5 ms·

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 su

by feral 14y ago

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 tell where the density of points is. Its possible that there is a dense line of points in there that still gives a strong correlation, that we just can't see. A heatmap with some level of binning, or a hex bin chart, or 2d histogram, would surely be more appropriate in these circumstances? Something like this: http://matplotlib.org/examples/pylab_examples/hexbin_demo.html http://matplotlib.org/examples/pylab_examples/hexbin_demo.ht... Maybe along with some direct math analysis of the correlation. Am I missing something?

drcode 14y ago
Exactly- It's pointless to counter a misleading graph with another misleading graph.
randomknowledge 14y ago
Correlation means how well the points fit on the line. It is unfortunate that the author did not include a regression line or R^2 value but none the less one can tell there is no line which most of the points lie near. That data is absolutely not strongly correlated. You seem to be confusing correlation and significance. Having lots of data points makes the correlation more significant, it does not make the correlation stronger.
- streptomycin 14y ago
  one can tell there is no line which most of the points lie near No, that's not correct. You can't conclude anything from looking at a big blob because you don't know the density of points at different places in the blob. This is the point the guy you replied to was making. As an extreme example, imagine a billion data points that fit perfectly on a straight line. Then superimpose a million data points randomly on top of it. What does it look like? A big blob. But almost every point is highly correlated with that straight line.
  - randomknowledge 14y ago
    Okay hows this: unless the author of the original blog post is deliberately deceiving the audience but putting a bunch of points on top of each other then one can tell there is no line which most of the points lie near. I agree that a scatter plot is not the best for showing data with that many data points, but frankly it's kind of irrelevant. The point the author was making was just that it isn't that hard to cook up some data that is not highly correlated, but will be if you bin and average it.
    - yummyfajitas 14y ago
      It's not a matter of being deliberate. He's just being lazy, using the standard excel scatterplot. To fix that graph, he needs to carefully choose an opacity, which he didn't do. See the discussion here: http://news.ycombinator.com/item?id=4027337 http://news.ycombinator.com/item?id=4027337 I stand by the claim made in my blog post. Don't use scatterplots, use a density plot instead. Incidentally, according to a comment the author made, the correlation is actually 0.3. That's far better than his graph suggests.
      - randomknowledge 14y ago
        I agree he could have presented his data better. I also recognize that there is some correlation. None the less, the story was about how averaging can be used to make a correlation appear strong than it is, and going from 0.3 to 0.99 certainly meets that criteria. I was responding to the claim that the data might already have a strong correlation (presumably on the order of 0.99), and I was arguing that no "natural" strongly correlated data would have a scatter plot like that. I might add that discussions like these, Illustrate a broader problem with the hacker news community. Rather than discussing the facts of the story, many of the posts are instead about the less relevant detail about how the author chose to present them, despite the fact the the authors point is still effectively made (and if the 0.3 were included there wouldn't have been any doubt) BTW: tmoertel did some analysis on the data: http://news.ycombinator.com/item?id=5125851 http://news.ycombinator.com/item?id=5125851 and the correlation appears to be around 0.2, which is pretty weak.
cousin_it 14y ago
It seems to me that the evidence of weak correlation isn't quite as weak as you imply :-) If there's a dense line in there, how come it's not sticking out of the blob even a little? Do actual phenomena often yield "deceptive" blobby scatterplots with dense lines hidden inside them? That seems apriori improbable to me, thus increasing the Bayesian probability that the blobby scatterplot is not a misleading picture after all. Though of course some more numbers would've been nice.
- ak217 14y ago
  >If there's a dense line in there, how come it's not sticking out of the blob even a little? It is. If you look at the density in the upper right of the cloud, it is clearly clustered around the x=y line. The correlation may not be very strong, but without extra information about how strong of an effect would constitute clear evidence in favor of including the value add metric, we can't say anything more about it. > Do actual phenomena often yield "deceptive" blobby scatterplots with dense lines hidden inside them? Absolutely. You're looking at a misapplication of a plotting technique - a scatterplot with substantial overplotting. The author may be convinced that there's no correlation there, but by presenting this plot they provide no evidence in support of their point.
  - cousin_it 14y ago
