heplots (version 1.3-1)

VocabGrowth: Vocabulary growth data

Description

Data from the Laboratory School of the University of Chicago. They consist of scores from a cohort of pupils in grades 8-11 on the vocabulary section of the Cooperative Reading Test. The scores are scaled to a common, but arbitrary origin and unit of measurement, so as to be comparable over the four grades.

Usage

data(VocabGrowth)

Format

A data frame with 64 observations on the following 4 variables.

grade8

Grade 8 vocabulary score

grade9

Grade 9 vocabulary score

grade10

Grade 10 vocabulary score

grade11

Grade 11 vocabulary score

Source

R.D. Bock, Multivariate statistical methods in behavioral research, McGraw-Hill, New York, 1975, pp453.

Details

Since these data cover an age range in which physical growth is beginning to decelerate, it is of interest whether a similar effect occurs in the acquisition of new vocabulary.

References

Friendly, Michael (2010). HE Plots for Repeated Measures Designs. Journal of Statistical Software, 37(4), 1-40. URL http://www.jstatsoft.org/v37/i04/.

Keesling, J.W., Bock, R.D. et al, "The Laboratory School study of vocabulary growth", University of Chicago, 1975.

Examples

data(VocabGrowth)

# Standard Multivariate & Univariate repeated measures analysis
Vocab.mod <- lm(cbind(grade8,grade9,grade10,grade11) ~ 1, data=VocabGrowth)
idata <-data.frame(grade=ordered(8:11))
Anova(Vocab.mod, idata=idata, idesign=~grade, type="III")

##Type III Repeated Measures MANOVA Tests: Pillai test statistic
##            Df test stat approx F num Df den Df    Pr(>F)    
##(Intercept)  1     0.653  118.498      1     63 4.115e-16 ***
##grade        1     0.826   96.376      3     61 < 2.2e-16 ***


heplot(Vocab.mod, type="III", idata=idata, idesign=~grade, iterm="grade",
	main="HE plot for Grade effect")

### doing this 'manually' by explicitly transforming Y -> Y M
# calculate Y M, using polynomial contrasts
trends <- as.matrix(VocabGrowth) %*% poly(8:11, degree=3)
colnames(trends)<- c("Linear", "Quad", "Cubic")

# test all trend means = 0 == Grade effect
within.mod <- lm(trends ~ 1)

Manova(within.mod)
heplot(within.mod, terms="(Intercept)", col=c("red", "blue"), type="3",
  term.labels="Grade",
  main="HE plot for Grade effect")
mark.H0()