Calling all Demographers

Double posted from here.

So, i’m fiddling with some citation data for a few Demography journals,* and came up with a couple of weird data points that i can’t account for, so thought i’d see what teh intertubes could tell me about it. Basically, early in the process of working with this sort of data, I like to take a look at “Citation Age” information.** This gives a sense of how old the literature is that people are drawing on in a given time period. These often roughly follow linear increases (though the rate of “aging” differs across fields). Anyway,  this one presented a couple of pretty extreme outliers (i’ve done this more than just a couple of times, and haven’t seen others this different). I can’t account for them, so am looking for any potential explanations.

Unfortunately the data isn’t currently in a format that would let me actually “solve” what’s accounting for this, but i should be able to soon, so it could be fun to see whose/which theories hold up. Anyway, if looking at this information by year, 1988 and 1991 are considerable outliers (see the plot after the jump). It would appear that most of the “blip” in 1988 comes from increased citations to work roughly 50 years before, while the one in 1991 comes from citations to work roughly 70 years earlier. Given that i’m not a “full fledged” demographer (training-wise), i’m guessing others of you might be able to help me out here. What happened in 1988 that led people to suddenly read/cite work from the 1930s and from 1991 to suddenly read things from the 1920s***? All potential explanations welcome.

The Role of the Federal Reserve

Most educated people have no idea how important the Fed is.  I’m not the best guy to explain it to you, but since not everyone reads Paul Krugman, let alone Scott Sumner, I should say something…

These past few years we’ve really really needed a bit more inflation and its finally coming.  Ben Bernanke recently announced that the Fed will finally do more to juice the economy.  This NyTimes article about it gives too much space to the “conventional wisdom,” and pays too little attention to the economists who have been, for years now, arguing that the Fed should be more active.  While not everyone agrees about the desirability of quantitative easing, the ranks of QE3 supporters have been growing and they include a diverse bunch: conservatives and liberals, monetarists and Keynesians of various stripes: Paul Krugman and Brad Delong,  Tyler Cowen and Scott Sumner.

Glass half-full: I think the Fed has just given us a big nudge towards better times.

Glass half-empty: A lot of people are hurting in this economy, and its incredibly sad that the Fed didn’t do more sooner to help.  This is arguably the most under-reported story of the recession.  It didn’t have to be this bad.

Social Networks for Charity

So this isn’t totally irrelevant:

My brother, Matt Bishop, has started a social enterprise whose goal is to use social media, and social network analysis (aka “social analytics”) to help charitable groups raise money, and help businesses do their part as well.

iGiveMore is launching tomorrow.  They already offer some services, but they need our help to raise money (and publicity) so that every month that passes they have more to offer.

Check out their website, and then I hope you’ll consider donating, and emailing or posting a link to iGiveMore on facebook.

Thanks,

Mike

Math Soc Pre-ASA Conference, Thurs. Aug 16

As was announced on the website last spring:

The fifth Joint Japan-North America Mathematical Sociology Conference will be held on Thursday, August 16, 2012 in the Colorado Convention Center, Denver, Colorado. The conference focuses on advancement of mathematical sociology worldwide and fosters friendship among those whose work is on mathematical sociology in all countries. Thus this is a wonderful opportunity to know cutting-edge topics in mathematical sociology and to meet people who share great enthusiasm for it.

To register, please download this form and follow the included instructions. If you have questions please e-mail Yoshimichi Sato.

Unfortunately I will miss most of it, but look forward to meeting people there towards the end of the day.

The Formation of the Mathematical Sociology Secion, 1994-1996

Young sociologists might assume that the section has been around a long time. I just came across this:

History of the Formation of the Section, 1994-6
by David Heise, Indiana University, Chair of the section 2003-4. This appeared in The Mathematical
Sociologist, Newsletter of the Mathematical Sociology Section of the American Sociological Association ,
Fall 2003
The first formal activity leading to the Mathematical Sociology Section occurred at a Professional
Workshop instigated and chaired by John Angle at the 1994 American Sociological Association annual
meeting in Los Angeles. After this workshop revealed genuine interest in creating a section, Eugene
Johnsen, with the assistance of a Steering Committee, produced a Mission Statement for a Mathematical
Sociology Section and, later, the By-Laws. The Steering Committee consisted of most of those involved in
the 1994 Workshop: John Angle, Stephen Berkowitz, Phillip Bonacich, Scott Feld, Sharlene Hesse-Biber,
James Hollander, Guillermina Jasso, Eugene Johnsen, Joel Levine, Timothy Liao, David McFarland, Alton
Okinaka, John Skvoretz, and Geoffrey Tootell.
A determined effort was made in the early years to bring the group’s interests to the attention of
sociologists in general and to display vital activities to the ASA. Eugene Johnsen organized and chaired a
Professional Workshop on “The Practice of Mathematical Sociology” at the 1995 ASA Meeting in
Washington D.C., with five invited speakers presenting papers. For the 1996 ASA Annual Meeting in New
York the section-in-formation proposed and received ASA approval for a Didactic Seminar by Stanley
Wasserman on social network analysis. At the 1997 ASA Meeting in Toronto, Phillip Bonacich presented
a Didactic Seminar, sponsored by the recently formed Mathematical Sociology Section

