A short intro to what’s up

In the slides below, I present what I’m currently (August 2016) up to in the health psychology front, and what I may be doing in the next couple of years, regarding employee well-being.

 

 

Note 1: If it’s not obvious, there are a lot of people besides me making these projects happen. I’m especially indebted to Nelli Hankonen, the principal investigator of both of them.

Note 2: The (Finnish only) web site of the Let’s Move It intervention is here.

Esittelyssä Raistlin Laplace

[See English version here.]

NY_posterised

Raistlin Laplace on juuri saanut psykiatriltaan diagnoosin, jota hän istuutuu lukemaan keväisenä päivänä New Yorkin aurinkoisen Keskuspuiston penkille. Ohuet huulet tapailevat luisevien sormien pitelemää tuomiota: “Epistemologinen meluyliherkkyys”. Se liittyi jotenkin siihen, kuinka hahmoja (signaali) erotetaan melun (tai “kohinan”) keskeltä, kuinka esimerkiksi kanavien välille viritetty radio ei kerro paljoakaan soittolistojen laatijoiden musiikkimauista, koska melua on liikaa signaaliin nähden. Toisaalta ihmisaivot ovat hahmontunnistuskone vailla vertaa, ja voivat vaivatta havaita saatanallisia säkeitä takaperin soitetussa musiikissa tai Jeesuksen koiran anuksessa. Herra Laplacen ongelma oli käänteinen raamatusta salattuja koodeja etsivän väen tulokulmaan nähden; pakkomielteinen satunnaisuuden luomien illusoristen hahmojen välttäminen. Diagnoosi kävi tietyllä tavalla järkeen, mutta hän oli kauan sitten lakannut luottamasta asioihin, jotka kävivät järkeen.

Raistlin vietti lapsuutensa Napoli-nimisessä pikkukylässä Yhdysvalloissa. Se oli kuulostanut sopivan eurooppalaiselta hänen ranskalais-venäläisille siirtolaisvanhemmilleen, jotka halusivat tarjota ainoalle lapselleen suvaitsevaisen kasvuympäristön jostain vähemmän sotaisasta maankolkasta. Vasta muutettuaan heille valkeni, että Napoli oli tosiasiassa punaniskakylä, jossa vanhemmat käyttivät suuren osan päivästään työmatkoihin ja lapset verisiin tappeluihin naapurikylien nuorten kanssa.

Raistlin oli aina ollut olemukseltaan sairaalloinen, vaikkei juurikaan tavannut sairastaa. Hänen hintelä ja kalvakka ulkonäkönsä, sekä pistävän sinisten silmien ja pikimustan tukan luoma kontrasti sai alusta lähtien taikauskoiset vanhukset kuiskimaan. Tietoisena tästä, hän ala-asteikäisenä paikallisen kirjaston löydettyään huomasi nauttivansa suunnattomasti rajatiede-nurkkauksen kirjoista, ammentaen itseensä kaikkea okkultismista ja samanismista new age-niteisiin. Urheilusta – tai sen puoleen mistään muustakaan, mikä muita lapsia kiinnosti – hän ei koskaan välittänyt, ja olisikin kaikkein mieluiten vain halunnut viettää aikaa yksin kirjojensa parissa.

Ensimmäisen kerran hänen informaatiomaailmankaikkeutensa romahti, kun hän kaikkea kokeilleena joutui ylä-asteella myöntämään, etteivät rajatiede-nurkkauksen kirjojen rituaalit ja tekniikat toimineetkaan luvatusti. Kaikki aikuisten kirjoittama ei ollutkaan erehtymätöntä, kaikki informaatio ei sisältänytkään tietoa. Mutta tämä oli vasta alkua.

