1/4 If you read one #depression#biomarker paper this year, read this one by Nils & the gang. They looked at a large sample of depressed and healthy participants, investigating numerous features (neuro, genetics, etc) in 2.4 million #MachineLearing models.
2/4 I'm not surprised by the results: depression is not a unitary, biological disease entity. It is a label that was historically developed for clinical utility: it is a heuristic superimposed on a complex landscape of mental health problems people experience.
3/4 The label has strenghts (and I believe labels can be helpful), but of course biological investigations into labels such as depression will have limited success.
1/5 This review by Gonthier (2022) tackles a crucial topic: are non-verbal intelligence tests culture fair? This is important because you often see the reasoning "ethnicity/race 1 has lower IQ than ethnicity/race 2, & it must be genetic because non-verbal tests are culture fair".
2/5 This takes ugly extremes such as that there is "some genetic component in Black–White differences in mean IQ" (Rushton & Jensen 2005) etc. So the review here really matters to address threats to such conclusions and can set the record straight.
3/5 Gonthier investigates numerous sources of evidence, from controlled lab experiments in the US to n=1 qualitative reports from ethnologists dating many decadesback, concluding that there is substantial evidence that non-verbal tests are *not* culture fair.
Highly recommended if you always wanted to know what colliders are or do—and if you ever "added x and y as covariates" to e.g. a linear regression because that is "what your field does".
2/ This is in my part my 'fault' bc I kept submitting it to applied journals, but I really didn't want this paper in a "journal specialized on measurement" (quote from 6 rejection letters): I wanted to reach clinicians & applied researchers.
3/ But I had submitted several papers that year, so didn't feel too bad abt waiting. Today, paper has ~400 citations & spawned little mini-literature of folks doing similar analyses w other scales. You can find a bit of a summary in this tweet here:
1/ So @UniLeiden has now "lost" a second Prof in a short period of time. In my reading of the news, he is no longer allowed on uni premises due to ‘extremely undesirable behaviour’, but keeps salary & title.
What did he do? See screenshot below from uni executive board.
2/ The other Prof we "lost" 3 yrs ago had committed fraud, tampered w data & grant applications, taken blood samples w/o ethics approval, fabricated experiments, removed participants, dropped & added authors (and was then hired by TU Dresden for .. I don't know exactly for what).
3/ Both cases reveal highly problematic practices that went on for years without uni doing something. And there is just no way *some* people in power weren't aware.
When practices did come to light, it is bc (often female & junior) folks spoke up, at their own peril.
Lots of new followers in the last few weeks, so here's a short thread introducing you to some of the work we conducted since 2020.
Broadly speaking, our work tackles how to best 1⃣understand,2⃣measure, and3⃣model mental health problems.
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Pillar 1: 𝗧𝗵𝗲𝗼𝗿𝘆 𝗯𝘂𝗶𝗹𝗱𝗶𝗻𝗴 𝗮𝗻𝗱 𝘁𝗲𝘀𝘁𝗶𝗻𝗴.
Don Robinaugh has led fanastic work on this topic. Our first recent paper I recommend is conceptual work on the importance of having clear theories.
Another project led by Don is our work on a formalized theory for panic disorder, sort of walking the theory walk instead of just talking the theory talk ;).
This is still in preprint stage, but there'll be some updates and news on this soon.
This thread has turned into neat lists of (1) things that can go wrong with Qualtrics surveys (and how to circumvent that), and (2) tricks of statistically interrogating data for anomalities that may cause unexpected findings.
(in our case, the zero correlation was due to a Qualtrics export error that was language specific—it was present in only half the sample, and not in the other, leading to group-level data that looked fine overall and behaved appropriately, but didn't correlate w other measures)