Source

Finding Data

A friend asked me about where he might find education data to practice/play with.

 

Here are some links I came up with:

 http://www.cpc.unc.edu/projects/addhealth/ has open-access and restricted data.  

 http://www.infochimps.com/tags/school 

http://www.factual.com/product/data?selected=education

http://stats.stackexchange.com/questions/27237/what-are-the-most-useful-sources-of-economics-data

http://stats.stackexchange.com/questions/7/locating-freely-available-data-samples

http://stats.stackexchange.com/questions/27061/how-is-research-based-on-the-u-s-census-organized

ugh!!!

It’s been a while, i know. So something dramatic must have happened. Multiple dramatic things have happened, but one in particular drove me to dust off my WP login. This article on obesity has been rapidly making the rounds (i’ve seen it linked no less than a dozen times since yesterday afternoon). It’s got lots of interesting information in it and easily quotable lines. But in virtually every single cite i’ve seen of it so far (on Facebook, news articles, etc.), the same finding has been quoted as the most striking punchline of the article, but has, in every case misrepresented what that finding actually is. The line from the article that matters is this (from the abstract):

North America has 6% of the world population but 34% of biomass due to obesity.

The quotes of that finding i’ve seen thus far however have inevitably been framed as:

North America has 6% of the world population but 34% of biomass.

A seemingly innocuous edit, but resulting in a complete misrepresentation of what the study actually found. What the analysis shows is that if you limit the analysis only to account for the obesity in the world (i.e., we look only at the “excess” weight) on the planet, N. America accounts for 34% of that amount. Not 34% of all biomass. In order for the citation as it’s making the rounds to be true, the average N. American would have to weigh 371 kg (or roughly 818 pounds; calculations stem from Table 3 in the paper). Now part of the problem is the way things are written as most people aren’t used to having to do a little simple math to digest their research blurbs, but sometimes it’s completely necessary to be sure you aren’t completely lead astray.

Now, i’m not trying to diminish the findings in the article, as i think they have done a good job of demonstrating how weight is unevenly distributed across the globe. Just not in the way people are saying they’ve said it is.

Neal Caren on citation patterns in sociology

What articles and books have sociologists been citing a lot recently?  See the list compiled by Neal Caren.  He also made a cool network diagram showing which articles are cited together.

The Promising Future of Mathematical Sociology

I strongly believe sociology, especially mathematical sociology, has an extremely promising future. The current trends in information technology clearly indicate a growth in quantitative modeling. Among other things, we are currently witnessing a tsunami of data from a globally-connected world (in fact, big data is the techno-geek buzzword), exponentially faster computing power (Markov Chain Monte Carlo simulations of complex models are now increasingly commonplace), and a rapid uptick in the volume and range of high-quality statistical programs (a great deal of which are open-source).

However, why would I think quantitatively-oriented sociologists are especially well-placed to gain from these structural developments?

The primary reason is that the underlying epistemology of modern quantitative sociology — grounded in complex predictive models, relational and nested data structures, and a folk-Bayesian approach to research design — represents the cutting edge and future direction of modeling in a shockingly vast array of fields. For example, multidimensional scaling, social network analysis, log-linear modeling, and finite mixture models (i.e., latent class analysis) are now at the forefront of disciplines ranging from machine learning to computational genetics (for example, see here, here, here, and here). However, most promising is the growing popularity of Bayesian multilevel models, which sociologists have in effect been using for several decades now. For instance, Bayesian multilevel models are now used by physicists to measure the mysterious properties of dark energy, geneticists to unlock the basic patterns of genomic population differentiation, and neuroscientists to describe the deepest structures of the brain. It is no exaggeration to claim that a human-level form of artificial intelligence, if it is ever developed, will probably be based on multilevel models of the type currently familiar to most quantitatively-oriented sociologists.