Eräänä New Yorkin syksyisenä sadepäivänä 28-vuotias Raistlin liimasi kiinni startup-yrityksensä konkurssihakemuksen sisältävän kirjekuoren ja mietti, mikä oli mennyt vikaan. Hän oli tehnyt kaiken oikein; lukenut oikeat kirjat, noudattanut menestysyritysten taktiikoita, kuunnellut satoja tunteja populaaripsykologiaa hyödyntäviä myyntikoulutusnauhoja, ottanut harkittuja riskejä ja tehnyt vuosia työtä periksiantamattomalla asenteella. Jossain vaiheessa rahat vain loppuivat ja –  velkojien hengittäessä niskaan – lisää ei tullut. Yksiönsä himmeässä valaistuksessa Raistlin hitaasti kasvavan kauhun vallassa pohti, mistä kirjailijat tiesivät niiden asioiden, joiden he väittivät tietävänsä todeksi, olevan totta? Erosivatko he tosiaan jollain tapaa hänestä itsestään, joka olisi voinut tällä hetkellä olla menestyvän teknologiayrityksen johtaja, mikäli vain muutama pikkuasia olisi sattunut menemään toisin?

Mies ei nukkunut sinä yönä. Hänen mielessään pyörivät ne lukemattomat tunnit, jotka hän oli viettänyt sanomalehtien parissa oppimatta mitään maailman toiminnasta. Uusista läpimurroista kertovat tiedeuutiset, joista kaikki olivat jälkeenpäin osoittautuneet ennenaikaisiksi; kaikki ne kirjat, joiden kirjoittajat luulivat kokemuksensa johtuvan satunnaistapahtumien sijaan omasta toiminnastaan ja kyvyistään.

Kaksi vuotta myöhemmin hän luki enää vain vertaisarvioituja tieteellisiä artikkeleita, kunnes matemaatikko-tilastotieteilijä John Ioannidisin artikkelista “Why most published research findings are false” seurannut keskustelu sai hänet sille kannalle, ettei tieteelliseenkään tietoon ole luottaminen. Informaation ja tiedon välinen suhde, josta hän oli ylä-asteella oppinut, alkoi muodostua hänelle pakkomielteeksi: Raistlin ei halunnut enää yhtään enempää informaatiota, hän janosi tietoa. Puhdas matematiikka todistettavissa olevine aksioomineen viimein tarjosi juuri tätä, ja hintelä oppimisaddiktimme paneutuikin siihen täysin rinnoin, päätyen sukulaisen suosituksen kautta pankkiin töihin. Hän asetti tavoitteekseen välttää kaikkea sellaista informaatiota, mikä ei ollut kosher – jos signaali oli heikko suhteessa meluun, mielen portit pysyivät visusti suljettuina.

Tästä seurasi odottamaton ongelma: mitä enemmän hän pyrki eristämään itsensä “hyödyttömältä hölynpölyltä”, sitä herkemmäksi hän sille tuli. Silloin harvoin kun hän enää käyskenteli ulkona, iltapäivälehtien shokkiotsikot tuntuivat vatsanpohjassa asti. Mainokset saivat hänet raivon valtaan. Hän alkoi myös välttelemään sosiaalisia tilanteita tajutessaan, kuinka helposti hyvät tarinat jäivät hänen mieleensä kummittelemaan. Hän työskenteli riskianalyytikkona, eikä halunnut alkaa pelätä lentokoneita, koska jonkun tutun tutun tuttu oli kokenut kauheita pakkolaskun tehneessä koneessa. Pakko-oireiden (kuten venäläisten matemaatikkojen nimien nopea peräkkäinen toistaminen jonkun perustellessa kantaansa anekdootein) pahetessa, Raistlinin huolestunut työnantaja ohjasi hänet ammattiavun piiriin.

Kevään voimistuvien auringonsäteiden lämmittämällä Keskuspuiston penkillä diagnoosiaan tarkasteleva Raistlin oli luvannut psykiatrilleen aloittaa terapiaryhmässä. Hänen ottamansa ahdistuslääkkeet olivat myös alkaneet tehota, mikä sai hänet ostamaan viereiseltä hodarikauppiaalta iltapäivälehden ja lukemaankin siitä pari sivua. Se ei tuntunut enää niin pahalta, suunnilleen yhtä järkevältä kuin hänen diagnoosinsakin; psykiatri oli selittänyt epistemologisen meluyliherkkyyden tarkoittavan tiedon alkuperään liittyvää ahdistusta siitä, ettei kohinan keskeltä löydykään signaalia, ja kuoleman hetkellä tajuaa eläneensä elämänsä reagoiden mielen melussa näkemiin aaveisiin, todellisten ilmiöiden sijaan.