    > Absolutely. Um, that's not much to go on. Can you give some examples?
vy8vWJlco 14y ago
A heatmap would be more revealing, but I'd eat my hat if it weren't a standard distribution (and I had a hat). It's nature (but hey, anything's possible). The scales are only slightly skewed, but we're not talking logarithms. The merit of the scales ("value-added") on the other hand, is very questionable. But the relation, whatever it is describing, is pretty weak since the ellipse isn't very irregular (it's only "slightly" irregular). http://www.emeraldinsight.com/content_images/fig/0870280109015.png http://www.emeraldinsight.com/content_images/fig/08702801090... Think Kepler's laws. Edit: The first post in the series shows a scatter of the direct (non-delta) scores, and it looks like San Fransisco in the morning: http://garyrubinstein.teachforus.org/2012/02/26/analyzing-released-nyc-value-added-data-part-1/ http://garyrubinstein.teachforus.org/2012/02/26/analyzing-re...
- spikels 14y ago
  Clearly all the overlapping points in the first graph are obscuring most of what's going on. This is a warning flag that the author may be trying color the facts to suit their argument. Not very clear what you are try to say: (1) What "standard deviation"? Perhaps you mean the standard error of the correlation coefficient is large relative to the its estimated value. Given the large number of data points it is likely to be quite small. Another warning flag is the failure to report either the correlation coefficient or its standard error. (2) What does "ellipse isn't irregular" mean? Given all the overlapping data points the shape of the plot is entirely driven outliers. To my eye this looks what you would get from plotting a bivariate normal distribution. (3) Kepler? The linked graph? How is this helpful in understanding what's going on? The first graph has a straightforward interpretation: you are making two imperfect measurements of each teacher's performance at two different times. There is noise in both measurements and teacher performance may have actually changed between measurements.
  - vy8vWJlco 14y ago
    "What "standard deviation"? Typo: should have been "standard distribution"... Less colloquially: "a two-variable normal distribution." "What does "ellipse isn't irregular" mean?" Eccentricity. Sorry `fer the double negative. "Kepler? The linked graph? How is this helpful in understanding what's going on?" If you take the scattered data and draw an ellipse containing a fixed probability (presumably represented with some confidence, by the data shown), the ellipse would be more eccentric for the same enclosed probability, the higher the correlation. It's a single statistic and is analogous to Kepler's 2nd law ("a line joining a planet and the Sun sweeps out equal areas during equal intervals of time."), except with probability rather than area... (To contain the same probability, you have to adjust the ellipse's size, while holding its enclosed probability constant, and accounting for the new eccentricity, which isn't under our control. If the area is fixed and the distance changes, solve for speed... I think visually. Alternatively, think V=IR.; if V is constant and R is known, solve for I.). The link was just illustrative of any such scatter plot. It was the best I could find, but I agree, it could have been better. I agree that the article is low on quantitative statistics though.
colanderman 14y ago
Not to mention that he says "this is a number between -1 and +1" and the graph clearly has data points outside that range.
kevinalexbrown 14y ago
Edit: since ColinWright explicitly asked us to indicate experience with hard statistics: Check the link in my profile. I work with very messy data. I've also found that the more experience people have with something, the less confident they are about their expertise so I won't claim to be NotWrong, but the statistics here really aren't hard. It's linear regression with data that appears, cursorily, to be gaussian, close-ish to iid, etc. The question isn't "did Gary do the math wrong?" it's more "is he interpreting the data reasonably?" I will make a strong claim: If doctor effectiveness were this stable, and it were the only data I had available, I would choose a doctor on this metric, ceteris paribus. I found a scatterplot by the author that uses smaller marks so it looks a lot less blob-ish and more heat-map-ish: http://garyrubinstein.teachforus.org/2012/02/26/analyzing-released-nyc-value-added-data-part-1/ http://garyrubinstein.teachforus.org/2012/02/26/analyzing-re... I'm trying to track down the original data to verify this myself, but a couple of quick points: There definitely seems to be good evidence of a relationship. It might be "weak" but that's not the same thing as "ineffective". Considering the number of data points, it makes it unlikely that the trend occurred by chance. This plot also makes it clear why the averaging made it look so pretty: the average in just about any percentile group in 08-09 matched 09-10 regardless of how finely it's divided (to a point). It's certainly the case that there's a lot of "noise". The question is how to interpret it. One might conclude that there is a lot of "measurement noise" -- that teacher evaluations