Selfie culture, tourism, and the challenge of the authentic self

Just kidding — who would give a blog post a title like that?

The author on vacation.

Our family vacation this summer, to London and Paris, was my first since the Year of the Selfie Stick (which is this year). This is also the year in which 59% of “millennials” (only 40% of whom understand that they are millennials) answered “yes” when asked whether the phrase “self-absorbed” describes “people in your generation overall”?

At Admiralty Arch, London

I can’t add much nuance to the theory of selfie culture, which has already taught me a lot. For example, I learned on NPR that vanity isn’t vanity if it’s part of constructing your “personal brand” — so it’s OK to edit your selfies:

“I think that while a lot of people find it easy to say, ‘Well, you’re taking a photo of yourself, and you’re posting it online for people to sort of rate.’ I could see how that superficially looks like vanity,” she says. “At the same time, I think that it really gives a lot of the younger generation a platform to express themselves without being incredibly harshly judged — because the selfie is such a casual form of expression, no one is going to expect you to look absolutely perfect.”

What interested me is the intersection of artificiality and authenticity that the selfie brings to the experience of tourism. Taking 1,000 pictures of yourself in order to post just the right spontaneous look is the normal business of personal brand construction. It creates a presentation version of yourself. It’s an expected form of inauthenticity, just like most people expect you won’t post pictures of your kid crying at her birthday party.

But when you’re a tourist, selfies are about authenticity. They’re the picture that proves you were really there — and really in that mood at that moment that you were there. As the culture studies journal Forbes Leadership put it:

In the age of social media, authenticity for Postmoderns is characterized by a consistency and continuity between their online personas and their lives in the real world. The more congruence there is between the two, the more authentic the Postmodern appears to be.

You can’t have that unless the famous thing is clearly visible behind you. Which produces the endlessly entertaining spectacle of tourists taking and retaking selfies with every different expression and pose in front of popular monuments and attractions.

At Buckingham Palace.

Social media is social. We share pictures and updates with real people. Even when you’re alone, you’re not alone on social media. Which I think is great. But like the person staring at his phone through dinner, it seems antisocial when you do it publicly. The selfie-takers are alone in their Instagram bubbles in the middle of a sea of other people.

At Buckingham Palace.

And when you’re going in for the money shot — say, the Eiffel Tower at sunset — you can’t afford to mess around. It seemed like these people were oblivious to me setting up for a shot of them from 10 feet away. (Also, like driving, it seems that operating the selfie stick in different-sex couples is the man’s job.)

At the Eiffel Tower.

Or, in this case, the daughter totally not noticing that her parents are collapsing from boredom while she works up her umpteenth Buckingham Palace selfie.

I have nothing against all this. (Our story is that every moment of the vacation was deliriously fun, and we have the pictures to prove it.) Having a good time seeing new people and things without hurting anyone is what I love about tourism.

Still, it’s funny that people go to such trouble to get the perfect picture of themselves — creating at least a moment that is artificial — in their quest for an image of authenticity. What would this picture mean if I weren’t really in Paris, contemplating all there is to contemplate in exactly that spot?

As affirmed by the behavior of thousands of people crowding around to take pictures of famous paintings, perfect reproductions of which are available online for free, it has to be real or it’s nothing. A lie.

The fake-smile selfie is a lie, too. I guess it’s just not a very important one.

One disappointing thing, though, is that suddenly the ritual of strangers asking each other to take their pictures is disappearing. That was a nice part of tourism, because it involved strangers smiling at each other.

Note: Help yourself to these photos, which are in this Flickr folder.

Herculean dimorphism

Who knows how many animated Disney movies I haven’t even seen yet? I never saw Hercules before.

I know, I know, Hercules is a demi-god. But he’s also all man. In Disney’s (1997) version, Hades says to Megara, “I need someone who can — handle him as a man.” And handle him she does:

And since they involve him in such matters of the human flesh (and heart), that means their measurements are fair game for the Disney dimorphism series. If Disney is going to eroticize the relationship and sell it to innocent children, then we should ask what they’re selling.

