Moved to Clevedon, UK!
This is just to say that I’ve moved with my family to Clevedon, UK - leaving Sarajevo after very nearly 20 years, … leaving a whole heap of dear friends and family behind, but meeting up with new and old friends and family over here in rainy Blighty. I’m still involved with proMENTE but obviously in a more remote capacity. I’m only using my personal email address steve AT pogol.net from now on.
I’m looking to meet up and network with people from the evaluation and social research worlds in South-West England, so do get in touch if you are interested.
I’m still blogging sporadically at LinkedIn - you’ll find more over there than here at this blog, which I mainly use for odds and ends.
Beaufort and Rubrics
A quick post about the Beaufort scale for wind speed, see below, as paradigm of a rubric.
Rubrics are really important in evaluation.
The Beaufort Scale is just a beautiful example of clearly defined, easily observable criteria. I love the way different features like smoke (lower wind speeds) become important at different wind speeds. At force 6, umbrella use becomes difficult - umbrellas are not mentioned elsewhere but it gives you a great idea of what force 6 feels like and so helps to anchor the scale.
In particular, I love the way the individual statements are mostly relatively objective, or rather, inter-subjective, i.e. they are likely to be understood by different people from different backgrounds in a similar way. In the social realm it is often hard to be so objective and so rubrics in project evaluation often include formulations like “effective”, “reasonably good overall” or “just about adequate” which are again not inter-subjective and so in a way beg the question.
This must have been a big improvement for the Navy before the advent of mechanical windspeed measurement.
Another fascinating thing is that it manages to break a quantity like wind speed into no less than 12 different levels. In evaluation, we mostly see rubrics limited to 4-7 levels.
Wikipedia says the Beaufort scale was extended in 1946, when forces 13 to 17 were added, but they are only used in typhoon countries.
I always thought it would be hard to really distinguish 10, 11 and 12 though. And on the other hand, numbers are still needed beyond 12 especially in the tropics.
I wonder if that is because the scale is linearly anchored to windspeed. I would have thought that perception of windspeed, like most other things like light and sound, would be logarithmic so that there should be more frequent divisions at the lower ends and bigger jumps at the higher extremes. If the number/windspeed ratio was logarithmic, 10 11 and 12 would cover ever increasing spreads of windspeed and would neatly extend to cover just about any hurricane.
An example of where assuming a linear relationship with a physical quantity has unfortunate consequences. The developers of the scale would probably have been better advised to ignore the physical windspeed and concentrate on what can be inter-subjectively distinguished. It is not even obvious that the span of each rubric in terms of physical windspeed, or its logarithm, or indeed in terms of anything else, need to be equal at all - even subjectively. That all depends on what the rubric-based rating is going to be used for.
| Beaufort number | Description | Sea conditions | Land conditions |
|---|---|---|---|
| 0 | Calm | Sea like a mirror | Calm. Smoke rises vertically. |
| 1 | Light air | Ripples with the appearance of scales are formed, but without foam crests | Smoke drift indicates wind direction. Leaves and wind vanes are stationary. |
| 2 | Light breeze | Small wavelets, still short but more pronounced; crests have a glassy appearance and do not break | Wind felt on exposed skin. Leaves rustle. Wind vanes begin to move. |
| 3 | Gentle breeze | Large wavelets. Crests begin to break; scattered whitecaps | Leaves and small twigs constantly moving, light flags extended. |
| 4 | Moderate breeze | Small waves with breaking crests. Fairly frequent whitecaps. | Dust and loose paper raised. Small branches begin to move. |
| 5 | Fresh breeze | Moderate waves of some length. Many whitecaps. Small amounts of spray. | Branches of a moderate size move. Small trees in leaf begin to sway. |
| 6 | Strong breeze | Long waves begin to form. White foam crests are very frequent. Some airborne spray is present. | Large branches in motion. Whistling heard in overhead wires. Umbrella use becomes difficult. Empty plastic bins tip over. |
| 7 | High wind, | Sea heaps up. Some foam from breaking waves is blown into streaks along wind direction. Moderate amounts of airborne spray. | Whole trees in motion. Effort needed to walk against the wind. |
| 8 | moderate gale, | Moderately high waves with breaking crests forming spindrift. Well-marked streaks of foam are blown along wind direction. Considerable airborne spray. | Some twigs broken from trees. Cars veer on road. Progress on foot is seriously impeded. |
| 9 | near gale | High waves whose crests sometimes roll over. Dense foam is blown along wind direction. Large amounts of airborne spray may begin to reduce visibility. | Some branches break off trees, and some small trees blow over. Construction/temporary signs and barricades blow over. |
| 10 | Gale, | Very high waves with overhanging crests. Large patches of foam from wave crests give the sea a white appearance. Considerable tumbling of waves with heavy impact. Large amounts of airborne spray reduce visibility. | Trees are broken off or uprooted, structural damage likely. |
| 11 | fresh gale | Exceptionally high waves. Very large patches of foam, driven before the wind, cover much of the sea surface. Very large amounts of airborne spray severely reduce visibility. | Widespread vegetation and structural damage likely. |
| 12 | Strong/severe gale | Huge waves. Sea is completely white with foam and spray. Air is filled with driving spray, greatly reducing visibility. | Severe widespread damage to vegetation and structures. Debris and unsecured objects are hurled about. |
Inventory & analysis of small conservation grants, C&W Africa - Powell & Mesbach! Lots of charts!
