Sociology

Next month, the US and Europe would like to make some progress in tearing down trade barriers, an archaic notion left over from the Colonial period in history.(1)Special trade agreements with blocs, like The Hanseatic League of the 12th century, were always common, but restrictions enjoyed a popularity boom after the collapse of the East India Trade Company in 1799 became a victim of free trade - 18th century globalization hysteria. --> read more

Frontiers of Science is a course offered as part of Columbia University’s Core Curriculum. The course is controversial, with some people praising its overview of several areas of science, and others feeling that a more traditional set of introductory science courses would do the job better. Last month, the faculty in charge of the course wrote the following public letter: The United States is in the midst of a debate over the value of a traditional college education. Why enroll in a place like Columbia College when you can obtain an undergraduate degree for $10,000 or learn everything from Massive Open Online Courses? In more parochial terms, what is the value added by approaches such as Columbia’s Core Curriculum? Recently students in our Core Course, Frontiers of Science (FoS), provided a partial answer. The FoS faculty designed a survey to gauge the scientific skills and knowledge of the Class of 2016 both before and after taking FoS. In an assembly held during orientation week last August, 966 first-year Columbia College students answered questions covering basic skills such as statistics, probability, and the reading and analysis of graphs, as well as content to be taught during the fall session of FoS. For the 519 College students taking FoS in the fall semester, the same survey was administered again as part of the final exam. The mean score for the initial orientation survey was less than 28%. The mean score for the same questions at the end of the semester was 76%! (The margin of error in both cases was ±1.0%.) To control for gains independent of FoS, 167 first-year College students, who did not take FoS in the fall, answered the same survey questions again at the start of the spring semester. The mean score of those students was 31%, not substantially greater than they and their classmates had scored on the survey during orientation. This type of research always has its limitations. However, the results with and without FoS are so different that the conclusion is inescapable. The scientific habits and knowledge that FoS imparts are new to, and effectively learned by, first-year students after one semester of intense study . . . This sounds pretty good, especially the part about “basic skills such as statistics, probability, and the reading and analysis of graphs.” My first thought is that, if these skills are so important, maybe all the students at Columbia should be taking a course in probability and statistics! But then I remembered that our intro stat course isn’t so great (I know, I’ve taught it several times), so maybe it’s just as well if some biologists, physicists, etc., create a new statistics module from scratch. Seriously, I have lots of ideas of how we could teach intro prob/stat better, but when I actually try to do it, I get all tangled in the details. So I can’t very well object to outsiders taking a shot at it. As users of statistics, they might have a better idea than I do of how to teach the subject. Similarly, I wouldn’t be surprised if the engineers could teach a better intro physics class than the physicists could. And it could well make sense to have biologists teach first-year chemistry, and psychologists teach first-year biology. What, then, would the statistician teach? First year math, of course. It would make the mathematicians cringe, but it might be closer to the students’ own level. Statisticians are users of math, and we could teach the subject from a user’s perspective. Just as, arguably, the collection of scientists who run Frontiers of Science might be teaching probability and statistics in a way more useful to freshman than whatever my colleagues and I in the stat department could come up with. OK, now that I’ve established my complete acceptance of the idea that this team can, and perhaps should, be teaching probability and statistics to Columbia’s freshmen, let me say that there are a bunch of thin

The other day we discussed that paper on ovulation and voting (you may recall that the authors reported a scattered bunch of comparisons, significance tests, and p-values, and I recommended that they would’ve done better to simply report complete summaries of their data, so that readers could see the comparisons of interest in full context), and I was thinking a bit more about why I was so bothered that it was published in Psychological Science, which I’d thought of as a serious research journal. My concern isn’t just that that the paper is bad—after all, lots of bad papers get published—but rather that it had nothing really going for it, except that it was headline bait. It was a survey done on Mechanical Turk, that’s it. No clever design, no clever questions, no care in dealing with nonresponse problems, no innovative data analysis, no nothing. The paper had nothing to offer, except that it had no obvious flaws. Psychology is a huge field full of brilliant researchers. Its top journal can choose among so many papers. To pick this one, a paper that had nothing to offer, that seems to me like a sign of a serious problem. A good study does not need to be methodologically original, but it should be methodologically sound. When we do surveys we worry about nonresponse. To take a few hundred people off MTurk and not even look for possible nonresponse bias, this is not serious. But, again, it’s not so much that this paper was flawed, as that it had nothing much positive to offer. Just to be clear: I’m really really really really not trying to censor such work, and I’m really really really really not saying this work should not be published. What I’m saying is that the top journal in a field should not be publishing such routine work. They should be publishing the best quality research, not just random things that happen to slip through the cracks. I mean, sure, the referees should’ve caught the problems with that paper. But I blame the editors for even considering publication. Even without noticing the paper’s methodological flaws, it was nothing special. And, once you decide to start publishing mediocre papers in your top journal, you’re asking for trouble. You’re encouraging more of the same. To clarify, let me compare this with some other high-profile examples: - Christakis and Fowler’s study of the contagion of obesity. This was published in top journals and was later found to have some serious methodological issues. But it’s not a mediocre work. They had a unique dataset, a new idea, and some new methods of data analysis. OK, they made some mistakes, but I can’t fault a leading journal for publishing this work. It has a lot of special strengths. - Bem’s paper claiming to demonstrate ESP. OK, I wouldn’t have published this one. But I can see where the journal was coming from on this. If the results had held up, it would’ve been the scientific story of the decade, and the journal didn’t want to miss out. The editors didn’t show the best judgment here, but their decision was understandable. - Kanazawa’s papers of schoolyard evolutionary biology. I’ve written about the mistakes here, and this work has a lot of similarities to the ovulation-and-voting study. The difference is that Kanazawa’s papers were published in a middling place—the Journal of Theoretical Biology—not in a top journal of their field. Don’t get me wrong, JTB is respectable, but it’s in the middle of the pack. It’s not expected that they are publishing the best of the best. - Hamilton’s paper in the American Sociological Review, claiming that college students get worse grades if their parents pay. This paper had a gaping hole (not adjusting for the selection effect arising from less well-funded students dropping out) and I think it was a mistake for it t

