family

The American Family – A demographic portrait

The New York Times has been running a variety of stories about American demography ever since the 2010 Census results were made publicly available. In this story (which came out last June…sorry for the delay), the article focused on today’s atypical families by spending time with a family comprised of a mom who used sperm donated by a gay friend of hers to have a baby. The biological dad stayed in the picture more than he had planned, as did his partner, though the end of the article hints that the biological mom and dad might be slowly coming closer to a shared living situation that more closely mirrors the traditional set-up.

That sort of one-off telling of the tale of a particular family is not what drew my attention. The larger demographic trends are what I find more fascinating and the interactive infographic offers a much less linear tool for exploring the changes in the demography of the American family than does the article. The article offers a narrative about a particular set of relationships. The infographic presents a question and then gives users enough historical and national context to poke around the possible answers to that question for themselves.

The site allows users to start with a head of household – that matches the way the Census is collected and makes sense. I picked a married couple above. Below I pick a variety of others but if you are sick of crappy screen grabs, feel free to go to the NYTimes site and choose the selections on your own.

What works

+ The graphic design is friendly without offending too many sensibilities (OK, the guy’s hair could be different to be more racially inclusive and it would be nice if the woman didn’t have to wear a skirt, but overall, I like the figures).

+ Another thing I like about the design is that they smack the percentage up there without feeling that they have to stick it in a pie chart or a graph or any other visual. They assume people have basic numeracy and can interpret a percentage without having to see it as a pac man…I mean pie chart. This leaves the visual field fairly clean and allows the focus to be on the family.

+ The graphs underneath the main family form do an excellent job of providing historical, racial, and income-based context. I love the history one – I think the big point about American family forms is that they are now and have always been subject to a fair amount of change despite the fact that it is fairly common to hear the “American family” referred to as if it were one kind of thing and had been since time immemorial.

+ The interactive component is excellent. Add some kids. Then kill them off. Or keep the kids and give them a different household head. Or get rid of the young kids and add adult kids. Or forget kids and spouses: just add siblings. Besides how much fun I had doing this, I ended up exploring many more angles of the American family demography question than I otherwise would have.

Of course, I was interested in what the story is for people like me (single women)…

if I were a man

…and whether or not my situation would be different if I were a man. I was surprised that there are more single women than men until I remembered that men die younger so I bet that the difference shows up at the later end of the life course, not so much among my age cohort.

on the other hand

…or had a child on my own like the woman in the article.

What about the same sex couples? Not exactly a huge percentage of the population, but the Census data upon which this was based are having trouble keeping tabs on the variations of legal statuses of same sex cohabiting couples. In some states same sex couples could marry in 2010, in some states not so much. This is a trend to watch in 2020 and 2030.

What needs work

I wish there were a way to visualize ‘any children’ instead of having everything broken down by age and number of children. I found myself curious to figure out how many households had kids, who they were, and whether or not they were single-headed, couple-headed, same-sex couple-headed and so on. But there’s no way to do the basic kids vs. no-kids comparison here.

Accessing good data online

This contemporary overview of American family demography was put together by some of the digital team at the New York Times and ran alongside “Baby Makes Four, and Complications”. It uses IPUMS data (which came from the US Census but had to be cleaned up and made properly malleable for crunching with statistical software before it could be analyzed).

The Integrated Public Use Microdata Series – IPUMS – is a project based at the Minnesota Population Center and used widely by American social scientists to study both domestic and international demography. Users – and just about anyone can become a user – can download subsets of the US Census suitable for data analysis on typical desktop computers. The subsets are random samples of the full Census and are generally considered to uphold the highest standards currently outlined for use with the statistical modeling techniques that common among social scientists. While IPUMS is an excellent, fantastic, extremely valuable resource for academic researchers. The Social Explorer, a website supported by Oxford University Press and headed up by Andrew Beveridge at Queens College and the CUNY Grad Center, tries harder to produce public-facing reports using data from IPUMS as well as the American Community Survey and other large-scale surveys. The Social Explorer also makes data available for others to analyze, so between IPUMS and The Social Explorer, it is much easier to get good data sets for analysis than it was in the past.