As usual, they’re selling extreme sex dimorphism. I did some simple measurements from one pretty straight shot in the movie, and compared it to this awesome set of measurements taken of about 4,000 U.S. Army men and women in the late 1980s. Since Hercules is obviously extremely strong and this woman seems to be on the petite side, I compared their measurements to those of the biggest man versus the smallest woman on each dimension in the entire Army sample. The numbers shown are the man/woman ratios: Hercules/Meg versus the Army maximum/minimum.

As you can see, this cartoon Hercules is more extremely big compared to his cartoon love interest than even the widest man-woman comparison you can find in the Army sample, by a lot. (Notice his relaxed hands – he’s not flexing that bicep.)

To show how unrealistic this is, we can compare it to images of the actual Hercules. Here’s one from about 1620 (“Hercules slaying the Children of Megara,” by Allessandro Turchi):

That Hercules is appallingly scrawny compared with Disney’s. Here’s another weakling version, from the 3rd or 4th century:

Now here is one from the 2014 Paramount movie, in which he is conveniently paired with the human female, Ergenia:

That bicep ratio is only 1.5-to-1. And that’s not normal.

Seriously, though, isn’t it interesting that both the Disney and the Paramount versions show more extreme dimorphism than the ancient representations? Go ahead, tell me he’s a demigod, that it’s a cartoon, that it’s not supposed to be realistic. I have heard all that before, and responded with counterexamples here. But that doesn’t explain why the modern versions of this myth should show more sex dimorphism than the old-school ones. That’s progress of a certain kind.

I’ve written so far about Frozen and Brave, Tangled, and Gnomeo and Juliet, and How to Train Your Dragon 2. It all goes back to the critique, which I first discussed here and Lisa Wade described here, of the idea that male and female humans aren’t just different, they’re opposites. This contributes to the idea that Mark Regnerus defends as the “vision of complementarity” — the insistence that children need a male and female parent — which drives opposition to same-sex marriage. If men and women are too similar, then we wouldn’t need them to be paired up in order to have complete families or sexual relationships.

In the more mundane aspects of relationships — attraction and mate selection — this thinking helps set up the ideal in which women should be smaller than men, the result of which is pairing couples by man-taller-woman-shorter much more than would occur by chance (I reported on this here, but you also could have read about it from 538’s Mona Chalabi 19 months later). The prevalence of such pairs increases the odds that any given couple we (or our children) observe or interact with will include a man who is taller and stronger than his partner. This is also behind some notions that men and women should work in different — and unequal — occupations. And so on.

So I’m not letting this go.

Telling the boys and girls how to tell the boys from the girls

From Reddit comes the story of an assignment given to high school students in a sex education unit of health class in Columbus, Ohio (as reported in the Dispatch).

The introduction reads (typos included):

Appreciating Gender Differences: Often there are many stereotypes attached to being male or female. Yet male and female together keep our species alive! Through knowing and appreciating the many differences in brain development and psychological processes of males vs. female one learn to accept and appreciate the differences.”

Then there’s this graphic:

Yes, boys and girls in the class all got the same handout, with the normal human described as “you” and the one in the dress labeled “she.”

After the graphic is a list of questions for the students to ponder in an essay, such as, “How might knowing these differences influence and impact an intimate relationship you might currently have or develop in the future?”

In her defense, the teacher naturally told the Dispatch that the point was to just “stimulate conversation.” But nothing in the assignment suggests the stereotypes might not be anything but true. None of the essay questions cast doubt on the facts presented.

Consider revising the text like this:

Appreciating Gender Similarities: Often there are many stereotypes attached to being male or female. Yet male and female together keep our species alive! Through knowing and appreciating the many similarities in brain development and psychological processes of males vs. female one learn to accept and appreciate the similarities.”

