Death Comes for the Appliance

/ bs king / 14 Comments

Our dryer died this week. Or rather, it died last weekend and we got a new one this week. When we realized it was dead (with a full load of wet clothes in it, naturally), the decision making process was pretty simple.

We’re only the third owners of our (early 1950s) house, and the previous owners spent most of the 5 years they had it trying to flip it for a quick buck. We’ve owned it for 6 years now, so any appliance that wasn’t new when we moved in was probably put in by them when they moved in. That made the dryer about 11 years old, and it was a cheap model. I was pretty sure a cheap dryer over a decade old (that had been slowly increasing in drying time for a year or so, unhelped by a thorough cleaning) would be more trouble to repair than it was worth, so we got a new one.

After making the assertion above, I got a little curious if there was any research backing up the life span of various appliances. As long as I can remember I’ve been fairly fascinated by dead or malfunctioning appliances, which I blame on my Yankee heritage. I’ve lived with a lot of half-functioning appliances in my lifetime, so I’ve always been interested in what appliance sounds/malfunctions mean “this is an appliance that will last three more years if you just never use that setting and jerry-rig (yes that’s a phrase) a way to turn it off/on” and which sounds mean “this thing is about to burst in to flames, get a new one”.

It turns out there actually is research on the topic, summarized here, and that there’s a full publication on the topic here:

So basically it looks like we were on schedule for a cheap dryer to go. Our washing machine was still working, but it was cheaper if we replaced them both at the same time.

This list suggests our dishwasher was weak as it went at about 7 years (they refused to repair it for under the cost of replacement), but our microwave is remarkably strong (10 years and counting). We had to replace our refrigerator earlier than should have been necessary (that was probably the fault of a power surge), but our oven should have a few more years left.

Good to know.

Interestingly, when I mentioned this issue to my brother this weekend, he asked me if I realized what the longest lasting appliance in our family history was. He stumped me until he told me the location….a cabin owned by our extended family. The refrigerator in it has been operational since my mother was a child, and I’m fairly sure it’s this model of Westinghouse that was built in the 1950s, making it rather close to 70 years old:

Wanna see the ad? Here you go!

It’s amusing that it’s advertised as “frost free”, as my strongest childhood memories of this refrigerator were having to unplug it at the end of the summer season and then put towels all around it until all the ice that had built up in it melted. We’d take chunks out to try to hurry the process along.

Interestingly, the woman in the ad up there was Betty Furness, who ended up with a rather fascinating career that included working for Lyndon Johnson. She was known for her consumer advocacy work, which may be why the products she advertised lasted so darn long, or at least longer than my dryer.

Judging Attractiveness

/ bs king / 8 Comments

From time to time, I see this graph pop up on Twitter: 

It’s from this blog post here, and it is almost always used as an example of how picky women are. The original numbers came from a (since deleted) OK Cupid blog post here. From what I can tell they deleted it because the whole “women find 80% of men below average” thing was really upsetting people.

Serious question though….has this finding been replicated in studies where men and women don’t get to pick their own photos?

As anyone who’s looked at Facebook for any length of time knows, photo quality can vary dramatically. For people we know, this is a small thing…”oh so and so looks great in that picture”, “oh poor girl looks horrible in that one”, etc etc. One only needs to walk in to a drug store to note that women in particular have a myriad of ways to alter their appearance….make up, hair products, hair styles, and I’m sure there are other things I am forgetting. Your average young male might use some hair product, but rarely alters anything beyond that.

So basically, women have a variety of ways to improve their own appearance, whereas men have very few. Women are also more rewarded for having a good looking photo on a dating site. From the (deleted) OK Cupid article:

So the most attractive male gets 10x the number of messages as the least attractive male, but the most attractive woman gets 25x the number of messages. A woman of moderate attractiveness has a huge incentive to get the best possible photo of herself up on the site, whereas a similarly placed man doesn’t have the same push. Back when I made a brief foray in to dating sites, I noted that certain photos could cause the number of messages in my inbox to triple overnight. With that kind of feedback loop, I think almost every woman would trend toward optimizing their photo pretty quickly. Feedback would be rather key here too, as research suggests we are actually pretty terrible at figuring out what a good photo of ourselves actually looks like.

