[R] Off topic --- underdispersed (pseudo) binomial data.

[Abby Spurdle]

Further to yesterday's posts:
I think the "n" value would be the maximum possible number of jumps,
not the number of students.
In theory, the minimum possible number is zero, so the distributions
are more binomial-like than they look.

Also, there was a mistake in my comments.
The jump is from non-A-grade to A-grade, not non-pass to pass.


On Sat, Mar 27, 2021 at 10:00 PM Abby Spurdle <spurdle.a using gmail.com> wrote:
>
> Sorry.
> I just realized, after posting, that the "n" value in the dispersion
> calculation isn't correct.
> I'll have to revisit the simulation, tomorrow.
>
> On Sat, Mar 27, 2021 at 9:11 PM Abby Spurdle <spurdle.a using gmail.com> wrote:
> >
> > Hi Rolf,
> >
> > Let's say we have a course called Corgiology 101, with a single moderated exam.
> > And let's say the moderators transform initial exam scores, such that
> > there are fixed percentages of pass rates and A grades.
> >
> > Rather than count the number of passes, we can count the number of "jumps".
> > That is, the number of people that pass the corgiology exam after
> > moderation, that would not have passed without moderation.
> >
> > I've created a function to test for underdispersion, based on your expression.
> > (I hope I got it right).
> >
> > Then I've gone on to create simulations, using both constant and
> > nonconstant class sizes.
> > The nonconstant simulations apply an (approx) discrete scaling
> > transformation, referred to previously.
> >
> > We can see from the examples that there are a lot of these jumps.
> > And more importantly, they appear to be underdispersed.
> >
> > ----code----
> > PASS.SCORE <- 0.5
> > A.SCORE <- 0.8
> >
> > #target parameters
> > PASS.RATE <- 0.8
> > A.RATE <- 0.2
> > #unmoderated parameters
> > UNMOD.MEAN.SCORE <- 0.65
> > UNMOD.SD.SCORE <- 0.075
> >
> > NCLASSES <- 2000
> > NSTUD.CONST <- 200
> > NSTUD.NONCONST.LIMS <- c (50, 800)
> >
> > sim.njump <- function (nstud, mean0=UNMOD.MEAN.SCORE, sd0=UNMOD.SD.SCORE,
> >     pass.score=PASS.SCORE, a.score=A.SCORE,
> >     pass.rate=PASS.RATE, a.rate=A.RATE)
> > {   x <- rnorm (nstud, mean0, sd0)
> >     q <- quantile (x, 1 - c (pass.rate, a.rate), names=FALSE)
> >     dq <- diff (q)
> >     q <- (a.score - pass.score) / dq * q
> >     y <- pass.score - q [1] + (a.score - pass.score) / dq * x
> >     sum (x < a.score & y >= a.score)
> > }
> >
> > sim.nclasses <- function (nclasses, nstud, nstud.std)
> > {   nstud <- rep_len (nstud, nclasses)
> >     njump <- integer (nclasses)
> >     for (i in 1:nclasses)
> >         njump [i] <- sim.njump (nstud [i])
> >     if (missing (nstud.std) )
> >         njump
> >     else
> >         round (nstud.std / nstud * njump)
> > }
> >
> > is.under <- function (x, n)
> >     var (x) < mean (x) * (1 - mean (x) / n)
> >
> > njump.hom <- sim.nclasses (NCLASSES, NSTUD.CONST)
> > nstud <- round (runif (NCLASSES, NSTUD.NONCONST.LIMS [1],
> > NSTUD.NONCONST.LIMS [2]) )
> > njump.het <- sim.nclasses (NCLASSES, nstud, NSTUD.CONST)
> >
> > under.hom <- is.under (njump.hom, NSTUD.CONST)
> > under.het <- is.under (njump.het, NSTUD.CONST)
> > main.hom <- paste0 ("const class size (under=", under.hom, ")")
> > main.het <- paste0 ("diff class sizes (under=", under.het, ")")
> >
> > p0 <- par (mfrow = c (2, 1) )
> > hist (njump.hom, main=main.hom)
> > hist (njump.het, main=main.het)
> > par (p0)
> > ----code----
> >
> > best,
> > B.
> >
> >
> > On Thu, Mar 25, 2021 at 2:33 PM Rolf Turner <r.turner using auckland.ac.nz> wrote:
> > >
> > >
> > > I would like a real-life example of a data set which one might think to
> > > model by a binomial distribution, but which is substantially
> > > underdispersed. I.e. a sample X = {X_1, X_2, ..., X_N} where each X_i
> > > is an integer between 0 and n (n known a priori) such that var(X) <<
> > > mean(X)*(1 - mean(X)/n).
> > >
> > > Does anyone know of any such examples?  Do any exist?  I've done
> > > a perfunctory web search, and had a look at "A Handbook of Small
> > > Data Sets" by Hand, Daly, Lunn, et al., and drawn a blank.
> > >
> > > I've seen on the web some references to underdispersed "pseudo-Poisson"
> > > data, but not to underdispersed "pseudo-binomial" data.  And of course
> > > there's lots of *over* dispersed stuff.  But that's not what I want.
> > >
> > > I can *simulate* data sets of the sor that I am looking for (so far the
> > > only ideas I've had for doing this are pretty simplistic and
> > > artificial) but I'd like to get my hands on a *real* example, if
> > > possible.
> > >
> > > Grateful for any pointers/suggestions.
> > >
> > > cheers,
> > >
> > > Rolf Turner
> > >
> > > --
> > > Honorary Research Fellow
> > > Department of Statistics
> > > University of Auckland
> > > Phone: +64-9-373-7599 ext. 88276
> > >
> > > ______________________________________________
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> > > and provide commented, minimal, self-contained, reproducible code.