That could be a useful opening to a unit on gender and development for high school sex education (without the graphic).

Where did this come from?

The teacher said it came from “an outdated book.” With the power of Google image search, you can follow this image around the Internet, where it has been used by a lot of people to illustrate supposedly funny-but-oh-so-true stereotypes, like “Hilarious differences between men and women,” and on pages with sexist aphorisms such as, “A woman worries about the future until she gets a husband; a man never worries about the future until he gets a wife,” and on relationship advice pages, with conclusions such as, “If we understand this basic fundamental, there will be better relationships … steadier !!,” and even “Real, Honest Female Advice” for men who want to “start having unbelievable success with women.”

It always has the same typo (“Figure Our Her Needs”). I can’t find an original use, or any serious attempt at educational use, but I’d love to know who came up with it.

Pregnancy discrimination and the gender gap, involuntary job choice edition

From Rachel Swarns at the New York Times comes the story of a woman, Angelica Valencia, fired from her $8.70-an-hour produce packing job because her doctor said she couldn’t work overtime because she was three months into a risky pregnancy. There actually is a new law on her side, but her employer somehow didn’t get around to notifying her of her right to reasonable accommodation.

Before reading my comment on this, why not check out this new video from the chapter on gender in my book. The video accompanies a much more compelling version of this graphic, showing the gender composition of some occupations, calculated from the American Community Survey:

Count that gender gap

OK, Back to Angelica Valencia. I’m not an expert on pregnancy discrimination, but I want to use this to comment on how we look at the gender gap in pay. The Census Bureau reports on the gender gap this way:

In 2013, the median earnings of women who worked full time, year-round ($39,157) was 78 percent of that for men working full time, year-round ($50,033).

Critics complain that this doesn’t account for occupational choice, time out of the labor force, and so on. As Ruth Davis Konigsberg sneeringly put it in Time:

Women don’t make 77 cents to a man’s dollar. They make more like 93 cents, as long as they don’t major in art history.

And Hanna Rosin helpfully explained:

Women congregate in different professions than men do, and the largely male professions tend to be higher-paying.

So what does the story of Angelica Valencia pregnancy tell us (besides the pitfalls of majoring in art history)? Valencia may end up winning some back pay in a lawsuit. But let’s assume someone just like her didn’t, and ended up instead in a lower-paying job that doesn’t like overtime, such as at McDonald’s. If we insist on statistically controlling for occupation, hours, job tenure, and time out of the labor force in order to see the real wage gap, people like Valencia may not show up as underpaid women — if they’re paid the same as men in the same jobs, holding constant hours, job tenure, and time out of the labor force. So the very thing that makes Valencia earn less — being fired for getting pregnant — disappears from the wage gap analysis. Instead, the data shows that women take more time off work, work fewer hours, change jobs more often, and “choose” less lucrative occupation.

Sure, a lot of women chose to get pregnant (and a lot of men choose to become fathers). But getting fired and ending up in a lower paid job as a result is not part of that choice (and it doesn’t happen to fathers). The overall difference in pay between men and women, which reflects a complicated mix of factors, is a good indicator of inequality.

For background on the motherhood penalty in wage, you might start here or here (including the sources citing these).

The number one cause of traffic fatalities

Please don’t text while driving.

Note: I have updated this post to reflect a response I received from Matt Richtel.

A data illustration follows the rant.

I don’t yet have a copy of Matt Richtel’s new book, A Deadly Wandering: A Tale of Tragedy and Redemption in the Age of Attention. Based on his Pulitzer-prize winning reporting for the New York Times, however, I’m afraid it’s unlikely to do justice to the complexity of the relationship between mobile phones and motor vehicle accidents. Worse, I fear it distracts attention from the most important cause of traffic fatalities: driving.

A bad sign

The other day Richtel tweeted a link to this old news article that claims texting causes more fatal accidents for teens than alcohol. The article says some researcher estimates “more than 3,000 annual teen deaths from texting,” but there is no reference to a study or any source for the data used to make the estimate. As I previously noted, that’s not plausible.

