andrew_gelman_stats andrew_gelman_stats-2010 andrew_gelman_stats-2010-326 knowledge-graph by maker-knowledge-mining
Source: html
Introduction: Partly in response to my blog on the Harlem Children’s Zone study, Mark Palko wrote this : Talk of education reform always makes me [Palko] deeply nervous. Part of the anxiety comes having spent a number of years behind the podium and having seen the disparity between the claims and the reality of previous reforms. The rest comes from being a statistician and knowing what things like convergence can do to data. Convergent behavior violates the assumption of independent observations used in most simple analyses, but educational studies commonly, perhaps even routinely ignore the complex ways that social norming can cause the nesting of student performance data. In other words, educational research is often based of the idea that teenagers do not respond to peer pressure. . . . and this : When you isolate a group of students, they will quickly arrive at a consensus of what constitutes normal behavior. It is a complex and somewhat unpredictable process driven by personali
sentIndex sentText sentNum sentScore
1 Partly in response to my blog on the Harlem Children’s Zone study, Mark Palko wrote this : Talk of education reform always makes me [Palko] deeply nervous. [sent-1, score-0.224]
2 Part of the anxiety comes having spent a number of years behind the podium and having seen the disparity between the claims and the reality of previous reforms. [sent-2, score-0.404]
3 Convergent behavior violates the assumption of independent observations used in most simple analyses, but educational studies commonly, perhaps even routinely ignore the complex ways that social norming can cause the nesting of student performance data. [sent-4, score-0.779]
4 In other words, educational research is often based of the idea that teenagers do not respond to peer pressure. [sent-5, score-0.288]
5 and this : When you isolate a group of students, they will quickly arrive at a consensus of what constitutes normal behavior. [sent-9, score-0.358]
6 It is a complex and somewhat unpredictable process driven by personalities and random connections and any number of outside factors. [sent-10, score-0.391]
7 You can however, exercise a great deal of control over the outcome by restricting the make-up of the group. [sent-11, score-0.163]
8 Dobbie and Fryer address the question of self-selection, “[R]esults from any lottery sample may lack external validity. [sent-15, score-0.251]
9 The counterfactual we identify is for students who are already interested in charter schools. [sent-16, score-0.381]
10 The effect of being offered admission to HCZ for these students may be different than for other types of students. [sent-17, score-0.206]
11 ” In other words, they can’t conclude from the data how well students would do at the Promise Academies if, for instance, their parents weren’t engaged and supportive (a group effective eliminated by the application process). [sent-18, score-0.691]
12 (There was a control group of lottery losers but there is no evidence that they were kept together as a group. [sent-20, score-0.42]
13 Because we are thinking about spending an enormous amount of time, effort and money on a major overhaul of the education system when we don’t have the data to tell us if what we’ll spend will wasted or, worse yet, if we are to some extent playing a zero sum game. [sent-22, score-0.355]
14 If you remove all of the students whose parents are willing and able to go through the application process, the norms of acceptable behavior for those left behind will move in an ugly direction and the kids who started out with the greatest disadvantages would be left to bear the burden. [sent-24, score-1.029]
15 Educational reform is not like climate change where observational data is our only reasonable option. [sent-26, score-0.137]
wordName wordTfidf (topN-words)
[('hcz', 0.341), ('norming', 0.227), ('students', 0.206), ('educational', 0.185), ('lottery', 0.144), ('reform', 0.137), ('palko', 0.118), ('process', 0.118), ('group', 0.112), ('parents', 0.111), ('address', 0.107), ('behind', 0.105), ('convergent', 0.103), ('academies', 0.103), ('anxiety', 0.103), ('nesting', 0.103), ('podium', 0.103), ('teenagers', 0.103), ('application', 0.1), ('fryer', 0.098), ('overhaul', 0.098), ('complex', 0.096), ('amount', 0.095), ('disparity', 0.093), ('isolated', 0.093), ('left', 0.091), ('unpredictable', 0.09), ('charter', 0.09), ('behavior', 0.089), ('education', 0.087), ('personalities', 0.087), ('disadvantages', 0.085), ('arrive', 0.085), ('counterfactual', 0.085), ('eliminated', 0.083), ('control', 0.083), ('instruction', 0.081), ('constitutes', 0.081), ('losers', 0.081), ('restricting', 0.08), ('tremendous', 0.08), ('zone', 0.08), ('isolate', 0.08), ('words', 0.079), ('violates', 0.079), ('engaged', 0.079), ('promise', 0.079), ('greatest', 0.076), ('wasted', 0.075), ('norms', 0.075)]
