Evangelicals and Catholics first joined forces in the suburbs

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In the middle of an article about how Rick Santorum has appealed to evangelicals, one of the factors mentioned is geographic: evangelicals and Catholics both moved to the suburbs after World War Two.

The plate tectonics of social mobility also figure into the Santorum surprise, note scholars like the political scientist John C. Green of the University of Akron. In the post-World War II years, many Catholics moved out of insular urban neighborhoods while many evangelicals left their rural and small-town homes for the suburbs and exurbs. In subdivisions, in office parks, in colleges, the young people of the two religions began to encounter one another as benign acquaintances rather than alien enemies.

It is no coincidence, then, that a Santorum voter like Carissa Wilson has grown up in the suburban sprawl between two cities with strong Catholic heritages, Dayton and Cincinnati. Like the Michigan autoworkers in 1980 who made a break with Democratic tradition to vote for Ronald Reagan, Miss Wilson just may be the embodiment of a new wave.

In other words, evangelicals and Catholics met and learned to like each other in the suburbs. United by suburban values and perhaps a dislike for both cities and rural areas, these two groups settled into the land of single-family homes and found that they could find common ground on some social and theological issues.

This brings several questions to mind:

1. Are Catholics and evangelicals more interested in preserving suburban values than finding common theological ground? Perhaps this is crassly put but the way the argument is written in the article, it suggests that the suburbs came first before the social and theological common ground.

2. How do race and class play into the process? In other words, while both groups came from different places to the suburbs, they were probably mostly white and the educational status of both groups was rising. Does this mean that the older city/rural divide was transcended by common status interests based on race and class?

My, your lawn is lush and green…especially where the dogs were!

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Record temperatures in Chicago have meant green lawns ahead of schedule. This is not usually considered a bad thing: the brown or dormant grass of winter has given way to verdant lawns that wouldn’t look out of place in the many lawn commercials one can see at this time of year. However, in walking around, I noticed that these lawns are often punctuated by more lush spots, presumably from the work of dogs. Here is one picture from an adjacent neighborhood:

Some thoughts about this:

1. The typical “perfect lawn” doesn’t include such spots. So if someone has pets and wants a great-looking lawn, how do you balance these two interests? Cut the lawn a lot? I haven’t noticed any products talking about this kind of fertilization.

2. Perhaps this is a bigger problem in townhome/condo/apartment neighborhoods where there are common lawns. To curb their dog, people walk about the neighborhood and use the common areas. Why use spaces close to your home when you can take advantage of other areas? (Additionally: you are paying for those other areas so why not?)

3. Some patterns emerge: I would estimate at least 80% of the spots were within four feet of the sidewalk. This likely says more about the dog owners than the dogs: the owners want to stay on the sidewalk so the dogs have to stay close by. Also, taller objects, signs, mailboxes, trees, etc. tended to have lusher grass around them. Here is another shot that also shows the first pattern:

Does anyone get upset about this desecration of the lawns? If the battle is between dogs and a perfect lawn, it looks like the dogs win at this time of year.

City wants to avoid McMansion development because the new residents would then demand upgrades to the sewege treatment plant

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I’ve seen a number of objections to McMansions over the years but I’ve never seen this particular argument made by the city of Santa Rosa, California:

Santa Rosa has renewed its interest in buying a former dairy to create a buffer zone at the regional sewage treatment plant on Llano Road…

The dairy is no longer in operation, but part of the property continues to be leased as pasture, Maresca told the board. There also are four rental homes on the property and a cellular tower.

The property has previously been marketed as suitable for as many as seven “McMansions” with “little hobby vineyards,” Maresca told the board.

That’s what the city wants to avoid. If such homes were built near the plant, future neighbors might complain about noise, odors and glare from plant operations and try to force the city to spend millions in upgrades.