In fact, only 2,823 teens teens died in motor vehicle accidents in 2012 (only 2,228 of whom were vehicle occupants). So, I get 7.7 teens per day dying in motor vehicle accidents, regardless of the cause. I’m no Pulitzer-prize winning New York Times journalist, but I reckon that makes this giant factoid on Richtel’s website wrong, which doesn’t bode well for the book:

In fact, I suspect the 11-per-day meme comes from Mother Jones (or someone they got it from) doing the math wrong on that Newsday number of 3,000 per year and calling it “nearly a dozen” (3,000 is 8.2 per day). And if you Google around looking for this 11-per day statistic, you find sites like textinganddrivingsafety.com, which, like Richtel does in his website video, attributes the statistic to the “Institute for Highway Safety.” I think they mean the Insurance Institute for Highway Safety, which is the source I used for the 2,823 number above. (The fact that he gets the name wrong suggests he got the statistic second-hand.) IIHS has an extensive page of facts on distracted driving, which doesn’t have any fact like this (they actually express skepticism about inflated claims of cellphone effects).

After I contacted him to complain about that 11-teens-per-day statistic, Richtel pointed out that the page I linked to is run by his publisher, not him, and that he had asked them to “deal with that stat.” I now see that the page includes a footnote that says, “Statistic taken from the Insurance Institute for Highway Safety’s Fatality Facts.” I don’t think that’s true, however, since the “Fatality Facts” page for teenagers still shows 2,228 teens (passengers and drivers) killed in 2012. Richtel added in his email to me:

As I’ve written in previous writings, the cell phone industry also takes your position that fatality rates have fallen. It’s a fair question. Many safety advocates point to air bags, anti-lock brakes and wider roads — billions spent on safety — driving down accident rates (although accidents per miles driven is more complex). These advocates say that accidents would’ve fallen far faster without mobile phones and texting. And they point out that rates have fallen far faster in other countries (deaths per 100,000 drivers) that have tougher laws. In fact, the U.S. rates, they say, have fallen less far than most other countries. Thank you for your thoughtful commentary on this. I think it’s a worthy issue for conversation.

I appreciate his response. Now I’ll read the book before complaining about him any more.

The shocking truth

I generally oppose scare-mongering manipulations of data that take advantage of common ignorance. The people selling mobile-phone panic don’t dwell on the fact that the roads are getting safer and safer, and just let you go on assuming they’re getting more and more dangerous. I reviewed all that here, showing the increase in mobile phone subscriptions relative to the decline in traffic accidents, injuries, and deaths.

That doesn’t mean texting and driving isn’t dangerous. I’m sure it is. Cell phone bans may be a good idea, although the evidence that they save lives is mixed. But the overall situation is surely more complicated than TEXTING-WHILE-DRIVING EPIDEMIC suggests. The whole story doesn’t seem right — how can phones be so dangerous, and growing more and more pervasive, while accidents and injuries fall? At the very least, a powerful part of the explanation is being left out. (I wonder if phones displace other distractions, like eating and putting on makeup; or if some people drive more cautiously while they’re using their phones, to compensate for their distraction; or if distracted phone users were simply the worst drivers already.)

Beyond the general complaint about misleading people and abusing our ignorance, however, the texting scare distracts us (I know, it’s ironic) from the giant problem staring us in the face: our addiction to private vehicles itself costs thousands of lives a year (not including the environmental effects).

To illustrate this, I went through all the trouble of getting data on mobile phone subscriptions by state, to compare with state traffic fatality rates, only to find this: nothing:

What does predict deaths? Driving. This isn’t a joke. Sometimes the obvious answer is obvious because it’s the answer:

If you’re interested, I also put both of these variables in a regression, along with age and sex composition of the states, and the percentage of employed people who drive to work. Only the miles and drive-to-work rates were correlated with vehicle deaths. Mobile phone subscriptions had no effect at all.

