5 More Things About Fertility Rates

Normally when I write a blog post, it’s because some topic was rattling around in my head too much and I want to get it out of there. This works most of the time, and after hitting publish I tend to stop thinking as often about whatever it is I wrote about. Sometimes however, this works in reverse and my initial post sparks me and various readers/others in my life to keep talking about the topic. My last post on fertility rates was of the latter group, and I’ve spent the past week discussing it with people both online and in real life. The roundup below is 5 of the most interesting things that came out of those discussions:

  1. Male fertility is dropping I mentioned last week that while fertility rates are always counted in children/woman, we shouldn’t forget the role of men in the whole thing. To help prove that point, commenter Christopher B pointed me to an interesting article I hadn’t seen about dropping sperm counts in Western men. According to the meta-analysis cited, sperm counts have dropped 50-60% since about 1973. There wasn’t a particular reason cited, but the Assistant Village Idiot mentioned sleep deprivation, and the authors didn’t rule out chemical exposures or increasing obesity. I also found a paper that found that “After adjusting for female age, conception during a 12-month period was 30% less likely for men over age 40 years as compared with men younger than age 30 years”. This is almost certainly playing a role in dropping fertility rates, particularly if you approach it from the “why don’t people have 3 or more children as often anymore?” angle. If you struggle to have a first child, you may pay for infertility treatments, but very few people go through the time and expense of them for a third child. The biggest impact however, may be on my next topic…..
  2. Reducing unplanned pregnancies reduces fertility rates The sentiments “lower teen pregnancy rate” and it’s close cousin “reduce unintended pregnancies” are pretty non-controversial as far as public health goals go. While the methods proposed to meet these goals can be quite controversial (abortion, free birth control, abstinence only education, etc), most people actually agree on the end game. Thus when we look at the fertility rate and why it’s dropping, we have to consider that 45% of pregnancies in America are still considered “unintended”, with about 40% of those ending in an abortion. This got me wondering a few things. First, I wonder if the dropping sperm counts have actually impacted how frequently unplanned pregnancies occur. Teen pregnancy rates have been trending downward for quite some time, and one wonders if that’s been helped by things like dropping sperm counts. It’s probably not the whole reason, but it certainly seems unlikely to hurt.
  3. Our messages around teen and unplanned pregnancies may bleed over in to our thinking about planned pregnancies. One of the posts that kicked off all my thoughts on fertility rates was this one by the Assistant Village Idiot. I don’t know that I agreed with the example he gave, but the core thought of his post seems true: it is really really hard to discourage teens from having babies without saying things about how challenging kids are or how important it is that you have your ducks in a row before you have them. I mean, imagine that you find out that a 15 year old you know and care about is having unprotected sex with a partner. What do you say to them? Your first thoughts are almost certainly about how many opportunities they’ll be giving up and how much work kids are. This is the dominate message most kids receive until at least 18, longer if they’re college bound, and almost always including some time to figure yourself out. Even groups that don’t necessarily support the “figure yourself out” phase tend to have their own pressures. For example, in my Baptist high school, you definitely needed to find someone to marry first (that you wouldn’t divorce), and you needed to have enough money to make sure you never had to rely on welfare. The point here is not that any of this advice is wrong, but rather that it’s the dominant message for the first 10-15 years most people are biologically capable of having children, and people likely take them to heart for much longer than that.
  4. Kin influence One of the more interesting theories I read while reading up on fertility rates was the theory of “kin influence”. As I mentioned, it’s been noted that increased education drops fertility rates quite quickly. One proposed mechanism for this is that it’s not necessarily what education adds, but what it subtracts: 24-7 time around your family. The idea is that biologically, your family has a high motivation to encourage you to have kids, because this helps your families DNA continue. Educators and friends may care for you, but they don’t not have the same interest in encouraging you to have kids. Interestingly, even in the developed world, people who live closer/are closer emotionally to their family tend to have more children. Some of this is likely also related to resources…most people take advantage of grandma/grandpa babysitters before they look at other options. The paper didn’t mention it, but I have to wonder how this theory overlaps with the issues in #3. Parents tend to be some of the strongest voices telling teens not to get pregnant, which suggests that development doesn’t just shift the attitudes of those who might be having children, but the generation above them as well. When fertility rates fall rapidly in a country like Iran, is that all men and women of childbearing age deciding to have fewer children, or are their own parents there encouraging them to take advantage of more educational opportunities first?
  5. Child mortality rates To end on a sad note, it’s terrible to realize that some of the very high fertility rates in the developing world may actually be driven by child mortality. While it’s hard to prove causality, it appears that everywhere child mortality drops, fertility rates drop with it. From Our World in Data:  This is a good reminder that countries with total fertility rates of 6 children/woman or more almost never result in families of 6 adult children, and that our drops in fertility rate aren’t always as dramatic as they sound. For example, in the year 1800 in the US, the fertility rate was nearly 7 children/woman, while today it is just under 2. However, if you factor child mortality in, the drop is much less dramatic: I don’t know exactly what to make of this, but I can speculate that if you have good confidence your children will live, you may plan more for each of their births. It also just reminds me how grateful I am to live in this time period.

