Tag Archives: statistics

Os Guinness on how evangelicals view and use sociology

Posted on by

Os Guinness tries to explain how evangelicals view and use sociology:

CP: How are we as Christians failing to live the Way of Jesus?

Guinness: Sadly, when we look at many movements within evangelicalism today, the world and the spirit of the age are dominant, rather than the Word and Spirit.

I feel this very deeply as one trained in the social sciences. When I wrote “The Gravedigger File” nearly thirty years ago, very few evangelicals knew much about sociology. It was considered a “dangerous” field, along with psychology. Now it is cited almost universally, especially in the constant quoting of the latest statistics. I have heard mega-church sermons in which “Gallup or Barna says” far out-stripped “God or the Bible says.

But whereas sociology was once unused, it is now used uncritically. One of the key places where sociology should be used is in analyzing “the world” of our times, so that we can be more discerning. To resist the dangers of the world you have to recognize the distortions and seductions of the world. I have revised and updated my book under a new title, “The Last Christian on Earth”, but understanding the world through cultural criticism, as this parable encourages, is still unfashionable. Rather than use sociology that way, most pastors use it in a way that leads to adapting to the world, and they are encouraged to do so by half-baked versions of “seeker-sensitive” mission, and so on.

Guinness suggests sociology is used by evangelicals in several ways:

1. As a source of data. Several commentators have suggested in recent years that this data is often used in an alarmist way and to rally people to a particular cause or way of thinking. See an example here.

2. It is used by religious leaders who are trying to adapt or connect to culture rather than critique or understand culture.

From what Guinness is saying, it sounds like evangelicals are taking what they want from sociology rather than engaging with some of the bigger ideas and methods of the discipline. This seems to fit with the pragmatic culture of evangelicalism that is always looking for ways to reach the broader culture without thinking everything through.

I would also argue with the suggestion that sociology is no longer viewed as “dangerous” by many evangelicals. They may hear sociological snippets at church but I think there is still a decent amount of resistance and more so than psychology.

Quick Review: Scorecasting

Posted on by

I have written about Scorecasting several times (see here and here) so I figured I had better read it. Here are my thoughts on what I read about “the hidden influences” in sports:

1. This book truly aims for the Freakonomics crowd: there is a blurb both at the top of the cover and the back from Freakonomics author Steven D. Levitt. Those University of Chicago professors stick together…

2. I know that I have heard a number of these arguments before, particularly ones about why football teams should not punt, the unfairness of coin flips at the beginning of overtime in the NFL, and the phenomenon of the “hot hand.” Perhaps this indicates that I read too much sports news or that the sports world in recent years really has taken a liking to new kinds of statistics and statistical analysis.

3. A number of the explanations included psychology, just like Spousonomics. Is this because psychological terms and studies are better known (compared to disciplines like sociology) or because psychology truly does provide a lot of helpful information about sports situations? A lot of sports can be broken down into individual performances and efforts – see all of the recent psychoanalyzing of LeBron James – but they are also team games that require cooperation. Could we get more analysis of units or collectives?

4. There were particular chapters and insights that I found fascinating – here are a few:

4a. The overvaluing of round numbers, such as 20 home runs in a season or a .300 batting average, compared to hitters with 19 home runs and/or a .299 average. I don’t know if teams could really save a lot of money doing this but there is a fixation on certain figures.

4b. The trade value chart used around the NFL Draft and pioneered by the Dallas Cowboys needs to be revised.

4c. Two things about home field advantage. First, it is fairly consistent within sports across time and across countries. Second, officiating make up a decent amount of this advantage. I like the evidence of how baseball umpires suddenly started advantaging the road team on close calls when they knew that technology was being used to evaluate their calls.

4d. The chapter on the Cubs curse shows again that the idea is irrational.

5. In reading through this, I was reminded again of the wealth of statistics available in baseball. Other sports have to try to catch up to quantify as much as baseball can. But there is clearly a revolution underway with more professional teams taking these numbers seriously, including the new NBA champions. Could we get an analysis of whether teams that pay more attention to advanced statistics and analysis actually have better records? “Moneyball” was a big idea for a while as well but doesn’t seem to get as much attention now that Billy Beane isn’t competing as well out in Oakland.

5a. I’m sure someone has to have translated an undergraduate statistics course into an all-sports data format. How appealing would students find this and does this improve student learning outcomes?

Overall, I enjoyed this book: this should be of little surprise since it involves sports and statistics, two things that interest me. While some of the arguments may be familiar to sports fans, it does provide some more fodder for future sports conversations.