Also, pickups?

Failing to find a demographic predictor that accounts for any of the variation after that explained by miles driven, I tried one more thing. I calculated each state’s deviation from the line predicted by miles driven (for example Alaska, where they only drive 6.3 thousand miles per person, is predicted to have 4.5 deaths per 100,000 but they actually have 8.1, putting that state 3.6 points above the line). Taking those numbers and pouring them into the Google correlate tool, I asked what people in those states with higher-than-expected death rates are searching for. And the leading answer is large, American pickup trucks. Among the 100 searches most correlated with this variable, 10 were about Chevy, Dodge, or Ford pickup trucks, like “2008 chevy colorado” (r = .68), shown here:

I could think of several reasons why places where people are into pickup trucks have more than their predicted share of fatal accidents.

So, to sum up: texting while driving is dangerous and getting more common as driving is getting safer, but driving still kills thousands of Americans every year, making it the umbrella social problem under which texting may be one contributing factor.

I used this analogy before, and the parallel isn’t perfect, but the texting panic reminds me of the 1970s “Crying Indian” ad I used to see when I was watching Saturday morning cartoons. The ad famously pivoted from industrial pollution to littering in the climactic final seconds:

Conclusion: Keep your eye on the ball.

New York City police killings: 1964 (life) – 1989 (art) – 2014 (life)

In July 1964, just after the passage of the Civil Rights Act, White New York City police officer Thomas Gilligan killed Black 15-year-old James Powell. After two days of peaceful protest, police and protesters clashed and six nights of violence followed. This is not James Powell being killed, just another guy being beaten:

In the summer of 1989, Spike Lee’s movie Do the Right Thing featured the killing of Radio Raheem by White police — using the already-infamous chokehold — after they swept into the sweltering neighborhood, where a fight had broken out. The climactic incident sparked an explosive riot (watch the scene on Hulu with membership):

Now, another quarter century later, police on Staten Island have apparently choked 43-year-old Eric Garner to death after he refused to cooperate with whatever random demand they had, as captured on video (and posted by the Daily News):

Now the chokehold is against police department rules, but the number of chokehold complaints — a statistic the department keeps — has been rising and last year reached 233, only a “tiny fraction” of which are substantiated. In the Daily News video, Garner is heard saying, “I can’t breathe” many times.

UPDATE: Spike Lee has now produced a video splicing together the chokehold scenes of Eric Garner and Radio Raheem. It’s embedded on Indiwire here.

Global inequality, within and between countries

Most of the talk about income inequality is about inequality within countries – between rich and poor Americans, versus between rich and poor Swedes, for example. The new special issue of Science magazine about inequality focuses that way as well, for example with this nice figure showing inequality within countries around the world.

But what if there were no income inequality within countries? If everyone within each country had the same income, but we still had rich and poor countries, how unequal would our world be? It turns out that’s an easy question to answer.

Using data from the World Bank on income for 131 countries, comprising 91% of the world population, here is the Lorenz curve showing the distribution of gross national income (GNI) by population, with each person in each country assumed to have the same income (using the purchasing power parity currency conversion). I’ve marked the place of the three largest countries: China, India, and the USA:

The Gini index value for this distribution is .48, which means the area between the Lorenz curve and the blue line – representing equality, is 48% of the lower-right triangle. (Going all the way to 1.0 would mean one person had all the money.)

But there is inequality within countries. In that Science figure the within-country Ginis range from .24 in Belarus to .67 in South Africa. (And that’s using after-tax household income, which assumes each person within each household has the same income. So there’s that, too.)

The World Bank data I’m using includes within-country income distributions broken into 7 quantiles: 5 quintiles (20% of the population each), with the top and bottom further broken in half. If I assume that the income is shared equally within each of these quantiles, I can take those 131 countries and turn them into 917 quantiles (just assigning each group its share of the country’s GNI). These groups range in average income from $0 (due to rounding) in the bottom 10th of Bolivia and Guyana, or $43 per person in the bottom 10th of the Democratic Rep. of Congo, up to $305,800 per person in the top 10th of Macao.

