Obesity and C-Section StatLit Materials

Obesity and C-Section StatLit Materials

Some stuff from Thursday’s class. Here’s the facilitator’s notes as well, if you want to run this in your own class.

It’s a sort of “case-study lite” approach. I gave the students the following in a packet: 

  • An article talking about research which showed people born by C-section are at a 50% greater risk of obesity than those that weren’t, and speculating C-sections may be behind the obesity epidemic
  • An abstract of that research study
  • A chart showing growth of C-sections since 1970
  • An article talking about why C-sections have increased since 1970 (It’s not for the reasons you think). 
  • A chart showing the growth of obesity over since 1970
Their role was described as follows:

For this scenario, you will play the role of an obesity researcher who has been asked by a hospital to see if there are extraneous variables in this study that were not accounted for. The hospital is trying to decide whether they should include the following sentence in their materials on C-section:

“Choosing to deliver your child by C-section may increase your child’s risk of future obesity.”

The instructions were: 
  • Produce a brief predictor-outcome statement. What is predicting what? How is it measured? What is the magnitude and direction of the association? 
  • Using the charts, produce a statement on whether the U.S. gains in childhood obesity roughly mirror the growth in C-sections.  
  • Produce a statement on whether the base rate of C-sections is large enough to have the suggested impact on obesity. 
  • Produce a list of all potential lurking variables controlled for. 
  • Produce a list of some lurking variables not controlled for. 
  • Give your gut-level take on whether any of the potential lurking variables not controlled for might dramatically reduce the magnitude of the association, or potentially reverse it. If you believe one of the lurking variables could do that, name it, and explain why it might account for the apparent association.

As with many simulations, we’ve loaded the dice a bit here. The news articles we used had reference to some confounders, but we removed those references. There is a very obvious lurking variable, one which was later controlled for in a subsequent study which found no effect of C-section on obesity.

The background information on the growth of C-sections holds the key to understanding what’s going on. In that article, it explains much of the growth of C-sections is due to overweight mothers — expecting mothers who are overweight often have to have C-sections due to obesity or obesity related illness. 

That’s a classic lurking variable scenario. Overweight mothers will be over-represented in the C-section group due to medical reasons. Due to genetics, overweight mothers will also have overweight children at a higher rate than mothers not overweight. So it is completely expected that the C-section group would have a larger percentage of children who grow up to be obese.

The one mistake I made running this was to run it too slow, and without stages. I would suggest you budget at LEAST 45 minutes for this activity, and rather than have the student groups report out all the questions at the end, have them report out the answers to the first half of the questions, then give them some more time to put together answers for the second half.

I ran this in a much more compressed time frame, and one of my eight groups got it, and another very nearly got it — which wasn’t bad. But ideally you’d have at least half the groups get in the vicinity of the answer (or produce other, equally compelling answers). 

If you try it, tell me how it goes.

‘Adrift’ in Adulthood: Students Who Struggled in College Find Life Harsher After Graduation

‘Adrift’ in Adulthood: Students Who Struggled in College Find Life Harsher After Graduation

From the article:

Here is what they found: Graduates who scored in the bottom 20 percent on a test of critical thinking fared far more poorly on measures of employment and lifestyle when compared with those who scored in the top 20 percent. The test was the Collegiate Learning Assessment, or CLA, which was developed by the Council for Aid to Education.

The students scoring in the bottom quintile were three times more likely than those in the top quintile to be unemployed (9.6 percent compared with 3.1 percent), twice as likely to be living at home with parents (35 percent compared with 18 percent), and significantly more likely to have amassed credit-card debt (51 percent compared with 37 percent).

“That’s a dramatic, stunning finding,” said Mr. Arum, referring to the sharp difference in unemployment so early in the students’ lives after college. “What it suggests is that the general higher-order skills that the Council for Aid to Education assessment is tracking is something of significance, something real and meaningful.”

I’m really curious about this, but initially it raises more questions for me than it answers. Most of the effects seem consequences of not finding a first job (debt, living with parents), and it is hard to see how much raw critical thinking would figure into that (securing the job vs. keeping it). 