Possible Fermilab “breakthough” illustrates statistical significance

Posted on by

Scientists at Fermilab may be on the verge of a scientific breakthrough regarding “a new elementary particle or a new fundamental force of nature.” There is just one problem:

But scientists on the Fermilab team say there is about a 1 in 1,000 chance that the results are a statistical fluke — odds far too high for them to claim a discovery.

“That’s no more than what physicists tend to call an ‘observation’ or an ‘indication,’ ” said Caltech physicist Harvey Newman.

For the finding to be considered real, researchers have to reduce the chances of a statistical fluke to about 1 in a million.

One of the key concepts in a statistics or social research course is statistical significance, where researchers say that they are 95% certain (or more) that their result is not just the result due to their sample or chance but that it actually reflects the population or reality. These scientists at Fermilab then want to be really sure that the results reflect reality as they want to reduce their possible error to 1 in a million.

Beyond working with the calculations, the scientists are also hoping to replicate their findings and rule out other explanations for what they are seeing:

Researchers hope that more data compiled at Fermilab will shed light on the matter, or that the Large Hadron Collider in Geneva will be able to replicate the findings. “We will know this summer when we double the data sets and see if it is still there,” said physicist Rob Roser of Fermilab, who is a spokesman for the project…

What the team must to do now, Roser said, is “eliminate all the mundane explanations.” They have been working on that, he said, and decided it was time to go public and let others know what they had found so far.

And science rolls on.

Statistic: “More Than 1,000 Mexicans Leave Catholic Church Daily”

Posted on by

Statistics are often put into terms that the average citizen might understand. Or, more cynically, into terms that grabs attention. Here is an example from a sociologist/historian looking at data about Catholicism in Mexico:

More than 1,000 Mexicans left the Catholic Church every day over the last decade, adding up to some 4 million fallen-away Catholics between 2000 and 2010, sociologist and historian Roberto Blancarte told Efe.

Put this way (and a headline built around this daily figure), this statistic seems noteworthy as it looks like a lot of people are making this decision every day. But later in the article, we get a broader perspective:

In 1950, 98.21 percent of Mexicans said they were Catholic, in 1960 the percentage dropped to 96.47 percent, in 1970 to 96.17 percent, in 1980 to 92.62 percent, in 1990 the percentage dropped to 89.69 percent, in 2000 the country was only 88 percent Catholic, and now that percentage is lower still at 83.9 percent.

This signifies that the last decade has seen a drop of more than 4 percentage points, equivalent to almost 4 million people or an average of 1,300 people a day leaving the Catholic Church.

From this decade-by-decade perspective, there is a clear decline from 98.21 percent to 83.9 percent in 2010, a drop of just over 14 percent over 60 years. But this longer-term perspective also helps show that the daily average isn’t really that helpful: are there really 1,300 people each day that make a conscious decision to leave the church? Is this how it works among individual citizens? In this case, it might be better to look at the percentage change each decade and see that the 4.1 percent drop in the 2000s is the largest since 1950.

Additionally, can the average person easily envision what exactly 1,300 people means? This is a large room of people, bigger than even a decent size college classroom but not quite enough to fill a decent sized theater. The Metro in Chicago holds about 1,150 so this is a close approximation.

At least we didn’t get down to another type of common statistic: this data from Mexico breaks down to about 1 Catholic leaving the church every 1.11 minutes.

Chart of total carbon emissions and emissions per capita

Posted on by

Miller-McCune has put together two charts showing total carbon emissions by country and also emissions per capita by country. See the two charts here.

This is colorful and vibrant. And it is nice to have the charts side-by-side as one can easily make comparisons. For example, the US is #2 in total emissions but #9 in per capita emissions. As The Infrastructurist points out, the chart gives some insights into how many countries might need to deal with per capita emissions rather than point fingers at countries with the largest amount of carbon emissions.

But there is a lot of information compressed in this chart – it is hard to see a lot of the smaller countries with small circles. Additionally, why are the countries in the order they are? It appears that regions are together but the order is not the same for both charts and it certainly isn’t rank-ordered (China and US are on opposite ends of the chart for total emissions). The color and vibrancy seems to be more important to the chart-makers than having a logical order to the countries.

h/t The Infrastructurist

Graphic comparing US to other developed nations on nine measures

Posted on by

This particular graphic provides a look at how the United States stacks up against other developed nations on nine key measures, such as a Gini index, Gallup’s global wellbeing index, and life expectancy at birth.

