These are slides from my lecture on significance testing, which took place in a course on research methods for social scientists. Some thoughts:
I tried to emphasise that this stuff is difficult, that people shouldn’t be afraid to say they don’t know, and that academics should try doing that more, too.
I tried to instill a deep memory that many uncertainties are involved in this endeavour, and that mistakes are ok as long as you report the choices you made transparently.
Added a small group discussion exercise at about 2/3 of the lecture: What was the most difficult part to understand so far? I think this worked quite well, although “Is this what an existential crisis feels like?” was not an uncommon response.
I really think statistics is mostly impossible to teach, and people learn when they get interested and start finding things out on their own. Not sure how successful this attempt was in doing that. Anyway, slides are available here.
TLDR: If you’re a seasoned researcher, see this. If you’re an aspiring one, start here or here, and read this.
For some years, I’ve been partly involved in the Let’s Move It intervention project, which targeted dysfunctional physical activity and sedentary behaviour patterns of older adolescents, by affecting their school environment as well as social and psychological factors.
I held a talk at the closing seminar; it was live streamed and is available here (on stage starting from about 1:57:00 in the recording). But if you were there, or are otherwise interested in the slides I promised, they are now here.
For a demonstration of non-stationary processes (which I didn’t talk about but which are mentioned in these slides), check out this video and an experimental mini-MOOC I made. Another blog post touching on some of the issues is found here.
The gist: to avoid getting fooled by them, we need to name our simplifying assumptions when modeling social scientific data. I’m experimenting with this visual approach to delivering information to those who think modeling is boring; feedback and improvement suggestions very welcome! [Similar presentation with between-individual longitudinal physical activity networks, presented at the Finnish Health Psychology conference: here]
I’m not as smooth as those talking heads on the interweb, so you may want just the slides. Download by clicking on the image below or watch at SlideShare.
Note: Jan Vanhove thinks we shouldn’t become paranoid with model assumptions; check his related blog post here!
These are the slides of my presentation at the annual conference of the European Health Psychology Society. It’s about presenting data visually, and taking publishing culture from the journals to our own hands. I hint to a utopia, where the journal publication is a side product of a comprehensively reported data set.
Please find a 14min video walkthrough of the slides (which can be found here) below. The site presented in the slides is here, and the tutorial by the most awesome Lisa DeBruine is here!
After the talk, I saw what was probably the best tweet about a presentation of mine ever. For a fleeting moment, I was happy to exist:
Matti Heino; Reijo Sund; Ari Haukkala; Keegan Knittle; Katja Borodulin; Antti Uutela; Vera Araújo-Soares, Falko Sniehotta, Tommi Vasankari; Nelli Hankonen
Background: Comprehensive reporting of results has traditionally been constrained by limited reporting space. In spite of calls for increased transparency, researchers have had to choose carefully what to report, and what to leave out; choices made based on subjective evaluations of importance. Open data remedies the situation, but privacy concerns and tradition hinder rapid progress. We present novel possibilities for comprehensive representation of data, making use of recent software developments.
Methods: We illustrate the opportunities using the Let’s Move It trial baseline data (n=1084). Descriptive statistics and group comparison results on psychosocial correlates of physical activity (PA) and accelerometry-assessed PA were reported in an easily accessible html-supplement, directly created from a combination of analysis code and data using existing tools within R.
Findings: Visualisations (e.g. network graphs, combined ridge and diamond plots) enabled presenting large amounts of information in an intelligible format. This bypasses the need to create narrative explanations for all data, or compress nuanced information into simple summary statistics. Providing all analysis code in a readily accessible format further contributed to transparency.
Discussion: We demonstrate how researchers can make their extensive analyses and descriptions openly available as website supplements, preferably with abundant visualisation to avoid overwhelming the reader with e.g. large numeric tables. Uptake of such practice could lead to a parallel form of literature, where highly technical and traditionally narrated documents coexist. While we may have to wait for fully open and documented data, comprehensive reporting of results is available to us now.
In this post, I present a property of averages I found surprising. Undoubtedly this is self-evident to statisticians and people who can think multi-variately, but personally I needed to see it to get a grasp of it. If you’re a researcher, make sure you do the single-item quiz before reading, to see how well your intuitions compare to those of others!
UPDATE: The finding regarding average intervention participants’ prevalence is published in this paper, in case you want a citable reference for it.
Ooo-oh! Don’t believe what they say is true
Ooo-oh! Their system doesn’t work for you
Ooo-oh! You can be what you want to be
Ooo-oh! You don’t have to join their f*king army
– Anti-Flag: Their System Doesn’t Work For You
In his book “The End of Average”, Todd Rose relates a curious story. In the late 1940s, the US Air Force saw a lot of planes crashing, and those crashes couldn’t be attributed to pilot error nor equipment malfunction. On one particularly bad day, 17 pilots crashed without an obvious reason. As everything from cockpits to helmets had been built to conform to the average pilot of the 1926, they brought in Lt. Gilbert Daniels to see if pilots had gotten bigger since then. Daniels measured 4063 pilots—who were preselected to not deviate from the average too much—on ten dimensions: height, chest circumference, arm length, thigh circumference, and so forth.