So the city wants to avoid McMansions because it will then lead to spending more money on the sewage treatment plant? This is an unusual rationale: cities often avoid McMansions because of concerns about teardowns or homes that “don’t fit” with the character of the community or objections to sprawl. This is out of concern about possible NIMBY concerns that the city wouldn’t want to deal with. This is one way to try to avoid NIMBY situations…

There could be other ways around this issue rather than framing it as an issue of trying to avoid future problems. Why not purchase the land and then zone it for a commercial or industrial or agricultural use (apparently on the table before) that wouldn’t be so harmed by being near the sewage treatment plant? Why not make it some sort of park or open space (also on the table before)? It seems odd to me to argue about contentious future residents rather than framing this as an opportuntiy for the city to make better use of this land.

One does have to wonder: how bad is it near this sewage treatment plant if Santa Rosa is really concerned about how much the McMansions residents might complain?

Sociology experiment: mixing strong academics and athletics at Northwestern

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Chicago Tribune columnist David Haugh suggested yesterday that Northwestern University is facing a sociology experiment by wanting strong academics and athletics:

So continued America’s fascinating sports sociology experiment in Evanston: Can a major-college sports program thrive in an environment in which winning clearly isn’t the No. 1 determinant of success? As Final Four week begins, it would behoove every basketball campus to reconsider its definitions of thrive, winning and success…

So I reached a different conclusion about Carmody but loved the way Phillips defended his. I loved the idea of a Big Ten school espousing ideals more typically found in Division III programs, of an AD taking an unpopular route by taking a stand for something noble. I can applaud a decision I wouldn’t have made because of what it symbolizes.

On one hand, Northwestern shows it recognizes the Big Ten basketball arms race by working on plans for $250 million worth of necessary facility upgrades. On the other, it stayed true to an underlying mission colleges usually ignore by keeping a coach who does things the right way…

Roll your eyes and look up Pollyannaish if you wish. But ultimately Phillips’ decision embodied the mandate for college sports programs Secretary of Education Arne Duncan outlined in a news conference on the eve of the NCAA tournament intended to remind schools of their priorities. Theoretically, Northwestern’s stance also reflected the emphasis more Big Ten and BCS-conference universities must consider in light of the NCAA linking academic progress rate with tournament eligibility beginning in 2013.

Haugh defends Northwestern’s actions in trying to do both: have high academic standards and have competitive sports programs. A few thoughts about this:

1. I’ve heard a lot of this argument at both Notre Dame and Northwestern. The situations are slightly different (Northwestern doesn’t have the past football glory of Notre Dame) but the argument generally go like this: the schools need to lower their academic standards in football and basketball if they really hope to compete for national championships. Perhaps this is right – neither school is the kind of powerhouse that brings athletes in and spits them out. But, as Haugh suggests, the schools have some different priorities.

2. These different priorities are not just tertiary concerns: Northwestern is a serious academic school (as is Notre Dame). According to the US News and World Report rankings, Northwestern is the #12 undergraduate school (Notre Dame is #19), #4 among business graduate schools, and #9 among education graduate schools (among other high rankings). So this isn’t quite a high-ranking Division III school; Northwestern is a strong academic university where there are many things going on besides athletics.

3. In other sports, Northwestern and Notre Dame can do just fine. Let’s be honest here: what is really driving these arguments is football (and maybe a little basketball). Interestingly, both Northwestern and Notre Dame are not bad at these sports but also not great. Northwestern football has been improved since the mid 1990s but they are not going to compete for a national championship. Northwestern basketball just missed the NCAA tournament but they played in perhaps the toughest conference this year and had a number of chances to make their season really memorable.

3a. If you look at the Director’s Cup rankings which account for all sports, some more academic schools do just fine. For example, look at the most recent March 22 rankings: Stanford is #1, Duke is #28, Notre Dame is #34, and Northwestern is #63. Granted, the big public schools seem to do well in these rankings across the sports but it’s not like academic schools can’t compete in other sports. For example, Northwestern has been known in recent years for two other sports: fencing and women’s lacrosse. While these are not high profile, the athletes have proven can be champions as well as high-performing athletes.