Overall this has been an interesting discussion and I appreciate everyone’s comments!

5 Things About Fertility Rates

Birth order is a hot topic in my family. I’m the oldest of four, and for as long as I can remember I’ve been grousing that being the oldest child is a bad deal. Your parents try out all their bright shiny untested parenting theories on you, relaxing the rules for all the subsequent kids, you’re held responsible for everything, and generally it’s just not faaaaaaaaaaaaaaaaair. Of course all this extra pressure does have some upsides later in life, like an increased likelihood of being a CEO or President. Anyway, given how often I’ve brought this up over the years, my parents (a youngest-of-3 and middle-of-5, respectively) were quick to point me to this article about the disappearance of the middle child in the US. After reading this article and the AVIs post about birthrates earlier this week, I went on a bit of a Google-bender on the whole topic. I figured I’d do a roundup of the most interesting numbers I found.

A quick note before I get started: for ease-of-counting purposes, fertility rates and family sizes are normally measured by “number of kids per woman”. This makes the data less messy, since you don’t have to worry about controlling for people who have children with multiple partners. However, it does often make discussions of fertility rates sound as though women are having kids in a vacuum and that men have nothing to do with it. This is simply not true. Social and economic pressures that encourage women to have fewer kids are almost certainly impacting men as well, and the compounding effect can decrease birthrates quite quickly.  So basically while I’ll be making a lot of references to women below, that’s just a data thing, not a “this is how it actually works” thing. Also, I’m going to mostly stick to numbers here as opposed to speculate on causality, because that’s just how I roll.

Alright, with that out of the way, let’s get started!