References

Kleinfield, N.R. (19 June 2011) Baby Makes Four, and Complications New York Times, NY/Region Section.

Steven Ruggles, J. Trent Alexander, Katie Genadek, Ronald Goeken, Matthew B. Schroeder, and Matthew Sobek. Integrated Public Use Microdata Series: Version 5.0 [Machine-readable database]. Minneapolis: University of Minnesota, 2010.IPUMS USA website

Beveridge, Andrew, et al. The Social Explorer website. New York: Oxford University Press.

Kids and happiness

Thanks to my twitter feed I landed on Philip Cohen’s blog post “Children beget happiness, eventually” on his blog Family Inequality. In the post, Cohen discusses A Global Perspective on Happiness and Fertility which appeared in Population and Development Review last March.

Margolis and Myrskyla used the World Values Survey from 1981 – 2005 for a total of 201,988 responses across 86 countries to perform their inquiry into the relationship between having kids and being happy. They measured happiness by asking people “taking all things together, would you say you are very happy, quite happy, somewhat happy, or not at all happy?”. They controlled for all sorts of things that probably matter like socioeconomic status, country level effects, and state welfare regimes. This is global evidence, folks, not US-only.

Cohen included the graph below and discussed the author’s findings which, in summary, are as follows:

1. Having kids does not lead to happiness when parents are actively involved in raising said children.
2. Older parents consistently report being happier than their childless counterparts. [My editorial comment: It is reasonable to believe that, for the most part, the children are no longer living with their parents by the time their parents start to report increases in happiness. At the very least, the kids are at least spending more time out of the home by the time mom and dad are between ages 40 and 49. The majority of kids are almost surely out of the house by the time their parents are 50+ which is the ‘happiest’ time to be a parent. Perhaps it’s because parents are proud of their kids’ accomplishments, perhaps it’s because the parents are no longer anxiously worrying about their kids well-being on a day-to-day basis. Who can say.]
3. Results in the 15 – 19 age cohort have fewer data points and are thus somewhat less representative. It’s hard to have three or four kids while in that age cohort.

An experiment

I used the exact same evidence to create the graph at the top of the blog because I wasn’t satisfied that the results were being clearly communicated by the graph above. Instead of plotting the happiness of age cohorts, I flipped it around and looked at happiness by number of children. Since I used the exact same information – pulling it directly from the graph because I couldn’t find a corresponding table in the paper – I do not have distinctly different findings to report. Duh. However, this is an excellent example of why visualizations are meaningful. It’s the same information, plotted in two different ways.

In my version, it is clearer to see that having 1 – 3 children represents extremely similar patterns of happiness across the life course. I discount the results at the very low age range because we know that the data at that end is less-than-representative. If we just look from the 20-29 cohort through to the 50+ cohort, we see that having more kids eventually represents more happiness for parents but that they are about equally unhappy during the most active years of child-rearing.

Having four or more kids breaks the pattern. This is evident in both graphic representations. In my opinion, it is more evident in the first version of the graph than the second version, as they appear in this post. I used a similar sensibility for the colors of 1, 2, and 3 children trends and a different kind of color for the 4+ kids scenario.

The graphs do not explain why having four (or more) kids would be so different than having, say, three kids. More study is needed.

My #1 take-away: do not have four or more children if you value your happiness.
My #2 take-away: Think twice about having any children at all if you would prefer to be happy for the twenty or so years it’s going to take those kids to move out.
My #3 take-away: Thanks, mom and dad. I hope you’re happy now.

References

Cohen, Philip. (14 May 2011) Children beget happiness, eventually [blog post] on Family Inequality.

Conley, Dalton. (2005) The Pecking Order: A Bold New Look at How Family and Society Determine Who We Are, New York: Vintage.

Margolis, Rachel and Mikko Myrskyla. (9 March 2011) A Global Perspective on Happiness and Fertility in Population and Development Review, Vol.37(1): 29-56.