3b. I wonder at times if Northwestern isn’t lucky on this front to be located in Chicago. Since Chicago doesn’t care much about college sports, schools like Northwestern and the University of Chicago (who used to be in the Big 10 but now competes at the Division III level) don’t have to go the athletic route.

In the end, I think Northwestern will be just fine. This is a sociology experiment that doesn’t have to happen – not all colleges need to be athletic powerhouses.

The median college loan debt: $12,800

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Growing calls for ways to deal with college loan debt can lead to a statistical question: just how much does “the average” college student owe? Here are some of the figures:

Meet Kelli Space. She went to Northeastern University to get a degree in sociology. And she graduated in $200,000 of student loan debt. In the economy’s newest trillion-dollar crisis, she is the 1 percent.

Kelli is not the face of America’s student debt problem. Among the 37 million people in this country with student loans to pay off, the median balance is $12,800. A whole 72 percent of borrowers have less than $25,000 left in debt, according to data from the Federal Reserve Bank of New York.

No, students like Kelli are the rarities, the white rhinos. Only about 5 percent of borrowers owe more than $75,000.

The key figures for me: the median is $12,800, meaning that half of people with student loans have less than this figure, half have more. Yet, nearly three-quarters of those with loans have less than $25,000 to pay off. Only 11% have more than $50k in debt.

So why do we keep hearing stories about those who owe mega amounts of money? Perhaps we might think of them as canaries in the mine shaft, students who show how bad the college finance system might be today. But, on the other hand, the statistics suggest that these students are rarities, people who have unusual debt. From these anecdotal and relatively rare stories, it seems like there is a pattern: a student goes to a prestigious school banking on the name of the school to pay off. (One common argument you will find online is that the major should be blamed – this usually puts more creative disciplines, the humanities, and subjects like sociology at the center of blame.) But, the name doesn’t always pay off, the student can’t find a good enough job to start paying off these debts, and the interest just continues to grow.

Overall, we need to work with the statistics more than the anecdotes: most college students do not have more than $25,000 of debt. This is not a small amount but it can be tackled (though the economy doesn’t help).

Michael Jackson didn’t die in a McMansion; he died in a mansion

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Perhaps this is a very minor point about the life of Michael Jackson but as a researcher of McMansions, I think there are better ways to describe the house in which Michael Jackson died which is now for sale:

“McMansion” doesn’t even begin to describe the grandly ostentatious home, which sits on a massive 17,000-square-foot chateau-style property.

It boasts seven bedrooms and 13 bathrooms, with an elevator to zip you where you want to go.

Oh my, did you happen to get a little lost there? Must be because you took a wrong turn while passing the theater, the spa, the gym and the wine cellar, which has its own tasting room.

Feeling chilly? Pick a fireplace—there are 14 of them.

Feeling hot? Then won’t you take a dip in the pool? You can practice your Olympic laps there.

Oh, we almost forgot: the asking price. The digs will set you back a cool $23.9 million.

As I’ve argued before, this is not a McMansion because of its size. Yes, the home may be ostentatious but this is not your typical large, mass produced suburban home. Rather, this house is 17,000 square feet, far behind the reach of most homebuyers. Perhaps this home is lacking in architectural quality but it is far too big to be a McMansion.

I think this use of the term McMansion is meant to convey the idea of tacky or kitschy. I’m not quite sure how that applies here: isn’t it pretty normal for the uber-wealthy or uber-famous to live in a huge house? Is the idea that Jackson had poor decorating taste? Or is the term applicable because the person who buys this home would be doing a strange thing since Jackson died here?

The rise of “data science” as illustrated by examining the McDonald’s menu

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Christopher Mims takes a look at “data science” and one of its practitioners:

Before he was mining terabytes of tweets for insights that could be turned into interactive visualizations, [Edwin] Chen honed his skills studying linguistics and pure mathematics at MIT. That’s typically atypical for a data scientist, who have backgrounds in mathematically rigorous disciplines, whatever they are. (At Twitter, for example, all data scientists must have at least a Master’s in a related field.)