It makes me wonder if performance on the test might be a proxy for persistence or responsibility, or any of another ten qualities that will help people realize intellectual gains in college and help people in a job search as well. 

As noted in the article, the selectivity of their college also played a role. Selective colleges take smarter students, and a person from Dartmouth may beat out a person from Podunk State. But that has nothing to do with the skills, per se, but with the degree. They note this in the last paragraph, but they don’t tell us what those quintile comparisons look like when selectivity is controlled for. My guess is that they are a lot less dramatic.

Finally, depending on the correlation with the written part of the CLA, we may also just be seeing that people with writing and communication skills a) get hired over other candidates, and b) do better on written tests.

But hopefully more data coming soon.

On Sex After Prostate Surgery, Confusing Data [Problems with Term Definition]

On Sex After Prostate Surgery, Confusing Data [Problems with Term Definition]

A classic problem of term definition from the NYT (somewhat older article):

A notable study in 2005 showed that a year after surgery, 97 percent of patients were able to achieve an erection adequate for intercourse. But last month, researchers from George Washington University and New York University reviewed interim data from their own study showing that fewer than half of the men who had surgery felt their sex lives had returned to normal within a year.

So which of the studies is right? Surprisingly, they both are.

Basically, the first number hinges on whether the patient occasionally acheives an erection “adequate for intercourse”. The article goes on to say that this definition is pretty inadequate from the patient’s viewpoint:

“That definition is misleading,” said Dr. Jason D. Engel, director of the urologic robotic surgery program at the George Washington University Hospital. “It doesn’t mean it was good intercourse, and it doesn’t even mean your penis was hard. That man is going to say, ‘I’m impotent.’ But in the surgeon’s eyes, that man had an erection adequate for intercourse.”

The better question for men is whether they can have sex when they want to, with or without drugs like Viagra. In a recent series of patients, Dr. Engel found that after a year 47 percent of men who had robotic prostatectomy were able to have regular sex.

Although he could cite statistics to give men a more hopeful view, he said that did not help the patient.

What we stress in the statistical literacy course is that such faulty definitions are not wrong — just ill-suited to the questions they are trying to answer. A man who asked this question before surgery is likely asking a question for which definition #1 is clearly unsuited.

How Visa Predicts Divorce

How Visa Predicts Divorce

From TDB

Hunch then looks for statistical correlations between the information that all of its users provide, revealing fascinating links between people’s seemingly unrelated preferences. For instance, Hunch has revealed that people who enjoy dancing are more apt to want to buy a Mac, that people who like The Count onSesame Street tend to support legalizing marijuana, that pug owners are often fans of The Shawshank Redemption, and that users who prefer aisle seats on planes “spend more money on other people than themselves.”

Stuff like this is usually overblown a bit (writers always get a case of Gladwell-itis when talking statistics) but it’s also the future as more and more data about us gets logged in ways that allow for association. 

On thing that occurs to me reading this is that the shift in statistics consumption is likely to mirror the post-internet shift in traditional publishing — strong associations, like books, used to be hard and expensive to churn out, so a lot of filtering went into the process up front — people would run statistics on things they thought might matter for other reasons.

With the advent of total life-logging via credit card, smartphones, and social media and the with rise of large and extensive cohort databases, associations become cheap — but the significance filter is passed on to the consumer.

In other words, if your publication filter is insufficient for the modern world (as Shirky claims) just imagine how inadequate your statistics filter is for the deluge about to come…

Udacity and the future of online universities

Udacity and the future of online universities

Felix Salmon on Sebastian Thrun, the open course runner extraordinaire who built the Stanford AI course:

Thrun was eloquent on the subject of how he realized that he had been running “weeder” classes, designed to be tough and make students fail and make himself, the professor, look good. Going forwards, he said, he wanted to learn from Khan Academy and build courses designed to make as many students as possible succeed — by revisiting classes and tests as many times as necessary until they really master the material.