  1. Birthrates are declining worldwide. It’s not surprising that most discussions of birthrates and family size in the US immediately start with a discussion of the factors in the US that could have led to falling birthrates. However, it’s important to realize that declining fertility rates is a global phenomena. Our World in Data shows that in 1950, the total fertility rate (TFR) for women everywhere was 5 children. In 2015, it was at 2.49. In that same time period, the US went from about 3 children per woman to 1.84.  This is notable because sometimes the explanations that are offered for declining birthrates in the US (like expensive daycare or lack of parental leave policies) don’t hold when you compare them to other countries. Sweden and Denmark are both known for having robust childcare/time off policies for parents, yet their fertility rates are identical to or lower than ours. Whatever it is that pushes birth rates lower, it seems to have a pretty cross cultural impact.
  2. Birthrates can fall fast. Like, really really fast. Growing up in the US, I always thought of birthrates as something that sort of slowly trended downward as countries grew more developed. What I didn’t realize is that it doesn’t always happen this way. Our World in Data has an interesting chart that shows how long it took for various countries to go from a birthrate of 6 or more children to 3 or fewer:  What’s stunning about this is that some of these numbers are half a generation. For birthrates to fall that quickly in Iran for example, it doesn’t just mean women were having fewer children than their mothers, it means they started having fewer children than their older sisters. In case you’re curious if these trends were just a product of instability in those countries during those times: today the birthrate in Bangladesh is 2.17, South Korea is 1.26, China is 1.60, Iran is 1.97 (per Wiki/CIA Factbook). It seems like all the downward trends shown here kept up or accelerated. China obviously made this a formal policy, but it does not appear the other countries did. I found this interesting because we often hear about subtle factors/cultural messages that impact birthrates, but there’s nothing subtle about these drop offs.
  3. A reduction in those having large families impacts the average as much (or more) than the number of women going childless. One of the first things that comes up when you talk about dropping fertility rates is the number of women who remain childless. While childless women certainly cause a drop in fertility rates, it’s important to note that they are also lowered by the number of women who don’t have large numbers of kids. I don’t have the numbers, but I would guess that the countries in point #2 ended up with lower fertility rates not because of a surge in childless women, but by a major decrease in women having 6 or more children. If we look at the change in family size in the US since 1976, the most notable drop is women having 4+ kids. From Pew Research:My first takeaway from this is that the appeal of having 3 children is timeless. My second takeaway is that it appears a large number of people aren’t crazy about having a large family. This matches my experience, because while you often hear people ask those without children or with one child “why don’t you have more kids?” you don’t often hear people ask those with 2 children the same thing. My friends with 3 children inform me that they actually start getting”you’re not having more are you?” type comments and I’d imagine those with 4 or more get the same thing routinely. Now I grew up going to Baptist school and my siblings were all home schooled at some point, so I am well aware that there are still groups that support/encourage big families. However, even among those who like “big families”, I think the perception of what “big” is has shrunk. I have friends who talked incessantly about wanting big families, married early and were stay at home moms, and none of them have more than 5 children. Most of us don’t have to go more than a generation or two back in our family trees to find a family of 5 kids or more. It seems like even those who want a big family think of it in terms of “more children than others” as opposed to an absolute number. Yes, the Duggars exist, but they are so rare they got a TV show out of the whole thing.
  4. International adoption likely doesn’t get factored in. As mentioned above, I probably know an above average number of people with 4+ children. Many of these families have a mix of biological and adopted children, frequently foreign adoptions. According the the CDC though, it doesn’t appear those adopted children are not counted in birthrate data, as they calculate that off of birth certificates issued for live births taking place in the US during a given year. Now of course this isn’t a huge impact on overall numbers: there are currently only about 5,000 international adoptions/year in the US, down from a high of 15,000 or so, vs 4,000,000 overall births. However, it is interesting to note that “number of kids” does not always equal birthrate. Since the US is the biggest adopter of foreign children in the world, it is a thing to keep in mind here.
  5. The demographics of who doesn’t have kids are changing When you mention “women without children” the vision that immediately springs to mind is a well educated white woman who put her career first. Interestingly enough, this stereotype is increasingly untrue, and is changing in many countries. According to Pew Research, childlessness among women with post-graduate degrees has dropped quite a bit in the last 20 years, and the number of women in that group with 3+ kids has gone up:According to the Economist, in Finland women with a basic education are less likely to have children than their more educated peers, and other countries are trending the same way. The US is nowhere near flipping, but it is an interesting trend to keep an eye on. Historically, education has always been associated with dropping fertility rates, so this would be huge if it switched.

Overall, I thought the data out there on the topic was pretty interesting. The worldwide trends make it interesting to try to come up with a hypothesis that fits all scenarios. For example, we know that effective birth control must impact the number of children people have, but Britain and the US both had birthrates under 3 decades before oral contraceptives came in to play. Economic resources must play a part, and yet it’s the richest countries that have the lowest birthrates. Wealth is sometimes linked to higher numbers of children (particularly among men), but sometimes it’s not. Education always lowers fertility rates, except that’s started to reverse. Things to puzzle over.

