Econstudentlog

Stuff

i. Better Colleges Failing to Lure Talented Poor, by David Leonhardt.

applicants“Only 34 percent of high-achieving high school seniors in the bottom fourth of income distribution attended any one of the country’s 238 most selective colleges […] Among top students in the highest income quartile, that figure was 78 percent. […]

Among high-achieving, low-income students, 6 percent were black, 8 percent Latino, 15 percent Asian-American and 69 percent white […]

The researchers defined high-achieving students as those very likely to gain admission to a selective college, which translated into roughly the top 4 percent nationwide. Students needed to have at least an A-minus average and a score in the top 10 percent among students who took the SAT or the ACT.

Of these high achievers, 34 percent came from families in the top fourth of earners, 27 percent from the second fourth, 22 percent from the third fourth and 17 percent from the bottom fourth. (The researchers based the income cutoffs on the population of families with a high school senior living at home, with $41,472 being the dividing line for the bottom quartile and $120,776 for the top.) […]

If they make it to top colleges, high-achieving, low-income students tend to thrive there, the paper found. Based on the most recent data, 89 percent of such students at selective colleges had graduated or were on pace to do so, compared with only 50 percent of top low-income students at nonselective colleges.”

For people with access to nber papers, here’s the direct link to the study.

ii. What effect size would you expect?

The p-value isn’t the only thing you should care about when evaluating small-N studies and larger N replication attempts. It shouldn’t be news, but lots of people get this stuff wrong. Do remember that even in the replication studies, N may be quite small.

iii. Will we ever regenerate limbs?

“Seifert doubts we will ever have an injectable cocktail of molecules that triggers regeneration. There’s too much complexity in the transition from wound to blastema to new limb, he says. It will also be a lengthy process. […] “Even if a human could grow a limb back, it might take 15-20 years,” says Seifert. A finger might be more realistic.”

iv. New insights into differences in brain organization between Neanderthals and anatomically modern humans. Razib Khan’s blog has some comments in case you’re curious.

iv. ‘The 99% percent’ weren’t really all that representative, it seems: The Geospatial Characteristics of a Social Movement Communication Network:

“Social movements rely in large measure on networked communication technologies to organize and disseminate information relating to the movements’ objectives. In this work we seek to understand how the goals and needs of a protest movement are reflected in the geographic patterns of its communication network, and how these patterns differ from those of stable political communication. To this end, we examine an online communication network reconstructed from over 600,000 tweets from a thirty-six week period covering the birth and maturation of the American anticapitalist movement, Occupy Wall Street. We find that, compared to a network of stable domestic political communication, the Occupy Wall Street network exhibits higher levels of locality and a hub and spoke structure, in which the majority of non-local attention is allocated to high-profile locations such as New York, California, and Washington D.C. Moreover, we observe that information flows across state boundaries are more likely to contain framing language and references to the media, while communication among individuals in the same state is more likely to reference protest action and specific places and times. Tying these results to social movement theory, we propose that these features reflect the movement’s efforts to mobilize resources at the local level and to develop narrative frames that reinforce collective purpose at the national level.”

Figure 2. Divergences in geographic distribution of users.

v. Cognitive Performance and Heart Rate Variability: The Influence of Fitness Level.

“we investigated the relation between cognitive performance and heart rate variability as a function of fitness level. We measured the effect of three cognitive tasks (the psychomotor vigilance task, a temporal orienting task, and a duration discrimination task) on the heart rate variability of two groups of participants: a high-fit group and a low-fit group. Two major novel findings emerged from this study. First, the lowest values of heart rate variability were found during performance of the duration discrimination task, compared to the other two tasks. Second, the results showed a decrement in heart rate variability as a function of the time on task, although only in the low-fit group. Moreover, the high-fit group showed overall faster reaction times than the low-fit group in the psychomotor vigilance task, while there were not significant differences in performance between the two groups of participants in the other two cognitive tasks. In sum, our results highlighted the influence of cognitive processing on heart rate variability. […] results suggested that the main benefit obtained as a result of fitness level appeared to be associated with processes involving sustained attention.”

N = 28, so it’s a small sample size. But at least the results “seem to support the idea that aerobic training produces selective benefits in cognitive performance.”

vi. How you behave online can tell (a lot? something? a bit? – people seem to disagree about how ‘impressive’ the findings are…) about who you are: Private traits and attributes are predictable from digital records of human behavior, by Kosinski, Stillwell & Graepel.

Figure 2 is probably the main figure from this paper – it “shows the prediction accuracy of dichotomous variables expressed in terms of the area under the receiver-operating characteristic curve (AUC), which is equivalent to the probability of correctly classifying two randomly selected users one from each class (e.g., male and female)”:

Fig 2

vii. Farm Use of Antibiotics Defies Scrutiny.

“Eighty percent of the antibiotics sold in the United States goes to chicken, pigs, cows and other animals that people eat, yet producers of meat and poultry are not required to report how they use the drugs — which ones, on what types of animal, and in what quantities. This dearth of information makes it difficult to document the precise relationship between routine antibiotic use in animals and antibiotic-resistant infections in people”

This is insane. I had no idea the problem in the US was this big.

viii. One of my guilty pleasures:

(If you just want to watch the chess, you can skip the first 3 minutes or so.)

March 17, 2013 - Posted by | Chess, economics, medicine, papers, Psychology, random stuff, statistics, studies

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