andrew_gelman_stats andrew_gelman_stats-2011 andrew_gelman_stats-2011-606 knowledge-graph by maker-knowledge-mining
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Introduction: Mark Palko points to a news article by Michael Winerip on teacher assessment: No one at the Lab Middle School for Collaborative Studies works harder than Stacey Isaacson, a seventh-grade English and social studies teacher. She is out the door of her Queens home by 6:15 a.m., takes the E train into Manhattan and is standing out front when the school doors are unlocked, at 7. Nights, she leaves her classroom at 5:30. . . . Her principal, Megan Adams, has given her terrific reviews during the two and a half years Ms. Isaacson has been a teacher. . . . The Lab School has selective admissions, and Ms. Isaacson’s students have excelled. Her first year teaching, 65 of 66 scored proficient on the state language arts test, meaning they got 3′s or 4′s; only one scored below grade level with a 2. More than two dozen students from her first two years teaching have gone on to . . . the city’s most competitive high schools. . . . You would think the Department of Education would want to r
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1 Mark Palko points to a news article by Michael Winerip on teacher assessment: No one at the Lab Middle School for Collaborative Studies works harder than Stacey Isaacson, a seventh-grade English and social studies teacher. [sent-1, score-0.332]
2 , takes the E train into Manhattan and is standing out front when the school doors are unlocked, at 7. [sent-4, score-0.141]
3 Her first year teaching, 65 of 66 scored proficient on the state language arts test, meaning they got 3′s or 4′s; only one scored below grade level with a 2. [sent-16, score-0.887]
4 More than two dozen students from her first two years teaching have gone on to . [sent-17, score-0.452]
5 Instead, the department’s accountability experts have developed a complex formula to calculate how much academic progress a teacher’s students make in a year — the teacher’s value-added score — and that formula indicates that Ms. [sent-28, score-0.751]
6 Isaacson ranks in the 7th percentile among her teaching peers — meaning 93 per cent are better. [sent-31, score-0.345]
7 Everyone who teaches math or English has received a teacher data report. [sent-40, score-0.432]
8 Isaacson’s students had a prior proficiency score of 3. [sent-43, score-0.539]
9 69 — based on the scores of comparable students around the city. [sent-46, score-0.329]
10 Remember, the exam is on a 1-4 scale, and we were already told that 65 out of 66 students scored 3 or 4, so an average of 3. [sent-53, score-0.553]
11 57 is “the average prior year proficiency rating of the students who contribute to a teacher’s value added score. [sent-57, score-0.694]
12 ” I assume that the “proficiency rating” is the same as the 1-4 test score but I can’t be sure. [sent-58, score-0.186]
13 The predicted score is, according to Winerip, “based on 32 variables — including whether a student was retained in grade before pretest year and whether a student is new to city in pretest or post-test year. [sent-59, score-0.912]
14 Isaacson’s best guess about what the department is trying to tell her is: Even though 65 of her 66 students scored proficient on the state test, more of her 3s should have been 4s. [sent-64, score-0.683]
15 A “3″ is a passing grade, but if you’re teaching in a school with “selective admissions” with the particular mix of kids that this teacher has, the expectation is that most of your students will get “4″s. [sent-67, score-0.787]
16 We don’t know this teacher’s students did this year so I’ll use the data given above, from her first year. [sent-69, score-0.356]
17 Suppose that x students in the class got 4′s, 65-x got 3′s, and one student got a 2. [sent-70, score-0.584]
18 So the gap would be covered by four students (in a class of 66) moving up from a 3 to a 4. [sent-85, score-0.31]
19 This gives a sense of the difference between a teacher in the 7th percentile and a teacher in the 50th. [sent-86, score-0.753]
20 I wonder what this teacher’s value-added scores were for the previous two years. [sent-87, score-0.105]
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Introduction: Mark Palko points to a news article by Michael Winerip on teacher assessment: No one at the Lab Middle School for Collaborative Studies works harder than Stacey Isaacson, a seventh-grade English and social studies teacher. She is out the door of her Queens home by 6:15 a.m., takes the E train into Manhattan and is standing out front when the school doors are unlocked, at 7. Nights, she leaves her classroom at 5:30. . . . Her principal, Megan Adams, has given her terrific reviews during the two and a half years Ms. Isaacson has been a teacher. . . . The Lab School has selective admissions, and Ms. Isaacson’s students have excelled. Her first year teaching, 65 of 66 scored proficient on the state language arts test, meaning they got 3′s or 4′s; only one scored below grade level with a 2. More than two dozen students from her first two years teaching have gone on to . . . the city’s most competitive high schools. . . . You would think the Department of Education would want to r
