Tag Archives: methods/use of data

Sunday Fun: Statistically Significant Others

boyfriend2By xkcd.

Lisa Wade is a professor of sociology at Occidental College and the co-author of Gender: Ideas, Interactions, Institutions. You can follow her on Twitter and Facebook.

On Women’s Comfort with Topless Sunbathing

What should we make of changes in fashion? Are they the visible outward expression of new ways of thinking? Or do fashions themselves influence our sentiments and ideas? Or are fashions merely superficial and without any deeper meaning except that of being fashionable?

It’s summer, and once again magazines and newspapers are reporting on beachwear trends in France, proclaiming “the end of topless.” They said the same thing five years ago.

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As in 2009, no systematic observers were actually counting the covered and uncovered chests on the beach. Instead, we are again relying on surveys – what people say they do, or have done, or would do.  Elle cites an Ipsos survey: “In 2013, 93% of French women say that they wear a top, and 35% find it ‘unthinkable’ to uncover their chest in public.”

Let’s assume that people’s impressions and the media stories are accurate and that fewer French women are going topless. Some of stories mention health concerns, but most are hunting for grander meanings. The Elle cover suggests that the change encompasses issues like liberty, intimacy, and modesty.  Marie-Claire says,

Et en dehors de cette question sanitaire, comment expliquer le recul du monokini : nouvelle pudeur ou perte des convictions féministes du départ ?

But aside from the question of health, how to explain the retreat from the monokini: a new modesty or a loss of the original feminist convictions? [my translation, perhaps inaccurate]

The assumption here is that is that ideas influence swimwear choices.  Women these days have different attitudes, feelings, and ideologies, so they choose apparel more compatible with those ideas.  The notion certainly fits with the evidence on cultural differences, such as those between France and the U.S.

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Americans are much more likely to feel uncomfortable at a topless beach. But they are also much less likely to have been to one. (Northern Europeans – those from the Scandinavian countries and Germany – are even more likely than the French to have gone topless.) (Data are from a 2013 Harris survey done for Expedia.)

This second graph could also support the other way of thinking about the relation between fashion and ideas: exposing your body changes how you think about bodies.  If people take off their clothes, they’ll become more comfortable with nudity. That is, whatever a woman’s original motivation, once she did try going topless, she would develop ideas that made sense of the experiences, especially since the body already carries such a heavy symbolism. She would not have to invent these topless-is-OK ideas all by herself. They would be available in the conversations of others. So unless her experiences were negative, these new ideas would add to and reinforce the thoughts that led to the original behavior.

This process is much like the general scenario Howie Becker outlines for deviance.

Instead of deviant motives leading to deviant behavior, it is the other way around; the deviant behavior in time produces the deviant motivation.  Vague impulses and desires … probably most frequently a curiosity … are transformed into definite patterns of action through social interpretation of a physical experience. [Outsiders, p. 42]

With swimwear, another motive besides “vague impulses” comes into play:  fashion –  the pressure to wear something that’s within the range of what others on the beach are wearing.

Becker was writing about deviance.  But when the behavior is not illegal and not all that deviant, when you can see lots of people doing it in public, the supportive interpretations will be easy to come by.  In any case, it seems that the learned motivation stays learned.  The fin-du-topless stories,  both in 2009 and 2014, suggest that the change is one of generations rather than a change in attitudes.  Older women have largely kept their ideas about toplessness. And if it’s true that French women don’t get fat, maybe they’ve even kept their old monokinis.  It’s the younger French women who are keeping their tops on. But I would be reluctant to leap from that one fashion trend to a picture of an entire generation as more sexually conservative.

Jay Livingston is the chair of the Sociology Department at Montclair State University. You can follow him at Montclair SocioBlog or on Twitter.

Sunday Fun: Confirmation Bias for Everyone!

1By David Malki at Wondermark.  H/t to @annettecboehm.

Lisa Wade is a professor of sociology at Occidental College and the co-author of Gender: Ideas, Interactions, Institutions. You can follow her on Twitter and Facebook.

Overweight Americans Have the Lowest Risk of Premature Death

Last year the Journal of the American Medical Association released a study aiming to determine the relationship between body mass index and the risk of premature death. Body mass index, or BMI, is the ratio between your height and weight. According to the National Institutes of Health, you are “normal weight” if your ratio is between 18.5-24.9.  Everything over that is “overweight” or “obese” and everything under is “underweight.”

This study was a meta-analysis, which is an analysis of a collection of existing studies that systematically measures the sum of our knowledge.  In this case, the authors analyzed 97 studies that included a combined 2.88 million individuals and over 270,000 deaths.  They found that overweight individuals had a lower risk of premature death than so-called normal weight individuals and there was no relationship between being somewhat obese and the rate of early death. Only among people in the high range of obesity was there a correlation between their weight and a higher risk of premature death.

Here’s what it looked like.

This is two columns of studies plotted according to the hazard ratio they reported for people.  This comparison is between people who are “overweight” (BMI = 25-29.9) and people who are “normal weight” (BMI = 18.5-24.9).  Studies that fall below the line marked 1.0 found a lower rate of premature death and studies above the line found a higher rate.

