Jonathan Wynn is an urban/cultural sociologist and blogger whose book The Tour Guide, Walking and Talking New York was recently published by the University of Chicago Press.

From the Everyday Sociology Blog:
A Sociologist Visits Occupy Wallstreet

Perhaps we will invite Dr. Wynn to stop by during SXSW, included in his research examining how city stakeholders have adopted “festivalization” as an urban cultural policy, and the affects of city branding on both locals and visitors, event organizers and participants.

Sociologists have long been familiar with metaphors, analogies, and theories imported to the social realm from the natural sciences. One of our founders Durkheim viewed society as a living “social organism.”

Fast forward to 2011 where the proliferation of new communications technologies such as Twitter have once again spawned (pardon my biological lingo) new comparisons with the biological realm.

In his recent blog post, Anthropologist John Postill expounds upon some of the basic ideas of his new book Democracy in the age of viral reality: a media epidemiography of Spain’s indignados movement, which we may find applicable to the socially networked social movements/protests/revolts/revolutions happening around the world.

Many of us have already become familiar with the term “viral” when referring to the way comic and political videos quickly become popular on youtube. But Postill applies this analogy to social protests, believing that social media have been a game changer that created a “new media ecology” that foments “outbreaks” of this virus of opposition that regimes in the Arab world and even governments in the developed world have found difficult to “quarantine.”

Contrast this media ecology view with the more material perspective of the Latin American intellectual and journalist Raúl Zibechi who was quoted here as saying:

I don’t believe in virtual spaces, spaces are always material as well as symbolic. It’s another matter to speak of virtual media of communication among people in movement….

This raises the question of the material cost of protest and whether online social media actually constitute a “space” for protest, or rather are simply the latest tool in a long line of printing presses, telegraph machines, telephones, radios, fax machines, cassette tapes, satellite television, and other media used to spread messages of dissent and mobilize protesters and revolutionaries.

Ithiel de Sola Pool seemed to lean more toward the latter in his famous book, Technologies of Freedom.

Is becoming a facebook fan of a protest page anything other than simply a convenient barometer for public sentiment? Is it a viral outbreak of resistance that must be “quarantined”, or is armchair revolution simply not revolution?

In the Egyptian protests last January, Mohammed Bamyeh notes here that some of the most critical moments that led to the downfall of Hosni Mubarak came during the period when the government had actually shut down all internet in the country, and protesters used more traditional means to organize and still were able to have millions protesting around the country.

Contrast this with the “viral” Occupy Wall Street movement, which uses the slogan “We are the 99%”, but is only able to get a thousand or so at a time to show up, even with the full force of social media, the organization of volunteers providing food, and the attraction of performers coming to put on concerts for the protesters.

In the end of the day, is the difference between Tahrir Square and Zucotti Park, even after more than 40 years of authoritarian rule in Egypt, more about family, neighborhood and community than it is about technology? Is it possible to organize a mass social movement in a society that is “bowling alone”?

Aside from the very interesting theoretical and political-sociology oriented posts of late, some of us at the UT Austin Soc blog would also like to encourage other types of posts with a more methodological angle.

Since many of us use STATA for statistical work, I thought a series of posts on STATA tips and tricks would be a good place to start our “geek out” and share some time-saving, or just plain cool commands.

So here I’m going to give a little bit of sample code for getting tables and graphs out of STATA for a more manageable look at results.

TABLES

When it comes to tables, there are a number of useful programs built in to STATA to export results of regressions and other data. STATA 12 now comes with an improved menu button for exporting certain parts of the raw data to excel. “Tabout” is a useful tool for creating summary excel tables of tabbed data, for example average income by gender, if the data is from a certain country.

But many times it is not simple data or tabs that we want to see in excel, but rather more complex results of our regressions. Our eyes can only take so much of staring at the output window and it is hard to make connections without seeing things in neat tables. Sometimes we even need to create publication quality tables to insert into articles.

Now, copying and pasting and formatting by hand is always an option. But over the course of just one project, not to mention an entire PhD program, the countless hours spent making revisions by hand until perfection seem to justify the short-term time-expenditure on learning how to automate tables in your STATA code.

For this purpose then, there are two excellent little programs called “estout” and “outreg”. “estout” enables you to output a specific set of regression (or other analysis) results after first saving them with “esttab.” “outreg” is a bit more automated, and in fact my favorite, which I will demonstrate here is “outreg2”, which has more bells and whistles and seems to work well even with more advanced models beyond simple regression.

For this purpose, I will use a simple country level data set collected from the World Bank website, which includes three variables: 1) country, 2) hiv (average hiv infection rate for the past 5 years), and 3) pov (average poverty rate for the past five years).

DISCLAIMER: I came across the question of whether poverty has a significant effect on HIV infection rates in some development literature, much of which assumes that the two are linked. However, the jury is still out and this simple regression exercise does not in any way claim to offer answers. Rather it aims only to demonstrate some techniques for data analysis in STATA. Statistics probably can tell us something about this question, but for that, a much more complicated model would be appropriate.

So, there are several steps to get our neat excel output. (In case you are totally new to STATA, note that the actual code you type comes on the lines below that begin with periods, and what follows is the output.)