This was an interesting job.
We visited three countries, did a lot of interesting interviews, and a lot of data analysis.
This project really made me shocked about how fast Western civilisation and Chinese money are eating up Africa’s nature.
I admit I got a bit distracted by the coding side of it.
It went like this:
We wrote to a lot of the agencies funding small conservation grants but of course we didn’t get much data from many of them. So I wrote long scripts to scrape all the websites and automated the whole report. Also, data was gathered from aiddata.org, which gathers and refines the OECD data, using the aiddata API, so it was a fully reproducible Rmarkdown product really and looked quite nice, with about 150 charts. Until of course it had to get squeezed into Microsoft Word in the end.
Is this “big data”? Not really.
Data from one of the funds
February 19, 2016 r dataviz conservation IUCN Africa reproducibleResearch
Students in predominantly ethnic minority classes want segregated education very much. The others don’t.
I just did this for what will hopefully be a book chapter on our Divided Education - Divided Citizens research project with NEPC. Explanation further below for anyone more interested in the actual topic ;-)
About the graphic:
I like raw-data plots like this, made possible by Hadley Wickhams’s amazing ggplot2 package for the stats package R. Every school class in the survey is on there somewhere. The line types need making a bit more distinct though. Oh, and it would presumably get printed in greyscale, so no colours.
I have spent weeks trying to analyse this data properly - spent ages with a stuctural equation model, and then scrapped that and went for a series of mixed effects models because of the class-level variance, but I found trying to implement that kind of model very difficult.
In the end I will probably just go for this graph and one very simple regression at class level and perhaps just one mixed effects regression. I think together they say all that needs to be said. In statistics as everywhere else the difficulty is in not saying too much. I am not sure I succeeded here yet, true there is only one graph but it is a pretty complicated one.
About the topic:
It’s about how support amongst students for segregated education is much stronger in minority schools. Minority students, or rather, students in predominantly minority classes, want segrated education very much. The others don’t.
The graph shows mean support for segregated schooling in each class by percentage of majority children in each class. Dots represent individual classes. Lines represent smoothed means per country.
The sample consisted of 2417 students from 134 schools in Bosnia & Herzegovina, Estonia, Latvia and Slovakia. To represent the school-level differences, the means of student support for segregation in each school were calculated along with the percentage of majority students in each school.
The figure shows that nearly all the classes had either heavily majority or heavily minority composition; very few classes lie between the 20% and 80% lines, and there are many classes which are 100% majority or minority children. Also, there are rather more minority classes with a few majority children than the other way round. The figure also shows clearly that the larger the proportion of minority students in a school, the stronger the support for segregation. In Bosnia & Hergovina the children are not such strong supporters of segregation. A linear regression predicting support for segregation at class level on the basis only of country and percentage majority children as predictors confirms this, with a remarkable 63% of the class-level variance explained (adjusted R2).
Then another analysis shows that majority children in predominantly minority classes do not differ from their classmates in their strong support for segregation, whereas minority children in predominantly majority classes are a little stronger than their classmates in their support for segregation, but not as strong as their peers in minority classes.
October 7, 2010 Bosnia-Herzegovina education ggplot research
Title: Tenses in Colour
Decades ago I spent a lot of time working on how to present the English tenses to my language students. I came up with a set of colour-coded charts which were pretty popular with my students. Since them they have been languishing.
So now I have published them here as an open source project.
This is one of the charts:
The charts themselves are available as a powerpoint file for downloading (in this version you can switch the explanations on and off).
There is also a corresponding list of tenses available as a pdfor wordfile.
This work is licensed under the Creative Commons Attribution-NonCommercial-ShareAlike License. To view a copy of this license, visit http://creativecommons.org/licenses/by-nc-sa/2.0/ or send a letter to Creative Commons, 559 Nathan Abbott Way, Stanford, California 94305, USA.