Side note: as we went over in a previous post, measuring first messages puts guys at a disadvantage from the get go. Men as a group receive far fewer messages from women on these sites. This means their feedback loop is going to be much more subtle than women’s, making it harder for them to figure out what to change.

My point is, I’m not sure we should take this data seriously until we compare it to what happens when all the pictures used are taken under the same conditions. The idea that the genders select their photos differently is a possible confounder.

I did some quick Googling to see if I could find a similar distribution of attractiveness rankings for a general research study, and I did find this one from a Less Wrong post about a study on speed dating: 

They note that men did rate the average woman slightly higher (6.5) than women rated the average man (5.9), but note that we see a bell curve rating in both cases. The standard deviation was noted to be the same (0.5). At a minimum, I feel this suggests that online perceptions do not translate cleanly in to real life. I suspect that’s a statement that can be applied to many fields.

I’d be interested to see any other non-dating site data sets people know about, to see what distribution they follow.

Measuring Compromise

/ bs king / 5 Comments

There’s a report that’s been floating around this week called Hidden Tribes: A Study of America’s Polarized Landscape. Based on a survey of about 8,000 people, the aim was to cluster people in to different political groups, then figure out what the differences between them were.

There are many interesting things in this report and others have taken those on, but the one thing that piqued my interest was the way they categorized the groups as either “wings” of the party or the “exhausted majority”. Take a look:

It’s rather striking that traditional liberals are considered part of the “exhausted majority” whereas traditional conservatives are considered part of the “wings”.

Reading the report, it seemed they made this categorization because the traditional liberals were more likely to want to compromise and to say that they wanted the country to heal.

I had two thoughts about this:

  1. The poll was conducted in December 2017 and January 2018, so well in to the Trump presidency. Is the opinion of the “traditionalist” group on either side swayed by who’s in charge? Were traditional liberals as likely to say they wanted to compromise when Obama was president?
  2. How do you measure desire to compromise anyway?

It was that second question that fascinated me. Compromise seems like one of those things that it’s easy to aspire to, but harder to actually do. After all, compromise inherently means giving up something you actually want, which is not something we do naturally. Almost everyone who has ever lived in a household/shared a workplace with others has had to compromise at some point, and two things become quickly evident:

  1. The more strongly you feel about something, the harder it is to compromise
  2. Many compromises end with at least some unhappiness
  3. Many people put stipulations on their compromising up front…like “I’ll compromise with him once he stops being so unfair”

That last quote is a real thing a coworker said to me last week about another coworker.

Anyway, given our fraught relationship with compromise, I was curious how you’d design a study that would actually test people’s willingness to compromise politically rather than just asking them if it’s generically important. I’m thinking you could design a survey that would give people a list of solutions/resolutions to political issues, then have them rank how acceptable they found each solution. A few things you’d have to pay attention to:

  1. People from both sides of the aisle would have to give input in to possible options/compromises, obviously.
  2. You’d have to pick issues with a clear gradient of solutions. For example, the recent Brett Kavanaugh nomination would not work to ask people about because there were only two outcomes. Topics like “climate change” or “immigration” would probably work well.
  3. The range of possibilities would have to be thought through. As it stands today, most of how we address issues already are compromises. For example, I know plenty of people who think we have entirely too much regulation on emissions/energy already, and I know people who think we have too little. We’d have to decide if we were compromising based on the far ends of the spectrum or the current state of affairs. At a minimum, I’d think you’d have to include a “here’s where we are today” disclaimer on every question.
  4. You’d have to pick issues with no known legal barrier to implementation. Gun control is a polarizing topic, but the Second Amendment does give a natural barrier to many solutions. I feel like once you get in to solutions like “repeal the second amendment” the data could get messy.