Happy Halloween

This graphic is a bit too cartoon-ish for my tastes but it does a good job of illustrating a health care gap that, even during the health care debate, went over-looked. I figured Halloween – a holiday whose commercialization revolves around candy – might be a good time to post the dental health care graphics developed over at the GOOD magazine transparency blog.

In the spirit of full disclosure: I was a dental assistant for a summer. The numbers here are accurate and have very real consequences. I used to see kids who did not know (they had no idea) that drinking soda was bad for their teeth. These kids sometimes had 7 and 8 cavities discovered in one check up. For older people, dry mouth would lead them to suck on lozenges or hard candy all day and they’d end up with a bunch of cavities, too. Bathing the mouth in sugar is bad. Combining the sugar with the etching acid in soda is even worse.

Once a tooth has a cavity, it needs to be filled or the bacteria causing the decay will continue to eat away at the tooth, eventually hitting the pulp in the middle of the tooth. Once that happens, the person is usually in pain and needs a root canal. Even if they aren’t in pain, they need to have the infected tissue removed (that’s what a root canal treatment does) or the infection can spread, sometimes into the jaw bone. There is no way for the body to fight an infection in a tooth because the blood supply is just too little to use the standard immune responses.

Dental decay progresses slowly. Kids lose their primary teeth any decay in those teeth goes with them. Therefore, it’s not all that common to see teenagers needing root canals. But it does happen. Root canals are expensive. It’s a lengthy procedure requiring multiple visits and a crown. Pricey stuff. BUT, this process allows the tooth to be saved. Without dental insurance, sometimes folks opt for the cheaper extraction option. Once a tooth is extracted, that’s it. It’s gone. (Yes, there is an option to have a dental implant but that’s even more expensive.) So a teenager who likes to suck on soda all day long and who may not be all that convinced about the benefits of flossing could end up losing teeth at a young age. I can tell you because I’ve seen it: a mouth without teeth is not a happy mouth. All those teeth tend to hold each other in place. Once some of them are extracted, the others can start to migrate. Extract some more and things get more interesting and people start to build diets around soft foods. Eventually, once enough of them are extracted the entire shape of the mouth flattens out – not even a denture can hang on to help the person eat.

Unfortunately, poor dental health disproportionately impacts poor people, as these graphics demonstrate. But that disproportionate impact can double down. Dental health is often seen as a sign of class status. People with poor dental health have trouble getting good jobs, especially in a service economy. For what it’s worth, I bet they also have more trouble in the dating/marriage market.

References

Di Ieso, Robert. (2 September 2010) How well do we take care of America’s Teeth? [Centers for Disease Control; Pediatrics]

Bad title

Why draw attention only to the fathers? Clearly there must be quite a few unmarried mothers out there as well. I hope this isn’t suggesting that deciding to take a relationship into marriage is somehow only or primarily the man’s responsibility. Both women and men have agency around the marital decision. It would be nice if cultural constructs supported equal opportunity for popping the question…but headlines that emphasize men’s agency over women aren’t going to get us any closer to equality on that front.

What works

It’s nice to see that this graph points out where definitions of racial categories change. It is also nice that it draws attention to the problem that many American children are being born into poverty or at least situations where resources are extremely constrained. In another graph elsewhere, the same group also reminds us that these births are largely NOT happening to teen parents.

The other critical point is that out of wedlock births are on the rise even though birth rates for teen mothers are declining. If in the past it was possible to think that the problem is just that teens are out having unprotected sex that leads to accidental births, we can no longer be so sure that this is what is happening. Age at first sex is decreasing which means that most of the people having children out of wedlock are capable of having sex without getting pregnant. They probably have been doing just that for years. Having children out of wedlock is best understood to be a choice, then, not an accident. Any efforts to prevent child poverty are probably not going to be successful if they rest on sex ed or free condoms (though I personally believe those things are important for other reasons). The American Heritage Foundation believes that if people would just get married, these kids wouldn’t be born into poverty. Others aren’t so sure it’s that simple.