To illustrate this, here are India, China, and the USA, showing average incomes for the quantiles and the countries as a whole:

This shows that the average income of China’s top 10th is between the second and third quntiles of the US income distribution, and the top 10th of India has an average income comparable to the US 10-19th percentile range. Obviously, this breakdown shows a lot more inequality.

So here I add the new Lorenz curve to the first figure, counting each of those 917 quantiles as a separate group with its own income:

Now the Gini index has risen a neat 25%, to an even .60. Is that a big difference? Clearly, between country inequality — the red line — is vast. If every country were a household, the world would be almost as unequal as Nigeria. In this comparison, you could say you get 80% of the income inequality to show up just looking at whole countries. But of course even that obscures much more, especially at the high end, where there is no limit.

Years ago I followed the academic debate over how to measure inequality within and between countries. If I were to catch up with it again, I would start with this article, by my friends Tim Moran and Patricio Korzeniewicz. That provoked a debate over methods and theory, and they eventually published this book, which argues: “within-country analyses alone have not adequately illuminated our understanding of global stratification.” There is a lot more to read, but their work, and the critiques they’re received, is a good place to start.

Note: I have put my Excel worksheet for this post here. It has the original data and my calculations, but not the figures.

Ridiculous NY Times Magazine data graphics

A series of ridiculous data graphics posts from the NY Time Magazine, collected in one post (with crummy photo-pic renderings).

These are examples of the abuse of data graphic techniques to spread ignorance, distract people from anything of actual importance, and contribute to the perception that statistics – especially graphic statistics – are just an arbitrary way of manipulating people rather than a set of tools for exploring data and attempting to answer real questions. (If you are already convinced of this and just want to see awesome real graphics, I would start with Healy and Moody’s Annual Review of Sociology paper.)

First, an innocent graphic that merely wastes space and contributes nothing — it really communicates less than the 8 simple data points it has because the bats all over are just confusing and the points are in no order (who even notices that the number of segments each bat is cut into is the data point?):

Maybe a little better, I suppose, is this one, where the number of trees shown at least corresponds to the data points. But you would still learn more, faster, from a simple list:

Here is an interesting mistake. I first thought these bars were out of order, but it turns out it’s just the top part of the bars that are out of order. If they were flat-topped bars it would be okay:

Here’s one that combines useless graphics with data that is itself completely misleading. These are the fees associated with different parks in NY City. But the units of time are different. What is the point of comparing the annual tennis fee to the hourly roller hockey fee? At least they didn’t make the cards different sizes to show this meaningless comparison more clearly.

The magazine also does text “analytics.” These are on the letters page, and they show the type of letters received. This is interesting to sociologists, who sometimes try to find ways to categorize text. They make two errors here that render these meaningless or worse.

First, they sometimes present them in order – as represented by graphic elements – when the sentiments expressed are not in that logical order. Like this one, in which the dial and shading implies these are in some logical order, but they aren’t:

They also did that here, with the shading implying some continuum that is not present. (In this one, also, is it the proportion of the state’s area the determines the size of the cuts, or the angle of the cuts at the center?). Come on!

A final point holds for all these letter “analytics.” You really shouldn’t determine the number of categories you are going to use before you read the texts, “Here, go break these letters into four categories.” For the love of God, they don’t even have an “other” category, and always ways add to 100%.

How well do teen test scores predict adult income?

Now with new figures and notes added at the end — and a new, real life headline and graph illustrating the problem in the middle!

The short answer is, pretty well. But that’s not really the point.

In a previous post I complained about various ways of collapsing data before plotting it. Although this is useful at times, and inevitable to varying degrees, the main danger is the risk of inflating how strong an effect seems. So that’s the point about teen test scores and adult income.

