Reading through the report from the ASA (that doesn't really "slam" the VAM statistic but rightly points out the flaws inherent to any attempt to use statistics in areas with many confounding factors), it appears as if the VAM is usually derived thusly:
1. Calculate a regression model for a student's expected standardized test scores based off of background variables (like previous scores, socioeconomic status etc). This includes having teacher's as variables.
2. Use the coefficient for the teacher as determined by the model to determine the teacher's "Value Added" metric.
The weaknesses in such an approach are also spelled out in the report: namely, missing background variables, lack of precision, and a lack of time to test for the effectiveness of the statistics themselves.
What's interesting is that the teacher in question was rated as "effective" the year before. The question becomes whether that was based off of her VAM score that year as well as what the standard error was on her regression coefficient. Unfortunately, the article doesn't mention any of that.
The problem with regression models is, in skilled hands, it's easy to manipulate the results. And that is without even opening up the rats nest that is causality.
For instance, want to raise the R^2, a value foolishly used to characterize how well the model explains? Add more variables. R^2 is monotonically increasing in the number of variables. So, for example, add the first letter of the teachers' middle names as an explanatory variable. R^2 will probably increase a bit.
Is there homoskedasticity? How much? What did they do to reduce it?
What observations are considered outliers and dropped, and who makes that determination?
Or, want to tank a teachers' score? Assuming teachers are added as something like indicator variables, there are lots of techniques to make the standard deviation increase, allowing you to say that at 0 is within the CI of B_{teacher}.
If they are using glmms -- as they probably ought to be -- there's even more room for a skilled statistician to pick outcomes, as more and more of the setup is a judgement call.
Finally, there's an open question of how well the exams were designed and if they accurately measured the student pre and post effect; there's a whole field -- psychometrics -- devoted to testing alone.
You've got to be kidding right? Are you really surprised that an English language site that is sometimes blocked in China and lacks a Chinese language interface doesn't have obviously Chinese contributors? Do you expect a conical hat on the profile pics of all the Chinese contributors? How exactly do you expect to see who's Chinese and who's not on a site like Github?
You can easily check out Chinese language code hosting sites, like
Unlike Pudn, csdn.net seems pretty legit. However it looks like there are only about 8000 projects give or take. Compare this with github which has a few million projects and that's just one repo service I'm not even counting stuff like bitbucket and google code. Again I'm not pointing out a deficiency in skill or talent, this is just about culture.
I work in an ad tech company on the mobile product, and all of my coworkers are amazed by this. Congrats on actually innovating the mobile advertising space instead of another native ad/geolocation startup.
Why is that a problem? They wouldn't bring in H1Bs unless there was an obvious benefit. Your "problem" just reduces the gap between value produced and compensation.
I've never understood advertising on Reddit. It's basically a very anti-advertising community, who will very likely be hostile to your product/service.
XX (or is it Dos Equis) and Oldspice would beg to differ, and they didn't even advertise there. I've found reddit to be rather accepting of advertising as long as it is done transparently. IAMAs by celebrities every time they star in a new movie aren't treated with hostility - just put the fact that you're in a new film in the submission text.
There have been many HN posts and comments about successful paid ads in specialized communities.
That seems to vary between subreddits. I've encountered a few quite successful ads (with comments from regular visitors to the sub further supporting the product!). /r/sysadmin comes to mind, but I'm sure there are others.
Well, there you go. When you know the audience, just adjust your advertising to fit. Not that hard. Not every product should be advertised on reddit tho.
I found a similar question on Quora [1], with an answer by an ex-quant. It seems to boil down to tradition and bureaucratic inefficiencies by the involved parties. So I agree, this makes the whole liquidity argument seem weak.
India has always felt that they had an understanding with China about the border. However they ignore the fact that China felt threatened by Soviet/Indian friendship.
Often they blame Nehru for being naïve, whilst at the same time blaming China for betrayal. I don't think those two arguments are very compatible.
In the end it was probably beneficial for India. They modernised their army, which let them defeat (Chinese ally) Pakistan pretty comprehensively.
But anyway, the Chinese military was pretty efficient in this war, which goes against the original article.
Every country by default assumes that they have an understanding with neighbours about it's borders. Of Course that doesn't stop anybody from trying to grab a piece.
As far as the two arguments go, I don't see the incompatibility. Can you share your thoughts about this?
Chinese approach was rather brute force, they just poured a lot of soldiers there, far from efficient.
As far as the two arguments go, I don't see the incompatibility. Can you share your thoughts about this?
If it was a "betrayal" then Nehru wasn't naïve (because betrayal implies deliberately misleading). OTOH, if Nehru was naïve, then it wasn't a betrayal (because it was so clear to everyone except Nehru that something was going to happen).
I think the truth is somewhere in between. I haven't studied it the conflict in depth, but it seems to me that China's policy towards the Indian border region changed (at least partially because of Indian actions), and India didn't realize it quickly enough.
Blatant abuse being the hostile behaviour shown by the Chinese just after the "hindi, chini bhai bhai" (Indian and Chinese are brothers.) catch phrase was what every Indian used to say. They attacked India and took away a huge part of Indian land.
This behaviour is typical for PRC. All they seem to want is new territory for themselves. Even now they dispute about Indian territory and frequently undermine borders by intruding.
1. Calculate a regression model for a student's expected standardized test scores based off of background variables (like previous scores, socioeconomic status etc). This includes having teacher's as variables. 2. Use the coefficient for the teacher as determined by the model to determine the teacher's "Value Added" metric.
The weaknesses in such an approach are also spelled out in the report: namely, missing background variables, lack of precision, and a lack of time to test for the effectiveness of the statistics themselves.
What's interesting is that the teacher in question was rated as "effective" the year before. The question becomes whether that was based off of her VAM score that year as well as what the standard error was on her regression coefficient. Unfortunately, the article doesn't mention any of that.