As I pondered this further, it occurred to me that the wings of the parties may actually be the most useful people in writing a survey like this. Since most “wing” type folks actually pride themselves on being unwilling to compromise, they’d probably be pretty clear sighted about what the possible compromises were and how to rank them.

Anyway, I think it would be an interesting survey, and not because I’m trying to disprove the original survey’s data. In the current political climate we’re so often encourage to pick a binary stance (for this, against that) that considering what range of options we’d be willing to accept might be an interesting framing for political discussions. We may even wind up with new political orientations called “flexible liberals/conservatives”. Or maybe I just want a good excuse to write a fun survey.

Media Coverage vs Actual Incidence

/ bs king

The month of October is a tough on for me schedule-wise, so I’m probably going to be posting a lot of short takes on random things I see. This study popped up on my Twitter feed this week and seemed pretty relevant to many of the themes of this blog: “Mediatization and the Disproportionate Attention to Negative News“.

This study took a look at airplane crashes, and tracked the amount of media attention they got over the years. I’ll note right up front that they were tracking Dutch media attention, so we should be careful generalizing to the US or other countries. The authors of the study decided to track the actual rate of airplane crashes over about 25 years, along with the number of newspaper articles dedicated to covering those crashes as a percentage of all newspaper articles published.

The whole paper is interesting, but the key graph is this one:

Now the authors fully admit that the MH17 airplane crash in 2014 (plane brought down by a missile, mostly Dutch passengers,) does account for that big spike at the end, but it appears the trend still holds even if you leave that out.

It’s an interesting data set, because it puts some numbers behind the idea that things are not always covered in the media in proportion to their actual occurrence. I think we all sort of know this intuitively in general, but it seems hard to remember when it comes to specific issues.

Even more interesting is that the authors did some analysis on exactly what these articles covered, to see if they could get some hints as to why the coverage has increased. They took 3 “eras” of reporting, and categorized the framing of the articles about the plane crashes. Here were there results:

Now again, the MH17 incident (with all its international relations implications) is heavily skewing that last group, but it’s interesting to see the changes anyway. The authors note that the framing almost definitely trends from more neutral to more negative. This supports their initial thesis that there is some “mediatization” going on. They define mediatization as “a long-term process through which the importance of the media and their spillover effects on society has increased” and theorize that “Under the conditions of mediatization, certain facets have become more prominent in media coverage, such as a focus on negativity, conflicts, and human-interest exemplars”. This tendency is the fault of “the decreasing press–party parallelism and media’s growing commercial orientation has strengthened the motives and effort to gain the largest possible audience media can get”.

As a result of this, the authors show that within the month after a plane crash is reported by the media, fewer people board planes. They don’t say if this effect has lessened or increased over time, but regardless, the media coverage does appear to make a difference. Interestingly, the found that airline safety was not related (time-series wise) to press coverage. Airlines were not more or less safe the month after a major crash than they were the month before, suggesting that crashes really aren’t taking place due to routine human error any more.

Overall, this was a pretty interesting study, and I’d be interested to see it repeated with some new media such as blogs or Twitter. It’s harder to get hard numbers on those types of things, but as their effect is felt more and more it would be interesting to quantify how they feed in to this cycle.

Wansink Link Roundup

/ bs king

I was away for most of this week so I’m just getting to this now, but Brian Wansink has announced he’s retiring at the end of this academic year after a series of investigations in to his work.  I’ve blogged about the Wansink saga previously (also here and here and here) , and have even had to update old posts to remove research of his that I referenced.

Christopher B passed along a good summary article from the AP , which I was pleased to see included a note that they had frequently used him as a source for stories. The New York Times followed suit, and this article mentions that he was cited as a source in at least 60 articles since 1993.