5 Things About Precognition Studies

Several months ago now, I was having dinner with a friend who told me he was working on some science fiction based on some interesting precognition studies he had heard about. As he started explaining them to me and how they was real scientific proof of ESP, he realized who he was talking to and quickly got sheepish and told me to “be gentle” when I ended up doing a post about it. Not wanting to kill his creative momentum, I figured I’d delay this post for a bit. I stumbled on the draft this morning and realized it’s probably been long enough now, so let’s talk about the paranormal!

First, I should set the stage and say that my friend was not actually wrong to claim that precognition has some real studies behind it. Some decent research time and effort has been put in to experiments where researchers attempt to show that people react to things that haven’t happened yet. In fact the history of this work is a really interesting study in scientific controversy and it tracks quite nicely with much of the replication crisis I’ve talked about. This makes it a really interesting topic for anyone wanting to know a bit more about the pluses/minuses of current research methods.

As we dig in to this, it helps to know a bit of background: Almost all of the discussions about this are referencing a paper by Daryl Bem from 2011, where 9 different studies were run on the phenomena. Bem is a respected psychological researcher, so the paper made quite a splash at the time. So what did these studies say and what should we get out of them, and why did they have such a huge impact on psychological research? Let’s find out!

  1. The effect sizes were pretty small, but they were statistically significant Okay, so first things first….let’s establish what kind of effect size we’re talking about here. For all 9 experiments the Cohen’s d was about .22. In general, a d of .2 is considered a “small” effect size, .5 would be moderate, .8 would be large. In the real world, this translated in to participants picking the “right” option 53% of the time instead of the 50% you’d expect by chance.
  2. The research was set up to be replicated One of the more interesting parts of Bem’s research was that he made his protocols publicly available for people trying to replicate his work, and he did this before he actually published the initial 2011 paper. Bem particularly pointed people to experiments #8 and #9, which showed the largest effect sizes and he thought would be the easiest to replicate. In these studies, he had people try to recall words off of a word list, writing down those they could remember. He then gave them a subset of those words to study more in depth, again writing down what they could remember. When they looked back, they found that subjects had recalled more of their subset words than control words on the first test. Since the subjects hadn’t seen their subset words at the time they took the first test, this was taken as evidence of precognition.
  3. Replication efforts have been….interesting. Of course with interesting findings like these, plenty of people rushed to try to replicate Bem’s work. Many of these attempts failed, but Bem published a meta-analysis stating that on the whole they worked. Interestingly however, the meta-analysis actually analyzed replications that pre-dated the publication of Bem’s work. Since Bem had released his software early, he was able to find papers all the way back to 2001. It has been noted that if you remove all the citations that pre-dated the publication of his paper, you don’t see an effect. So basically the pre-cognition paper was pre-replicated. Very meta.
  4. They are an excellent illustration of the garden of forking paths. Most of the criticism of the paper comes down to something Andrew Gelman calls “The Garden of Forking Paths“. This is a phenomena in which researchers make a series of tiny decisions as their experiments and analyses progress, which may add up to serious deviation from the original results. In the Bem study for example, it has been noted that some of his experiments actually used two different protocols, then combined the results. It was also noted that the effect sizes got smaller as more subjects were added, suggesting that the number of subjects tested may have fluctuated based on results. There are also decisions so small you mostly wouldn’t notice. For example, in the word recall study mentioned above, word recall was measured by comparing word lists for exact matches. This meant that if you spelled “retrieve” as “retreive”, it didn’t automatically give you credit. They had someone go through and correct for this manually, but that person actually knew which words were part of the second experiment and which were the control words. Did the reviewer inadvertently focus on or give more credit to words that were part of the “key word” list? Who knows, but small decisions like this can add up. There were also different statsticall analyses performed on different experiments, and Bem himself admits that if he started a study and got no results, he’d tweak it a little and try again. When you’re talking about an effect size of .22, even tiny changes can add up.
  5. The ramifications for all of psychological science were big It’s tempting to write this whole study off, or to accept it wholesale, but the truth is a little more complicated. In a thorough write-up over at Slate, Daniel Engber points out that this research used typical methods and invited replication attempts and still got a result many people don’t believe is possible. If you don’t believe the results are possible, then you really should question how often these methods are used in other research. As one of the reviewers put it “Clearly by the normal rules that we [used] in evaluating research, we would accept this paper. The level of proof here was ordinary. I mean that positively as well as negatively. I mean it was exactly the kind of conventional psychology analysis that [one often sees], with the same failings and concerns that most research has”. Even within the initial paper, the word “replication” was used 23 times. Gelman rebuts that all the problems with the paper are known statistical issues and that good science can still be done, but it’s clear this paper pushed many people to take good research methods a bit more seriously.

