AddHealth {heplots}

Adolescent Mental Health Data

Description

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

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"

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/

Examples

data(AddHealth)

if(require(dplyr) & require(ggplot2)) {
# find means & std.errors by grade
means <- AddHealth |>
group_by(grade) |>
  summarise(
    n = n(),
    dep_se = sd(depression, na.rm = TRUE) / sqrt(n),
    anx_se = sd(anxiety, na.rm = TRUE) / sqrt(n),
    depression = mean(depression),
    anxiety = mean(anxiety) ) |> 
  relocate(depression, anxiety, .after = grade) |>
  print()
  
# plot means with std.error bars
ggplot(data = means, aes(x = anxiety, y = depression, 
color = grade)) +
  geom_point(size = 3) +
  geom_errorbarh(aes(xmin = anxiety - anx_se, 
                     xmax = anxiety + anx_se)) +
  geom_errorbar(aes(ymin = depression - dep_se, 
                    ymax = depression + dep_se)) +
  geom_line(aes(group = 1), linewidth = 1.5) +
  geom_label(aes(label = grade), 
             nudge_x = -0.015, nudge_y = 0.02) +
  scale_color_discrete(guide = "none") +
  theme_bw(base_size = 15)
}

# fit mlm
AH.mod <- lm(cbind(anxiety, depression) ~ grade, data=AddHealth)

car::Anova(AH.mod)
summary(car::Anova(AH.mod))

heplot(AH.mod, hypotheses="grade.L", 
       fill=c(TRUE, FALSE),
       level = 0.4)