Visualizing the Sociology of Liana Sayer and Time Use Research

Originally posted at Feminist Reflections.

People are often shockingly wrong about how much time they dedicate to various tasks.  In general, we tend to overestimate how much time it takes to do things we dislike and underestimate how much time we spend on tasks we enjoy.  So, people ritualistically overestimate how much time they spend on laundry, cleaning bathrooms, working out and underestimate how much time they spend watching television, napping, eating, or doing any number of tasks that provide them with joy.

Asking about how people use their time has been a mainstay on surveys dealing with households and family life.  We ask people to assess how much time they spend on all manner of mundane tasks in their lives–everything from shopping, sleeping, watching television, attending to their children, and household labor is divided up into an astonishing number of variables.  The assumption, of course, is that people can provide meaningful information or that their responses are an accurate (or approximately accurate) portrayal of the time they actually spend (see here).  This is why time diary studies came into being–they produce a more accurate picture of how people use their time.  People record their actual time use throughout the day in a diary, marking starting and stopping points of various activities.  And there are a number of different scholars who rely on this method and these data.  But Liana Sayer is among the leading scholars in the field.  When I’ve seen her present, or others present on time use data, the data are almost always visualized in the same way (as stacked column charts).  Personally, I love seeing the data this way.  The changes jump out at me and I feel like I instantly recognize trends and distinctions they discuss. But I have learned in my classroom that students do not always have the same reaction.

I’m interested because I use data visualizations in class a lot.  And in my (admittedly limited) experience, students have an easier time interpreting the story of time use data when it is visualized in some ways over others.

All of the examples here are pretty basic changes in data visualizations.  But, learning these basics are necessary to help students read the more complex data visualizations they may encounter.  Being able to interpret visualizations of temporal data is important; it’s part of what helps social scientists consider, measure, and critique the idea of “feminist change.”  Distinguishing between men’s and women’s time use is only one pocket of this field.  But, it’s the one I’ll focus on here, and on which Liana Sayer is among the foremost experts.  The data I’m visualizing below come from one of Sayer’s most cited articles: “Gender, Time and Inequality: Trends in Women’s and Men’s Paid Work, Unpaid Work, and Free Time” (here – behind a paywall).

It’s fairly common to present time use data with a series of stacked columns (the same way the Census often illustrates shifts in household types).  Below is a visualization of the differences between women’s and men’s minutes per day allocated to paid work, unpaid work, free time, and time dedicated to self care.  It’s all time diary data and we talk about why this is more reliable and a better measure – but also why it is more difficult to collect, etc.  Some students see the story of this graph immediately.  I do too.  Men’s time allocated to paid labor decreased while women’s increased.  And women perform more unpaid work than do men.  Lots of students, however, are stumped. stacked column

But when I present the data differently, students often have an easier time seeing the story the chart is produced to illustrate.  The Pew Research Center visualizes a lot of their data using stacked bar graphs.  And maybe it’s because these are more easily recognized by people with less experience with data visualization.  I have found that more of my students are able to read the chart below than the one above (at least for temporal time use data comparisons).stacked bar

Another way of presenting these data might be to use clustered columns.  I have also found that students are more quick to recognize the trend in these data with the graph below than they are with the initial stacked column chart.cluster column

But, I’ve found that students have the easiest time with line charts for temporal time use data.  On the chart below, I deleted the grid lines because The New York Times sometimes displays time trend data this way (see Philip Cohen’s piece on NYT Opinionater, “How Can We Jump-Start the Struggle for Gender Equality?” for an example).  Students that struggle to recognize the trend in the clustered column chart, are much faster to see the trend here.line

These aren’t an exhaustive set of examples, and all of them are basic visualizations.  For instance, we might use a stacked area chart to show these data (as trends in the racial composition of the U.S. are often depicted), a scatterplot (as data on GDP and fertility rates are often illustrated), a series of pie charts (as men’s and women’s various compositions in different economic sectors are sometimes visualized), or something else entirely. Screen Shot 2016-03-22 at 9.59.28 AMIn fact, The New York Times produced a really incredible interactive stream graph visualizing data from The American Time Use Survey that illustrates differences in time use between groups. I sometimes have students explore this graph in my course on the sociology of gender.  But many struggle interpreting it.  This is, I think, in part due to the fact that we often take data visualization literacy for granted.  It’s a skill, and it’s one we should be better at teaching.