are inherently messy and rather uninformative except as a larger trend. There seems to be reasonable support for this! Students aren't the same, even from year to year. Of course it could "signal noise" - maybe teachers themselves change from year to year. Perhaps one year you're more motivated, the next your not, or vice versa. To understand the implications of this noise, we also need to ask what we're using it for. For instance, if this were a plot of the correlation between money donated to Watsi and quality-of-life, you'd probably think you were doing pretty well! You might point out that a particular quality-of-life metric is flawed or noisy, or that the quality-of-life depends on lots of things besides health, but likely you'd be happy that you're clearly causing some benefit. So when we ask whether the data suggest the viability of value-added measurements in aggregate, to me that seems to be a yes. But if we ask whether the data suggest using these measurements to make a decision on a per-teacher basis, that depends on the context of the alternatives! If this is the only thing we have to go by, perhaps it's not so bad. I would love to see how stable the other parts of the teacher evaluation fit in. If you combine multiple noisy signals, you often get a much better picture of what's going on! This accounts for only 1/3 of the teacher's evaluation. I would like to think that if you appeared not to be a value-added teacher, but your principals and coworkers spoke highly of you, that you'd still get that raise. Also, I'm looking forward to multiple years of this data - if your score accounts for 35 percent of the variance from one year to the next, multiple years might look a lot better. Perhaps in deciding teacher tenure, scores over 5-10 years could be really useful.
yummyfajitas 14y ago
I'm really surprised to see Gary Rubinstein continuing to post bad graphs which obscure the data. 8 months ago I wrote a blog post about why you should avoid scatterplots, and used his graphs as examples of what not to do. http://www.chrisstucchio.com/blog/2012/dont_use_scatterplots.html http://www.chrisstucchio.com/blog/2012/dont_use_scatterplots... He is certainly aware of my post - he even commented on it and I offered him some constructive suggestions (such as adding alpha if he wants to use scatterplots). I'm beginning to think that maybe his goal is to make VAM look bad, rather than to actually clarify and explain the data. (I also left a comment on his blog expressing my concerns with his plot. Strangely, I seem to be hellbanned on his blog - the post is invisible when I view the post in an incognito window.)
- patio11 14y ago
  That is common for pre-moderated comments on blogs rather than evidence of hell banning, fwiw.
  - yummyfajitas 14y ago
    My mistake. Apologies to GR if that is the case.
    - yummyfajitas 14y ago
      Looks like I overreacted. My post is showing up fine. I often warn against assuming the worst of those you disagree with, and I should have heeded my own warnings.
tmoertel 14y ago
Important expectation-calibration note: I'm not making any claims about teacher performance, the usefulness of the value-added idea, or any lofty topics in general. I'm just trying to figure out how to recreate the first graph in the initial posting and get a closer look at the data behind it. I grabbed the data set [1], loaded it into R, and tried to recreate the first graph in the original post. Observations: 1. The value-added scores are quantized so, as some people have suggested, it's hard to see the true density relationships in the original plot. There's a lot of overlapping data points. 2. I re-plotted the data using jittering to more accurately show the density distribution. I also added a GAM smoother to show the trend. Result: https://docs.google.com/file/d/0B2Tqdso8AsFNS3lWQTdPUGpHZlk/edit https://docs.google.com/file/d/0B2Tqdso8AsFNS3lWQTdPUGpHZlk/... 3. I also plotted the change in each teacher's value-added score from one year to the next. The resulting histogram: https://docs.google.com/file/d/0B2Tqdso8AsFNeUNiM3hHUGwzdlk/edit https://docs.google.com/file/d/0B2Tqdso8AsFNeUNiM3hHUGwzdlk/... 4. If we model the relationship as next_year_value_added = this_year_value_added + random_error, here's the fitted model: Call: lm(formula = va_0910 ~ va_0809, data = ds_cmp) Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 0.009157 0.002315 3.956 7.67e-05 *** va_0809 0.820085 0.017646 46.475 < 2e-16 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 0.2153 on 8691 degrees of freedom Multiple R-squared: 0.1991, Adjusted R-squared: 0.199 F-statistic: 2160 on 1 and 8691 DF, p-value: < 2.2e-16 If you want to see exactly what I did, I threw my R code and the data sets up on GitHub: https://github.com/tmoertel/nyva-cursory https://github.com/tmoertel/nyva-cursory [1] http://www.ny1.com/content/top_stories/156599/now-available--2007-2010-nyc-teacher-performance-data#doereports http://www.ny1.com/content/top_stories/156599/now-available-...
- DataWorker 14y ago
  One question would be whether or not to match the files on subject/grade/teacher, or just the teacher. You use subject/grade/teacher level, but you get a different answer if you use aggregated teacher level.
  - DataWorker 14y ago