Before Daniels began, the general assumption was, that these pilots were mostly if not exclusively average, and Daniels’ task was to find the most accurate point estimate. But he had a more fundamental idea in mind. He defined “average” generously as person who falls within the 30% band around the middle, i.e. the median ±15%, and looked at whether each individual fulfills that criterion for all the ten bodily dimensions.
So, how big a proportion of pilots were found to be average by this metric?
This may be surprising, until you realise that each additional dimension brings with it a new “objective”, making it less likely that someone achieves all of them. But actually, only a fourth were average on a single dimension, and already less than ten percent were average on two dimensions.
As you saw in the quiz, I wanted to figure out how big a proportion of our intervention participants could be described as “average” by Daniels’ definition, on four outcome measures. The answer?
A lousy 1.5 percent.
I’m a bit slow, so I had to do a of simulation to get a better grasp of the phenomenon (code here). First, I simulated 700 intervention participants, who were hypothetically measured on four random, uncorrelated, normally distributed variables. What I found was that 0.86 % of this sample were “average” by the same definition as before. But what if we changed the definition?
Here’s what happens:
As you can see, you’ll describe more than half of the sample only when you extend the definition of “average” to about the middle 85% percent (i.e. median ±42.5%).
But what if the variables were highly correlated? I also simulated 700 independent participants with four variables, which were correlated almost perfectly (within-individual r = 0.99) with each other. Still, only 22.9 % percent of participants were described by defining average as the middle 30% around the median. For other definitions, see the plot below.
What have we learned? First of all: When you see averages, do not go assuming that they describe individuals. If you’re designing an intervention, you don’t just want to see which determinants correlate highly with the target behaviour on average, or seem changeable in the sense that the mean on those variables is not very high to begin with in your target group (see the CIBER approach, if you’re starting from scratch and want to get a preliminary handle on the data). This, because a single individual is unlikely to have the average standing on more than, say, two of the determinants, and individuals are who you’re generally looking to target. One thing you could do, is a cluster analysis where you’d look for the determinant profile, which is best associated with e.g. hospital visits (or, attitude/intention), and try to target the changeable determinants within that.
As a corollary: If you, your child, or your relationship doesn’t seem to conform to the dimensions of an average person in your city, or a particular age group, or whatever, this is completely normal! Whenever you see yourself falling behind the average, remember that there are plenty of dimensions where you land above it.
But wait, what happened to USAF’s problem of planes crashing? Well, the air force told the plane manufacturers to fix the problem of cockpits which don’t fit any individuals. The manufacturers said it was impossible and extremely costly. But when the air force said didn’t listen to excuses, cheap and easy solutions appeared quickly. Adjustable seats—now standard equipment in cars—are an example of the new design philosophy of individual fit, where we don’t try to fit the individual to the system, but the system to the individual.
Let us conclude with Daniels’ introduction section:
Three additional notes about the average:
Note 1: I’m taking it for granted, that we already know that the average is a useless statistic to begin with, unless you know the variation around the average, so I won’t pound on that further. But remember that variables generally aren’t perfectly normally distributed, as in the above simulations; my guess is that the situation would be even worse in those cases. Here’s a blog post you may want to check out: On Average, You’re Using the Wrong Average.
Note 2: There’s a curious tendency to think that deviations from the average represent “error” regardless of domain, whereas it’s self-evident that individuals can survive both if they’re e.g. big and bulky, or small and fast. With psychological measurement, is it not madness to think all participants have an attitude score, which comes from a normal distribution with a common mean for all participants? To inject reality in the situation, each participant may have their own mean, which changes over time. But that’s a story for another post.
Note 3: Did I already say, that you generally shouldn’t make individual-level conclusions based on between-individual data, unless ergodicity holds (which, in psychology, would be quite weird)?
I recently had a great experience with a StackOverflow question, when I was thinking about how to visualise ordinal data. This post shows an option for how to do that. Code for the plots is in the end of this post.
Update: here’s an FB discussion, which mentions e.g. a good idea of making stacked % graphs (though I like to see the individuals, so they won’t sneak up behind me) and using the package TramineR to visualise and analyse change.
Update 2: Although they have other names too, I’m going to call these things flamethrower plots. Just because it reflects the fact, that even though you have the opportunity to do it, it may not always be the best idea to apply them.
Say you have scores on some likert-type scale questionnaire items, like motivation, in two time points, and would like to visualise them. You’re especially interested in whether you can see detrimental effects, e.g. due to an intervention. One option would be to make a plot like this: each line in the plot below is one person, and the lighter lines indicate bigger increases in motivation scores, whereas the darker lines indicate iatrogenic development. The data is simulated so, that the highest increases take place in the item in the leftmost plot, the middle is randomness and the right one shows iatrogenics.
I have two questions:
Do these plots have a name, and if not, what should we call them?
How would you go about superimposing model-implied-changes, i.e. lines showing that when someone starts off at, for example, a score of four, where are they likely to end up in T2?
The code below first simulates 500 participants for two time points, then draws plot. If you want to use it on your own data, transform the variables in the form scaleName_itemNumber_timePoint (e.g. “motivation_02_T1”).