What needs work

The problem with the write-up accompanying this chart is that it implies that the causal mechanism goes something like this: for whatever reason couples have children together but do not get married. The failure to get married means that these children will be far more likely to be raised in poor or impoverished conditions. For emphasis, I’ll restate: the parents’ failure to marry one another leads to children being raised in poverty.

Now. Here’s what I have to say about the chart. First, if that is the message, why not depict the out-of-wedlock birth rate by poverty status, preferably poverty status prior to pregnancy? I’d settle for poverty status at some set time – like the child’s birth or first birthday, but that isn’t as good. I feel like showing these numbers by race is subtly racist, implying that race matters here when what really matters is poverty, at least according to the story that they are telling and the story that many marriage scholars care about. Yes, it is true that poverty and racial status (still) covary rather tightly in America, but if the story being told is about poverty, I’d like to see the chart address that directly rather than through the lens of race. Furthermore, if race DOES matter, where are Asians? American Indians?

Moving away from the chart for a moment and getting back to the causal story, marriage researcher Andrew Cherlin finds that the causal arrow might go the other way. Being poor may be a critical factor in preventing folks from getting married. William Julius Wilson was an earlier proponent of this concept, especially with respect to poor African Americans. His work suggested that during and after the post-industrial decline in urban manufacturing jobs, African American men were systematically excluded from the work force and this made them appear to be poor marital material. Cherlin’s more recent work applies more broadly, not specifically to African American men, and bolsters the idea that marriage is something Americans of all backgrounds feel they shouldn’t get into until they are economically comfortable. What ‘comfortable’ means varies a lot, but most people like to have steady full-time jobs, they like to be confident that they won’t get evicted, that the heat or electricity will not be turned off, that they will have enough to eat.

The more important question would be: why don’t these assumptions apply to having children? Whereas getting married can represent an economic gain if you are marrying a working spouse, having children certainly does not (state subsidies do not cover the full cost of having children no matter how little the children’s parents make). Perhaps what we are faced with is people for whom getting married may not represent an economic gain. Marrying a person without a steady job could present more of a drain on your resources than staying single, whether or not you have kids.

What Works

I love that recessionary periods are included in this graphic. They are the lavender columns and it is obvious that recessions tend to correlate with increases in the number of people in poverty and that the current recession is really a doozy.

What saddens me the most is the graphic that depicts how poverty breaks down by age. First, note that people over age 65 have the lowest poverty rate of any age group. Then remember that they receive social security and health care. Now wonder what would happen to the economy if every US citizen were supported at that level or above. I cannot answer that question, but this graphic compels me to pose it.

Second, note that the age group most likely to be poverty stricken is children. Over 20 percent of people under the age of 18 are living in poverty. Think about it: if a parent with three kids loses his or her job, that means four people are negatively impacted from that single job loss. In this economy, I’m guessing that is part of the reason we see children sliding into poverty.

Third, have a look at this next graphic. Note that beyond the absolute number of poor kids and the rate of poverty among children, the proportion of impoverished Americans who are under 18 shows over-representation. Growing up poor is not only difficult for the kids, but it is not good for the future of the country. Being poor comes with all sorts of baggage for kids – they are more likely to live in poorer school districts with lower quality schools, they are more likely to live in more dangerous neighborhoods, they are more likely to have food insecurity (just try studying for a math test or writing a composition when you’re hungry), poor kids are more likely to be African or Hispanic American which might mean they are also dealing with face-to-face and institutional racism all throughout their lives, and so forth. Not trying to sound like Stevie Wonder here, but these kids are our future. As a country we’re doing a crap job at making sure they have the basic physical, social, and educational support they need to live up to their best potential. Quite stupid. Decision making made by people who have trouble seeing past the end of their own nose, perhaps?

Forgive me. I know I am supposed to keep politics out of my blog but it’s hard to see how making sure kids are not living in poverty is a political issue. It’s a human issue. I would hope we can at least agree kids should not be living in poverty. I realize that it is much more difficult to agree on how to go about getting them out of poverty and preventing others from becoming poverty-stricken in the first place.

What Needs Work

Right. So what needs work here is our economy. But that is not news so I’ll let that debate sit.