simIndex simValue blogId blogTitle
same-blog 1 0.9999997 326 andrew gelman stats-2010-10-07-Peer pressure, selection, and educational reform
Introduction: Partly in response to my blog on the Harlem Children’s Zone study, Mark Palko wrote this : Talk of education reform always makes me [Palko] deeply nervous. Part of the anxiety comes having spent a number of years behind the podium and having seen the disparity between the claims and the reality of previous reforms. The rest comes from being a statistician and knowing what things like convergence can do to data. Convergent behavior violates the assumption of independent observations used in most simple analyses, but educational studies commonly, perhaps even routinely ignore the complex ways that social norming can cause the nesting of student performance data. In other words, educational research is often based of the idea that teenagers do not respond to peer pressure. . . . and this : When you isolate a group of students, they will quickly arrive at a consensus of what constitutes normal behavior. It is a complex and somewhat unpredictable process driven by personali
2 0.13594306 542 andrew gelman stats-2011-01-28-Homework and treatment levels
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
3 0.12453963 957 andrew gelman stats-2011-10-14-Questions about a study of charter schools
Introduction: Phil Harris and Bruce Smith write: We have only recently chanced upon your blog while we were looking for responses to the “decline effect” in medical and scientific studies. We were especially taken by your comment that there is something wrong with the scientific method “if this method is defined as running experiments and doing data analysis in a patternless way and then reporting, as true, results that pass a statistical significance threshold.” To us, that seems to be standard operating procedure in much of social science, including our own field of education. Indeed quasi-experimental designs are the stock in trade of those who attempt to use “science” — we dare not say haruspicy, but you can if you like — to influence the course of public policy. Now, a new entrant into the cherry pickers sweepstakes seems to have emerged. It is on Charter School Performance in Indiana Schools . We are by no means professional statisticians or data analysts, but we have some background in
4 0.12328018 1517 andrew gelman stats-2012-10-01-“On Inspiring Students and Being Human”
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
5 0.11374483 71 andrew gelman stats-2010-06-07-Pay for an A?
Introduction: Judah Guber writes about his new company : What we have done with Ultrinsic is created a system of incentives for students to allow them to invest in their ability to achieve a certain grade and when they achieve that grade we reward them with a cash incentive on top of receiving their original investment. This helps remove one of the large barriers students have to studying and staying motivated over the course of long semesters of college by giving them rewards on a much more immediate basis. We have been doing a pilot program in 2 schools, NYU and Penn, for the past year or so, and are currently in the process of a major roll out of our services to 37 schools all across the country. This is due to our popularity and inquiries from students in tons of schools all around the country regarding getting Ultrinsic’s services in their school. In the Fall 2010 semester, Ultrinsic will be revolutionizing student motivation on a grand scale . This is the dream of many economists: to c
6 0.11016263 344 andrew gelman stats-2010-10-15-Story time
7 0.10716142 943 andrew gelman stats-2011-10-04-Flip it around
9 0.10538118 1683 andrew gelman stats-2013-01-19-“Confirmation, on the other hand, is not sexy”
10 0.10478884 1688 andrew gelman stats-2013-01-22-That claim that students whose parents pay for more of college get worse grades
11 0.10460947 261 andrew gelman stats-2010-09-07-The $900 kindergarten teacher
12 0.10261651 93 andrew gelman stats-2010-06-17-My proposal for making college admissions fairer
13 0.099390179 1352 andrew gelman stats-2012-05-29-Question 19 of my final exam for Design and Analysis of Sample Surveys
14 0.09751901 1910 andrew gelman stats-2013-06-22-Struggles over the criticism of the “cannabis users and IQ change” paper
15 0.094083175 236 andrew gelman stats-2010-08-26-Teaching yourself mathematics
16 0.093441144 1803 andrew gelman stats-2013-04-14-Why girls do better in school
17 0.091497809 1265 andrew gelman stats-2012-04-15-Progress in U.S. education; also, a discussion of what it takes to hit the op-ed pages
18 0.090014927 2046 andrew gelman stats-2013-10-01-I’ll say it again
19 0.088681854 370 andrew gelman stats-2010-10-25-Who gets wedding announcements in the Times?
