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.
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
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 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.
Today's breakdown was a huge warning flag for me. I'm heavily integrated with Google Services, and use Hangouts as my main messenger and SMS app on my Nexus phone. While Gmail was down, I couldn't respond to anyone who was using hangouts to contact me, couldn't share any documents on Google Drive, etc.
I'm going to have to seriously think about the risks of being so heavily reliant on Google services.
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.