The New York Times article about this topic pointed out that what needs work is the way the poverty line is calculated. On one hand, at about $11,000 for a single adult and $22,000 for a family of four it’s awfully low. This is because when the original formula for calculating poverty was adopted, it was tied to food prices and food budgets now make up a smaller proportion of the overall family budget than they did when the formula was concocted. [Remember this example folks: equations are not unbiased.] Over the years, family food budgets have experienced a real drop due to subsidies (the true costs are not passed to consumers), technological advances (we can grow more for less $ with fertilizer, GMOs, antibiotics for livestock, and pesticides for greens/grains), and ‘advances’ in corporate agriculture (economies of scale, see Michael Pollan’s work, Eric Schlosser’s “Fast Food Nation”, Marion Nestle’s “Food Politics”). Other critical costs for surviving from day to day like housing and health care have risen. On the other hand, benefits from programs like food stamps are not included in ‘income’ so there might be a few people bouncing above that poverty line once we take their food stamps (and a few other benefits) into account. Then again, the poverty line is too low so even if food stamps sends a family above it, they are still likely to experience poverty even if they don’t fit the current fiscal definition of poverty. The other problem with the calculation is that it does not take into account differences in regional costs of living. Living in New York City is expensive. Living in a rural area may be less so though paying to own, insure, maintain, and fuel a car or two to drive to work, school, and the grocery store could hike up the rural cost of living more than I know. With an annual budget of $22,000 for a family of four, a car or two would be a real cost, one that an NYC resident would not need to handle.

There is a graphic in the report that shows where poverty rates would be if the poverty line were adjusted upward or downwards.

References

DeNavas-Walt, Carmen; Proctor, Bernadette; Smith, Jessica. (September 2010) Income, Poverty, and Health Insurance Coverage in the United States, 2009 US Census Bureau, Current Population Reports: Consumer Income.

Eckholm, Eric. (16 September 2010)
Poverty Rate Rose Sharply in 2009, Says Census Bureau. New York Times.

What works

Thank you, Pew Research, for all of your hard work.

This set of lines does not tell a story about marriage and wages, it poses a question. Let me first take a moment to stop and praise the graph maker for choosing lines instead of bars. This is basically a series of timelines presented on the same axes. When displaying trends, lines are better than boxes. A line can travel over time, a box just sits there. Of course, then, for time series data, unless there is a compelling reason to discourage people from feeling a sense of movement over time, then go with a line. You might want to choose a box or series of boxes if you have reason to believe your dataset is not truly continuous.

Second, let me say that I enjoy the way the context provided here forces the viewer to wonder why it was that the wages of single people flattened out. While it might be tempting – and some have done it – to assume that getting married makes you rich, looking at the trends presented the way they are here makes it hard to jump to that conclusion. We can see changes over time in the relative wages of married and single men and women, but we cannot see any reason to think that it is marriage that leads to increased wages. Folks who study marriage and wages (Andrew Cherlin, Betsy Stevenson and Justin Wolfers, Kathleen Gerson, and many others) have long pointed out that even though there has long been a correlation between marriage and wages (married people tend to have higher wages) we have no idea whether being married leads to higher wages or having higher wages leads to getting/staying married. The set of lines above does a good job of making sure it is difficult to jump to a causal conclusion.

Karen Sternheimer at Everyday Sociology blog which is part of Norton publishing covered this question a long time ago, but she focused on the gender difference in wage returns. It used to be that women benefited economically by getting married but now that women’s and men’s salaries are getting closer to parity, men see a bit more of a per capita bump than do women when they get married.

This still does not explain why single people make so much less or whether marriage preceeds the wage increase or the actual or promised high wages attract marriage partners.

