This data was taken from the National Longitudinal Study of Adolescent Health. It is a cross-sectional sample of participants from grades 7--12, described and analyzed by Warne (2014).
Format
A data frame with 4344 observations on the following 3 variables.
grade
an ordered factor with levels
7
<8
<9
<10
<11
<12
depression
a numeric vector
anxiety
a numeric vector
Source
Warne, R. T. (2014). A primer on Multivariate Analysis of Variance (MANOVA) for Behavioral Scientists. Practical Assessment, Research & Evaluation, 19 (1). https://scholarworks.umass.edu/pare/vol19/iss1/17/
Details
depression
is the response to the question "In the last month, how
often did you feel depressed or blue?"
anxiety
is the response to the question "In the last month, how often
did you have trouble relaxing?"
The responses for depression
and anxiety
were recorded on a
5-point Likert scale, with categories 0="Never", 1="Rarely",
2="Occasionally", 3="Often", 4="Every day"
Examples
data(AddHealth)
# fit mlm
AH.mod <- lm(cbind(depression, anxiety) ~ grade, data=AddHealth)
car::Anova(AH.mod)
#>
#> Type II MANOVA Tests: Pillai test statistic
#> Df test stat approx F num Df den Df Pr(>F)
#> grade 5 0.022415 9.834 10 8676 < 2.2e-16 ***
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
summary(car::Anova(AH.mod))
#>
#> Type II MANOVA Tests:
#>
#> Sum of squares and products for error:
#> depression anxiety
#> depression 6058.911 3021.691
#> anxiety 3021.691 5210.233
#>
#> ------------------------------------------
#>
#> Term: grade
#>
#> Sum of squares and products for the hypothesis:
#> depression anxiety
#> depression 112.76722 87.57399
#> anxiety 87.57399 75.02650
#>
#> Multivariate Tests: grade
#> Df test stat approx F num Df den Df Pr(>F)
#> Pillai 5 0.0224153 9.833964 10 8676 < 2.22e-16 ***
#> Wilks 5 0.9776192 9.872584 10 8674 < 2.22e-16 ***
#> Hotelling-Lawley 5 0.0228579 9.911186 10 8672 < 2.22e-16 ***
#> Roy 5 0.0211939 18.387786 5 4338 < 2.22e-16 ***
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
heplot(AH.mod, hypotheses="grade.L", fill=c(TRUE, FALSE))