As a graphic, this is both interesting and confusing. It is interesting in that one can take a quick glance at all of these measures at once and the color shading helps mark the higher and lower values. This is the goal of graphics or charts: condense a lot of information into an engaging format. However, there are a few problems: there is a lot of information to look at, it is unclear why the countries are listed in the order they are, and it takes some work to compare the countries marked with the different colors because they may be at the top or bottom of the list.

(By the way, the United States doesn’t compare well to some of the other countries on this list. Are there other overall measures in which the United States would compare more favorably?)

Scorecasting looks at data: Cubs not unlucky, just bad

Posted on by

The recently published book Scorecasting (read a quick summary here) has a chapter that tackles the question of whether the Chicago Cubs are cursed or not. Their conclusion after looking at the data: the team has simply been bad.

But how can anyone disprove the existence of a curse? According to the authors, teams that frequently field good teams but finish in second place, or make the playoffs but fail to win a title, justifiably can claim to be unlucky. So, too, can teams that have impressive batting, hitting and defensive statistics, but whose strong numbers don’t translate into victories.

On both scores, the Cubs proved to be “less unlucky” than the average team. That is, not unlucky, just bad.

“Relative to other teams, we could easily explain the Cubs lack of success from the data — both their on the field statistics and where they finished in the standings,” Moscowitz said.

Since their last Series appearance in 1945, the Cubs have finished second fewer times than they have finished first. They also have finished last or next to last close to 40 percent of the time. According to the book, the odds of this happening by chance are 527 to 1.

The authors of “Scorecasting” believe that what has been stopping the Cubs the last three decades is the extreme loyalty of their fans, which has served to reduce the incentive for Cubs management to win.

According to their analysis, which is primarily based on attendance records and the team’s won-loss percentage from 1982-2009, Cubs fans are the least sensitive to the team’s winning percentage, while White Sox fans are among the most sensitive.

There are two interesting arguments going on here, both of which commonly come up in conversation in Chicago:

1. The data suggests that the Cubs have just been a bad team. It is not as if they have reached the playoffs or World Series multiple times and lost. It is not that they have impressive statistics and this hasn’t translated into wins. They just haven’t been very good. It would be interesting to read the rest of this chapter to see if the authors talk about the MLB teams that have been truly unlucky. I don’t know if a chapter like this will put the talk of a Cubs curse to rest but it is good to hear that there is data that could quiet the curse talk. (But perhaps the curse is what Cubs fans want to believe – it means that the team or the fans aren’t at fault.)

2. Cubs fans like to think that they are loyal while White Sox fans argue that Cubs fans will go to Wrigley Field no matter what. So is the answer for more Cubs fans to stay away from the ballpark until the team and the Ricketts show that they are serious about winning?

Meteorologists debate whether recent Chicago snowstorm was 3rd or 4th largest on record

Posted on by

Headlines after the recent Chicago blizzard suggested that the storm had the third largest amount of snow in Chicago history. But when this was later changed to the 4th largest storm, an argument erupted among meteorologists about what exactly counted as part of this particular storm:

After a brief drop to No. 4, the Blizzard of 2011 has now been put back in its rightful spot as the No. 3 worst blizzard in Chicago history.

Earlier in the day, the National Weather Service downgraded the Ground Hog Day Blizzard to 20 inches, taking away .2 inches of snow they say fell hours before the actual blizzard hit. At the same time, they decided that the 1979 storm lasted three days, not the two generally cited. That upped the storm’s total to 20.3 from the 18.8 inches generally credited to the storm…

But during a teleconference with meteorologists from Chicago area media outlets, there was such outcry over the weather service’s decision to lower the total snowfall from this year’s blizzard that the decision was reversed.

“You really are getting into hazardous territory,” WGN meteorologist Tom Skilling warned National Weather Service officials during the teleconference. “To downgrade this storm in any way shape or form is highly subjective. You guys are the arbiters of this, but I don’t agree with it.”…

Allsopp emphasized that these storm totals are more for the public’s benefit than for the record books. The official snow records are listed by calendar days.

Even the weather, data we might consider “hard data,” is open to different interpretations. It is interesting that the final decision went the way of the local forecasters. While Skilling is right to suggest that the decision to downgrade the storm was subjective, wasn’t ranking the storm 3rd also subjective?

Perhaps the key is the final statement in the article: this is for the public, not the record books. In the long run, does it make Chicago area residents feel better or more proud to know that the recent storm was the 3rd largest? If we went by the official snowfall by calendar day, this website suggests the record was 18.6 inches on January 2, 1999.

