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abortion secrets in sociological science

Sociological Science has a new paper by Sara Cowan discusses when people share information using data on abortion:

Though abortion is a more common event in the United States than miscarriage, this article shows that more Americans hear of women who have had miscarriages than they hear of women who have had abortions. This is a result of both the patterns of secret telling and keeping: more Americans tell miscarriage secrets to more people than abortion secrets, and more Americans keep abortion secrets from more people than  miscarriage secrets.
In the introduction, I described two scenarios: one in which people tend to hear secrets they previously approved, and this pattern would contribute to a stasis in public opinion and a second scenario in which people hear secrets they previously condemned and this scenario would inspire social influence and facilitate social change. The data analyzed here illustrate the first scenario. They show a strong trend whereby individuals who hold restrictive views toward abortion are less likely than their liberal peers to report knowing someone who has had one. People tend to hear those secrets about which they already approve and are less likely to hear secrets about which they disapprove. Secret keeping and selective disclosure intensify this experience of homophily above and beyond any objective network segregation.
Check it out.
50+ chapters of grad skool advice goodness: Grad Skool Rulz/From Black Power 

Written by fabiorojas

November 6, 2014 at 12:08 am

“work in progress” forum on organizational sociology

Work in Progress, the blog of ASA’s organizations, occupations, and work section, just launched a new series on the future of organizational sociology. It launched today with a introduction from Liz Gorman and a first post by Howard Aldrich. Liz has an impressive slate of sociologists lined up — in the days to come, you can expect to hear from:

Martin Ruef (Duke)
Harland Prechel (Texas A&M)
Elisabeth Clemens (University of Chicago)
Ezra Zuckerman (MIT Sloan)
Gerald F. Davis (University of Michigan)
Heather Haveman (UC-Berkeley)
Brayden King (Northwestern)
Charles Perrow (Yale)
W. Richard Scott (Stanford)
Mark Suchman (Brown)
Patricia Thornton (Duke)
Marc Ventresca (Oxford)
Elizabeth Gorman (University of Virginia)
Matt Vidal (King’s College London)

Thanks to Liz and OOW for organizing this conversation and here’s hoping it gets the attention it deserves.

Written by epopp

October 30, 2014 at 7:19 pm

Posted in academia, sociology

dear steven fazzari

Hi, Steve, Fabio here. I recently read about how you are now the chair of the new sociology department at Washington University, St. Louis. It seems that you are getting advice from some excellent sociologists. Still, I wanted to offer a suggestion about how to build your program that I think has some merit  but that may not be obvious.

Here it is: build a program that, roughly speaking, is about 2/3 quantitative and 1/3 qualitative. However, don’t use the traditional criteria for “quantitative research,” which means anyone who does regression analysis or, as in economics, people who do research in theoretical statistics. Instead, the quantitative sector of the department should focus on unique and important quantitative types of data that sociologists are, or can be, good at. Roughly speaking, that means network analysis, social simulations, “big data,” and quantitative analysis of text. You might also toss in the experimenter or survey design guru.

Why? No one else is building such a program, but it would have a huge immediate impact on the profession of sociology. You would have an enormous first mover advantage. It also has other  benefits. For example, the graduate students would be immediately employable inside and outside academia; the faculty would be able to do some fundraising, though not as much as a demography center; and this sort of critical mass would increase the chance that WUSTL would be the origin of the next big quantitative advance in the social sciences.

The other 1/3 of the program should be filled with mid to late career qualitative scholars. You need this for a few reasons. First, sociology, especially the younger folks, has converged on the view that mixed methods is the way to go. So you will need top notch ethnographers, historical types, and interviewers to make sure that your PhD graduates have a proper view of sociology. Also, graduate students may opt for a qualitative PhD and you will need good faculty to make sure they don’t get lost in the cracks. The most important reason is that older scholars will be able to maintain a distinct identity and forge bonds in a program that is, by design, tilted in one direction.

As a well regarded private school, you might be tempted to mimic your peers and chase the “best people,” which means whoever recently graduated from high status programs with good publications. It’s not a bad idea, but you will directly compete with all the other top 2o programs that claims these graduates. Instead, you might consider a more focused mission that has a very specific, and achievable, intellectual goal. It’s worth a thought.

50+ chapters of grad skool advice goodness: Grad Skool Rulz/From Black Power 

Written by fabiorojas

October 28, 2014 at 12:01 am

Posted in academia, fabio, sociology

the latest at wustl

As many of you know, Washington University decided to reestablish a sociology department after notoriously shutting theirs down some two decades ago. The Chronicle of Higher Ed has reported that the university has chosen the department’s first chair and associate chair — Steven Mazzari, a macroeconomist at Wash U., and Mark Rank, who started in Washington’s sociology department before moving to the School of Social Work in 1989.