I think the point I want to make is that we (or I, at least) need to think more carefully about how we visualize our data and findings to different audiences.  Liana Sayer has an incredible mastery of this (she presents data in all of the ways mentioned in this post and more throughout her work).  One thing that I’m thinking more about as I write and teach about research and findings amenable to data visualization is which visualizations are best suited to which kinds of data (something all scientists are concerned with), but also which visualizations work with which kinds of audiences.  This is new territory to me.

Visualizing feminist change in a single chart is difficult.  And it’s often accompanied by, “Well, this is true, but let me tell you about what these data don’t show…”  But, I’m interested in how we make choices about visualizing feminist research and whether we need to make different kinds of choices when we talk about the findings with different kinds of audiences.

Gender Segregation By Victorian Design

Homes have always illustrated a great deal about those who inhabit them.  And changes in architectural design reflect much more than simply new techniques and styles.  They also reflect changing relationships between groups of people.  Victorian architecture is famous for a number of things, but one of my favorites is the notion that rooms really ought to only have one purpose.*  One of my favorite ways that this is illustrated is by highlighting the lack of a bedside table in most bedrooms in the 1800s in England.  Reading (or anything else for that matter) was an activity that was best undertaken in a separate (and more appropriate) room of its own.

To accomplish this, larger houses had an extraordinary number of rooms.  Smaller houses were forced to shift furniture around depending on what was going on that particular day.  While one of the premises of modern architectural design involves breaking down walls and opening up space, the Victorians were much more concerned with erecting walls and closing spaces off.  There are all sorts of remnants of this time still present in homes today – though they are often put to separate use.  For instance, parlors are still present in many homes.  They’re typically small rooms near the front of the house where household guests would have congregated, and within which Victorian forms of courtship took place (see Bailey on courtship here).  But few of us use these spaces as they were originally intended.  They feel impractical by today’s standards.

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The U.S. Gender Gap in Traffic

“[A]n analysis of traffic can enrich sociological theory.” (Schmidt-Relenberg, 1968: 121)

Almost everywhere we go is a “gendered space.”  Although men and women both go to grocery stores, different days of the week and times of the day are associated with different gender compositions of shoppers.  Most of our jobs are gendered spaces.  In fact, Census data show that roughly 30% of the 66,000,000 women in the U.S. labor force occupy only 10 of the 503 listed occupations on the U.S. Census.  You’d probably be able to guess what some of these jobs are just as easily as you might be able to guess some of the very few Fortune 500 companies have women CEOs.  Sociologists refer to this phenomenon as occupational segregation, and it’s nothing new.  Recently, I did read about a gender segregated space that is new (at least to me): traffic.

When I picture traffic in my head, I think of grumpy men driving to jobs they hate, but this is a horrible stereotype of traffic that’s misleading.  Women actually make up the vast majority of congestion on the roads.  One way of looking at this is to argue that women are causing more congestion on our roads.  But another way to talk about this issue (and the way to talk about this issue that is consistent with actual research – and ought to make us feminists smile) is to say that women endure more congestion on the roads.

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Wikipedia – Gendered Space, Gendered Knowledge

I would rather be a cyborg than a goddess.
–Donna Haraway

Google just about anything these days and Wikipedia’s answer is sure to be high on the list of results.  New technology brings with it new expectations and many of us have grown accustomed to instant access to answers to just about any question we can imagine.  Have you ever have a conversation with someone and been unsure about a date, a name, or the title of a movie?  It’s fun.  You rack your brains and sometimes come up with the right answer or sometimes agree to move on without the information.  Ever had a similar conversation with an iPhone owner?  Less fun.

Part of the attractiveness of the internet and internet search engines and wiki’s is that they feel like they ought to be more democratic.  The reason that Google works is based on the collective wisdom of internet users (though certainly people have found ways of attempting to exploit it).  Wikipedia is similar.  It’s basically an online, evolving encyclopedia.  Anyone can contribute, edit posts, add new information, or even new items currently lacking a post.  The interesting finding, however, is that although anyone can participate, it’s not just anyone that does participate.

Wikipedia has a huge gender gap in contributors to the site.  The results from Wikipedia’s survey of users found that less than 15% of contributors to the site are women.  Less than 15%?!??!  This gap in contribution is compounded by the fact that Wikipedian women, on average, post on fewer topics such that women’s overall contribution to Wikipedia in terms of actual material is less than 10%.  Seen from a different angle, men produce more than 90% of the material on Wikipedia!

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