Dalton Conley, in Elsewhere, USA, pointed out that what could be more alarming than the distance between single and married people is the way that equality in marriage partners (folks are starting to equalize their strategy for choosing mates – more and more we all want to marry wealthy, attractive people who are likely to continue to be wealthy and attractive. This holds regardless of whether we are men or women.). This means that folks with high incomes marry other folks with high incomes and increase the distance between top earning households and lower earning households. He calls it doubling down, though I suppose if you are a high earner married to another high earner you might consider it doubling up. Either way, the distance between the haves and the have-nots may actually be exacerbated (in some ways) by the sexual revolution, especially if single people’s wages flatten out. I’m thinking in particular of single parents, who are going to be raising kids on sole salaries lower than their married counterparts, for whatever reason. Their kids are competing for spots in the good high schools and colleges with all the kids whose high earning parents doubled up.

I love graphics that make me ask questions.

What needs work

The married men’s trend line ends up looking like a shadow of the married women’s trend line even though men are not actually women’s shadows. I would have recommended a different color scheme to make sure we don’t read men as existing in women’s shadows.

References

Sternheim, Karen. (2010, February) “Men and Marriage” at Everyday Sociology by W.W. Norton Publishing.

What works

1. Menlo is my favorite font of the moment for information graphics.
2. I have no idea why I haven’t seen this Venn diagram before. In my humble opinion, if you are a social scientist and you are attempting to display a concept that may or may not have solid numbers to back it up, start with the Venn diagram because:
a. Venn diagrams are easy to make.
b. Venn diagrams are easy to understand.
c. Venn diagrams are not expected to represent solid numbers. They certainly can be employed in that way, but they are not always employed in that way so you are not likely to mislead readers that you are backing your claim up with census data.
3. I am doing a bit of research on marriage and I have run up against many arguments that seem to believe that marriage and childbearing always go together, or at least that they OUGHT to always go together. News flash: 36.9% of children are born out of wedlock (Cherlin, 2008). Other adults get married but do not have children. Yet other adults get married, have children, and then end up unmarried again because divorce and death ended their marriage. The above graphic should help clear up what actually happens in the world. Marriage and child raising frequently have no overlap.

What needs work

I was so upset that I didn’t stop and look up the actual data for each of these segments. In part, I wanted to leave it as a universal concept and NOT tie it to US data. But yeah, I realize it would be better if I had sat down and figured out how many people are in each of these three areas. That’s coming in the article version. And after I take a deep breath to disperse the anger I feel at people who make illogical arguments.

References

Cherlin, Andrew. (2008) “The Marriage Go-Round.” New York: Vintage.

What works

I am not a huge fan of this graphic though I admit it works better in print than it does in this crappy scan of the print article. My apologies. Click through here for a crisp version.

In summary, the article is about the way that research is done in the presence of many more data points (specifically, complete DNA maps of numerous individuals) and much more processing capacity. They argue using a case study revolving around the personal story of Sergey Brin who is at risk of developing the as-yet-untreatable Parkison’s disease, that data mining means research will progress much faster with no loss of accuracy over traditional research methods. They use a medical research case so they get to conclude that moving to data mining will mean people who might have died waiting around for some peer review committee (or other tedious component of double-blind research methodology) will live. Hallelujah for data mining!

They summarize their happiness in this Punky Brewster of a timeline.

What needs work

First, why did the art director order a timeline and not a diagram about how the assumptions underlying the research method have changed? It is clear that the article is taking a stand that the new research methods are better because they are faster and, in the case of Parkinson’s, could save lives by speeding things up. That is undoubtedly true, as it would be for any disease for which we currently don’t have anything that could be referred to as a “cure”. However, as a skeptical sort of reader, I find it difficult to simply believe that the new data-mining variety research is always going to come up with such a similar result – “people with Parkinson’s are 5.4 times more likely to carry the GBA mutation” (hypothesis driven method) vs. “people with Parkinson’s are 5 times more likely to carry the GBA mutation” (data-mining method). If the article is about research methods, which is ostensibly what it claims. However, featuring the chosen cause of e-world celebrity Sergey Brin could indicate that Wired doesn’t so much care about changing research methods as it cares about selling magazines via celeb power. Fair enough. It’s kind of like when Newsweek runs a cover story about AIDS in Africa accompanied by a picture of Angelina Jolie cradling a thin African child. Are we talking about the issue or the celebrity? In this particular article, it seems to me that if the core message were to focus appropriately on the method, the graphic could have depicted all of the costs and benefits of each research model. The traditional model is slower but it makes more conservative assumptions and subjects all findings to a great deal of peer review which offers fairly robust protection against fallacies of type 1 and type 2 (ie it protects us from rejecting a true hypothesis as false and accepting a false hypothesis as true). In the data mining scenario, since the process begins not with a hypothesis but with the design of a tool, there are reasons to believe that we may be more likely to run into trouble by designing tools that too narrowly define the problem. A graphic describing just how these tools are constructed and where the analogous checks and balances come in – where are the peer reviewers? What is the hypothesis? How do data-miners, who start by developing tools to extract data rather than hypotheses in line with the current literature, make sure they aren’t prematurely narrowing their vision so much that they only end up collecting context-free data (which is basically useless in my opinion)?