This seems like a surprising decision. The Chronicle writes:

Administrators had considered appointing a senior figure in American sociology to be chair, but, “lacking an obvious candidate,” as Mr. Fazzari puts it, they turned to him. Along with several teaching awards, he has six years of experience as chair of the economics department, and has done stints on campus-planning and hiring committees. He was a member of the campus advisory panel formed last year to consider how to revive sociology.

“There is much overlap between the problems addressed by economics and sociology,” he says. “Economics also provides a firm grounding in technical modeling and data analysis that is part of much advanced work in many social sciences, including sociology.”

I can imagine various reasons they might have taken this approach. Luring a top senior person in to build a department from scratch has to be a challenge. Still, Washington has a lot of resources and is a highly respected university (outside of sociology, where it has no presence). And there are some definite downsides to launching the department without a highly visible sociologist at the helm. I’m curious what the back story is here but, having no inside information, will leave it to you to speculate.

Written by epopp

October 23, 2014 at 3:03 pm

Posted in academia, sociology

race and genomics: comments on shiao et al.

Shiao et al in Sociological Theory, the symposioum, Scatterplot’s discussion, Andrew Perrin’s comments, last week’s discussion.

Last week, I argued that many sociologists make a strong argument. Not only are social classifications of race a convention, but there is no meaningful clustering of people that can be derived from physical or biological traits. To make this claim, I suggested that one would need to have a discussion of what meaningful traits would include, get a huge sample people, and then see if there are indeed clusters. The purpose of Shaio et al (2012) is to claim that when someone conducts such an exercise, there is some clustering.

Before I offer my own view of the evidence that Shiao et al offer, we need to set some ground rules. What are the logical possible outcomes of such an exercise?

  1. The null hypothesis: your clustering methods yield no clusters (e.g., there are no detectable sub-groups of people).
  2. The weak hypothesis: clustering algorithms yield ambiguous results. It’s like getting in regression analysis a small correlation with a p=.07. This is important because it should shift your prior moderately.
  3. The “conventional” strong hypothesis: unambiguous groups that correspond to social classifications of people. E.g., there really is a “White” group of people corresponding to people from Europe.
  4. The “unconventional” strong hypothesis: unambiguous groups that do not correspond to common social classifications of people. For example, there might be an extremely well defined group of people that combines Hawaiians and Albanians.

A few technical points, which are important. First, any such exercise will need top incorporate robustness checks because clustering methods require the use to set up initial parameters. Clustering algorithms do not tell you how many groups there are. Instead, they answer the question of how well the model fits the hypothesis that you have X groups. Second, sociologists tend to mix up these possible outcomes. They correctly point out that there is a social construction called “race” which is real in its effects and influence on people. But that doesn’t logically entail anything about the presence or absence of human populations that are differentiated due to random variation of inherent physical traits over time. Also, they fail to consider #4. Their might be actual differences, but they might not match up to our common beliefs.

So what does Shiao at al offer and where does it lie in this spectrum of possibilities? Well, the article is a not a systematic review of genomic research that searches for clusters or people. Rather, it offers a few important points drawn from anthropology and genomics. First, Shiao et al point out that there is a now undisputed (among academics) human history. Humans originated in East Africa and then spread out (“Out of Africa thesis”). Second, as people spread out, genomic variation emerges as people mate with people close by. Third, genetic drift implies that geography will predict variations in genes. As you move from X to Y, you will see measurable differences in people. Fourth, these differences are gradual in character.

Shiao then switch gears and talk about clustering of people using genomic data. They tell us that there are statistically detectable and stable group differences and that these do not rigidly determine behavior. They also cite research suggesting these statistical groups correlate with self-described racial groupings. Then, the authors discuss a “bounded” approach to social theory where biology imposes some constraints on the variation on behavior but in a non-deterministic fashion.

I’ll get to the symposium next week, but here’s my response: 1. There is a real tension. At some points, Shiao et al suggests a world of gradual variation, which suggests no distinct racial groups (outcome #1) but then there’s a big focus clusters.  2. If we do live in a world of gradual, but real, variation in human biology, then the whole clustering approach is misleading. Instead, we might live in a world that’s like a contour map. It’s all connected, there are no groups, but you see some variables increase as you move along the map. 3. If that’s true, we need an outcome #5 – “race is not real but biology is real.” 4. I definitely need more detail on the clustering methods and procedures. Some critics have pointed out that the clusters found in research are endogenously produced, which makes me suspect that the underlying science might be hovering around outcomes #1 (it all depends on the algorithm and its parameters) or #2 (there might be some clustering, but it is very poorly defined).