Joitain tunteja myöhemmin nuori, koiraa ulkoiluttava opiskelija löysi ilokseen puistosta päivän lehden, jonka hän vei kotiinsa ja avasi murokulhon ääressä. Se näytti muuten lähes koskemattomalta, mutta usean artikkelin perään oli hyvin pienellä mutta varmalla käsialalla kirjoitettu: “Kolmogorov. Kolmogorov. Kolmogorov.

Introducing Raistlin Laplace

In this post, you meet Raistlin Laplace. You will hear more of him at a later time. Please find the Finnish version here.

NY_posterised

Raistlin Laplace has just received a diagnosis from his psychiatrist. It’s a sunny day of early spring, as he sits down on a bench in New York’s Central Park and opens an envelope. His thin lips hesitate upon the judgement held in his bony fingers: “Epistemological Hypersensitivity”. It had something to do with how patterns are distinguished from the midst of noise. Like how a radio tuned in the middle of two channels doesn’t tell much of the DJs’ music taste; too much noise, too little signal. The human brain is a signal detection machine without comparison, as it can detect satanic verses in backwards-played metal music or see Jesus in a dog’s anus (has happened). But Mr. Laplace’s problem was at odds with the one of those who seek secret codes in the bible. He was obsessed with avoiding randomness-created illusory patterns. The diagnosis made sense in a way, but he had long ago given up trust in things that made sense.

Raistlin spent his youth in a small town called Naples in southwest Florida. It had sounded aptly European to his French-Russian immigrant parents, who wanted to offer their only child a more tolerant environment from a less war-prone part of the world. It wasn’t immediately clear that Naples was, in fact, a red-neck village where parents spent most of their days commuting, and children in bloody fights with the youngsters of nearby villages.

Raistlin had always had a sickly appearance, although he was seldom ill. He had a feeble posture and pale complexion, combined with the contrast between his icy blue eyes and jet-black hair. This was more than enough to make the superstitious elderly whisper. Knowing this, and upon discovering the local library in elementary school, he realised he took great delight in the books found at the corner marked occultism. He devoured everything from shamanism to theosophy and new age. Sports—or, to that matter, anything else which interested other children—he couldn’t care less about. Having a hard time fitting in, he would’ve most wanted just to spend time alone with his books.

The first time his information universe collapsed was, when in junior high, he had to admit that the rituals and techniques of the occult-corner didn’t work as promised. Everything adults wrote wasn’t unerring; all information wasn’t knowledge. But the shock waned quickly and little did he know that this was only the beginning.

On a rainy New York day, 28-year-old Raistlin sealed the envelope containing a bankruptcy application of his startup company. He pondered on what had gone wrong. He had done everything right; read all the right books, followed the strategies of highly successful companies, listened to hundreds of hours of popular psychology-inspired sales training tapes, taken educated risks, and for years worked with a relentless, never-give-up attitude. At some point the money just run out and, as creditors breathed down his neck, more wasn’t coming. In the dim lighting of his studio apartment, Raistlin felt horror escalate. How could those writers, who so confidently spew out facts of the world, actually know how things truly worked? Were they really different than him, who—had any of the myriad small things gone differently—could now well be the CEO of a highly successful tech company?

He didn’t sleep that night. He watched an agonising replay of all those hours he had spent reading newspapers without learning anything about how the world actually worked. All the popular science news touting great new discoveries, all of which had later turned out to be premature. All the books written by those who thought their success was caused by their own actions and aptitude, instead of random occurrences of serendipity.

