UCAS, ethnicity and admission rates

Stata

UCAS, the UK university admissions clearing house, have released data relating to ethnicity and admissions to English universities, in part in response to Vikki Boliver‘s research in Sociology suggesting that members of ethnic minorities are less likely to be admitted to Russell Group universities.

The analysis note with the release is sober and correct, showing a mostly consistent pattern of offer rates for ethnic minority students being lower (but not far lower) than expected. However, UCAS’s press release seems to have suggested that the effect is almost explained away, and attributes it to ethnic minority students disproportionately applying to courses with low acceptance rates. This does not seem to be the case.

Update: see also next blog entry.
Continue reading UCAS, ethnicity and admission rates →

Substitution costs from transition rates

Uncategorized

Given that determining substitution costs in sequence analysis is such a bone of contention, many researchers look for a way for the data to generate the costs. The typical way to do this is, is by pooling transition rates and defining the substitution cost to be:

2 – p(ij) – p(ji)

where p(ij) is the transition rate from state i to state j. Intuitively, states that are closer to each other will have higher transitions, and vice versa. Continue reading Substitution costs from transition rates →

Mapping with Python and Stata

Elevation data for large swathes of the planet have been collected by NASA and are available to download from http://dds.cr.usgs.gov/srtm/.

The data is contained in binary files, each representing a 1-degree by 1-degree “square”. Here are five lines of Python and four lines of Stata that will turn the data into a simple graph:

import struct
file = open("data/N52W011.hgt", "r")
for y in range(1201):
for x in range(1201):
print y, x, struct.unpack(">h",file.read(2))[0]

Do python file.py > map.dat. Then run this Stata code:

infile i j height using /tmp/ext.dat
gen h2 = int(sqrt(height))
replace h2 = 30 if h2<=0
hmap j i h2, nosc

Low res version of map

(Hi-res version.)

You may need to install Python’s struct package, and Stata’s hmap add on, but they’re available from the usual locations.

There are better ways of doing this, of course: it’s slow, the aspect ratio is wrong, the colours are not ideal and the axis labelling is bad. Even worse, it is a complete abuse of the hmap add-on. It’s a quick and dirty way to turn binary data into pictures, all the same.

Hedstrom’s Desires-Believes-Acts model in Emacs lisp

Uncategorized

Emacs-lisp is a pretty functional language for managing Emacs and automating complex tasks within it, particularly to do with text processing. It’s probably not wise to use it for more general programming or analytical tasks, but every now and then (when I need to procrastinate, mostly) I get carried away.

A few years ago I was reading Peter Hedstrom’s book, Dissecting the Social, and realised his Desires-Believes-Acts model (a kind of cellular automaton) would be easy enough to implement. More recently, I noticed that Emacs’ tools for displaying simple games like Tetris (do “M-x tetris”) would permit a clean display.

In Hedstrom’s model, every cell in a grid may desire an outcome, and may believe they are able to achieve it. If they do both, they act. Belief and desire depend on the beliefs and desires of your neighbours. Generally, even starting from random and low distributions of belief and desire, within a number of iterations stable configurations emerge, with systematic segregation; often everyone acts in the end but sometime stable oscillating systems emerge.

Continue reading Hedstrom’s Desires-Believes-Acts model in Emacs lisp →

Discrepancy analysis in Stata

sequence_analysis, Stata

In Studer et al (2011) an important new tool is introduced to the field of sequence analysis, the idea of “discrepancy” as a way of analysing pairwise distances. This quantity is shown to be analogous to variance, and is thus amenable to ANOVA-type analysis, which means it is a very attractive complement to cluster analysis of distance matrices.

This has been implemented in TraMineR (under R), along with a raft of other innovations coming out of Geneva and Lausanne. Up to now it hasn’t been available elsewhere. I spoke to Matthias Studer at the LaCOSA conference, and he convinced me that it was easy to code, and that all the information required was in the paper. This turned out to be the case, and I have written an initial Stata implementation. Continue reading Discrepancy analysis in Stata →

Cluster analysis is unstable, we knew that!

sequence_analysis, Stata

Experience tells me that small changes in the data can lead to substantial changes in the solution of a cluster analysis. This is especially true when the space is sparsely populated, as is the case with sequence analysis of lifecourses. Small changes in parameterisation (e.g., substitution costs) can lead to substantial differences in the cluster solution.

However, recently I came across an extreme case of sensitivity. Continue reading Cluster analysis is unstable, we knew that! →