If someone told you that the test scores people get in their late teens were highly correlated with their incomes later in life, you probably wouldn’t be surprised. If I said the correlation was .35, on a scale of 0 to 1, that would seem like a strong relationship. And it is. That’s what I got using the National Longitudinal Survey of Youth. I compared the Armed Forces Qualifying Test scores, taken in 1999, when the respondents were ages 15-19 with their household income in 2011, when they were 27-31.*

Here is the linear fit between between these two measures, with the 95% confidence interval shaded, showing just how confident we can be in this incredibly strong relationship:

That’s definitely enough for a screaming headline, “How your kids’ test scores tell you whether they will be rich or poor.”

In fact, since I originally wrote this, the Washington Post Wonkblog published a post with the headline, “Here’s how much your high school grades predict your future salary,” with this incredibly tidy graph:

No doubt these are strong relationships. My correlation of .35 means AFQT explains 12% of the variation in household income. But take heart, ye parents in the age of uncertainty: 12% of the variation leaves a lot left over. This variable can’t account for how creative your children are, how sociable, how attractive, how driven, how entitled, how connected, or how White they may be. To get a sense of all the other things that matter, here is the same data, with the same regression line, but now with all 5,248 individual points plotted as well (which means we have to rescale the y-axis):

Each dot is a person’s life — or two aspects of it, anyway — with the virtually infinite sources of variability that make up the wonder of social existence. All of a sudden that strong relationship doesn’t feel like something you can bank on with any given individual. Yes, there are very few people from the bottom of the test-score distribution who are now in the richest households (those clipped by the survey’s topcode and pegged at 3 on my scale), and hardly anyone from the top of the test-score distribution who is now completely broke.

But I would guess that for most kids a better predictor of future income would be spending an hour interviewing their parents and high school teachers, or spending a day getting to know them as a teenager. But that’s just a guess (and that’s an inefficient way to capture large-scale patterns).

I’m not here to argue about how much various measures matter for future income, or whether there is such a thing as general intelligence, or how heritable it is (my opinion is that a test such as this, at this age, measures what people have learned much more than a disposition toward learning inherent at birth). I just want to give a visual example of how even a very strong relationship in social science usually represents a very messy reality.

Post-publication addendums

1. Prediction intervals

I probably first wrote about this difference between the slope and the variation around the slope two years ago, in a futile argument against the use of second-person headlines such as “Homophobic? Maybe You’re Gay.” Those headlines always try to turn research into personal advice, and are almost always wrong.

Carter Butts, in personal correspondence, offered an explanation that helps make this clear. The “you” type headline presents a situation in which you — the reader — are offered the chance to add yourself to the study. In that case, your outcome (the “new response” in his note) is determined by the both the line and the variation around the line. Carter writes:

the prediction interval for a new response has to take into account not only the (predicted) expectation, but also the (predicted) variation around that expectation. A typical example is attached; I generated simulated data (N=1000) via the indicated formula, and then just regressed y on x. As you’d expect, the confidence bands (red) are quite narrow, but the prediction bands (green) are large – in the true model, they would have a total width of approximately 1, and the estimated model is quite close to that. Your post nicely illustrated that the precision with which we can estimate a mean effect is not equivalent to the variation accounted for by that mean effect; a complementary observation is that the precision with which we can estimate a mean effect is not equivalent to the accuracy with which we can predict a new observation. Nothing deep about that … just the practical points that (1) when people are looking at an interval, they need to be wary of whether it is a confidence interval or a prediction interval; and (2) prediction interval can (and often should be) wide, even if the model is “good” in the sense of being well-estimated.

And here is his figure. “You” are very likely to be between the green lines, but not so likely to be between the red ones.

2. Random other variables

I didn’t get into the substantive issues, which are outside my expertise. However, one suggestion I got was interesting: What about happiness? Without endorsing the concept of “life satisfaction” as measured by a single question, I still think this is a nice addition because it underscores the point of wide variation in how this relationship between test scores and income might be experienced.

