe word in the sentence. The dependent measure is response speed = k(1/reaction time).

Sample sentence:


*       The tall man met the young girl who got the new hat.
Pos'ns:      1    2            3    4    5
Function:   ADJ1 SUBJ        ADJ2  OBJ  REL.PN

In Probe2, there are two groups of subjects, pre-selected on a test of short term memory.

These data sets (fictitious) are used as examples of single-sample and two-sample profile analysis or simple repeated measure designs with structured contrasts.

Examples


data(Probe1)
boxplot(Probe1)


pmod1 <- lm(cbind(p1,p2,p3,p4,p5) ~ 1, data=Probe1)
idata <- data.frame(position=factor(1:5))

library(car)
(pmod1.aov <- car::Anova(pmod1, idata=idata, idesign=~position))
#> 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   0.96312   261.15      1     10 1.705e-08 ***
#> position     1   0.75177     5.30      4      7   0.02767 *  
#> ---
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

# using default contrasts (p5 as reference level)
heplot(pmod1, manova=pmod1.aov, 
       iterm="position", 
       type="III", 
       idata=idata, idesign=~position)

pairs(pmod1, manova=pmod1.aov, 
       iterm="position", 
       type="III", 
       idata=idata, idesign=~position)


# contrasts for substantative hypotheses regarding
# sentence position effects
C <- matrix(c(
  1, 1, -1, -1, 0,
  1, -1, 1, -1, 0,
  1, -1, -1, 1, 0,
  1, 1, 1, 1, -4), 5, 4)
rownames(C) <- paste("p", 1:5, sep="")
colnames(C) <- c("SubPred", "AdjNoun", "SPxAN", "RelPN")

contrasts(idata$position)<- C
(pmod1.aov <- car::Anova(pmod1, idata=idata, idesign=~position))
#> 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   0.96312   261.15      1     10 1.705e-08 ***
#> position     1   0.75177     5.30      4      7   0.02767 *  
#> ---
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

heplot(pmod1, manova=pmod1.aov, 
       iterm="position", type="III", idata=idata, idesign=~position)

pairs(pmod1, manova=pmod1.aov, 
       iterm="position", type="III", idata=idata, idesign=~position)