by feral 14y ago

drcode 14y ago

Exactly- It's pointless to counter a misleading graph with another misleading graph.

randomknowledge 14y ago

Correlation means how well the points fit on the line. It is unfortunate that the author did not include a regression line or R^2 value but none the less one can tell there is no line which most of the points lie near. That data is absolutely not strongly correlated. You seem to be confusing correlation and significance. Having lots of data points makes the correlation more significant, it does not make the correlation stronger.

streptomycin 14y ago
one can tell there is no line which most of the points lie near No, that's not correct. You can't conclude anything from looking at a big blob because you don't know the density of points at different places in the blob. This is the point the guy you replied to was making. As an extreme example, imagine a billion data points that fit perfectly on a straight line. Then superimpose a million data points randomly on top of it. What does it look like? A big blob. But almost every point is highly correlated with that straight line.
- randomknowledge 14y ago
  Okay hows this: unless the author of the original blog post is deliberately deceiving the audience but putting a bunch of points on top of each other then one can tell there is no line which most of the points lie near. I agree that a scatter plot is not the best for showing data with that many data points, but frankly it's kind of irrelevant. The point the author was making was just that it isn't that hard to cook up some data that is not highly correlated, but will be if you bin and average it.
  - yummyfajitas 14y ago
    It's not a matter of being deliberate. He's just being lazy, using the standard excel scatterplot. To fix that graph, he needs to carefully choose an opacity, which he didn't do. See the discussion here: http://news.ycombinator.com/item?id=4027337 http://news.ycombinator.com/item?id=4027337 I stand by the claim made in my blog post. Don't use scatterplots, use a density plot instead. Incidentally, according to a comment the author made, the correlation is actually 0.3. That's far better than his graph suggests.
    - randomknowledge 14y ago
      I agree he could have presented his data better. I also recognize that there is some correlation. None the less, the story was about how averaging can be used to make a correlation appear strong than it is, and going from 0.3 to 0.99 certainly meets that criteria. I was responding to the claim that the data might already have a strong correlation (presumably on the order of 0.99), and I was arguing that no "natural" strongly correlated data would have a scatter plot like that. I might add that discussions like these, Illustrate a broader problem with the hacker news community. Rather than discussing the facts of the story, many of the posts are instead about the less relevant detail about how the author chose to present them, despite the fact the the authors point is still effectively made (and if the 0.3 were included there wouldn't have been any doubt) BTW: tmoertel did some analysis on the data: http://news.ycombinator.com/item?id=5125851 http://news.ycombinator.com/item?id=5125851 and the correlation appears to be around 0.2, which is pretty weak.

cousin_it 14y ago

It seems to me that the evidence of weak correlation isn't quite as weak as you imply :-) If there's a dense line in there, how come it's not sticking out of the blob even a little? Do actual phenomena often yield "deceptive" blobby scatterplots with dense lines hidden inside them? That seems apriori improbable to me, thus increasing the Bayesian probability that the blobby scatterplot is not a misleading picture after all. Though of course some more numbers would've been nice.

ak217 14y ago
>If there's a dense line in there, how come it's not sticking out of the blob even a little? It is. If you look at the density in the upper right of the cloud, it is clearly clustered around the x=y line. The correlation may not be very strong, but without extra information about how strong of an effect would constitute clear evidence in favor of including the value add metric, we can't say anything more about it. > Do actual phenomena often yield "deceptive" blobby scatterplots with dense lines hidden inside them? Absolutely. You're looking at a misapplication of a plotting technique - a scatterplot with substantial overplotting. The author may be convinced that there's no correlation there, but by presenting this plot they provide no evidence in support of their point.
- cousin_it 14y ago
  > Absolutely. Um, that's not much to go on. Can you give some examples?