While the initial coverage was mostly shock, I’ve been pleased to see how many subsequent articles point to the many bigger problems in science (and science reporting) that led to Wansink’s rise.

The New York Times article I just cited delves in to the statistical games that the field of nutrition often plays to get significant results, and how the press generally reports them uncritically. For those of you who have been following this story, you’ll remember that this whole drama was kicked off by a blog post Wansink wrote where he praised a grad student for finding publishable results in a data set he admitted looked like it had yielded nothing. While this wouldn’t be a problem if he had admitted that’s what he was doing, his papers never corrected for multiple comparisons or clarified that they were running hundreds of different comparisons to try to find something significant.

The Atlantic put up a good piece about the “scientist as celebrity” angle, discussing how we should think about scientists who get a lot of attention for their work. The “buzz cycle”, where we push findings we like and scientists respond by trying to generate findings that will be likable. This is a good point, as many people who don’t know Wansink’s name know of his findings (health halos, use small plates, we eat more from bottomless soup bowls, etc).

This Washington Post op-ed has an interesting discussion of science education, and wonders if we did more to educate kids about scientific mistakes and fraud if we’d be more skeptical about scientific findings in general. It’s an interesting thought…we do hear science mostly presented as an unbroken march towards truth, not always hearing how many side roads there are along the way.

Overall it’s a fascinating and sad story, made slightly worse by the fact that it appears to have come to a head at the same time that Wansink’s mother died and his father broke his hip. While this is a good reminder to limit any gratuitous potshots against him as a person, it still raises many interesting discussion points about how we got here. Any other articles, feel free to pass them along!

Take Your Best Guess

/ bs king / 1 Comment

The AVI passed on an interesting post about a new study that replicates the finding that many psychological studies don’t replicate. Using 21 fairly randomly selected studies (chosen specifically to avoid being too sensational…these were supposed to be run of the mill), replication efforts showed that about 60% of studies held up while almost 40% could not be replicated.

This is a good an interesting finding, but what’s even more interesting is that they allowed people to place bets ahead of time on exactly which studies they thought would fail and which ones would bear out. Some of the people were other psych researchers, and some were placing bets for money. Turns out that everyone was pretty darn good at guessing which findings would replicate:

Consistently, studies that failed to replicate had fewer people guessing they would replicate. In fact, most people were able to guess correctly on at least 17 or 18 out of the 21.

Want to try your hand at it? The 80,000 hours blog put together a quiz so you can do just that! It gives you the overview of the study finding with an option to read more. about exactly what they found. Since I’m packing up for a work trip this week, I decided not to read any details and just go with my knee jerk guess from the description. I got 18 out of 21:

I encourage you to try it out!

Anyway, this is an interesting finding because quite often when studies fail to replicate, there are outcries of “methodological terrorism” or that the replication efforts “weren’t fair”. As the Put A Number on It blog post points out though, if people can pretty accurately predict which studies are going to fail to replicate, then those complaints are much less valid.

Going forward, I think this would be an interesting addendum to all replication effort studies. It would be an interesting follow up to particularly focus on the studies that were borderline….those that just over 50% of people thought might replicate, but that didn’t end up replicating. It seems like those studies might have the best claim to change the methodology and repeat.

Now go take the quiz, and share your score if you do! The only complaint I had was that the results don’t specifically tell you (I should have written it down) if you were more likely to say a study would replicate when it didn’t or vice versa. It would be an interesting personal data point to know if you’re more prone to Type 1 or Type 2 errors.

 

Tornadoes in the Middle of America

/ bs king / 4 Comments

I was talking to my son (age 6) a few days ago, and was surprised to hear him suddenly state “Mama, I NEVER want to go to the middle of America”. Worried that I had somehow already managed to inadvertently make him in to one of the coastal elite, I had to immediately ask “um, what makes you say that?”. “The middle of America is where tornadoes are, and I don’t want to be near a tornado”, he replied. Oh. Okay then.