Two years later he only read peer-reviewed scientific journals. That is, until the discussion which followed mathematician-statistician John Ioannidis’ article “Why most published research findings are false” persuaded him of the fallibility of the scientific method (outside of physics, at least). The relationship between information and knowledge he had learnt about in junior high, began forming as an obsession: Raistlin wanted no more information, and he hungered for knowledge. Pure mathematics with it’s provable axioms finally offered just this, and our bony learning addict delved into it. By a stroke of luck and a relative’s recommendation, he ended up working in a bank. He vowed to avoid all information which wasn’t kosher; if the signal-to-noise ratio was low, the gates of his mind remained sealed.

This resulted in an unexpected problem: the more he aspired to isolate himself from “useless nonsense”, the more sensitive to it he became. On those few days he strolled outside, the shock headlines at newspaper stands turned his stomach to knots. Advertisements filled him with outrage. He also started avoiding social situations when he realised how easy it was for good stories to get stuck in his brain. He worked as a risk analyst, and didn’t want to start fearing airplanes just because some acquaintance of an acquaintance had experienced dread during an emergency landing. Compulsions—like fast repetition of names of old Russian mathematicians, when someone used anecdotes to advocate a position—got worse and eventually his worried employer steered him towards professional help.

Rays of intensifying sunlight warmed up Raistlin’s bench and whispered promises of summer to the people wandering about Central Park. Raistlin had promised his psychiatrist to begin participating in a therapy group. The anxiety meds he took had also started to kick in, which made him buy a newspaper from a nearby hot dog stand and even read a couple of pages. It didn’t feel as bad anymore, perhaps about as sensible as his diagnosis. The psychiatrist had explained that epistemological hypersensitivity meant anxiety stemming from the origin of knowledge. That there wouldn’t be a signal in the noise. Fear of realising, upon the moment of one’s death, that he had spent his life reacting to ghosts the mind saw in the noise, instead of real phenomena.

Some hours later, a young student walking a dog in the park, to her delight stumbled upon a pristine newspaper. She took it home and opened it in front of a bowl of cereal. At first glance, the paper looked untouched. Only after reading several articles, she noticed some very small but resolute handwriting. In the margins, someone had written: “Kolmogorov. Kolmogorov. Kolmogorov.

The myth of the magical “Because”

In this post I try to answer the call for increased transparency in psychological science by presenting my master’s thesis. I ask for feedback about the idea and the methods. I’d also appreciate suggestions for which journal it might be wise to submit the paper I’m now starting to write with co-authors. Check OSF for the documents: thesis is here (33 pages), analysis code and plots here (I presented the design analysis in a previous post).

In my previous career as a marketing professional, I was often enchanted by news about behavioral science. Such small things could have such large effects! When I moved into social psychology, it turned out that things weren’t quite so simple.

One study that intrigued me was done in the 70’s, and has since gained huge publicity (see here and here, for examples). The basic story is, that you could use the word because to get people to do things, due to a learned “reason → compliance” link.

because20in20media

Long story short, I was able to experiment in a within-trial setting of a health psychology intervention. Here’s a slideshow adapted from what I presented in the annual conference of the European Health Psychology Society:

 

Things I’m happy about:

  • Maintaining a Bayes Factor / p-value ratio of about 1:2. It’s not “a B for every p“, but it’s a start…
  • Learning basic R and redoing all analyses in the last minute, so I wouldn’t have to mention SPSS🙂
  • Figuring out how this pre-registration thing works, and registering before end of data collection.
  • Using the word “significant” only twice and not in the context of results.

Things I’m not happy about:

  • Not having pre-registered before starting data collection.
  • Not knowing what I now know, when the project started. Especially about theory formation and appraisal (Meehl).
  • Not having an in-depth understanding of the mathematics underlying the analyses (although math and logic are priority items on my stuff-to-learn-list).
  • Not having the data public… yet. It will be in 2017 the latest, but hopefully already this autumn.

A key factor for fixing psychological science is transparency; making analyses, intentions and data available for all researchers. As a consequence, anyone can point out inconsistencies and use the findings to elaborate on the theory, making accumulation of knowledge possible.