Don’t get me wrong, I am excited by the vast quantities of data that are both available and easy to analyze on desk top computers (even more can be done on big work stations and so forth). Caution is in order lest we throw out all that is reliable and robust about current research methods in favor of getting to a result more quickly. We could use the traditional hypothesis driven, double-blind kind of trial procedure coupled with the power of DNA analysis and greater processing capacity. It’s somewhat unclear why we would abandon the elements of the traditional scientific method that have served us well. There is a way to integrate the advances in technology to smooth over some of our stumbling blocks from the past without reinventing the wheel.

Concerns about the graphic

My second major problem is that this graphic is one of a type commonly referred to as a ‘time line’. In this case, what we appear to have is a time line warped by a psychedelic drug. This might, in fact, be appropriate give that the article is about neurology and neuropathy. Yet, the darn thing is much harder to read in the Rainbow Brite configuration than it would be if it were, well, a line. Time. Line. And the loop back factor implies that there is going to be a repetition of the research cycle starting with the same question (or dataset) all over again. That’s sort of true – the research cycle has a repetitive quality – but it is not strictly true because hopefully the researchers will have learned enough not to ask the exact same question, following the exact same path all over again.

References

Goetz, Thomas. (July 2010) Sergey’s Story Wired Magazine.

Wired magazine. (12 March 2009) Science as Search: Sergey Brin to Fund Parkinson’s Study on the Wired Science blog.

23andme (11 March 2009) A New Approach to Research: The 23andMe Parkinson’s Disease Initiative. [This was an early announcement about this project from 23andme who offered the DNA analysis].

What works

What Terri Chiao and Deborah Grossberg Katz from Columbia University’s GSAPP design school have done is come up with a way to represent percentages using a flow-chart. Not only is it creative in the sense that this sort of data rarely gets displayed this way, but it helps turn the data into a narrative. In order to figure it out, the viewer quite literally has to reconstruct a story that sounded something like this in my head: “The population they are concerned about has 40% of people already experiencing homelessness with another 60% at risk of homelessness. The folks who are already homeless are the only ones living on the street, but really, 75% of already homeless people live in shelters. As for the at-risk-of-homelessness people, 60% live with family or friends. Twenty-five percent of the at-risk population owns their homes … why, then, are they at risk of homelessness? Both the at-risk and already homeless groups have far more families than single folks. And what does it mean to be homeless in jail/prison? That you aren’t sure where you will go when you exit? Somehow I feel like that could describe a lot of the prison population. And what about half-way houses? Those still exist, right?”

The flow-chart concept is not typically used to describe the breakdown of percentages and what works here is that it forces the viewer to walk through the narrative. As a pedagogical maneuver, it’s quite successful. Because of the way the information is presented, it invites questions in a way that a pie chart or a bar graph may not. It’s also a little harder to interpret. Graphics that invite questions often are a bit more challenging to ingest, not quite so perfectly sealed as other more common strategies might appear.

What needs work

I spent a good deal of time looking at this chart trying to figure out what the blue means. I still don’t understand what the blue means.