Apparently one of his friends at school had started telling him all about tornadoes, and he wanted to know more. Where were most of the tornadoes? Where was the middle of America anyway? And (since I’m headed to Nebraska in a week), what state had the most tornadoes?

We decided to look it up, and the first thing I found on Google image search was this map from US Tornadoes:

Source here. I was surprised to see  the highest  concentration was  in the Alabama/Mississippi area, but then I realized this was tornado warnings, not tornadoes themselves. The post that accompanies the map suggests that the high number of tornado warnings in the Mississippi area is because they have a much longer tornado season there than the Kansas/Oklahoma region that we (or at least I) normally think of as the hotbed for tornadoes.

Areas impacted by tornadoes vary a bit depending on what you’re counting, but this insurance company had a pretty good map of impacted areas here:

Measuring can vary a bit for two reasons: what you count as a tornado, and how you calculate frequency. The National Oceanic and Atmospheric Administration puts out a few different types of numbers: average number of tornadoes, average number of strong to violent tornadoes, tornadoes by state and tornado average per 10,000 square miles. Those last two are basically to help account for states like Texas, which gets hit with more tornadoes than any other state (155 between 1991 and 2010), but mostly because it’s so big. If you correct that to look at a rate over 10,000 square miles, it dips to 5.9….well below Florida (12.2) and Kansas (11.7).

Florida coming in ahead of Kansas surprised me, but this is where strength of tornadoes comes in. Apparently Florida has lots of weak tornadoes. Looking at only strong to violent tornadoes only, we get this:

The NOAA also breaks down risk by month, so I decided to take a look and see what the risk in Nebraska was for September:

I think I can reassure the kiddo that mommy is going to be just fine. Apparently if you want to go to the middle of America but avoid tornadoes, fall is a pretty good bet.

Of course after we got the numbers down, we went to YouTube and started watching storm chaser videos. While he thought those were fascinating, he did have a reassuring number of questions along the lines of “mama, why did the people in the video see the tornado but not run away?”. Good impulse kid. Also, continuing his mother’s habit of rampant anthropomorphizing, he informed me that this video made him “very sad for the trees” (see the 35-40 second mark):

https://www.youtube.com/watch?v=jO2VVjlHe8o&t=122s

 

5 Things About the Challenges of Nutritional Epidemiology

/ bs king

Anyone who’s been reading this blog for any amount of time knows that I’m a pretty big fan of the work of John Ioannidis, and that I like writing about the challenges of nutrition research. Thus, you can imagine my excitement when I saw that JAMA had published this opinion piece from him called “The Challenge of Reforming Nutritional Epidemiologic Research“. The whole article is quite good, but for those who don’t feel like wading through it, I thought I’d pull together some of the highlights. Ready? Let’s go!