So there you have it. Interestingly, Bem actually works out of Cornell and has been cited in the whole Brian Wansink kerfluffle, a comparison he rejects. I think that’s fair. Bem has been more transparent about what he’s doing, and did invite replication attempts. In fact his calls for people to look at his work were so aggressive, there’s a running theory that he published the whole thing to make a point about the shoddiness of most research methods. He’s denied this, but that certainly was the effect. An interesting study on multiple levels.

5 Things About IQ Errors in Intro Psych Textbooks

A few months ago I did a post on common errors that arise when people try to self-estimate their IQ.  One concern I sort of covered at the time was that many people may not truly understand what IQ was. For example, there seems to be a tendency to confuse educational attainment with IQ, which is likely why many of us think our grandparents were not nearly as smart as we are.

I was thinking about this issue this past week when I saw a newly published study called “What Do Undergraduates Learn About Human Intelligence? An Analysis of
Introductory Psychology Textbooks“. As the study suggests, the authors took a look at intro psych textbooks to see what they say about IQ, and how well it aligns with the actual published research on IQ. So what did they find? Let’s take a look!

  1. Most of what undergrads learn about intelligence will be learned in intro psych. To back up the premise of the study, the authors looked at the topics covered in psych programs around the country. They determined that classes on intelligence were actually pretty rare, and that the primary coverage the topic got was in intro psych. Once they’d established this, they were able to pull the 30 most popular intro psych textbooks, and they chose to analyze those. Given the lack of subsequent classwork and the popularity of the textbooks used, they estimate that their study covers a huge proportion of the formal instruction/guidance/learning on intelligence that goes on in the US.
  2. The percent of space dedicated to discussing intelligence has dropped The first research question the authors wanted to look at was how much space was dedicated to explaining IQ/intelligence research to students. In the 80s, this was 6% of textbook space, but now it’s about 3-4%. Now it’s possible that this is because textbooks got longer (and thus the percent dropped), or it could be that the topic got de-emphasized. Regardless, an interesting note.
  3. IQ Fallacies were pretty common The list of possible IQ “fallacies” was drawn from two sources. The first was from this article by Gottfredson et al, which was published after “The Bell Curve” came out and had 52 signatories who wanted to clear up what current research on IQ said. The second paper was a statement from the American Psychological Association, also in response to the publicity around the Bell Curve. They used these two papers to generate the following list:  The most common fallacies they found were #2, 3 4 and 6. These were present in 8 books (2 and 3) and 6 books (4 and 6) respectively. Interestingly, for #3 they specifically clarified that they only called it a fallacy if someone asserted that you could raise IQ by adding a positive action as opposed to eliminating a negative action. Their example was that lead poisoning really does provably lower IQ, but fish oil supplements during pregnancy have not been proven to raise IQ. The initial two papers explain why these are viewed as fallacies.
  4. Briefs discussions led to inaccuracies In addition to fallacies, the authors also took a look at inaccuracies, questionable theories, and the proportionate amount of time authors spent looking at various topics. Many of the textbooks committed the errors of citing part of the story, but not the full story. For example, it was noted that testing bias was well covered, but not the efforts that have been made to correct for testing bias. Some textbooks went so far as to say that all IQ tests required you to speak English, where as nonverbal tests have been available as far back as 1936. Additionally, some theories of intelligence that have not born out well (Gardner’s theory of multiple intelligences and Sternberg’s triarchic theory of intelligence) were two of the most discussed topics in textbooks, but did not include a discussion of the literature supporting those vs the g theory of intelligence. I imagine the oversimplification issue is one that affects many topics in intro textbooks, but this does seem a bit of an oversight.
  5. Overall context of intelligence scores was minimized Despite good proof that intelligence scores are positively correlated with various good outcomes, the most surprising finding was that several textbooks said directly that IQ only impacted education and had little relevance to every day life (4 textbooks). This directly contradicts most current research, and also a certain amount of common sense. Even if IQ only helped you in academia, having a degree helps you in many other areas of life, such as income and all the advantages that brings.

