Grades in a Sociology Course

The data set SocGrades contains four outcome measures on student performance in an introductory sociology course together with six potential predictors. These data were used by Marascuilo and Levin (1983) for an example of canonical correlation analysis, but are also suitable as examples of multivariate multiple regression, MANOVA, MANCOVA and step-down analysis in multivariate linear models.

Format

A data frame with 40 observations on the following 10 variables.

class

Social class, an ordered factor with levels 1 > 2 > 3

sex

sex, a factor with levels F M

gpa

grade point average

boards

College Board test scores

hssoc

previous high school unit in sociology, a factor with 2 no, yes

pretest

score on course pretest

midterm1

score on first midterm exam

midterm2

score on second midterm exam

final

score on final exam

eval

course evaluation

Source

Marascuilo, L. A. and Levin, J. R. (1983). Multivariate Statistics in the Social Sciences Monterey, CA: Brooks/Cole, Table 5-1, p. 192.

Details

midterm1, midterm2, final, and possibly eval are the response variables. All other variables are potential predictors.

The factors class, sex, and hssoc can be used with as.numeric in correlational analyses.

Examples

data(SocGrades)
# basic MLM
grades.mod <- lm(cbind(midterm1, midterm2, final, eval) ~ 
  class + sex + gpa + boards + hssoc + pretest, data=SocGrades)
  
car::Anova(grades.mod, test="Roy")
#> 
#> Type II MANOVA Tests: Roy test statistic
#>         Df test stat approx F num Df den Df    Pr(>F)    
#> class    2   1.56729  11.7547      4     30 7.322e-06 ***
#> sex      1   0.55300   4.0092      4     29  0.010419 *  
#> gpa      1   1.20780   8.7566      4     29 9.195e-05 ***
#> boards   1   0.73142   5.3028      4     29  0.002489 ** 
#> hssoc    1   0.03496   0.2535      4     29  0.905171    
#> pretest  1   0.31307   2.2697      4     29  0.085881 .  
#> ---
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

clr <- c("red", "blue", "darkgreen", "magenta", "brown", "black", "darkgray")
heplot(grades.mod, col=clr)

pairs(grades.mod, col=clr)


if (FALSE) {
heplot3d(grades.mod, col=clr, wire=FALSE)
}

if (require(candisc)) {
  # calculate canonical results for all terms
  grades.can <- candiscList(grades.mod)
  # extract canonical R^2s
  unlist(lapply(grades.can, function(x) x$canrsq))
  # plot class effect in canonical space
  heplot(grades.can, term="class", scale=4)  

  # 1 df terms: show canonical scores and weights for responses
  plot(grades.can, term="sex")
  plot(grades.can, term="gpa")
  plot(grades.can, term="boards")
  }