It’s complicated — common sense associations with those terms are neither totally wrong nor totally right.
Here are the “standard” political science questions for placing people on these scales (as with all the data here, all courtesy of the British Election Study):The standard approach to turning this questionnaire data into scales for Economic Left-Right/Social Liberalism-Conservatism … is just to add up the individual scores.
That works fine for most political science needs but the resulting distributions are neither nicely Gaussian (bell curves) nor are they well spread which makes visualisation — particularly comparing different distributions — a chore.
What I’ve done is to divide the distribution equally along each axis — e.
the right-most 1/200th of the chart contains the most Economic Right 1/200th of the sample — *every* vertical and horizontal slice of the chart contains the corresponding 1/200th slice of all participants in the British Election Study.
If the two axes were completely independent — and these axes were originally created by political scientists to reflect two independent political factors — the result of charting all participants in one plot would then be beautifully, boringly monochrome because each position on the chart would contain exactly the same number of people:Clearly that was a technical failure (albeit a big step forward from dense misshapen blob).
The problem is that the axes are mostly but not wholly independent.
That reflects a mixture of something about the questions and something about the actual distribution of values.
The Economic Left *really does* just polarise more along the Social dimension than the Economic Right.
That’s a real-world problem for the people on the Economic Left.
But — more importantly — it’s a problem for me making visualisations so I’m going to keep looking into whether I can synthesise better behaved axes by adding/dropping questions from the mix and/or means of directly minimally deforming that distribution (automated panel beating) into something a bit more even.
Until then, I’m afraid you *do* have to view all these charts while trying to remember that the baseline distribution has these three clusters — e.
The Sun readership mean position is bang in the middle of the Economic axis — the Ec.
Left-Social Conservative blob is just pronounced because people in that quadrant cluster together in the top left corner.
The Mirror *is* significantly to the Economic Left of The Sun … but only because less Economic Right-Social Conservatives/more Economic Left-Social Liberals read it.
Now you know all that, here’s that chart again — as an animated slide show broken down for specific “waves” (dates that the polling was carried out).
As far as I can tell, no really meaningful trends — but a really cheap, easy way to get a sense of how stable this method is (kinda)/what to look for:So, about that opening-question/flimsy-bait-and-switch for a methodology dominated blog article?You can use this approach to define “Right Wing Press” as Economic Right (by readership).
But that puts The Sun/Star as neither definitively RWP (or not-RWP).
Or you can use it to define “Right Wing Press” as Socially Conservative (by readership) — but that means The Mirror becomes RWP, the Telegraph fall into ‘neither’ like The Sun/Star above and The Times/Financial Times conclusively become not-RWP.
I propose a new, objective categorisation:Left-bloid: Daily Mirror/RecordMid-bloid: The Sun/StarRight-bloid: The Express/Daily MailLeft-sheet: The Guardian/IndependentRight-sheet: The Times/Financial TimesRight-broadbloid: The TelegraphClearly, the naming scheme is unquestionably perfect, but what about the methodology?.Don’t people pick newspapers for reasons other than their personal politics (e.
TV pages)?.What about people who stick with a newspaper when it shifts its positions?.Yeah, well … let’s see you do better!Next Up: What’s up with the Broadsheet/Tabloid split, huh?Recommendation: If you liked this, you’d probably enjoy Paula Surridge’s blog *even more*Code: Pan-Dataset Values notebook (warning: this notebook is currently just a vast uncommented mishmash of hacks, dead-ends and random approaches — will try to refactor something cleaner — also I haven’t integrated the R code that does the multiple imputation yet)Data: pan_dataset_allr_values.
csv (includes both uniform and Gaussian-ish distributed versions and the id column from for all 14 waves There’s a great deal to be said for using academic labels that are obviously labels rather than descriptions (or descriptions that are so overtly out of date — which side of the French King would you sit? — that they’ve become labels).
 There are variants, but they don’t vary “a lot” and they “tend” to give the same answers.
 “Strongly Disagree” ->0 … “Strongly Agree” -> 4, DKs get replaced by the mean for whole sample, al_scale=al1+al2+al3+al4+al5, lr_scale=20-(lr1+lr2+lr3+lr4+lr5) Data processing: replace DKs with nans, run Hmisc (R package) multiple imputation on all BES al/lr variables across all waves (one at a time), then run sparsePCA (python module Sklearn) to spit out two orthogonal-ish components made from blending all those variables together.
Then I box-cox (python Scipy stats) it to get something Gaussian/use pandas qcut (python module) to transform into a discrete uniform distribution.
Visualisation is Seaborn (python module built on Matlplotlib) using FacetGrid and kde plots.
 Health Warning: I didn’t apply the British Election Study’s demographic weighting partly because “profile_newspaper” is a variable across all the waves and I know they’re going to bring out some code to generate a proper cross-wave weighting.
But mostly because it’s a hassle to build those charts with it due to the python module I’m using.
It’s doable, but it’s the last item on the to-do list and, from past experience, I know it doesn’t solve problems of lumpy distributions/make a huge visual difference.
 lr1 — redistribution — correlates quite heavily with Education/Social Liberalism — that’s why most Political Compass style analyses give the impression that there *are no* Economic Right-Social Liberals because their support for redistribution auto-converts them to Economic Centre-Social Liberals.
As HE provision has expanded, it looks like this has become more or a problem for this sort of analysis — so there’s a weird-but-reasonable case for dropping “support for redistribution” from the definition of Economic Left! Please contact me if you know of a nice, formal way to do this — for some reason, I just keep failing to find the right google keywords — ideally with a functional python module!.