Overall this was a pretty interesting paper, especially when they gave examples of the type of statements they were talking about. Reading the statement from the APA and comparing it to the textbooks was rather interesting, as it shows how far it is possible it is to drift from consensus if you’re not careful.

Additionally, the authors cited some interesting work to show that some popular public misconceptions around IQ are directly mirrored in the intro psych textbooks errors. Overall I think the point is well taken that intro to anything textbooks should be given a lot of scrutiny in making sure their claims are factual before being assigned.

5 Things About that “Republicans are More Attractive than Democrats” Study

Happy Valentine’s Day everyone! Given the spirit of the day, I thought it was a good time to post about a study Korora passed along a few days ago called “Effects of physical attractiveness on political beliefs”, which garnered a few headlines for it’s findings that being attractive was correlated with being a Republican. For all of you interested in what was actually going on here, I took a look at the study and here’s what I found out:

  1. The idea behind the study was not entirely flattering. Okay, while the whole “my party is hotter than your party” thing sounds like compliment, the premise of this study was actually a bit less than rosy. Essentially the researchers hypothesized that since attractive people are known to be treated better in many aspects of life, those who were more attractive may get a skewed version of how the world works. Their belief/experience that others were there to help them and going to treat them fairly may cause them to develop a “blind spot” that caused them to believe people didn’t need social programs/welfare/anti-discrimination laws  as much as less attractive people might think.
  2. Three hypotheses were tested Based on that premise, the researchers decided to test three distinct hypotheses. First, that attractive people were more likely to believe things like “my vote matters” and “I can make a difference”, regardless of political party. Second, they asked them about ideology, and third partisanship. I thought that last distinction was interesting, as it drew a distinction between the intellectual undertones and the party affiliation.
  3. Partisans are more attractive than ideologues. To the shock of no one, better looking people were much more likely to believe they would have a voice in the political process, even when controlled for education and income. When it came to ideology vs partisanship though, things got a little interesting. Attractive people were more likely to rate themselves as strong Republicans, but not necessarily as strong conservatives. In fact in the first data set they used (from the years 1972, 1974 and 1976) only one year should any association between conservatism and attractiveness, but all 3 sets showed a strong relationship between being attractive and saying you were a Republican. The later data sets (2004 and 2011) show the same thing, with the OLS coefficient for being conservative about half (around .30) of what the coefficient for Republicanism was (around .60). This struck me as interesting because the first headline I saw specifically said “conservatives” were more attractive, but that actually wasn’t the finding. Slight wording changes matter.
  4. We can’t rule out age cohort effects When I first saw the data sets, I was surprised to see some of the data was almost 40 years old. Then I saw they used data from 2004 and 2011 and felt better. Then I noticed that the 2004 and 2011 data was actually taken from the Wisconsin Longitudinal Study, whose participants were in high school in 1957 and have been interviewed every few years ever since. Based on the age ranges given, the people in this study were born between 1874 and 1954, with the bulk being 1940-1954. While the Wisconsin study controlled for this by using high school yearbook photos rather than current day photos, the fact remains that we only know where the subjects politics ended up (not what they might have been when they were young) and we don’t know if this effect persists in Gen X or millenials. It also seems a little suspect to me that one data set came during the Nixon impeachment era, as strength of Republican partisanship dropped almost a whole point over the course of those 4 years. Then again, I suppose lots of generations could claim a confounder.
  5. Other things still  are higher predictors of affiliation. While overall the study looked at the effect of attractiveness by controlling  for things like age and gender, the authors wanted to note that those other factors actually still played a huge role. The coefficients for the association of Republican leanings with age (1.08) and education (.57) for example  were much higher than attractiveness the coefficient for attractiveness (.33). Affinity for conservative ideology/Republican partisanship was driven by attractiveness (.37/.72) but also by income (.60/.62) being non-white (-.59/-1.55) and age (.99/1.45). Education was a little all over the place…it didn’t have an association with ideology (-.06), but it did with partisanship (.94). In every sample, attractiveness was one of the smallest of the statistically significant associations.

