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http://ed2worlds.blogspot.com/2013/01/gates-foundation-waste...
Key quote:
"...it has been known for more than 60 years now that correlating averages of groups grossly overstates the strength of the relationship between two variables"
Apparently, this was also used in the past to show high correlation between African-Americans and illiteracy.
Also, see this blog for some more discussion along the same lines: http://ed2worlds.blogspot.com/2013/01/gates-foundation-waste...
Seems pretty clear that Gates' study presents the data in a very misleading manner to make a very weak correlation look like a very strong correlation.
This is a surprising blog post in that I draw the complete opposite conclusion the author does. The author seems to think that the averaging hides volatility (which it does), which leads to incorrect conclusions drawn. Whereas to me it looks like it removes visual noise to show an actual trend. In his original scatter plot, because of the big ball in the middle it's easy to handwave and say, "look a random blob" -- b…
I agree 100%. A scatter plot, with a low but very obvious correlation, shown for two years only seems to me to be absolutely fantastic evidence that progress is being made.
But anyway, each one is free to reach his own conclussions.
I know nothing about the domain of teacher measurement, and have no opinion on it. But the first chart in the blog post, with the big mass of blue on it, is surely not the strong evidence of weak correlation, that the author is making it out to be? There could still be a strong correlation in that data, even if it looks like a blob of blue - because there are so many data points on that chart, that we can no longer t…
Grouping data points does mask volatility. This doesn't make the effect any less real on the societal level. If we seek to improve student performance, skewing the pool of teachers towards the better ones will do that.
Yet there is also an enormous amount of volatility which is also real. Ideally we would just create better predictors. Constraining ourself for the moment to this set, since they have high error rates on the individual level, it's also reasonable to assert that in relying on this metric, we will make unfair decisions for many teachers.
The right solution depends on how much you value optimizing student outcomes vs. optimizing fairness as well as second order effects such as driving away teachers. My bias would be to use these scores for economic incentives to attempt to ensure that retention for the best teachers is higher than for the worst. Without additional work, termination likely isn't warranted based on this data.
To those who argue that pay differentials require better evidence, I would suggest comparing the validity of performance evaluations across any other job. It's imperfect but better for society than doing nothing.
For long-term fairness, it's important that there isn't a single value-added model. Competitive pressure can do wonders to improve model quality and it's also fairer since those who don't do well on one model can move to a different rubric.
I suspect there'll be a lot of differing opinions on this, and I'm looking forward to seeing the discussion. What would be helpful would be if people could say how much experience they have in hard statistics, and how much what they say is driven a priori from the data. I know that hackers, in particular, have real problems with "Argument from Authority", but stats is one place where it's really, really easy to go wr…
Typically when someone posts a baiting title like that I know not to trust them. Scholarship and evidence don't need hyperbolic headlines.
And really you would be amazed at the titles (and abstracts) of papers in journals of Pure Mathematics. Really. I've seen thing you people would't believe.
Earlier quoted context omitted.
The other thing the author does is to derive absolute findings when the author himself admits to making an assumption about how the numbers were obtained. To me, using the word "lie" in the title is therefore nothing more than click-baiting. And on a separate note - there's a lot of anti-Gates sentiment flying around lately. Whether his methods are the best or not, he's using his own money . I find it really difficul…
Bill Gates's position as a famous wealthy guy means he has more of a responsibility than the rest of us to not put out misleading graphs. Even if he is spreading misinformation with his own money.
This looks to me like someone trying to make the report look good so that Bill Gates will get positive press, think this was worthwhile, and fund them again.
This is a surprising blog post in that I draw the complete opposite conclusion the author does. The author seems to think that the averaging hides volatility (which it does), which leads to incorrect conclusions drawn. Whereas to me it looks like it removes visual noise to show an actual trend. In his original scatter plot, because of the big ball in the middle it's easy to handwave and say, "look a random blob" -- b…
"Whereas to me it looks like it removes visual noise to show an actual trend." Except for that in this case the noise is more important than the trend. Think about it, if you're firing or sanctioning perhaps 30%+ of teachers each year for no reason, then only complete morons would go into teaching. It's the same as airport security, where a .1% false positive rate is unacceptable, whereas a 75% false negative rate is…
So I'm thinking they're not too worried about this.