vy8vWJlco 14y ago

A heatmap would be more revealing, but I'd eat my hat if it weren't a standard distribution (and I had a hat). It's nature (but hey, anything's possible). The scales are only slightly skewed, but we're not talking logarithms. The merit of the scales ("value-added") on the other hand, is very questionable. But the relation, whatever it is describing, is pretty weak since the ellipse isn't very irregular (it's only "slightly" irregular). http://www.emeraldinsight.com/content_images/fig/0870280109015.png http://www.emeraldinsight.com/content_images/fig/08702801090... Think Kepler's laws. Edit: The first post in the series shows a scatter of the direct (non-delta) scores, and it looks like San Fransisco in the morning: http://garyrubinstein.teachforus.org/2012/02/26/analyzing-released-nyc-value-added-data-part-1/ http://garyrubinstein.teachforus.org/2012/02/26/analyzing-re...

spikels 14y ago
Clearly all the overlapping points in the first graph are obscuring most of what's going on. This is a warning flag that the author may be trying color the facts to suit their argument. Not very clear what you are try to say: (1) What "standard deviation"? Perhaps you mean the standard error of the correlation coefficient is large relative to the its estimated value. Given the large number of data points it is likely to be quite small. Another warning flag is the failure to report either the correlation coefficient or its standard error. (2) What does "ellipse isn't irregular" mean? Given all the overlapping data points the shape of the plot is entirely driven outliers. To my eye this looks what you would get from plotting a bivariate normal distribution. (3) Kepler? The linked graph? How is this helpful in understanding what's going on? The first graph has a straightforward interpretation: you are making two imperfect measurements of each teacher's performance at two different times. There is noise in both measurements and teacher performance may have actually changed between measurements.
- vy8vWJlco 14y ago
  "What "standard deviation"? Typo: should have been "standard distribution"... Less colloquially: "a two-variable normal distribution." "What does "ellipse isn't irregular" mean?" Eccentricity. Sorry `fer the double negative. "Kepler? The linked graph? How is this helpful in understanding what's going on?" If you take the scattered data and draw an ellipse containing a fixed probability (presumably represented with some confidence, by the data shown), the ellipse would be more eccentric for the same enclosed probability, the higher the correlation. It's a single statistic and is analogous to Kepler's 2nd law ("a line joining a planet and the Sun sweeps out equal areas during equal intervals of time."), except with probability rather than area... (To contain the same probability, you have to adjust the ellipse's size, while holding its enclosed probability constant, and accounting for the new eccentricity, which isn't under our control. If the area is fixed and the distance changes, solve for speed... I think visually. Alternatively, think V=IR.; if V is constant and R is known, solve for I.). The link was just illustrative of any such scatter plot. It was the best I could find, but I agree, it could have been better. I agree that the article is low on quantitative statistics though.

colanderman 14y ago

Not to mention that he says "this is a number between -1 and +1" and the graph clearly has data points outside that range.

kevinalexbrown 14y ago

Edit: since ColinWright explicitly asked us to indicate experience with hard statistics: Check the link in my profile. I work with very messy data. I've also found that the more experience people have with something, the less confident they are about their expertise so I won't claim to be NotWrong, but the statistics here really aren't hard. It's linear regression with data that appears, cursorily, to be gaussian, close-ish to iid, etc. The question isn't "did Gary do the math wrong?" it's more "is he interpreting the data reasonably?" I will make a strong claim: If doctor effectiveness were this stable, and it were the only data I had available, I would choose a doctor on this metric, ceteris paribus. I found a scatterplot by the author that uses smaller marks so it looks a lot less blob-ish and more heat-map-ish: http://garyrubinstein.teachforus.org/2012/02/26/analyzing-released-nyc-value-added-data-part-1/ http://garyrubinstein.teachforus.org/2012/02/26/analyzing-re... I'm trying to track down the original data to verify this myself, but a couple of quick points: There definitely seems to be good evidence of a relationship. It might be "weak" but that's not the same thing as "ineffective". Considering the number of data points, it makes it unlikely that the trend occurred by chance. This plot also makes it clear why the averaging made it look so pretty: the average in just about any percentile group in 08-09 matched 09-10 regardless of how finely it's divided (to a point). It's certainly the case that there's a lot of "noise". The question is how to interpret it. One might conclude that there is a lot of "measurement noise" -- that teacher evaluations are inherently messy and rather uninformative except as a larger trend. There seems to be reasonable support for this! Students aren't the same, even from year to year. Of course it could "signal noise" - maybe teachers themselves change from year to year. Perhaps one year you're more motivated, the next your not, or vice versa. To understand the implications of this noise, we also need to ask what we're using it for. For instance, if this were a plot of the correlation between money donated to Watsi and quality-of-life, you'd probably think you were doing pretty well! You might point out that a particular quality-of-life metric is flawed or noisy, or that the quality-of-life depends on lots of things besides health, but likely you'd be happy that you're clearly causing some benefit. So when we ask whether the data suggest the viability of value-added measurements in aggregate, to me that seems to be a yes. But if we ask whether the data suggest using these measurements to make a decision on a per-teacher basis, that depends on the context of the alternatives! If this is the only thing we have to go by, perhaps it's not so bad. I would love to see how stable the other parts of the teacher evaluation fit in. If you combine multiple noisy signals, you often get a much better picture of what's going on! This accounts for only 1/3 of the teacher's evaluation. I would like to think that if you appeared not to be a value-added teacher, but your principals and coworkers spoke highly of you, that you'd still get that raise. Also, I'm looking forward to multiple years of this data - if your score accounts for 35 percent of the variance from one year to the next, multiple years might look a lot better. Perhaps in deciding teacher tenure, scores over 5-10 years could be really useful.