20 0.088665679 1611 andrew gelman stats-2012-12-07-Feedback on my Bayesian Data Analysis class at Columbia
topicId topicWeight
[(0, 0.203), (1, -0.048), (2, 0.021), (3, -0.073), (4, 0.036), (5, 0.106), (6, 0.038), (7, 0.092), (8, -0.05), (9, 0.013), (10, -0.006), (11, 0.047), (12, -0.042), (13, -0.027), (14, 0.002), (15, -0.038), (16, 0.065), (17, 0.008), (18, -0.038), (19, 0.039), (20, -0.027), (21, -0.003), (22, -0.022), (23, 0.001), (24, 0.037), (25, -0.014), (26, 0.032), (27, 0.014), (28, -0.01), (29, -0.003), (30, -0.034), (31, -0.023), (32, 0.032), (33, 0.017), (34, 0.02), (35, 0.034), (36, 0.014), (37, 0.025), (38, 0.044), (39, 0.042), (40, -0.002), (41, 0.022), (42, 0.01), (43, -0.024), (44, 0.025), (45, -0.012), (46, 0.004), (47, 0.006), (48, -0.0), (49, 0.016)]
simIndex simValue blogId blogTitle
same-blog 1 0.96971053 326 andrew gelman stats-2010-10-07-Peer pressure, selection, and educational reform
Introduction: Partly in response to my blog on the Harlem Children’s Zone study, Mark Palko wrote this : Talk of education reform always makes me [Palko] deeply nervous. Part of the anxiety comes having spent a number of years behind the podium and having seen the disparity between the claims and the reality of previous reforms. The rest comes from being a statistician and knowing what things like convergence can do to data. Convergent behavior violates the assumption of independent observations used in most simple analyses, but educational studies commonly, perhaps even routinely ignore the complex ways that social norming can cause the nesting of student performance data. In other words, educational research is often based of the idea that teenagers do not respond to peer pressure. . . . and this : When you isolate a group of students, they will quickly arrive at a consensus of what constitutes normal behavior. It is a complex and somewhat unpredictable process driven by personali
Introduction: Howard Wainer writes : When we focus only on the differences between groups, we too easily lose track of the big picture. Nowhere is this more obvious than in the current public discussions of the size of the gap in test scores that is observed between racial groups. It has been noted that in New Jersey the gap between the average scores of white and black students on the well-developed scale of the National Assessment of Educational Progress (NAEP) has shrunk by only about 25 percent over the past two decades. The conclusion drawn was that even though the change is in the right direction, it is far too slow. But focusing on the difference blinds us to what has been a remarkable success in education over the past 20 years. Although the direction and size of student improvements are considered across many subject areas and many age groups, I will describe just one — 4th grade mathematics. . . . there have been steep gains for both racial groups over this period (somewhat steeper g
3 0.85258138 1803 andrew gelman stats-2013-04-14-Why girls do better in school
Introduction: Wayne Folta writes, “In light of your recent blog post on women in higher education, here’s one I just read about on a techie website regarding elementary education”: Why do girls get better grades in elementary school than boys—even when they perform worse on standardized tests? New research . . . suggests that it’s because of their classroom behavior, which may lead teachers to assign girls higher grades than their male counterparts. . . . The study, co-authored by [Christopher] Cornwell and David Mustard at UGA and Jessica Van Parys at Columbia, analyzed data on more than 5,800 students from kindergarten through fifth grade. It examined students’ performance on standardized tests in three categories—reading, math and science-linking test scores to teachers’ assessments of their students’ progress, both academically and more broadly. The data show, for the first time, that gender disparities in teacher grades start early and uniformly favor girls. In every subject area, bo
4 0.8505277 71 andrew gelman stats-2010-06-07-Pay for an A?