Science is all about predicting, and everyone knows how anyone can say “yeah, I knew that’d happen”. The most impressive predictions are those made well before things start happening. So don’t be like me, and pre-register your study before the start of data collection. It’s not as hard as it sounds! For clinical trials, this can be done for free in the WHO-approved German Clinical Trials Register (DRKS). For all trials, the Open Science Framework (OSF) website can be used for pre-registering plans and protocols, as well as making study data available for researchers everywhere.There’s also an extremely easy-to-use pre-registration site AsPredicted.

One can also use the OSF website as a cloud server to privately manage one’s workflow (for free). As a consequence, automated version control protects the researcher in the case of accusations of fraud or questionable research practices. Check the site out by browsing my thesis here (33 pages) or analysis code and plots here.

ps. If there’s anything weird in that thesis, it’s probably because I have disregarded some piece of advice from Nelli Hankonen, Keegan Knittle and Ari Haukkala, for whose comments I’m indebted to.

Analyse your research design, before someone else does

In this post, I demonstrate how one could use Gelman & Carlin’s (2014) method to analyse a research design for Type S (wrong sign) and Type M (exaggeration ratio) errors, when studying an unknown real effect. Please let me know if you find problems in the code presented here.

[Concept recap:]

Statistical power is the probability you detect an effect, when it’s really there. Conventionally disregarded completely, but often set at 80% (more is better, though).

Alpha is the probability you’ll say there’s something when there’s really nothing, in the long run (as put by Daniel Lakens). Conventionally set at 5%.

type-i-and-ii-errors1-625x468
Two classic types of errors. Mnemonic: with type 1, there’s one person and with type 2, there are two people. Not making a type 2 error is called ‘power’ (feel free to make your own mnemonic for that one). Photo source.

Why do we need to worry about research design?

If you have been at all exposed to the recent turbulence in the psychological sciences, you may have bumped into discussions about the importance of a bigger-than-conventional sample sizes. The reason is, in a nutshell, that if we find a “statistically significant” effect with an underpowered study, the results are likely to be grossly overestimated and perhaps fatally wrong.

Traditionally, if people have considered their design at all, they have done it in relation to Type 1 and Type 2 errors. Gelman and Carlin, in a cool paper, bring another perspective to this thinking. They propose considering two things:

Say you have discovered a “statistically significant” effect (p < alpha)…

  1. How probable is it, that you have in your hands a result that’s of the wrong sign?  Call this a Type S (sign) error.
  2. How exaggerated is this finding likely to be? Call this a Type M (magnitude) error.

Let me exemplify this with a research project we’re writing up at the moment. We had two groups with around 130 participants each, and exposed one of them to a message with the word “because” followed by a reason. The other received a succinct message, and we observed their subsequent behavior. Note, that you can’t use the observed effect size to figure out your power (see this paper by Dienes). That’s why I figured out a minimally interesting effect size of around d=.40 [defined by calculating the mean difference considered meaningful, and dividing the result by the standard deviation we got in a another study].

First, see how we had an ok power to detect a wide array of decent effects:

power

So, unless the (unknown) effect is smaller than what we care about, we should be able to detect it.

TypeS

Next, above we see that the probability we would observe an effect of the wrong sign would be miniscule for any effect over d=.2. This would mean it’d look like the succinct message worked better than the reason message, when it really was the other way around. typeM

Finally, and a little surprisingly, we can see that even relatively large true effects would actually be exaggerated by a factor of two!

Dang.

But what can you do, those were all the participants we could muster up with our resources. An interesting additional point is brought by looking at the “v-statistic”. This is the measure of how your model compares to random guessing. 0.5 represents coin flipping accuracy (see here for full explanation and the original code I used).

vstat

Figure above shows how we start exceeding random guessing at R^2 around 0.25 (d=.32 according to this). The purple line is in there to show how an additional 90 people help a little but do not do wonders. I’ll write about the results of this study in a later post.

Until then, please let me know if you spot errors or find this remotely helpful. In case of the latter, you might be interested in how to calculate power in cluster randomised designs.

Oh, and the heading? I believe it’s better to do as much of this sort of thinking, before reviewer 2 (or an adversary) does it for you.