yummyfajitas 14y ago

I'm really surprised to see Gary Rubinstein continuing to post bad graphs which obscure the data. 8 months ago I wrote a blog post about why you should avoid scatterplots, and used his graphs as examples of what not to do. http://www.chrisstucchio.com/blog/2012/dont_use_scatterplots.html http://www.chrisstucchio.com/blog/2012/dont_use_scatterplots... He is certainly aware of my post - he even commented on it and I offered him some constructive suggestions (such as adding alpha if he wants to use scatterplots). I'm beginning to think that maybe his goal is to make VAM look bad, rather than to actually clarify and explain the data. (I also left a comment on his blog expressing my concerns with his plot. Strangely, I seem to be hellbanned on his blog - the post is invisible when I view the post in an incognito window.)

patio11 14y ago
That is common for pre-moderated comments on blogs rather than evidence of hell banning, fwiw.
- yummyfajitas 14y ago
  My mistake. Apologies to GR if that is the case.
  - yummyfajitas 14y ago
    Looks like I overreacted. My post is showing up fine. I often warn against assuming the worst of those you disagree with, and I should have heeded my own warnings.

tmoertel 14y ago

Important expectation-calibration note: I'm not making any claims about teacher performance, the usefulness of the value-added idea, or any lofty topics in general. I'm just trying to figure out how to recreate the first graph in the initial posting and get a closer look at the data behind it. I grabbed the data set [1], loaded it into R, and tried to recreate the first graph in the original post. Observations: 1. The value-added scores are quantized so, as some people have suggested, it's hard to see the true density relationships in the original plot. There's a lot of overlapping data points. 2. I re-plotted the data using jittering to more accurately show the density distribution. I also added a GAM smoother to show the trend. Result: https://docs.google.com/file/d/0B2Tqdso8AsFNS3lWQTdPUGpHZlk/edit https://docs.google.com/file/d/0B2Tqdso8AsFNS3lWQTdPUGpHZlk/... 3. I also plotted the change in each teacher's value-added score from one year to the next. The resulting histogram: https://docs.google.com/file/d/0B2Tqdso8AsFNeUNiM3hHUGwzdlk/edit https://docs.google.com/file/d/0B2Tqdso8AsFNeUNiM3hHUGwzdlk/... 4. If we model the relationship as next_year_value_added = this_year_value_added + random_error, here's the fitted model: Call: lm(formula = va_0910 ~ va_0809, data = ds_cmp) Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 0.009157 0.002315 3.956 7.67e-05 *** va_0809 0.820085 0.017646 46.475 < 2e-16 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 0.2153 on 8691 degrees of freedom Multiple R-squared: 0.1991, Adjusted R-squared: 0.199 F-statistic: 2160 on 1 and 8691 DF, p-value: < 2.2e-16 If you want to see exactly what I did, I threw my R code and the data sets up on GitHub: https://github.com/tmoertel/nyva-cursory https://github.com/tmoertel/nyva-cursory [1] http://www.ny1.com/content/top_stories/156599/now-available--2007-2010-nyc-teacher-performance-data#doereports http://www.ny1.com/content/top_stories/156599/now-available-...

DataWorker 14y ago
One question would be whether or not to match the files on subject/grade/teacher, or just the teacher. You use subject/grade/teacher level, but you get a different answer if you use aggregated teacher level.
- DataWorker 14y ago