  1. Everything’s a problem (or maybe just our methods) Ioannidis starts out with an interesting reference to a paper from last year called “Food groups and risk of all-cause mortality: a systematic review and meta-analysis of prospective studies“. This meta-analysis looked at the impact of various food groups on mortality, and reported the significant associations. Ioannidis points out that almost every food they looked at had a statistically significant association with mortality, even at relatively small intakes. Rather than get concerned about any one finding, Ioannidis raises concerns about the ubiquitousness of significant findings. Is every food we eat really raising or lowering our all cause mortality all the time? Or are we using methods that predispose studies to finding things significant?
  2. Reported effect sizes are large aren’t necessarily cumulative The second thing Ioannidis points out is exactly how large the effect sizes are. The study mentioned in point #1 suggests you get  1.7 extra years of life for eating a few extra hazelnuts every day? And that eating bacon every day is worse than smoking? That seems unlikely. The fundamental problem here is that most food consumption is heavily correlated with other types of food consumption, making it really difficult to tease out which foods are helping or hurting. If (hypothetically) vegetables were actually bad for us, but people ate them a lot with fruit (which was good for us) we might come to the conclusion that vegetables were good merely because their consumption was tied to other things. As Ioannidis puts it “Almost all nutritional variables are correlated with one another; thus, if one variable is causally related to health outcomes, many other variables will also yield significant associations in large enough data sets. “
  3. We focus too much on food itself Speaking of confounders, Ioannidis goes on to make another interesting point about how food consumption is always assumed to be beneficial or risky based on properties of the food itself, with potential confounders being ignored. For example, he cites the concern that grilling meat can create carcinogens, and the attempts to disentangle the cooking method from the meat itself. Drinking scalding hot beverages is known to increase the risk for esophageal cancer, separate from what the beverage itself actually is. It’s entirely plausible there are more links like that out there, and entirely plausible that various genetic factors could make associations stronger for some groups than others. Teasing those factors out is going to be extremely challenging.
  4. Publication methods encourage isolation of variables One of the other interesting things Ioannidis points out is that even very large long term studies (such as the nurses health study) tend to spread their results out over hundreds if not thousands of papers. This is a problem that we talked about in the Calling Bullshit class I reviewed: researchers are more rewarded for publishing in volume rather than for the quality of each paper. Thus, it makes sense that each nutrient or food is looked at individually, and headline writers magnify the issue. Unfortunately this makes the claims look artificially strong, and is probably why randomized trials frequently fail to back up the observed claims.
  5. Nutritional epidemiology uniquely impacts the public So what’s so bad about an observational study failing to live up to the hype? Well, nothing, unless clinical recommendations are based on it. Unfortunately, this study found that in 56% of observational studies, the author recommended a change to clinical practice. Only 14% of those recommendations came with a caveat that further studies might be needed to corroborate the findings. This is particularly concerning when you realize that some studies have found that very few observational studies replicate. For example, this one looked at 52 findings from 12 papers, and found that none of them replicated in randomized trials, and 5 actually showed a reverse in correlation. Additionally, headlines do little to emphasize the type of study that was done, leading to a perception that science in general is unreliable. This has long term implications both for our health and for our perception of the scientific method.

Overall I enjoyed the piece, and particularly its link to promising new recommendations to help address these issues. While criticizing nutritional epidemiology has become rather popular, better ways of doing things have been more elusive. Given the level of public interest however, we definitely need more resources going in to this. Given that the NUSI model appears to have failed, new suggestions should be encouraged.

What I’m Reading: September 2018

/ bs king / 3 Comments

The news about the fire at the National Museum of Brazil was rather shocking, and I feel even worse about it now that I’ve read this roundup of some of the pieces that were lost in the flames.

Hat tip to Jonathan for sending me this great NPR piece on the school shootings that weren’t. Their reporting found that out of the 238 school shootings that got reported last year, 227 were due to errors filling out the form and 11 were actual shootings. A cautionary tale about what happens when you rely on people filling out online forms to report things like school shootings, and a good example of base rate issues in action.

As a proud member of the Oregon Trail Generation, I really liked this history of the game and why it became so ubiquitous for a certain age group.

In an interesting point/counter-point this week, we have a Vox article that explains how Alexandria Ocasio-Cortez is getting unfair amounts of criticism for her errors because she’s a young female, and this Washington Examiner piece points out that much of their criticism of the criticism is just wrong. For example, the Vox piece points to several incorrect statements Paul Ryan has made, then says “No one saw these statements and said Ryan is unfit to serve in Congress. No one told him to go put training wheels back on. No one told him he wasn’t ready for primetime.” The Washington Examiner piece points out that there’s an anti-Ryan super PAC named “Unfit to Serve“,  2 years ago Nancy Pelosi actually released a long fact check of Ryan that started with the phrase “time to take the training wheels off!“, and in 2012 Obama’s re-election campaign released a statement saying Ryan was “not ready for prime time“. Oops. Now regardless of your opinion of Ocasio-Cortez or Paul Ryan, this is a good moment to remember the Tim Tebow Effect. Paul Ryan’s approval rating has never been above 48%, and the last numbers I can find suggest it’s closer to 34% or so now, with 46% of the population viewing him unfavorably. He was also popular enough to be named speaker of the house. Neither liking him nor disliking him is an underrepresented viewpoint. Ocasio-Cortez has been called “the future of the Democratic Party” by the DNC chair, and roundly criticized by many others, as the original Vox article points out. She has no approval rating polls I can find (likely since she currently holds no office). In other words, if you’re going to claim “no one is criticizing” either of these people, you may want to Google a bit first. Otherwise you’ll be wandering in to premature expostulation territory pretty quickly.