While this study is interesting, I would like to see it replicated with a younger cohort to see if this was a reflection of an era or a persistent trend. Additionally, I would be interested to see some more work around specific beliefs that might support the initial hypothesis that this is about social programs. With the noted difference between partisanship and ideology, it might be hard to hang your hat on an particular belief as the driver.

Regardless, I wouldn’t use it to start a conversation with your Tinder date. Good luck out there.

5 Things About the GLAAD Accelerating Acceptance Report

This past week a reader contacted me to ask what I thought of a recent press release about a poll commissioned by GLAAD for their “Accelerating Acceptance” report. The report struck me as pretty interesting because the headlines mentioned that in 2017 there was a 4 point drop in LGBT acceptance, and I had actually just been discussing a Pew poll that showed a 7 point jump in the support for gay marriage in 2017. 

I was intrigued by this discrepancy, so I decided to take a look at the report (site link here, PDF here), particularly since a few of the articles I read about the whole things seemed a little confused about what it actually said. Here are 5 things I found out:

  1. The GLAAD report bases comfort/acceptance on reaction to seven different scenarios In order to figure out an overall category for each person, respondents were asked how comfortable they’d feel with seven different scenarios. The scenarios were things like “seeing a same sex couple holding hands” or “my child being assigned an LGBT teacher”. Interestingly, respondents were most likely to say they’d be uncomfortable if they found out their child was going to have a lesson in school on LGBT history (37%), and they were least likely to say they’d be uncomfortable if an LGBT person was at their place of worship (24%).
  2. The answers to those questions were used to assign people to a category Three different categories were assigned to people based on the responses they gave to the previous seven questions. “Allies” were respondents who said they’d be comfortable in all 7 situations. “Resisters” were those who said they’d be uncomfortable in all 7 situations. “Detached supporters” were those whose answers varied depending on the situation.
  3. It’s the “detached supporter” category that gained people this year. So this is where things got interesting. Every single question I mentioned in #1 saw an increase in the “uncomfortables” this year, all by 2-3%. While  that’s right at the margin of error for a survey this size (about 2,000 people), the fact that every single one went up by a similar amount give some credence to the idea that it’s an uptick. To compound that point, this was not driven by an uptick of people responding they were uncomfortable in every situation, but actually more people saying they were uncomfortable in some situations but not others:
  4. The percent of gay people reporting discrimination has gone up quite a bit. Given the headlines, you’d think the biggest finding of this study would be the drop in the number of allies for LGBT people, but I actually thought the most striking finding was the number of LGBT people who said they had experienced discrimination. That went from 44% in 2016 to 55% in 2017, which was a bigger jump than other groups: That red box there is the only question I ended up with. Why is the 27% so small? Given that I saw no other axis/scale issues in the report, I wondered if that was a typo. Not the biggest deal, but curiosity inducing nonetheless.
  5. Support for equal rights stayed steady For all the other findings, it was interesting to note that 79% of people continue to say they support equal rights for LGBT people. This number has not changed.