Here’s one of the wackier examples of the versatility of data science, from Chen’s own blog. In a post with the rousing title Infinite Mixture Models with Nonparametric Bayes and the Dirichlet Process, Chen delves into the problem of clustering. That is, how do you take a mass of data and sort it into groups of related items? It’s a tough problem — how many groups should there be? what are the criteria for sorting them? — and the details of how he tackles it are beyond those who don’t have a background in this kind of analysis.

For the rest of us, Chen provides a concrete and accessible example: McDonald’s

By dumping the entire menu of McDonald’s into his mathemagical sorting box, Chen discovers, for example, that not all McDonald’s sauces are created equal. Hot Mustard and Spicy Buffalo do not fall into the same cluster as Creamy Ranch, which has more in common with McDonald’s Iced Coffee with Sugar Free Vanilla Syrup than it does with Newman’s Own Low Fat Balsamic Vinaigrette.

This sounds like an updated version of factor analysis: break a whole into its larger and influential pieces.

Here is how Chen describes the field:

I agree — but it depends on your definition of data science (which many people disagree on!). For me, data science is a mix of three things: quantitative analysis (for the rigor necessary to understand your data), programming (so that you can process your data and act on your insights), and storytelling (to help others understand what the data means). So useful skills for a data scientist to have could include:

* Statistics, machine learning (on the quantitative analysis side). For example, it’s impossible to extract meaning from your data if you don’t know how to distinguish your signals from noise. (I’ll stress, though, that I believe any kind of strong quantitative ability is fine — my own background was originally in pure math and linguistics, and many of the other folks here come from fields like physics and chemistry. You can always pick up the specific tools you’ll need.)

* General programming ability, plus knowledge of specific areas like MapReduce/Hadoop and databases. For example, a common pattern for me is that I’ll code a MapReduce job in Scala, do some simple command-line munging on the results, pass the data into Python or R for further analysis, pull from a database to grab some extra fields, and so on, often integrating what I find into some machine learning models in the end.

* Web programming, data visualization (on the storytelling side). For example, I find it extremely useful to be able to throw up a quick web app or dashboard that allows other people (myself included!) to interact with data — when communicating with both technical and non-technical folks, a good data visualization is often a lot more helpful and insightful than an abstract number.

I would be interested in hearing whether data science is primarily after descriptive data (like Twitter mood maps) or explanatory data. The McDonald’s example is interesting but what kind of research question does it answer? Chen mentions some more explanatory research questions he is pursuing but it seems like there is a ways to go here. I would also be interested in hearing Chen’s thoughts on how representative the data is that he typically works with. In other words, how confident are he and others are that the results are generalizable beyond the population of technology users or whatever the specific sampling frame is. Can we ask and answer questions about all Americans or world residents from the data that is becoming available through new data sources?

h/t Instapundit

Quick Review: Hunger Games movie

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Lots of action and some story and less commentary about oppressive regimes. As I noted in my review of the book series in September 2010, these books were ready-made to be movies. Here area  few thoughts about the movie itself and the experience of seeing it in a full theater.

1. I thought the movie was engaging. At the same time, the movie takes a book that is relatively sparse in terms of character development and explicit commentary and is even thinner in these areas. But there is a lot of action and some of the key relationships, Katniss and Prim, Katniss and Rue, and Katniss and Peeta, are given more time.

2. I thought the best actor in the movie was Stanley Tucci who was perfect as Caessr Flickerman.

3. With not as much time to work with in the movie, the opening parts of the first book are really compressed. What we miss in the movie then is a more complete understanding of the despair and desolation in District 12. I felt like the movie wanted us to think that the Capitol and President Snow were bad people but we didn’t have enough of the backstory to really feel it.

