didactic example

Shading regions of the normal: The Stanine scale

For the presentation of norm values, often stanines are used (standard nine). These values mark a person’s relativ position in comparison to the sample or to norm values.
According to Wikipedia:

The underlying basis for obtaining stanines is that a normal distribution is divided into nine intervals, each of which has a width of 0.5 standard deviations excluding the first and last, which are just the remainder (the tails of the distribution). The mean lies at the centre of the fifth interval.

For illustration purposes, I wanted to plot the regions of the stanine values in the standard normal distribution – here’s the result:

First: Calculate the stanine boundaries and draw the normal curve:

# First: Calculate stanine breaks (on a z scale)
stan.z <- c(-3, seq(-1.75, +1.75, length.out=8), 3)

# Second: get cumulative probabilities for these z values
stan.PR <- pnorm(stan.z)

# define a color ramp from blue to red (... or anything else ...)
c_ramp <- colorRamp(c("darkblue", "red"), space="Lab")

# draw the normal curve, without axes; reduce margins on left, top, and right
par(mar=c(2,0,0,0))
curve(dnorm(x,0,1), xlim=c(-3,3), ylim=c(-0.03, .45), xlab="", ylab="", axes=FALSE)

Next: Calculate the shaded regions and plot a polygon for each region:

# Calculate polygons for each stanine region
# S.x = x values of polygon boundary points, S.y = y values
for (i in 1:(length(stan.z)-1)) {
    S.x  <- c(stan.z[i], seq(stan.z[i], stan.z[i+1], 0.01), stan.z[i+1])
    S.y  <- c(0, dnorm(seq(stan.z[i], stan.z[i+1], 0.01)), 0)
    polygon(S.x,S.y, col=rgb(c_ramp(i/9), max=255))
}

And finally: add some legends to the plot:

# print stanine values in white
# font = 2 prints numbers in boldface
text(seq(-2,2, by=.5), 0.015, label=1:9, col="white", font=2)

# print cumulative probabilities in black below the curve
text(seq(-1.75,1.75, by=.5), -0.015, label=paste(round(stan.PR[-c(1, 10)], 2)*100, "%", sep=""), col="black", adj=.5, cex=.8)
text(0, -0.035, label="Percentage of sample <= this value", adj=0.5, cex=.8)

And finally, here’s a short script for shading only one region (e.g., the lower 2.5%):

# draw the normal curve
curve(dnorm(x,0,1), xlim=c(-3,3), main="Normal density")

# define shaded region
from.z <- -3
to.z <- qnorm(.025)

S.x  <- c(from.z, seq(from.z, to.z, 0.01), to.z)
S.y  <- c(0, dnorm(seq(from.z, to.z, 0.01)), 0)
polygon(S.x,S.y, col="red")

Comments (1) | Trackback

The Evolution of Correlations

This is the evolution of a bivariate correlation between two questionnaire scales, “hope of power” and “fear of losing control”. Both scales were administered in an open online study. The video shows how the correlation evolves from r = .69*** (n=20) to r = .26*** (n=271). It does not stabilize until n = 150.

Data has not been rearranged – it is the random order how participants dropped into the study. This had been a rather extreme case of an unstable correlation – other scales in this study were stable right from the beginning. Maybe this video could help as an anecdotal caveat for a careful interpretation of correlations with small n’s (and with ‘small’ I mean n < 100) …

The evolution of correlations from Felix Schönbrodt on Vimeo.

Comments (3) | Trackback

Send this to a friend

© 2018 Felix Schönbrodt | Impressum | Datenschutz | Contact