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Just by eyeballing it, you can confirm that there is not a strong correlation between weight and premature death, at least in this population. When the scientists ran statistical analyses, the math showed that there is a statistically significant relationship between being “overweight” and a lower risk of death.

Here’s the same data, but comparing the risk of premature death among people who are “normal weight” (BMI = 18.5-24.9) and people who are somewhat “obese” (BMI = 30-34.9).  Again, eyeballing the results suggest that there’s not much correlation and, in fact, statistical analysis found none.

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Finally, here are the results comparing “normal weight” (BMI = 18.5-24.9) and people who are quite “obese” (BMI = 35 or higher). In this case, we do see a relationship between risk of premature death in body weight.

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It’s almost funny that the National Institutes of Health use the word normal when talking about BMI. It’s certainly not the norm – the average BMI in the U.S. falls slightly into the “overweight” category (26.6 for adult men and 25.5 for adult women) — and it’s not related to health. It’s clearly simply normative. It’s related to a socially constructed physical ideal that has little relationship to what physicians and public health advocates are supposed to be concerned with.  Normal is judgmental, but if they changed the word to healthy, they have to entirely rejigger their prescriptions.

So, do we even have an obesity epidemic? Perhaps not if we use health as a marker instead of some arbitrary decision to hate fat.  Paul Campos, covering this story for the New York Times, points out:

If the government were to redefine normal weight as one that does not increase the risk of death, then about 130 million of the 165 million American adults currently categorized as overweight and obese would be re-categorized as normal weight instead.

That’s 79%.

It’s worth saying again: if we are measuring by the risk of premature death, then 79% of the people we currently shame for being overweight or obese would be recategorized as perfectly fine. Ideal, even. Pleased to be plump, let’s say, knowing that a body that is a happy balance of soft and strong is the kind of body that will carry them through a lifetime.

Cross-posted at Adios Barbie.

Lisa Wade is a professor of sociology at Occidental College and the co-author of Gender: Ideas, Interactions, Institutions. You can follow her on Twitter and Facebook.

U.S. Jobs Are Back, but They’re No Match for Population Growth

Last week CNN triumphantly reported that the job market has recovered to its 2008 peak.  Here’s the headline:

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Not so fast, though.

Sociologist Philip Cohen observes that the real news is hidden in the fourth paragraph. There the author of the piece acknowledges that the job data are numbers, not proportions.  The numbers have bounced back but, because of the addition of almost 12 million people to the U.S. population, the percent of Americans who have jobs or are in school remains lower than it was in 2008.

From CNN:

Given population growth over the last four years, the economy still needs more jobs to truly return to a healthy place. How many more? A whopping 7 million, calculates Heidi Shierholz, an economist with the Economic Policy Institute.

Using the Bureau of Labor Statistics, Cohen offers us a clearer look at where we’re at:

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Lisa Wade is a professor of sociology at Occidental College and the co-author of Gender: Ideas, Interactions, Institutions. You can follow her on Twitter and Facebook.

Saturday Stat: Main, Mean, and Median Street

Mean and median are two measures of “average.”  The mean is the average as we typically think of it: the sum of things divided by the total number of things.  The median, in contrast, is literally the number in the middle if we align all the quantities in order.  People often use median instead of mean because it is insensitive to extreme outliers which may skew the mean in one direction or another.

For a quick illustration of the difference, I often use the example of income. I choose a plausible average (mean) for the classroom population and review the math. “If Bill Gates walks into the room,” I say, “the average income is now in the billions. The median hasn’t moved, but the mean has gone way up.” So has the Gini coefficient.

Here’s a more realistic and global illustration – the net worth of people in the wealthier countries.  The U.S. ranks fourth in mean worth – $301,000 per person…

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…but the median is far lower – $45,000, 19th out of the twenty nations shown.  (The graph is from Credit Suisse via CNN.)

The U.S. is a wealthy nation compared with others, but  “average” Americans, in the way that term is generally understood, are poorer than their counterparts in other countries. 

Jay Livingston is the chair of the Sociology Department at Montclair State University. You can follow him at Montclair SocioBlog or on Twitter.

Income is a Poor Measure of American Inequality

I’d hope that someone who has written a book about “What Shapes Our Fortunes” would have had Sociology 101 where he would have learned the fundamentally different ways that income and wealth work in our economy.  But apparently not.

In Rags to Riches to Rags Again,  Mark Rank writes that because of a great deal of turbulence in household earning over a lifetime “we have much more in common with one another than we dare to realize.”

One of the reasons for such fluidity at the top is that, over sufficiently long periods of time, most American households go through a wide range of economic experiences, both positive and negative. Individuals we interviewed spoke about hitting a particularly prosperous period where they received a bonus, or a spouse entered the labor market, or there was a change of jobs. These are the types of events that can throw households above particular income thresholds.

Ultimately, this information casts serious doubt on the notion of a rigid class structure in the United States based upon income. It suggests that the United States is indeed a land of opportunity, that the American dream is still possible — but that it is also a land of widespread poverty. And rather than being a place of static, income-based social tiers, America is a place where a large majority of people will experience either wealth or poverty — or both — during their lifetimes.