4. I wonder how many of the people in the theater tonight recognized any of the social commentary that is lurking in the books. The books could be taken in a couple of different directions. First, we could think about reality TV – how far away are we from a situation where people are killing each other for prizes on television? Second, the Capitol is supposed to represent tyranny and oppression and trying to stave off rebellion with a futuristic “bread and circuses.” But the movie seems to be more about the action itself and the audience members responded to this. I wonder how much the next two movies take up the social commentary and how they represent the growing rebellion against the Capitol.

4a. There were a couple of points during the Hunger Games themselves when a character was killed and people watching the movie laughed. This is an interesting reaction that sounded like it came from some teenagers or younger kids. While the action was violent (though a number of reviews said it was understated), I wonder how different it really was from what these kids have seen before. How many murders have they already seen in movies, on TV, and in video games? Plus, the kissing got a lot of reactions. Do both murders and kissing make teenagers nervous, thus the laughter?

5. I’m often amused by what “the future” looks like in movies. I was not impressed by the Capitol. Parts of the CGI were impressive (the people modeled in the large crowd scenes, for example) but it was clearly fake. The residents are shown in lively colors and interesting hair and makeup. The buildings are a little different but if you have seen a futuristic movie before, they look familiar. The special computer setup to control the Hunger Games is interesting but we’ve seen things like this before. They have 200 mph trains…which other parts of the world have now. So we’re supposed to be believe that the future includes some more avant garde style, a little better technology, and people are still glued to television screens? Not terribly futuristic.

6. The music during the closing credits was good. I’ve read some positive comments about the soundtrack and it may be worth checking out further.

7. I haven’t been in a full movie theater in quite a while. On one hand, there is a kind of buzz in the air and if the movie is good (and it apparently was tonight), people clap at the hand. On the other hand, you have lots of people going in and out and talking (and revealing key points of the plot to people next to them).

8. I was thinking earlier today that I have hopped on certain cultural bandwagons and not others. Why read all of the Hunger Games books and see the first movie or be an early adopter of Adele’s bestselling album from last year while waiting years to read Harry Potter and see all the movies? I don’t know. But if I do want to join the crowd, I can always say that I am engaging in cultural research…

Judging the validity of academic expertise in court

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It is common in the world of academia for academics to judge the credibility of other scholars. But what happens when academics step into the courtroom and a judge assesses whether they are experts or not? Consider the case of a Canadian sociologist who was going to testify as a gangs expert:

Mark Totten, an Ottawa sociologist, has “virtually no expertise with gangs in the Greater Toronto Area,” Ontario Superior Court Justice Robert Clark said in a 27-page ruling which had been under a publication ban until the jury in a gang-related case began deliberations Wednesday.

Yet, this is the same sociologist who, in 2009, was praised by the Ontario Court of Appeal for having “extensive and impressive credentials” in the field of street gang culture…

Totten himself admitted in an interview that he “didn’t handle it very well” after wilting under cross-examination in the voir dire, a preliminary examination to determine the competency of a witness…

The last time Totten’s expertise was questioned was in 2007, when Justice Todd Archibald disallowed his “expert” witness testimony on the meaning of a teardrop tattoo on the cheek of an accused killer, Warren Abbey…

On his website, Totten’s list of degrees includes a PhD in sociology (1996) from Carleton University. He is also the author of a book about to be released, Nasty, Brutish and Short: The Lives of Gang Members in Canada.

According to his 31-page resumé, most of Totten’s work with gangs has been in the Ottawa area and western Canada, and he says he has counselled hundreds of gang members.

Several things seem to be happening here:

1. In the most recent incident, Totten admitted he didn’t do a good job testifying. So perhaps he isn’t convincing and/or gets flustered.

2. Perhaps Totten’s knowledge is not specific enough for particular cases. While he has researched gangs, he may not know the particulars of gang activity in Toronto (or some other locations).

3. With the possibility of #1 and #2, why would either the prosecution or defense call on Totten for his expert testimony?