First, we run our basic regression:

. reg hiv pov

      Source |       SS       df       MS              Number of obs =      61
-------------+------------------------------           F(  1,    59) =    0.76
       Model |  19.0177362     1  19.0177362           Prob > F      =  0.3873
    Residual |  1478.91636    59  25.0663791           R-squared     =  0.0127
-------------+------------------------------           Adj R-squared = -0.0040
       Total |   1497.9341    60  24.9655684           Root MSE      =  5.0066

------------------------------------------------------------------------------
         hiv |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         pov |  -.0190502   .0218708    -0.87   0.387    -.0628136    .0247133
       _cons |     3.1443    .970026     3.24   0.002     1.203282    5.085317
------------------------------------------------------------------------------

Next, we can call up a list of the estimates obtained from the above regression.

. ereturn list

scalars:
                  e(N) =  61
               e(df_m) =  1
               e(df_r) =  59
                  e(F) =  .7586949891917172
                 e(r2) =  .0126959765324349
               e(rmse) =  5.006633506264527
                e(mss) =  19.01773619459277
                e(rss) =  1478.916364896987
               e(r2_a) =  -.0040379899670153
                 e(ll) =  -183.795077505511
               e(ll_0) =  -184.1847839095903
               e(rank) =  2

macros:
            e(cmdline) : "regress hiv pov"
              e(title) : "Linear regression"
          e(marginsok) : "XB default"
                e(vce) : "ols"
             e(depvar) : "hiv"
                e(cmd) : "regress"
         e(properties) : "b V"
            e(predict) : "regres_p"
              e(model) : "ols"
          e(estat_cmd) : "regress_estat"

matrices:
                  e(b) :  1 x 2
                  e(V) :  2 x 2

functions:
             e(sample)

With these estimates we can use outreg2 to create a simple table.

. outreg2 using OUTPUT_hiv_pov_countries, e(N df_m F rss ll) excel replace
OUTPUT_hiv_pov_countries.xml
dir : seeout

So, it’s as simple as that. Just run your analysis, call up the list of estimates, and plug those in to have outreg2 create a nice excel table like the one below. As you can see, one cool feature of outreg2 is that it automatically adds 1, 2 or 3 stars to your estimates in order to indicate whether they are statistically significant at the .1, .05, or .01 levels. This is not only a publication convention, but is also very useful for a quick eyeball look at your results, to see if you are on the right track.

NOTE ON AUTOMATION:

When you are running a large number of analyses however, it is useful to note a few things about automating outreg2.

1) Advanced formatting: Type “help outreg2” and take a closer look at the advanced features in order to be able to play with the formatting. This can save you from having to format every excel table by hand.

2) Replace: When running a number of analyses, for example the same regression over and over on individual countries, or separately for men and women… using the “replace” option on the very first analysis will make sure that you save over the old versions of your excel file when you re-run your code with the latest tweaks.

3) Append: Using the append option for your outreg2 code after each additional analysis that you wish to include in the same excel file will ensure that you have one big comparable table, which will list results for other regressions right along side the first one.

This was just a simple example to show outreg2 in action. However, one nice thing about this command is that it works well with more advanced analyses as well, including multi-level models, and can give you additional statistics such as Inter-Class Correlations… Basically, anything you can get STATA to estimate will appear in the “ereturn list” and can be outputted with outreg2.

Although it’s a bit basic, I hope you found this little geek out useful.

Please share with us some of your favorite STATA tips and tricks either in the comments, or perhaps as a guest blogger.

“It’s easy to imagine the end of the world. An asteroid destroying all life and so on. But you cannot imagine the end of capitalism.” – Slavoj Žižek, Senior Researcher, Institute of Sociology, University of Ljubljana; President, Society for Theoretical Psychoanalysis, Ljubljana, Slovenia

Full transcript: http://www.imposemagazine.com/bytes/slavoj-zizek-at-occupy-wall-street-transcript

UT SOC Alumna Corinne Reczek‘s research on Marriage’s Dark Side cited on MSNBC.com

Inside Higher Ed found the juxtaposition of Las Vegas and Sociology irresistible:

Sociologists in Sin City

There is something both jarring and perfectly apropos about bringing thousands of sociologists to Sin City. As the ASA press release delicately observed, “Las Vegas [is] vibrant and fascinating from a sociological perspective” – but it’s not difficult to conjecture why the conference had never been held here before. The very aspects of Las Vegas that might make it fascinating to a sociologist — the emphasis on consumerism and decadence; the unapologetic obsession with (and exploitation of) female flesh; and the city’s most celebrated pastime gambling, whose appeal is particularly mystifying to some with a background in statistics — are also the sorts of things that tend to be off-putting to academics, especially (or at least) in the presence of their colleagues. Little wonder that ol’ Lost Wages is one of the least-educated cities in the country. (As David Dickens, professor of sociology at the University of Nevada at Las Vegas, likes to say: “Thank god for Fresno.”) And little wonder, too, that even those who have dedicated their careers to studying human society weren’t wholly enthused about being thrust into the heart of this particular society, however fascinating it might be.

Celebrating the arrival of our new 2011 Cohort at Austin’s Pizza. We’re very happy to welcome you to UT Austin Sociology!