So overall, what’s going on here? Why is support for gay marriage going up, support for equal rights unchanged, but discrimination reports going up and individual comfort going down? I have a few thoughts.

First, for the overall “comfort” numbers, it is possible that this is just a general margin of error blip. The GLAAD survey only has 4 years of data, so it’s possible that this is an uptick with no trend attached. Pew Research has been tracking attitudes about gay marriage for almost 20 years, and they show a few years where a data point reversed the trend, only to change the next year. A perfectly linear trend is unlikely.

Second, in a tense political year, it is possible that different types of people pick up the phone to answer survey questions. If people are reporting similar or increased levels of support for concrete things (like legal rights) but slightly lower levels of comfort around people themselves, that may be a reflection of the polarized nature of many of our current political discussions. I know my political views haven’t changed much in the past 18 months, but my level of comfort around quite a few people I know has.

Third, there very well could be a change in attitudes going on here. One data point does not make a trend, but every trend starts with a data point. I’d particularly be interested in drilling in to those discrimination numbers to see what types of discrimination were on the uptick. Additionally, the summary report mentions that they’ve changed some of the wording (back in 2016) to make it clearer that they were asking about both LGB and T folks, which makes me wonder if the discrimination is different between those two groups. I wasn’t clear from the summary if they had separate answers for each or if they just mentioned each group specifically, so I could be wrong about what data they have here.

Regardless, the survey for next year should shed some light on the topic.

5 Things About the Perfect Age

When people ask me to explain why I got degrees in both family therapy and statistics, my go to answer is generally that “I like to think about how numbers make people feel.” Given this, I was extremely interested to see this article in the Wall Street Journal this weekend, about researchers who are trying to figure out what people consider the “perfect” age.

I love this article because it’s the intersection of so many things I could talk about for hours: perception, biases, numbers, self-reporting, human development, and a heavy dose of self-reflection to boot.

While the researchers haven’t found any one perfect age, they do have a lot of thought provoking commentary:

  1. The perfect age depends on your definition of perfect Some people pick the year they had the most opportunities, some the year they had the most friends, some the years they had the most time, others the year they were the happiest, and other the years they had a lot to reflect on. Unsurprisingly, different definitions lead to different results.
  2. Time makes a difference Unsurprisingly, young people (college students) tend to say if they could freeze themselves at one age, it would be sometime in their 20s. Older people on the other hand name older ages….50 seems pretty popular. This makes sense as I suspect most people who have kids would pick to freeze themselves at a point where those kids were around
  3. Anxiety is concentrated to a few decades One of the more interesting findings was that worry and anxiety were actually most present between 20 and 50.  After 50, well-being actually climbed until age 70 or so. The thought is that generally that’s when the kids leave home and people start to have more time on their hands, but before the brunt of major health problems hits.
  4. Fun is also concentrated at the beginning and end of the curve Apparently people in the 65 to 74 age range report having the most fun of any age range, with 35 to 54 year olds having the least. It’s interesting that we often think of young people as having the “fun” advantage due to youth and beauty, but apparently the “confusion about life” piece plays a big part in limiting how fun those ages feel. Sounds about right.
  5. How stressed you are in one decade might dictate how happy you are in the next one This is just me editorializing, but all of this research really makes me wonder how our stress in one decade impacts the other decades. For example, many parents find the years of raising small children rather stressful and draining, but that investment may pay off later when their kids are grown. Similar things are true of work and other “life building” activities. Conversely, current studies show that men in their 20s who aren’t working report more happiness than those in their cohort who are working….but one suspects by age 40 that trend may have reversed. You never know what life will throw at you, but even the best planned lives don’t get their highs without some work.

Of course after thinking about all this, I had to wonder what my perfect age would be. I honestly couldn’t come up with a good answer to this at the moment, especially based on what I was reading. 50 seems pretty promising, but of course there’s a lot of variation possible between now and then. Regardless, a good example of quickly shifting opinions, and how a little perspective tweak can make a difference.