All together now:  Income, that comes in *household* paychecks, regardless of how many earners are contributing to that household income, is not wealth.  Wealth is how much money a household has in the bank and in investments and the assets they own, like real estate, businesses, land, cars, boats, and planes.

Wealth inequality is much greater than income inequality. It looks like this:

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And breaking it down by race:

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It is no small thing for any household to attain an annual income of a million dollars for even one year.

But it is an entirely different experience to have enough wealth that one can no longer worry about income at all, can work the tax system to mask enormous amounts of income,  can essentially withdraw from everyday contact with everyday Americans, can use one’s wealth to leverage political and economic power, and can know that the children in one’s household will never, ever want for a thing.

The “1%” was never about income alone.

Jane Van Galen, PhD, is a professor of education at the University of Washington, Bothell.  Her research focus is on socioeconomic class, education, and digital media. She writes for Education and Class, where this post originally appeared.

Newsflash: Facebook has Always Manipulated Your Emotions

Emotional Contagion is the idea that emotions spread throughout networks. If you are around happy people, you are more likely to be happy. If you are around gloomy people, you are likely to be glum.

The data scientists at Facebook set out to learn if text-based, nonverbal/non-face-to-face interactions had similar effects.  They asked: Do emotions remain contagious within digitally mediated settings? They worked to answer this question experimentally by manipulating the emotional tenor of users’ News Feeds, and recording the results.

Public reaction was such that many expressed dismay that Facebook would 1) collect their data without asking and 2) manipulate their emotions.

I’m going to leave aside the ethics of Facebook’s data collection. It hits on an important but blurry issue of informed consent in light of Terms of Use agreements, and deserves a post all its own. Instead, I focus on the emotional manipulation, arguing that Facebook was already manipulating your emotions, and likely in ways far more effectual than algorithmically altering the emotional tenor of your News Feed.

First, here is an excerpt from their findings:

In an experiment with people who use Facebook, we test whether emotional contagion occurs outside of in-person interaction between individuals by reducing the amount of emotional content in the News Feed. When positive expressions were reduced, people produced fewer positive posts and more negative posts; when negative expressions were reduced, the opposite pattern occurred.

In brief, Facebook made either negative or positive emotions more prevalent in users’ News Feeds, and measured how this affected users’ emotionally expressive behaviors, as indicated by users’ own posts. In line with Emotional Contagion Theory, and in contrast to “technology disconnects us and makes us sad through comparison” hypotheses, they found that indeed, those exposed to happier content expressed higher rates of positive emotion, while those exposed to sadder content expressed higher rates of negative emotion.

Looking at the data, there are three points of particular interest:

  • When positive posts were reduced in the News Feed, people used .01% fewer positive words in their own posts, while increasing the number of negative words they used by .04%.
  • When negative posts were reduced in the News Feed, people used .07% fewer negative words in their own posts, while increasing the number of positive words by.06%.
  •  Prior to manipulation, 22.4% of posts contained negative words, as compared to 46.8% which contained positive words.

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Let’s first look at points 1 and 2 — the effects of positive and negative content in users’ News Feeds. These effects, though significant and in the predicted direction, are really really tiny. None of the effects even approach 1%. In fact, the effects are all below .1%. That’s so little! The authors acknowledge the small effects, but defend them by translating these effects into raw numbers, reflecting “hundreds of thousands” of emotion-laden status updates per day. They don’t, however, acknowledge how their (and I quote) “massive” sample size of 689,003 increases the likelihood of finding significant results.

So what’s up with the tiny effects?

The answer, I argue, is that the structural affordances of Facebook are such users are far more likely to post positive content anyway. For instance, there is no dislike button, and emoticons are the primary means of visually expressing emotion. Concretely, when someone posts something sad, there is no canned way to respond, nor an adequate visual representation. Nobody wants to “Like” the death of someone’s grandmother, and a Frownie-Face emoticon seems decidedly out of place.

The emotional tenor of your News Feed is small potatoes compared to the effects of structural affordances. The affordances of Facebook buffer against variations in content. This is clear in point 3 above, in which positive posts far outnumbered negative posts, prior to any manipulation. The very small effects of experimental manipulations indicates that  the overall emotional makeup of posts changed little after the study, even when positive content was artificially decreased.

So Facebook was already manipulating your emotions — our emotions — and our logical lines of action. We come to know ourselves by seeing what we do, and the selves we perform through social media become important mirrors with which we glean personal reflections. The affordances of Facebook therefore affect not just emotive expressions, but reflect back to users that they are the kind of people who express positive emotions.

Positive psychologists would say this is good; it’s a way in which Facebook helps its users achieve personal happiness. Critical theorists would disagree, arguing that Facebook’s emotional guidance is a capitalist tool which stifles rightful anger, indignation, and mobilization towards social justice. In any case, Facebook is not, nor ever was, emotionally neutral.

Jenny Davis is an Assistant Professor of Sociology at James Madison University and a weekly contributor to Cyborgology, where this post originally appeared. You can follow her on Twitter.