I also would like to see on the graphic some explanation of how they determined who was at risk of being homeless. Because when I got to the section of the flow-chart that showed how many of the at-risk population owned their homes, I began to get confused. By ‘own home’ do they not mean actually owning the home, but renting it or paying a mortgage on it? And if they do mean that folks actually own their homes outright, how can they be at risk of homelessness? Is the home about to be seized by eminent domain to make way for Atlantic Yards? At risk of being condemned (I hope NYC doesn’t have so many properties at risk of condemnation)? I’m sure if the makers of the graphic ever find their way to this page they will be upset because ‘at-riskness’ is described in the paper. But in life online, stuffing a little more text into the graphic is often a good idea because cheap folks like me will take the graphic out of context and whatever isn’t included will be lost. In this case, though, all is not lost. First, you can visit the blog on which I found this lovely graphic and get the whole story. But if you aren’t ready for all that, note that the authors define those who are at risk of homelessness as anyone who has spent some time in a shelter in the past year, regardless of whether they happened to have been homeless at the time of the survey.

Bonus Graphic

They also included the graphic below. I still don’t know what the blue means. This graphic does make it easier to understand that being truly homeless appears to mean running out of friends and family who have homes to share. Because none of the truly homeless live with family and friends. It’s also clear from both graphics that most homeless people are not visibly homeless. The folks you might see sleeping on the train or the street 1) may not be homeless, they could be sleeping away from home for reasons unrelated to homelessness per se and 2) if they are homeless, they may be quite different from the rest of the homeless population. They’re more likely to be single adults than families and more likely to be men than women.

References

Chiao, Terri and Grossberg Katz, Deborah. (11 November 2009) “Public/Private: Rethinking Design for the Homeless” at Urban Omnibus.

What Works

Last night this blog received a deluge of spam from someone with an IP address in Australia promoting wholesale wedding dresses. In response, I first exercised a wholesale ‘delete’ event. Now we’ve got a graph about the stability of marriage in the US since the 1950s. The next time someone tells you that 50% of marriages end in divorce, you’ll know how to show them that they’re wrong.

As you can see from the above graphic representation, marriages in the 1950’s were less likely to end in divorce within the first 25 years of marriage than any subsequent cohort of married folks. We have no idea if those were ‘good’ marriages that lasted, we just know that they were less likely to end in divorce. From the representation we see that divorce rates climbed through the 1960s and 1970s but started falling in the 1980s and continues to fall, inching back towards 1960s levels.

Furthermore, from this next graph, we can see that the decrease in the divorce rate is not only due to marriages lasting, but that any given person is less likely to experience divorce because we are now less likely to get married in the first place. If one doesn’t get married, one cannot get divorced. It would seem that people might actually be making fairly appropriate decisions around the ‘I do’ moment because the people who choose marriage are staying married longer. In other words, the folks less likely to stay married may somehow recognize this about themselves and opt out of marriage altogether.

Using multiple graphs tells a much more complete picture than relying on just one. The first graph was designed to debunk the notion that 50% of marriages end in divorce by showing that for a brief moment, marriages formed in the 1970s may have approached that dissolution rate but that marrieds have been sticking together more and more since then. The second graph is more interesting to me because it details overall trends in marriage, including the slow slide away from marriage altogether. It could be that people are just waiting longer to get married, in which case the decline in the marriage rate recently might just be a lag. Lifetime marriage rate is something I’d still be interested in checking out, though I feel that we haven’t maxed out on age at first marriage so it would be hard to see, at least not in 2010, if the trend is toward later marriage or no marriage at all. My prediction would be that age at first marriage will start to hit a plateau at around 30 for women because reproductive ability tends to decrease markedly starting at about 35, or so I’ve been told, and many people get married at least in part because they’d like to have some kids. But we’ve got a long way to go before we hit 30 for women’s marrying age. Median age at first marriage for women is just 26 and even though it is climbing, it isn’t skyrocketing.

References

Stevenson, Betsey and Wolfers, Justin. (2007) Trends in Marital Stability. Working Paper.

Wolfers, Justin. (21 March 2008) Misreporting on Divorce. on the Freakonomics blog at the New York Times.