In Europe, the arrival of the farmers who replaced Mesolithic hunter-gatherers happened in force 9,000 years ago but it was happening elsewhere prior to that. In Syria, there is even evidence of scientific trait selection in grains in 10,000 B.C. but in other parts of the world agriculture came much later. A region in sub-tropical China which did not have agriculture until the arrival of domesticated rice from elsewhere may have gotten agriculture prior to that - as far back as 3,000 B.C., according to a new paper. read more

This isn’t quite right—poetry, too, can be in paragraph form (see Auden, for example, or Frost, or lots of other examples)—but Basbøll is on to something here. I’m reminded of Nicholson Baker’s hilarious “From the Index of First Lines,” which is truly the poetic counterpart to Basbøll’s argument in prose: The post Prose is paragraphs, prose is sentences appeared first on Statistical Modeling, Causal Inference, and Social Science.

Hamdan Azhar writes: I came across this graphic of vaccine-attributed decreases in mortality and was curious if you found it as unattractive and unintuitive as I did. Hope all is well with you! My reply: All’s well with me. And yes, that’s one horrible graph. It has all the problems with a bad infographic with none of the virtues. Compared to this monstrosity, the typical USA Today graph is a stunning, beautiful masterpiece. I don’t think I want to soil this webpage with the image. In fact, I don’t even want to link to it. The post uuuuuuuuuuuuugly appeared first on Statistical Modeling, Causal Inference, and Social Science.

Cognition causes language, not the other way around. Correlations between changes in thought with changes in language abound. But the arguments are very weak for causality from language to cognition in this context.What do People Mean by Language Shapes Thought?Lera Boroditsky likes to spread the meme language shapes thought. Others have used it too when talking about Whorfian matters. --> read more

If you live in California, you can never get too amazed by the 9th Circuit Court of Appeals. If there is a progressive position that an activist court can take, they usually take it.If you can't go to a national chain store and get your eyes checked and buy glasses, the 9th is why - they ruled that is medical care and health care is not interstate in America. Sure, we can mandate health care and force people to pay for it under the Commerce clause, but for some reason we can't let people buy prescription glasses from an out-of-state company. --> read more