50+ chapters of grad skool advice goodness: Grad Skool Rulz/From Black Power

Written by fabiorojas

October 20, 2014 at 12:01 am

if sociology had an igm panel

The IGM panel of economic experts got some recent buzz because 63% of their experts — 81%, when weighted by confidence — disagree with the Piketty-inspired argument that r > g is driving recent wealth inequality in the U.S.

I always enjoy reading these surveys. The panel includes 50 or so top academic economists, from a variety of subfields and political orientations, and asks them whether they agree or disagree with a policy-relevant economic statement. Respondents answer on a Likert scale, and indicate their degree of certainty as well as their level of agreement. Sometimes they add a short comment.

The results usually aren’t incredibly surprising. Not really shocking that 100% of economists agree that

Letting car services such as Uber or Lyft compete with taxi firms on equal footing regarding genuine safety and insurance requirements, but without restrictions on prices or routes, raises consumer welfare.

They’re a little more nervous about selling kidneys (45% favor, but nearly 30% find themselves “uncertain” — the highest proportion for any recent question besides whether ending net neutrality is a good thing). The most interesting ones are those where there’s disagreement (Have the last decade of airline mergers improved things for travelers?) or that counter the stereotype (54% disagree that giving holiday presents — rather than cash — is inefficient. Okay, counters it a little).

Anyway, this got me wondering. What if sociology had a similar panel? I mean, aside from the fact that no one would care. I can think of empirical findings we’d have broad confidence in that much of the public wouldn’t buy — for example, that there’s lots of hiring discrimination against African-Americans. But are there policy prescriptions we’d agree on — ones that are grounded in the discipline, as opposed coming solely from our left-leaning tendencies, though of course the two are hard to separate — that would tell us, Yep, sociologists WOULD say that.

EDITED TO ADD: Yes, I know that Piketty does not actually argue r > g is the cause of recent inequality growth in the US, which is what the question asks. But if they can headline the poll “Piketty on Inequality,” it seems fair to call the statement “Piketty-inspired.”

Written by epopp

October 16, 2014 at 2:43 am

before you say race isn’t real, you need a definition of race

This week, I’d like to focus on the sociology of race. We’ll discuss Shiao et al.’s Sociological Theory article The Genomic Challenge to the Social Construction of Race, which is the subject of a symposium. After you read the article and symposium, you might enjoy the Scatterplot discussion.

In this first post, I’d like to discuss the definitional problems associated with the concept “race.” The underlying concept is that people differ in some systematic way that goes beyond learned traits (like language). One aspect of the “person in the street” view of race is that it reflects common ancestry, which produces correlated physical and social traits. When thinking about this approach to race, most sociologists adopt the constructivist view which says that: (a) the way we group people together reflects our historical moment, not a genuine grouping of people with shared traits and  (b) the only physical differences between people are superficial.

One thing to note about the constructivist approach to race is that the first claim is very easy to defend and the other is very challenging. The classifications used by the “person on the street” are essentially fleeting social conventions. For example, Americans used the “one drop rule” to classify people, but it makes little sense because putting more weight on Black ancestors than White ancestors is arbitrary. Furthermore, ethnic classifications vary by place and even year to year. The ethnic classifications used in social practice flunk the basic tests of reliability and validity that one would want from any measurement of the social world.

The second claim is that there are no meaningful differences between people in general. This claim is much harder to make. This is not an assessment of truth of the claim, but the evidence needed to make is of a tall order. Namely, to make the strong constructivist argument, you would need (a) a definition of which traits matter, (b) a systematic measurement of those traits from a very large sample of people, (c) criteria for clustering people based on data, and (d) a clear test that all (or even most) reasonable clustering methods show a single group of people. As you can see, you need *a lot* of evidence to make that work.

That is where Shiao et al get into the game. They never dispute the first claim, but suggest that the second claim is indefensible – there is evidence of non-random clustering of people using genomic data. This is very important because it disentangles two important issues – race as social category and race as intra-group similarity. It’s like saying the Average Joe may be mistaken about air, earth, water, and fire, but real scientists can see that there are elements out there and you can do real science with them.

50+ chapters of grad skool advice goodness: Grad Skool Rulz/From Black Power 

Written by fabiorojas

October 14, 2014 at 12:04 am

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