Introduction: Judah Guber writes about his new company : What we have done with Ultrinsic is created a system of incentives for students to allow them to invest in their ability to achieve a certain grade and when they achieve that grade we reward them with a cash incentive on top of receiving their original investment. This helps remove one of the large barriers students have to studying and staying motivated over the course of long semesters of college by giving them rewards on a much more immediate basis. We have been doing a pilot program in 2 schools, NYU and Penn, for the past year or so, and are currently in the process of a major roll out of our services to 37 schools all across the country. This is due to our popularity and inquiries from students in tons of schools all around the country regarding getting Ultrinsic’s services in their school. In the Fall 2010 semester, Ultrinsic will be revolutionizing student motivation on a grand scale . This is the dream of many economists: to c
5 0.83728379 93 andrew gelman stats-2010-06-17-My proposal for making college admissions fairer
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
6 0.83559978 1688 andrew gelman stats-2013-01-22-That claim that students whose parents pay for more of college get worse grades
7 0.83551776 542 andrew gelman stats-2011-01-28-Homework and treatment levels
8 0.8340888 606 andrew gelman stats-2011-03-10-It’s no fun being graded on a curve
9 0.82067132 825 andrew gelman stats-2011-07-27-Grade inflation: why weren’t the instructors all giving all A’s already??
10 0.80521232 1507 andrew gelman stats-2012-09-22-Grade inflation: why weren’t the instructors all giving all A’s already??
11 0.79935557 95 andrew gelman stats-2010-06-17-“Rewarding Strivers: Helping Low-Income Students Succeed in College”
12 0.78915769 94 andrew gelman stats-2010-06-17-SAT stories
13 0.7867862 261 andrew gelman stats-2010-09-07-The $900 kindergarten teacher
14 0.78440762 484 andrew gelman stats-2010-12-24-Foreign language skills as an intrinsic good; also, beware the tyranny of measurement
15 0.78243554 718 andrew gelman stats-2011-05-18-Should kids be able to bring their own lunches to school?
16 0.78078902 617 andrew gelman stats-2011-03-17-“Why Preschool Shouldn’t Be Like School”?
17 0.77843207 1620 andrew gelman stats-2012-12-12-“Teaching effectiveness” as another dimension in cognitive ability
18 0.76572698 1350 andrew gelman stats-2012-05-28-Value-added assessment: What went wrong?
20 0.76439208 957 andrew gelman stats-2011-10-14-Questions about a study of charter schools
topicId topicWeight
[(2, 0.032), (5, 0.014), (15, 0.029), (16, 0.078), (21, 0.052), (24, 0.139), (29, 0.014), (61, 0.019), (82, 0.188), (84, 0.018), (86, 0.03), (90, 0.011), (99, 0.266)]
simIndex simValue blogId blogTitle
1 0.98040831 940 andrew gelman stats-2011-10-03-It depends upon what the meaning of the word “firm” is.
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.