4. How does a judge decide whether a testifying expert has enough expertise. I’m sure there are guidelines to this but doesn’t this require the judge to assess the research ability of the expert? For example, the recent case involved questions about the methodology Totten used:

In a ruling released March 5, Clark flagged as a “problem” Totten’s data relating to the sample size of gang members he purportedly interviewed, calling it “inaccurate and misleading in several ways.”

Clark had listened for a day and a half as Misener challenged Totten about his research and methodology, including that used in the Abbey trial, and about his lack of knowledge about Toronto street gangs.

This is a very common academic argument: attack the methodology of another researcher and suggest they can’t reach the conclusions they do because the data is bad. Knowing this, many academics know they have to be able to respond to this which is why articles and books typically contain a defense of the methodology used for the study. In this case, the argument seems to be that Totten can’t really speak about Toronto gangs because there are important differences between these gangs and the ones Totten has studied. At what point is the judge convinced that Totten is not an expert for this case?

Even if the methodology is good, perhaps #1 and #2 are most important here – if the expert can’t speak well to the specific case and defend their methodology, it doesn’t matter if the expert really is an expert. Part of being an expert requires that the expert can effectively communicate their argument and the methodology behind it.

(My goal in this post is not to defend Totten or suggest his testimony should not be allowed. Rather, I was intrigued by the fact that these arguments about methodology and validity took place in court. While sociologists and researchers in other disciplines might know how the publishing system works for their own field, I assume the rules and standards in court differ even as there are some similarities between the two realms.)

We need a more complex analysis of how taxes affect income inequality

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One current blogosphere discussion about whether taxes could help reduce income inequality would benefit from more complex analyses. Here is the discussion thus far according to TaxProf Blog:

There have been a number of reports published recently that purport to show a link between rising inequality and changes in tax policy — especially tax cuts for the so-called rich. The latest installment comes from Berkeley professor Emmanuel Saez, Striking it Richer: The Evolution of Top Incomes in the United States.

Saez and others who write on this issue seem so intent on proving a link between tax policy and inequality that they overlook the major demographic changes that are occurring in America that can contribute to — or at least give the appearance of — rising inequality; a few of these being, differences in education, the rise of dual-earner couples, the aging of our workforce, and increased entrepreneurship.

Today, we will look at the link between education and income. Recent census data comparing the educational attainment of householders and income shows about as clearly as you can that America’s income gap is really an education gap and not the result of tax cuts for the rich.

The chart below shows that as people’s income rise, so too does the likelihood that they have a college degree or higher. By contrast, those with the lowest incomes are most likely to have a high school education or less. Just 8% of those at the lowest income level have a college degree while 78% of those earning $250,000 or more have a college degree or advanced degree. At the other end of the income scale, 69% of low-income people have a high school degree or less, while just 9% of those earning over $250,000 have just a high school degree.

This analysis starts in the right direction: looking at a direct relationship between two variables such as tax rates and income inequality is difficult to do in isolation of other factors. While some factors may be more influential than others, there are a number of reasons for income inequality. In other words, graphs with two variables are not enough. Pulling out one independent variable at a time doesn’t give us the full picture.

But, then the supposedly better way is that we were just looking at the wrong variable’s influence on income and should have been looking at education instead! So after saying that the situation was more complex, we get another two variable graph that shows that as education goes up, so does income so perhaps it really isn’t about taxes at all.

What we need here is some more complex statistical analysis, preferably including regression analysis where we can see how a variety of factors at the same time influence income inequality. Some of this might be a little harder to model since you would want to account for changing tax rates but arguing over two variable graphs isn’t going to get us very far. Indeed, I wonder if this is more common now in debates: both sides like simpler analyses because it allows each to make the point they want without considering the full complexity of the matter. In other words, easier to make graphs line up more with ideological commitments rather than an interest in truly sorting out what factors are more influential in affecting income inequality.