When the history of open education is written, I think one question will be whether the initial focus on participation from the “top colleges” was a good place to start. Undergraduate education in a place like Stanford can be divorced from the problems of universal education in very unhelpful ways.

If you have a “weeder” mentality, there is no failure. Those people that dropped out? Well, good riddance. The people that studied but didn’t learn? Probably not college material. 

You can’t have a universal access focus and a weeder mentality at the same time. The two are antithetical.

Thrun apparently agrees:

But that’s not the announcement that Thrun gave. Instead, he said, he concluded that “I can’t teach at Stanford again.” He’s given up his tenure at Stanford, and he’s started a new online university called Udacity. He wants to enroll 500,000 students for his first course, on how to build a search engine — and of course it’s all going to be free.

Adding: I’m probably way too harsh on Ivy League schools here — anybody who tries to do something in this space is a friend of mine. But I get frustrated with a press that ignores similar experiments from lower-tier institutions and a grant structure that seeks answers to problems of universal education from the most elite institutions on the planet. 

But those people working at top-tier schools to do this? Still my heroes, every one of you. 

Why the I Love Charts post is the most beautiful thing I’ve read today

Why the I Love Charts post is the most beautiful thing I’ve read today

There’s so much to like in this post. It starts with nuanced exploration of feminism, terminology, and privilege, but ends as a reflection of the difficulties of staying a good person on the internet, especially when you run a site.

Dealing with trolling makes you hard and reactive. Even non-trolls delight in deliberate misunderstandings. False outrage is the norm. As the level of dickishness increases on a site, everything pressures people to react senselessly, to look only for weak spots, to engage in the point-scoring that is the currency of mobs instead of the conversation that marks true communities.

And you have to do that, to some extent — to treat every interlocutor in a comment thread with equal credit is a recipe for disaster. So ultimately, the dicks on the internet pull us all down with them. 

Yet even given these things, beautiful things happen on the web. This post from the “I Love Charts” guy is one of them. 

ilovecharts:

Yesterday we posted a chart to which some people took offense. One person took the time to write us directly with her anger about the chart. I took exception to her tone and disagreed with her assertions and regrettably fell into one of the more simple traps of poor communication, writing a

Active Learning Not Associated with Student Learning in a Random Sample of College Biology Courses

Active Learning Not Associated with Student Learning in a Random Sample of College Biology Courses

I’ve been collecting these sorts research examples and making an effort to read them thoroughly, partially because I think we’ve become a bit too self-congratulatory on active learning, and partially because you learn more from these failures than yet another paper confirming active learning/constructivism/engaged pedagogy works.

This one is particularly interesting for a couple of reasons. First of all, it ends up showing that although active learning did not correlate with learning gains, using active learning to confront misconceptions did.

That’s really interesting, because if you look at well-designed physics clicker questions, for example, they really plug into common misconceptions — but it takes time in a discipline to really hone a set of questions like that. 

The study is also interesting because it reminds us of the normal state of affairs, where students are graduating biology with common misbeliefs about evolution:

Thirty-nine percent (n = 13) of courses had an effect size lower than 0.42, which corresponds to students answering only one more question (out of 10) correctly on the posttest than on the pretest.1 When learning was calculated as average normalized gain, the mean gain was 0.26 (SD = 0.17). On the cheetah question, learning gains were even lower. Effect sizes ranged from −0.16–0.58. The mean effect size was 0.15 (SD = 0.19) and the mean normalized gain for the cheetah question was 0.06 (SD = 0.08). These remarkably low learning gains suggest students are not learning to apply evolutionary knowledge to novel questions in introductory biology courses.

That’s 15 weeks or so to get one more answer out of ten on a post-test right. I’m not mocking that at all — in fact, quite the opposite. It’s worth remembering how hard it is to get gains in these areas.  When we see effect sizes of 1 or more, our jaw should be on the floor…

As far as weaknesses of the study — self-reports, self-reports, self-reports. They try to deal with this by doing a correlation with student impressions, but what I’d really like to see is a sample observed on video and coded.