Somewhat related: a new paper on tipping points in social conventions. Apparently once around 25% of people feel a certain way about a particular issue, the majority viewpoint begins to sway. Interesting to consider in light of political parties, which tend to be about a third of the country at baseline. How much of a party base needs to be on the same page before the party starts to sway?

Also related: a new study highlights the paradox of viral outrage. People view one person scolding a bad online post positively, but they view 10 people scolding that person negatively. Interesting research, with NeuroSkeptic raising some good counter questions.

John Ioannidis is back with a good piece on the challenge of reforming Nutritional Epidemiology. I’ll probably due a summary post of this sometime soon.

Another one I want to review soon: Many Analysts, One Data Set. A paper exploring how different choices during the analysis phase can lead to very different results.

Not a thing I’m reading, but I got in to a conversation this weekend about the most worthwhile eco-friendly trade offs people had made. Mine was buying these microfiber cleaning cloths and using them instead of paper towels. They clean better (both for scrubbing and dusting), can be thrown in with any load of laundry to get them clean, and last for a long time. At around $12 a pack for 24, I am guessing we got our money back pretty quickly in what we saved on paper towels.  I got so weirdly passionate about these that I apparently inspired others to buy them, so I figured I’d pass the link along.

5 Things About Peak Desirability

/ bs king / 3 Comments

A couple weeks ago after my College Educated White Women post, the AVI sent along an Atlantic article about how everyone on dating apps is trying to date almost exactly 25% out of their league. 

The bigger more attention grabbing headline from this study though, was the finding that women’s desirability peaked at age 18, whereas men’s peaked at age 50. They included this chart:

Since I always get hung up on how these things are calculated and what they’re really telling us, I decided to take a look at the paper and the supplementary materials. Here’s what I found:

  1. Desire = PageRank When looking at a study like this, one of the first things I always want to know is how they defined their terms. Here, the authors decided that using a model where desirability = the number of messages received would be too simplistic, so they decided to use the PageRank equation. Yes, from Google. This equation is useful because it doesn’t just measure overall number of messages received, but how desirable the people who got in touch with you were. So ten messages from desirable people were worth more than 100 from less desirable people…sort of like one link from a famous blogger is worth more than ten links from lesser known bloggers. This choice made a lot of sense as “desire” is not just about how many people want something, but also how hard it is to get. However, choosing this definition does have some interesting consequences, which I’ll get to in a minute.
  2. The pool was not randomly selected, and the most desirable people were the outliers When the AVI initially sent me this article, one of his first comments was that generalizing from a sample of dating website users was probably not a great idea. After looking at the sample, he was completely right. Not only are these dating website users, but they were exclusively dating website users in large cities. There were other interesting differences….like check out the demographics table:  As a reminder, only about a third of US adults have a college degree. Those numbers for NYC are really unusual. You’ll also note that the average age of a user tended to be just over 30. So where did our highly desirable 18 year old women and 50 year old men fall? On the long tails:  Yes, I drew pink and blue arrows to show where the most desirable men and women fell. Sorry about that. Anyway, as you can see, those who showed up as the most desirable were not the best represented. This makes a certain amount of sense….18 year olds don’t join dating sites as often because they are frequently still in high school and have lots of access to people their own age. 50 year old tend to be married, partnered, or otherwise not looking. This is important because it introduces the idea that those not in the peak age range for use (23-33 from what I can tell) may have some survivor bias going on. In other words, if they log on and are successful, they stay on the site. If they aren’t, they leave. From what I can tell in my friend group, a 30 year old will stick it out on dating sites until they find someone, because that’s simply what everyone does. Other age groups may have different strategies. Since all the data came from one month (January 2014) it would not capture people who came and went quickly.
  3. Desirable men and women probably don’t have the same experience One of the more interesting discussions in the “network analysis” section of the paper, was when the authors mentioned that they had to include two different measures of interest in order to cover both genders. Because men send 80% of the first messages, they realized that assessing “interest” only by first messages would basically mean they only knew who men were interested in. Given this, they decided to also include replies as markers of interest. Thus, while the same equation was applied to both genders, one suspects this plays out differently. Desirable women are likely those who get many messages from men, and desirable men are likely those who get a lot of replies from women. For example, the study authors note that the most popular person they found in their data was a 30 year old woman in NYC who received over 1500 messages (!) in the one month they studied. They don’t list how the most popular male did, but one has to imagine it’s an order of magnitude less than that woman. It’s simply much harder to compose messages than it is to receive them, and with reply rates hovering at 15-20% one imagines that even extremely popular men may only be hearing back from around 100 women a month. In other words, the experiences of the genders are hard to compare, even when you use the same grading criteria.
  4. Decreasing your messages out would increase your page rank Okay, back to the PageRank system. Ever since Google first released their PageRank algorithm, people have been trying to optimize their sites for it. While Google has definitely tweaked their algorithm since releasing it, this study used the original version, which used the number of links your site makes as a divisor. In other words, the less you link to other sites, the higher your own rank. An example: suppose an 18 year old woman and a 30 year old woman get 100 messages from the exact same group of men. The 18 year old kinda freaks out and only replies to 1 or 2. The 30 year old woman seriously wants to find someone and replies to 20. Per PageRank, the 18 year old is rated more highly than the 30 year old. Now take a 30 year old man and a 50 year old man. The 30 year old man is all in on his dating app game, and messages 100 women, receiving 20 replies. The 50 year old man isn’t quite as sure and carefully selects 10 messages to women he thinks he has a chance with, getting 3 replies. If those replies came from “higher ranking” women than the 20 the other guy got, the 50 year old is now more “highly desirable”. In other words, users who are highly engaged with the dating site and taking chances will not do as well ranking-wise. Being choosy about who you reply to/message helps.
  5. Some of this may be up front decision making rather than personal One of the weirder downsides to online dating is the ability to set hard stops on certain characteristics of others. While in pre-computer days you would generally find out someone’s attractiveness first, now you can ask the site only to show you matches that are taller than 6’/older than 25/younger than 40, and the algorithm will do exactly what you say. This almost certainly impacts messaging behavior, and it turns out men and women approach ages limits really differently. OKCupid pulled their data on this, and here’s what they found: So our median male keeps 18 year old women in his age range for 5 years of his life (18-23), while our median female will only date 18 year old men for 2 years (18-20). It appears once women get out of college and hop on a dating site they pretty much immediately want to drop college aged men. On the other end, 48 year old men have a preferred age range nearly double the size of the age range 48 year old women set. Men raise their floor as they age, just not nearly as quickly as women do. Both genders appear to raise their ceiling at similar rates, though women always keep theirs a little higher. Thus, younger women will always be receiving messages from a much larger pool of men than older women, particularly since participation in dating sites drops off precipitously with age. A 30 year old woman (the average age) has men 26-46 letting her through their filter, whereas a 30 year old man has women 26-35 letting him through theirs.

Well there you have it, my deep dive in to desirability and PageRank as applied to dating! For any of you single folks out there, it’s a good time to remind you that just like Google results, online dating can actually be hacked to optimize your results, and that the whole thing is not a terribly rational market. Good luck out there!