A secondary reason why the future looks so promising for mathematical sociology is that a vacuum has been created in the social sciences due to the rise of an alternative approach to quantitative modeling, frequently promoted by mainstream economists. According to this approach, the main goal of quantitative research is to estimate population-averaged causal effects, either by setting up a randomized (controlled) experiment or applying a small suite of techniques to observational data, such as instrumental variables regression, so-called “fixed” effects (rather than “random” effects) regression, difference-in-differences design, and so forth.

This approach is appealing because it promises the extraction of causal estimates with minimal theoretical insight, but it comes at enormous costs. For example, the assumptions of causality are rarely, if ever, satisfied for any particular model fit to observational data (as painfully but clearly outlined by the counterfactual model of causality, and evinced by the growing ranks of not-really-exogenous-but-we’ll-use-it-anyway instrumental variables). Furthermore, although it’s well-known randomized experiments are inferior to controlled experiments, the latter require strong theory that is often absent (and even then experiments in the social sciences often lack generalizability to other populations). Finally, an enormous amount of substantively-rich information is usually discarded when observational data are used primarily  for extracting causal estimates, so if we don’t believe our causal estimates then we’re left with a rather meager description of the data at hand (the worst offender is the so-called “fixed” effects technique, which can be viewed as a special case of a Bayesian multilevel model in which the groups are assumed, rather unreasonably, to have infinite variance between them).

Of course, both economics and sociology are large fields, encompassing a wide range of viewpoints, so I caution that my comments embody ideal types. Yet the dominant trends of a globally-networked world, combined with the rise of a distinctive approach to quantitative modeling popularized by economists, has created the conditions for a promising future for sociology more generally, and mathematical sociology in particular.

The Success of Stack Exchange: Crowdsourcing + Reputation Systems

You’ve heard me say it before… Crowdsourced websites like StackOverflow and Wikipedia are changing the world.  Everyone is familiar with Wikipedia, but most people still haven’t heard about the StackExchange brand question and answer sites.  If you look into their success, I think you’ll begin to see how the combination of crowdsourcing and online reputation systems is going to revolutionize academic publishing and peer-review.

Do you know what’s happened to computer programming since the founding of StackOverflow, the first StackExchange question and answer site?  It has become a key part of every programmer’s continuing education, and for many it is such an essential tool that they can’t imagine working a single day without it.

StackOverflow began in 2008, and since then more than 1 million people have created accounts, more than 3 million questions have been asked, and more than 6 million answers provided (see Wikipedia entry).  Capitalizing on that success, StackExchange, the company which started StackOverflow, has begun a rapid expansion into other fields where people have questions.  Since most of my readers do more statistics than programming, you might especially appreciate the Stack Exchange for statistics (aka CrossValidated).  You can start exploring at my profile on the site or check out this interesting discussion of machine learning and statistics.

How do the Stack Exchange sites work?

The four most common forms of participation are question asking, question answering, commenting, and voting/scoring.  Experts are motivated to answer questions because they enjoy helping, and because good answers increase their prominently advertised reputation score.  Indeed, each question, answer, and comment someone makes be voted up or down by anyone with a certain minimum reputation score.  Questions/answers/comments each have a score next to them, corresponding to their net-positive votes.  Users have an overall reputation score.  Answers earn their author 10 points per up-vote, questions earn 5, and comments earn 2.  As users gain reputation, they earn administrative privileges, and more importantly, respect in the community.  Administrative privileges include the ability to edit, tag, or even delete other people’s responses.  These and other administrative contributions also earn reputation, but most reputation is earned through questions and answers.  Users also earn badges, which focuses attention on the different types of contributions.

Crowdsourcing is based on the idea that knowledge is diffuse, but web technology makes it much easier to harvest distributed knowledge.  A voting and reputation system isn’t necessary for all forms of crowdsourcing, but as the web matures, we’re seeing voting and reputation systems being applied in more and more places with amazing results.

To name a handful the top of my head:

• A couple of my friends are involved in a startup called ScholasticaHQ which is facilitating peer-review for academic journals, and also offers social networking and question and answer features.
• The stats.stackexchange.com has an open-source competitor in http://metaoptimize.com/qa/ which works quite similarly.  Their open-source software can and is being applied to other topics.
• http://www.reddit.com is a popular news story sharing and discussion site where users vote on stories and comments.
• http://www.quora.com/ is another general-purpose question and answer site.

It isn’t quite as explicit, but internet giants like google and facebook are also based on the idea of rating and reputation.

A growing number of academics blog, and people have been discussing how people could get academic credit for blogging.  People like John Ioannidis are calling attention to how difficult it is to interpret the a scientific literature because of publication bias and other problems.  Of course thoughtful individuals have other concerns about academic publishing.  Many of these concerns will be addressed soon, with the rise of crowdsourcing and online reputation systems.