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Introduction: Jacob Hartog writes the following in reaction to my post on the use of value-added modeling for teacher assessment: What I [Hartog] think has been inadequately discussed is the use of individual model specifications to assign these teacher ratings, rather than the zone of agreement across a broad swath of model specifications. For example, the model used by NYCDOE doesn’t just control for a student’s prior year test score (as I think everyone can agree is a good idea.) It also assumes that different demographic groups will learn different amounts in a given year, and assigns a school-level random effect. The result is that, as was much ballyhooed at the time of the release of the data,the average teacher rating for a given school is roughly the same, no matter whether the school is performing great or terribly. The headline from this was “excellent teachers spread evenly across the city’s schools,” rather than “the specification of these models assume that excellent teachers are
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Introduction: Rachel Schutt (the author of the Taxonomy of Confusion) has a blog! for the course she’s teaching at Columbia, “Introduction to Data Science.” It sounds like a great course—I wish I could take it! Her latest post is “On Inspiring Students and Being Human”: Of course one hopes as a teacher that one will inspire students . . . But what I actually mean by “inspiring students” is that you are inspiring me; you are students who inspire: “inspiring students”. This is one of the happy unintended consequences of this course so far for me. She then gives examples of some of the students in her class and some of their interesting ideas: Phillip is a PhD student in the sociology department . . . He’s in the process of developing his thesis topic around some of the themes we’ve been discussing in this class, such as the emerging data science community. Arvi works at the College Board and is a part time student . . . He analyzes user-level data of students who have signed up f
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Introduction: Sharon Otterman reports : When report card grades were released in the fall for the city’s 455 high schools, the highest score went to a small school in a down-and-out section of the Bronx . . . A stunning 94 percent of its seniors graduated, more than 30 points above the citywide average. . . . “When I interviewed for the school,” said Sam Buchbinder, a history teacher, “it was made very clear: this is a school that doesn’t believe in anyone failing.” That statement was not just an exhortation to excellence. It was school policy. By order of the principal, codified in the school’s teacher handbook, all teachers should grade their classes in the same way: 30 percent of students should earn a grade in the A range, 40 percent B’s, 25 percent C’s, and no more than 5 percent D’s. As long as they show up, they should not fail. Hey, that sounds like Harvard and Columbia^H^H^H^H^H^H^H^H^H^H^H^H^H^H^H^H^H^H^H^H^H various selective northeastern colleges I’ve known. Of course, we^H^H
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Introduction: Stuart Buck writes: I have a question about fixed effects vs. random effects . Amongst economists who study teacher value-added, it has become common to see people saying that they estimated teacher fixed effects (via least squares dummy variables, so that there is a parameter for each teacher), but that they then applied empirical Bayes shrinkage so that the teacher effects are brought closer to the mean. (See this paper by Jacob and Lefgren, for example.) Can that really be what they are doing? Why wouldn’t they just run random (modeled) effects in the first place? I feel like there’s something I’m missing. My reply: I don’t know the full story here, but I’m thinking there are two goals, first to get an unbiased estimate of an overall treatment effect (and there the econometricians prefer so-called fixed effects; I disagree with them on this but I know where they’re coming from) and second to estimate individual teacher effects (and there it makes sense to use so-called
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Introduction: Mark Palko points to a news article by Michael Winerip on teacher assessment: No one at the Lab Middle School for Collaborative Studies works harder than Stacey Isaacson, a seventh-grade English and social studies teacher. She is out the door of her Queens home by 6:15 a.m., takes the E train into Manhattan and is standing out front when the school doors are unlocked, at 7. Nights, she leaves her classroom at 5:30. . . . Her principal, Megan Adams, has given her terrific reviews during the two and a half years Ms. Isaacson has been a teacher. . . . The Lab School has selective admissions, and Ms. Isaacson’s students have excelled. Her first year teaching, 65 of 66 scored proficient on the state language arts test, meaning they got 3′s or 4′s; only one scored below grade level with a 2. More than two dozen students from her first two years teaching have gone on to . . . the city’s most competitive high schools. . . . You would think the Department of Education would want to r