So here is the same figure, but with the individuals coded according to how they answered the following question in 2008, when they were age 24-28, “All things considered, how satisfied are you with your life as a whole these days? Please give me an answer from 1 to 10, where 1 means extremely dissatisfied and 10 means extremely satisfied.” In the figure, Blue is least satisfied (1-6; 21%), Orange is moderately satisfied (7-8; 46%), and Green is most satisfied (9-10; 32%)

Even if you squint you probably can’t discern the pattern. Life satisfaction is positively correlated with income at .16, and less so with test scores (.07). Again, significant correlation — not helpful for planning your life.

* I actually used something similar to AFQT: the variable ASVAB, which combines tests of mathematical knowledge, arithmetic reasoning, word knowledge, and paragraph comprehension, and scales them from 0 to 100. For household income, I used a measure of household income relative to the poverty line (adjusted for household size), plus one, and transformed by natural log. I used household income because some good test-takers might marry someone with a high income, or have fewer people in their households — good decisions if your goal is maximizing household income per person.

Does sleeping with a guy on the first date make him less likely to call back?

I have no idea. But there is a simple reason that it might seem like it does, even if it doesn’t.

Let’s imagine that a woman — we’ll call her “you,” like they do in relationship advice land — is trying to calculate the odds that a man will call back after sex. Everyone tells you that if you sleep with a guy on the first date he is less likely to call back. The theory is that giving sex away at a such a low “price” lowers the man’s opinion of you, because everyone thinks sluts are disgusting.* Also, shame on you.

Photo by Emily Hildebrand, from Flickr Creative Commons

So, you ask, does the chance he will call back improve if you wait till more dates before having sex with him? You ask around and find that this is actually true: The times you or your friends waited till the seventh date, two-thirds of the guys called back, but when you slept with him on the first date, only one-in-five called back. From the data, it sure looks like sleeping with a guy on the first date reduces the odds he’ll call back.

So, does this mean that women make men disrespect them by having sex right away? If that’s true, then the historical trend toward sex earlier in relationships could be really bad for women, and maybe feminism really is ruining society.

Like all theories, this one assumes a lot. It assumes you (women) decide when couples will have sex, because it assumes men always want to, and it assumes men’s opinion of you is based on your sexual behavior. With these assumptions in place, the data appear to confirm the theory.

But what if that those assumptions aren’t true? What if couples just have more dates when they enjoy each other’s company, and men actually just call back when they like you? If this is the case, then what really determines whether the guy calls back is how well-matched the couple is, and how the relationship is going, which also determines how many dates you have.

What was missing in the study design was relationship survival odds. Here is a closer look at the same data (not real data), with couple survival added:

By this interpretation, the decision about when to have sex is arbitrary and doesn’t affect anything. All that matters is how much the couple like and are attracted to each other, which determines how many dates they have, and whether the guy calls back. Every couple has a first date, but only a few make it to the seventh date. It appears that the first-date-sex couples usually don’t last because people don’t know each other very well on first dates and they have a high rate of failure regardless of sex. The seventh-date-sex couples, on the other hand, usually like each other more and they’re very likely to have more dates. And: there are many more first-date couples than seventh-date couples.

So the original study design was wrong. It should have compared call-back rates after first dates, not after first sex. But when you assume sex runs everything, you don’t design the study that way. And by “design the study” I mean “decide how to judge people.”

I have no idea why men call women back after dates. It is possible that when you have sex affects the curves in the figure, of course. (And I know even talking about relationships this way isn’t helping.) But even if sex doesn’t affect the curves, I would expect higher callback rates after more dates.

Anyway, if you want to go on blaming everything bad on women’s sexual behavior, you have a lot of company. I just thought I’d mention the possibility of a more benign explanation for the observed pattern that men are less likely to call back after sex if the sex takes place on the first date.

* This is not my theory.