Lee Sechrest sends along this article by Brian Haig and writes that it “presents what seems to me a useful perspective on much of what scientists/statisticians do and how science works, at least in the fields in which I work.” Here’s Haig’s abstract: A broad theory of scientific method is sketched that has particular relevance for the behavioral sciences. This theory of method assembles a complex of specific strategies and methods that are used in the detection of empirical phenomena and the subsequent construction of explanatory theories. A characterization of the nature of phenomena is given, and the process of their detection is briefly described in terms of a multistage model of data analysis. The construction of explanatory theories is shown to involve their generation through abductive, or explanatory, reasoning, their development through analogical modeling, and their fuller appraisal in terms of judgments of the best of competing explanations. The nature and limits of this theory of method are discussed in the light of relevant developments in scientific methodology. I found this very difficult to read and forwarded it to Cosma Shalizi, who writes: Like a lot of what I read about abduction, it seems much more a theory (or sketch of a theory) of how scientists think, than of scientific method. Put another way, the H-D account of scientific method has always tended to “black-box” the issue of where hypotheses come from, in favor of what to do with them once you have them. I think this is usually helpful, but there’s no reason not to try to open up the black box, and study the origin of hypotheses; if there’s a role for abduction, it’s there, in explicating the “generate” part of generate-and-test. (In fact, if memory serves, Peirce later repented of his term “abduction”, and just called it “hypothesis” or “hypothesizing”.) If one could show, or even plausibly suggest, that certain modes of hypothesizing are systematically more reliable or fruitful than others, that would be extremely valuable. This paper in particular seems to have some odd confusions of levels between what are presumably fairly permanent parts of how scientists think (analogy), and current technological artifacts — I love bootstrapping, but it hardly belongs in the same category as a component of scientific method. (And as for stem-and-leaf plots…) Sechrest wrote: Too much research seems to be addressed to determining whether “it is,” or “it isn’t.” The more important question very often is “Why is (or isn’t) it?” To me, abduction seems more likely to occur in the aftermath of having seen something. Isaac Asimov once said The most exciting phrase to hear in science, the one that heralds new discoveries, is not ‘Eureka!’ but ‘That’s funny…’ And that is when abduction begins, the attempt to identify explanations and reason toward the best one. For example, at least some drug trials begin with seemingly sensible expectations that a drug will work; those expectations are often wrong. Usually that is the end of the matter. But, to me, an important question may well be “Why didn’t the drug work as expected?” (A “why isn’t it? question) The abductive process cannot lead directly to a clear-cut answer, but it can get us closer. And that is what, I think, good scientists do. Ineffective scientists (and I have seen them many times) say, “Well, that didn’t work. Anybody got another idea?” I have nothing to add to the above discussion, except to point to our recent discussion of the challenges of systematizing model building. As I see it, new ideas arise from anomalies in data with respect to existing theories. The post Where do theories come from? appeared first on Statistical Modeling, Causal Inference, and Socia

I received two emails yesterday on related topics. First, Stephen Olivier pointed me to this post by Daniel Lakens, who wrote the following open call to statisticians: You would think that if you are passionate about statistics, then you want to help people to calculate them correctly in any way you can. . . . you’d think some statisticians would be interested in helping a poor mathematically challenged psychologist out by offering some practical advice. I’m the right person to ask this question, since I actually have written a lot of material that helps psychologists (and others) with their data analysis. But there clearly are communication difficulties, in that my work and that of other statisticians hasn’t reached Lakens. Sometimes the contributions of statisticians are made indirectly. For example, I wrote Bayesian Data Analysis, and then Kruschke wrote Doing Bayesian Data Analysis. Our statistics book made it possible for Kruschke to write his excellent book for psychologists. This is a reasonable division of labor. That said, I’d like to do even more. So I will make some specific suggestions for data analysis in psychology right here in this post, in the context of my next story: Dan Kahan sent me this note: The most egregious instance of totally bogus methods I had the misfortune to feel obliged to call foul on involved an econometrics study that purported to find that changes in law that never happened increased homicides by “lowering the cost” of committing them…) Actually, as you know, often times investigation of a “wtf?!” report like this discloses that the problem is in the news report & not in the study. I think you agree that many of the “bad statistics/methods” problems & even the “nonreplicability” problem are rooted in the perpetuation of a set of mindless statistical protocols associated with ossified conception of NHT (one from which all the thought that it might have reflected was drained away & discarded decades ago). But certainly another problem is the “wtf?!!!!!!” conception of psychology.  Its distinguishing feature is its supposed discovery of phenomena that are shocking bizarre & lack any coherent theory. The alternative conception of psychology is the “everything is obvious – once you know the answer.”  The main point of empirical research isn’t to shock people. It’s to adjudicate disputes between competing plausible conjectures about what causes what we see.  More accounts of what is going are plausible than are true; without valid inference from observation, we will never separate the former from the sea of the latter & will drown in a sea of “just so” story telling. I have zero confidence in “wtf?!!!” & am convinced that it is a steady stream of bogus, nonreplicable studies that hurt the reputation of psychology. I have lots of confidence in EIO–OYKTA. It’s not nearly so sexy — which is good, b/c it removes the temptation to cut corners in all the familiar, petty ways that researchers do (usually by coaxing out a shy “p Kahan points to a much-mocked and criticized study by Kristina Durante, Ashley Arsena, Vladas Griskevicius, “The Fluctuating Female Vote: Politics, Religion, and the Ovulatory Cycle,” which was reported then retracted from CNN under the title, “Study looks at voting and hormones: Hormones may influence female voting choices.” The relevance for the present discussion is that this paper was published in Psychological Science, a top journal in psychology. Here’s the abstract: Each month many women experience an ovulatory cycle that regulates fertility. Whereas research finds that this cycle influences women’s mating preferences, we propose that it might also change women’s political and religious views. Building on theory suggesting that political and religious orientation are linked to repr