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Introduction: After reading the Rewarding Strivers book , I had some thoughts about how to make the college admissions system more fair to students from varying socioeconomic backgrounds. Instead of boosting up the disadvantaged students, why not pull down the advantaged students? Here’s the idea. Disadvantaged students are defined typically not by a bad thing that they have, but rather by good things that they don’t have: financial resources, a high-quality education, and so forth. In contrast, advantaged students get all sorts of freebies. So here are my suggestions: 1. All high school grades on a 4-point scale (A=4, B=3, etc). No more of this 5-points-for-an-A-in-an-AP course, which gives the ridiculous outcomes of kids graduating with a 4.3 average, not so fair to kids in schools that don’t offer a lot of AP classes. 2. Subtract points for taking the SAT multiple times. A simple rule would be: You can use your highest SAT score, but you lose 50 points for every other time
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Introduction: Interesting discussion here by Mark Palko on the difficulty of comparing charter schools to regular schools, even if the slots in the charter schools have been assigned by lottery. Beyond the direct importance of the topic, I found the discussion interesting because I always face a challenge in my own teaching to assign the right amount of homework, given that if I assign too much, students will simply rebel and not do it. To get back to the school-choice issue . . . Mark discussed selection effects: if a charter school is popular, it can require parents to sign a contract agreeing they will supervise their students to do lots of homework. Mark points out that there is a selection issue here, that the sort of parents who would sign that form are different from parents in general. But it seems to me there’s one more twist: These charter schools are popular, right? So that would imply that there is some reservoir of parents who would like to sign the form but don’t have the opp
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Introduction: There’s been some discussion lately about grade inflation. Here’s a graph from Stuart Rojstaczer ( link from Nathan Yau): Rojstaczer writes: In the 1930s, the average GPA at American colleges and universities was about 2.35, a number that corresponds with data compiled by W. Perry in 1943. By the 1950s, the average GPA was about 2.52. GPAs took off in the 1960s with grades at private schools rising faster than public schools, lulled in the 1970s, and began to rise again in the 1980s at a rate of about 0.10 to 0.15 increase in GPA per decade. The grade inflation that began in the 1980s has yet to end. . . . These trends may help explain why private school students are disproportionately represented in Ph.D. study in science and engineering and why they tend to dominate admission into the most prestigious professional schools. People have discussed why the grades have been going up and whether this is a bad thing. I have a slightly different take on all this. As a t
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Introduction: Congressman Kevin Brady from Texas distributes this visualization of reformed health care in the US (click for a bigger picture): Here’s a PDF at Brady’s page, and a local copy of it. Complexity has its costs. Beyond the cost of writing it, learning it, following it, there’s also the cost of checking it. John Walker has some funny examples of what’s hidden in the almost 8000 pages of IRS code. Text mining and applied statistics will solve all that, hopefully. Anyone interested in developing a pork detection system for the legislation? Or an analysis of how much entropy to the legal code did each congressman contribute? There are already spin detectors , that help you detect whether the writer is a Democrat (“stimulus”, “health care”) or a Republican (“deficit spending”, “ObamaCare”). D+0.1: Jared Lander points to versions by Rep. Boehner and Robert Palmer .
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Introduction: David Hogg pointed me to this news article by Angela Saini: It’s not often that the quiet world of mathematics is rocked by a murder case. But last summer saw a trial that sent academics into a tailspin, and has since swollen into a fevered clash between science and the law. At its heart, this is a story about chance. And it begins with a convicted killer, “T”, who took his case to the court of appeal in 2010. Among the evidence against him was a shoeprint from a pair of Nike trainers, which seemed to match a pair found at his home. While appeals often unmask shaky evidence, this was different. This time, a mathematical formula was thrown out of court. The footwear expert made what the judge believed were poor calculations about the likelihood of the match, compounded by a bad explanation of how he reached his opinion. The conviction was quashed. . . . “The impact will be quite shattering,” says Professor Norman Fenton, a mathematician at Queen Mary, University of London.
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