2 0.96776938 335 andrew gelman stats-2010-10-11-How to think about Lou Dobbs
Introduction: I was unsurprised to read that Lou Dobbs, the former CNN host who crusaded against illegal immigrants, had actually hired a bunch of them himself to maintain his large house and his horse farm. (OK, I have to admit I was surprised by the part about the horse farm.) But I think most of the reactions to this story missed the point. Isabel Macdonald’s article that broke the story was entitled, “Lou Dobbs, American Hypocrite,” and most of the discussion went from there, with some commenters piling on Dobbs and others defending him by saying that Dobbs hired his laborers through contractors and may not have known they were in the country illegally. To me, though, the key issue is slightly different. And Macdonald’s story is relevant whether or not Dobbs knew he was hiring illegals. My point is not that Dobbs is a bad guy, or a hypocrite, or whatever. My point is that, in his setting, it would take an extraordinary effort to not hire illegal immigrants to take care of his house
3 0.96438742 1772 andrew gelman stats-2013-03-20-Stan at Google this Thurs and at Berkeley this Fri noon
Introduction: Michael Betancourt will be speaking at Google and at the University of California, Berkeley. The Google talk is closed to outsiders (but if you work at Google, you should go!); the Berkeley talk is open to all: Friday March 22, 12:10 pm, Evans Hall 1011. Title of talk: Stan : Practical Bayesian Inference with Hamiltonian Monte Carlo Abstract: Practical implementations of Bayesian inference are often limited to approximation methods that only slowly explore the posterior distribution. By taking advantage of the curvature of the posterior, however, Hamiltonian Monte Carlo (HMC) efficiently explores even the most highly contorted distributions. In this talk I will review the foundations of and recent developments within HMC, concluding with a discussion of Stan, a powerful inference engine that utilizes HMC, automatic differentiation, and adaptive methods to minimize user input. This is cool stuff. And he’ll be showing the whirlpool movie!
4 0.9632718 178 andrew gelman stats-2010-08-03-(Partisan) visualization of health care legislation
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 .
5 0.9451713 1749 andrew gelman stats-2013-03-04-Stan in L.A. this Wed 3:30pm
Introduction: Michael Betancourt will be speaking at UCLA: The location for refreshment is in room 51-254 CHS at 3:00 PM. The place for the seminar is at CHS 33-105A at 3:30pm – 4:30pm, Wed 6 Mar. ["CHS" stands for Center for Health Sciences, the building of the UCLA schools of medicine and public health. Here's a map with directions .] Title of talk: Stan : Practical Bayesian Inference with Hamiltonian Monte Carlo Abstract: Practical implementations of Bayesian inference are often limited to approximation methods that only slowly explore the posterior distribution. By taking advantage of the curvature of the posterior, however, Hamiltonian Monte Carlo (HMC) efficiently explores even the most highly contorted distributions. In this talk I will review the foundations of and recent developments within HMC, concluding with a discussion of Stan, a powerful inference engine that utilizes HMC, automatic differentiation, and adaptive methods to minimize user input. This is cool stuff.
6 0.94397688 1440 andrew gelman stats-2012-08-02-“A Christmas Carol” as applied to plagiarism
7 0.94356859 340 andrew gelman stats-2010-10-13-Randomized experiments, non-randomized experiments, and observational studies
same-blog 8 0.9379338 326 andrew gelman stats-2010-10-07-Peer pressure, selection, and educational reform
9 0.93578762 67 andrew gelman stats-2010-06-03-More on that Dartmouth health care study
11 0.93165988 1488 andrew gelman stats-2012-09-08-Annals of spam
13 0.92737764 1958 andrew gelman stats-2013-07-27-Teaching is hard
14 0.92729926 699 andrew gelman stats-2011-05-06-Another stereotype demolished
15 0.92561466 2003 andrew gelman stats-2013-08-30-Stan Project: Continuous Relaxations for Discrete MRFs
16 0.92031276 1134 andrew gelman stats-2012-01-21-Lessons learned from a recent R package submission
17 0.92009759 1553 andrew gelman stats-2012-10-30-Real rothko, fake rothko
18 0.91481036 193 andrew gelman stats-2010-08-09-Besag
19 0.90586561 1725 andrew gelman stats-2013-02-17-“1.7%” ha ha ha
20 0.90570974 1039 andrew gelman stats-2011-12-02-I just flew in from the econ seminar, and boy are my arms tired