Tag Archives: statistics

Evangelicals and their propensity to think that everyone is against them

Posted on by

Sociologist Bradley Wright draws attention to an issue among evangelicals: a common belief that fellow Americans do not like them:

Similarly, somewhere along the line we evangelical Christians have gotten it into our heads that our neighbors, peers, and most Americans don’t like us, and that they like us less every year. I’ve heard this idea stated in sermons and everyday conversation; I’ve read it in books and articles.

There’s a problem, though. It doesn’t appear to be true. Social scientists have repeatedly surveyed views of various religions and movements, and Americans consistently hold evangelical Christians in reasonably high regard. Furthermore, social science research indicates that it’s almost certain that our erroneous belief that others dislike us is actually harming our faith.

The statistics Wright presents suggests evangelicals are somewhere in the middle of favorability among different religious groups. For example, a 2008 Gallup survey suggests Methodists, Jews, Baptists, and Catholics are viewed more favorably than evangelicals while Fundamentalists, Mormons, Muslims, Atheists, and Scientologists are viewed less favorably.

Wright goes on to argue (as he also does in this book) that the perceptions evangelicals have might be harmful:

If American evangelicals do have an image problem, it’s not our neighbors’ image of us; it’s our image of them. The 2007 Pew Forum study found that American Christians hold more negative views of “atheists” than non-Christians do of evangelical Christians. (The most recent Pew survey found similar attitudes; see the chart above.) Now, I am not a theologian, but this seems to be a problem. We Christians are called to love people, and as I understand it, this includes loving people who believe differently than we do. I’m not sure how we can love atheists if we don’t like them.

Ultimately, evangelical Christians might do well not to spend too much time worrying about what others think of us. Christians in general, and evangelical Christians in particular (depending on how you ask the question), are well-regarded in this country. If nothing else, there’s little we can do to change other people’s opinions anyway. Telling ourselves over and over that others don’t like us is not only inaccurate, it also potentially hinders the very faith that we seek to advance.

This is an ongoing issue with several aspects:

1. There is a disconnect between the numbers and the perceptions. Wright looks like he is trying to make a prolonged effort to bring these statistics to the masses. Will this data make a difference in the long run? How many evangelicals will ever hear about these statistics?

2. There may be positive or functional aspects to continually holding the idea that others don’t like you. Subgroups can use this idea to enhance solidarity and prompt action among adherents. Of course, these alarmist tendencies might not be helpful in the long run. (See a better explanation of this perspective from Christian Smith here.)

In the end, this is useful data but there is more that could be done to explain how these perceptions are helpful or not and what could or should be done to move in a different direction. Providing people with the right data and good interpretations is a good start but then people will want to know what to do next.

More difficulty with housing vacancy data

Posted on by

I’ve written about this before but here is some more evidence that one should be careful in looking at housing vacancy data:

In early 2009 the Richmond, Virginia press wrote numerous articles after quarterly HVS data on metro area rental vacancy rates “showed” that the rental vacancy rate in the Richmond, Virginia metro area in the fourth quarter of 2008 was 23.7%, the highest in the country. This shocked local real estate folks, including folks who tracked rental vacancy rates in apartment buildings in the area. The Central Virginia Apartment Association, e.g., found that the rental vacancy rate based on a survey of 52 multi-family properties in the Richmond, VA metro area was around 8% — above a more “normal” 5%, but no where close to 23.7%. And while the HVS attempts to measure the overall rental vacancy rate (and not just MF apartments for rent), the data seemed “whacky.”

When I talked to Census folks back then, they said that there quarterly metro area vacancy rates were extremely volatile and had extremely high standard errors, and that folks should focus on annual data.

However, “annual average” data from the HVS showed MASSIVELY different rental vacancy rates in Richmond, Virginia than did the American Community Survey, which also produces estimates of the vacancy rate in the overall rental market…

There are several other MSAs where the HVS rental vacancy rates just look plain “silly.” Some Census analysts agree that the HVS MSA data aren’t reliable, and even that several state data aren’t reliable, but, well, er, the national data are probably “ok” – which they are not.

If you want to read more on the issue, there are a number of links at the bottom of the story.

If the estimates are so far off from other estimates generally regarded as being reliable like the American Community Survey or the decennial Census, it would look like a new system is needed to calculate the quarterly vacancy rates.

I wonder how much these figures could hurt a particular community. Take the case of Richmond: if data suggests the vacancy rate is the highest in the country even though it is not, is this simply bad publicity or would it actually affect decisions made by residents, businesses, and local governments?

A stable statistic since 1941: “Americans prefer boys to girls”

Posted on by

Amidst news that families in Asian countries are selecting boys over girls before they are born, Gallup reports that Americans also prefer boys:

Gallup has asked Americans about their preferences for a boy or a girl — using slightly different question wordings over the years — 10 times since 1941. In each instance, the results tilt toward a preference for a boy rather than a girl. The average male child-preference gap across these 10 surveys is 11 percentage points, making this year’s results (a 12-point boy-preference gap) just about average. Gallup found the largest gap in 1947 and 2000 (15 points) and the smallest in a 1990 survey (4 points).

The attitudes of American men drive the overall preference for a boy; in the current poll, conducted June 9-12, men favor a boy over a girl by a 49% to 22% margin. American women do not have a proportionate preference for girls. Instead, women show essentially no preference either way: 31% say they would prefer a boy and 33% would prefer a girl…

The degree to which Americans deliberately attempt to select the gender of their children is unclear. It is significant that 18- to 29-year-old Americans are the most likely of any age group to express a preference for a boy because most babies are born to younger adults. The impact of the differences between men and women in preferences for the sex of their babies is also potentially important. The data from the U.S. suggest that if it were up to mothers to decide the gender of their children, there would be no tilt toward boys. Potential fathers have a clear preference for boys if given a choice, but the precise amount of input males may have into a deliberate gender-selection process is unknown.

This seems to be one of those statistics that is remarkably constant since 1941 even though the relationships between and perceptions of genders has changed. Is this statistic a sign of a lack of progress in the area of gender?

Gallup suggests several traits lead to higher preferences for boys: being male, being younger, having a lower level of education (though income doesn’t matter), and Republican. So why exactly do these traits lead to these preferences? Outside of being younger, one could suggest these traits add up to a “traditionalist” understanding of families where boys are more prized.

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?