The Tableau “viz” analysis suite lets you do things like compare for any time period for which Google gave daily data, and even between two time periods. You can also compare any number of countries, regions, even US counties, with data filters. You can compare habits over the days of the week, or see what mobility behaviors people changed (or didn’t, on individual days in a range days). There are “fair expectations” set for each metric based on average to slightly above performance shown to be attainable over a 6 week period, to give further context to the numbers. There is a population filter to compare countries in select ranges of populations. Finally, there are ranks so you don’t have to memorize any numbers in comparing performance in different places and/or over different times. Lots of stuff you can do all kinds of analytics with, draw conclusions about (though be careful on assumptions), and such!
The Tableau viz will be updated roughly once a week, when Google puts out the latest data set. It doesn’t seem they’ll be too consistent with when they do that, but only varying between Thursday and Friday so far. In that Tableau viz is:
A table of content tab (at top of view) outlining what is in each tab;
A map of the world showing how countries compare for each of the 6 metrics;
Continental maps showing regional breakdowns in each country on the continent (where there is data);
Even a US county breakdown map;
Graphs showing ranks of countries and regions (US counties were too spotty with incomplete data for me to care and give it its own comparison dashboard);
Graphs showing select regions against others, allowing comparisons between countries and smaller regions like states and provinces, for example;
Graphs showing results over time;
Bundled sets of charts in logical order to produce what would be a good briefing report, without text that someone could write for their region/s if they wanted to; and
Please click on the link if you want to test out the analytical suite I built. It’s free! No ads or anything! 🙂
I owe this post to a pro-gun friend who debated me on more gun control. His arguments and stats used, including the graph below, led me to do the degree of research I did to counter, which I used here. I didn’t convince him, which I didn’t expect to, but you can judge for yourself from what I present following. I thought it might be valuable for people to understand the flaws in many anti-gun control, if not pro-gun, arguments presented, coming from someone who does analysis of all sorts for a living.
I was shown the graph below with data showing why tighter gun control was not the solution to America’s gun problem with gun related deaths and incidents. What I, as a professional analyst, saw, instead, was the very reasons why America needs tighter gun control. I will also counter a bunch of other points brought up by gun lobbyists that doesn’t involve data, because it isn’t just about the data, of course. But let’s first look at one graph with lots of data.
You can also try this for runners, triathletes or any other classification of people you know, not just runners. I have it for runners because I know many runners and have research to expand on the subject in future posts.
This experiment works best for people in North American culture because the “normal” rates used for comparison were based on United States 2004 census data. Other cultures have different social influences on divorce and so the national rates are probably different, which will lead to different answers to the question in the title. But it might be interesting to still try and speculate via approximation. In some cases, the answer someone gets will be so vastly different to the national ratio that the conclusion drawn would not be in much doubt. So get a pen or pencil, scrap piece of paper you can write a list on, and give this a try!
List all the runners you can think of.
No need to be too exact to time or capture everybody. You can stop where you start running out of names (no pun intended), but the more names you have, the more confident you can be in your personal experiment.
Cross off the never married people on that list, or anyone you aren’t sure about on that status.
Label the rest as either married to first spouse or divorced at least once. You might want to keep track of runners married to another runner for an additional consideration. Cross off anyone you aren’t sure here as well.
Count the number in each remaining, then divide the divorced at least once by the total number of people you have. That’s the number of people not crossed off. That’s because everyone you have not crossed off would have been married at least once.
You can do this calculation on Google by typing a number, a slash, and then the other number, like 23/44 and hit Return or Search.
If your answer is larger than 0.305, then Yes, runners you know divorce more than the national (US) average.
If you wouldn’t mind anonymously sharing your decimal results from above, please take the poll below and/or share your numerical score as a comment. I’d be curious to see some range among the results. The poll is not a scientific experiment, in case anyone was wondering, just a curiosity for me.
My score was 0.5, by fluke where I stopped being with 50 runners and I happened to have had a 25/50 total.
That’s definitely bigger than 0.305, but I don’t have enough people to create a respectable margin or error like those polls involving 1,000 or so people to conclude anything. Chances are, you won’t, either. But that’s why I said above that it’s for your situation only, not an end all and be all answer. I’ll write more by Sunday night to offer some real research on runners and their divorce rates relative to the general population.
Please get your friends to try if you want a comparison to your own that is not a totally anonymous result. This would be more effective with friends who don’t have a lot of friends in common with you because otherwise, it’d be a very similar result to yours. Your friends don’t need to be runners, just some people who know runners.
The common “marriage and divorce” stat everybody knows is that 50% of marriages end in divorce. However, you can’t predict the marriages of runners currently married so that’s a useless baseline standard. What I did was to take the total number of men and women who have ever been divorced, and divided it by those who have ever been married. That’s two things you can figure out about the “marriage” status of people you know, and what you did in the experiment above. So in the US:
75.56 million men ever marry (i.e. married at least once)
22.70 million men ever divorce 0.301 = men who ever marry and end up getting divorced
87.32 million women ever marry
26.95 million women ever divorce 0.309 = women who ever marry and end up getting divorced
162.88 million men and women ever marry
49.68 million men and women ever divorce 0.305 = men and women, combined, who ever married and end up getting divorced
Essentially, these stats encompass all the men and women captured by the 2004 US Census. One could make a good argument the 0.305 value should be lower for this experiment because there wouldn’t be very many runners captured in the over 60 category, meaning some people who may divorce then are skewing the results to the larger ratio than might be to compare with for runners. However, I’ll stand on 0.305 to avoid speculation on a substitute value that would only invite criticism. I think you’ll find the results generally different enough from 0.305 that you won’t care about whether that 0.305 ratio should have been 0.25, 0.305 or some other similar value.
Your age, and thus the likely average age of the running people you know, might affect your result. After all, if you’re younger and know lots of young married runners, they may not have divorced yet (if ever, of course). Divorce takes time if they happen. However, you are comparing it to the general population so you can’t skew it too high, just too low a ratio. For the record, I’m 36 and about 2/3 of my list was older than me.
The degree of commitment shown by the runners you know, probably of some correlation to how good they are and/or the distance they run, could well be another serious factor. About half those I had on my list were marathon runners instead of the shorter distance types. And on average, they’re quite a bit better than the average runner. So that’s likely my skew, but I’ll talk more on that in following posts.