Trying to count the people on the streets in Cairo

Posted on by

This is a problem that occasionally pops up in American marches or rallies: how exactly should one estimate the number of people in the crowd? This has actually been quite controversial at points as certain organizers of rallies have produced larger figures than official government or media estimates. And with the ongoing protests taking place in Cairo, the same question has arisen: just how many Egyptians have taken to the streets in Cairo? There is a more scientific process to this beyond a journalist simply making a guess:

To fact-check varying claims of Cairo crowd sizes, Clark McPhail, a sociologist at the University of Illinois and a veteran crowd counter, started by figuring out the area of Tahrir Square. McPhail used Google Earth’s satellite imagery, taken before the protest, and came up with a maximum area of 380,000 square feet that could hold protesters. He used a technique of area and density pioneered in the 1960s by Herbert A. Jacobs, a former newspaper reporter who later in his career lectured at the University of California, Berkeley, as chronicled in a Time Magazine article noting that “If the crowd is largely coeducational, he adds, it is conceivable that people might press closer together just for the fun of it.”

Such calculations of capacity say more about the size of potential gathering places than they do about the intensity of the political movements giving rise to the rallies. A government that wants to limit reported crowd sizes could cut off access to its cities’ biggest open areas.

From what I have read in the past on this topic, this is the common approach: calculate how much space is available to protesters or marchers, calculate how much space an individual needs, and then look at photos to see how much of that total space is used. The estimates can then vary quite a bit depending on how much space it is estimated each person wants or needs. These days, the quest to count is aided by better photographs and satellite images:

That is because to ensure an accurate count, some computerized systems require multiple cameras, to get high-resolution images of many parts of the crowd, in case density varies. “I don’t know of real technological solutions for this problem,” said Nuno Vasconcelos, associate professor of electrical and computer engineering at the University of California, San Diego. “You will have to go with the ‘photograph and ruler’ gurus right now. Interestingly, this stuff seems to be mostly of interest to journalists. The funding agencies for example, don’t seem to think that this problem is very important. For example, our project is more or less on stand-by right now, for lack of funding.”

Without any such camera setup, many have turned to some of the companies that collect terrestrial images using satellites, but these companies have collected images mostly before and after the peak of protests this week. “GeoEye and its regional affiliate e-GEOS tasked its GeoEye-1 satellite on Jan. 29, 2011 to collect half-meter resolution imagery showing central Cairo, Egypt,” GeoEye’s senior vice president of marketing, Tony Frazier, said in a written statement. “We provided the imagery to several customers, including Google Earth. GeoEye normally relies on our partners to provide their expert analysis of our imagery, such as counting the number of people in these protests.” This image was taken before the big midweek protests. DigitalGlobe, another satellite-imagery company, also didn’t capture images of the protests, according to a spokeswoman, but did take images later in the week.

Because these images are difficult to come by in Egypt, it is then difficult to make an estimate. As the article notes, this is why you will get vague estimates for crowd sizes in news stories like “thousands” or “tens of thousands.”

Since this is a problem that does come up now and then, can’t someone put together a better method for making crowd estimates? If certain kinds of images could be obtained, it seems like an algorithm could be developed that would scan the image and somehow differentiate between people.

One chart that situates the 2012 Republican presidential contenders

Posted on by

One of the key purposes of a chart or graph is to distill a lot of complicated information into a simple graphic so readers can quickly draw conclusions. In the midst of a crowded field of people who may (or may not) be vying to be the Republican candidate for president in 2012, one chart attempts to do just that.

This chart has two axes: moderate to conservative and insider to outsider. While these may be fuzzy concepts, creator Nate Silver suggests these axes give us some important information:

With that said, it is exceptionally important to consider how the candidates are positioned relative to one another. Too often, I see analyses of candidates that operate through what I’d call a checkbox paradigm, tallying up individual candidates’ strengths and weaknesses but not thinking deeply about how they will compete with one another for votes.

Silver then goes on to explain two other pieces of information for each candidate that is part of the circle used to place each candidate on the graph: the color indicates the region and the size of the circle represents their relative stock on Intrade.

Based on this chart, it looks like we have a diagonal running from top left to bottom right, from moderate insider (Mitt Romney) to conservative outsider (Sarah Palin) with Tim Pawlenty and Mike Huckabee trying to straddle the middle. We will have to see how this plays out.

But as a statistics professor who is always on the lookout for cool ways of presenting information, this is an interesting graphic.