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New paper out based on a massive sample (n = 355,358) finds that screens explain less than 0.4% of depression AND shows why previous research is deeply flawed - unless you are willing to believe potatoes and eyeglasses are also destroying a generation (Thread 1/13)pic.twitter.com/AF3GP68b94
2/13 Although most scholars would agree this effect is tiny – similar effect sizes using this SAME data set are the basis for the radical claims about the dangers of smart phones. But what the Nature: HB paper does next is exciting.pic.twitter.com/0mijUXGp6b
3/13 It examines how strongly various neutral items in these data sets were related to depression (below a 1x effect means it had the same effect as screen time). For example, the effect of wearing eyeglasses on depression is 1.45 times greater than the effect of screens!pic.twitter.com/VRp5FTiAdF
4/13 So, if we believe screens are destroying a generation that would mean that so are: Potatoes, having asthma, not drinking milk, going to movies, music, religion, being tall, biking, and wearing glasses!pic.twitter.com/pK7dWevmDS
5/13 Now before we tell kids to stop wearing glasses AND take away cell phones the authors point out that due to questionable research practices of past scholars even the tiny effect of screens on depression is likely overestimated.pic.twitter.com/HMbVzjv3I7
6/13 In other words, previous researchers using these large data sets made many “choices” before publishing their findings. For example, in the MtF data set there are various items that COULD be used to in different combinations measure depression.pic.twitter.com/yHt9GzbMep
7/13 In fact, this is exactly what past scholars have done. Here is a breakdown of the different items past scholars have used to measure the SAME construct from the SAME data set.pic.twitter.com/Jxx8JqpzPp
8/13 In addition to this choice there are many other choices past scholars made (e.g., how do they measure technology use, do they use covariates, etc.). In fact there are trillions of different combinations past researchers could have used in some of these data sets!
9/13 The problem is that with so many combinations it was easy for past scholars to “pick and choose” the findings the best fit their belief. This picking and choosing can have a dramatic impact on the final product.
11/13 The current authors demonstrate that various combinations produce very different results. For example, the relation with well-being and screens can vary between -.075 to +.05 (both significant) in the EXACT SAME DATA-simply depending on the choices made by a researcher.pic.twitter.com/CwjBtfbq54
12/13 This is problematic because it means a researcher can make the data appear to tell whatever story they want it to tell in order to fit their belief that screens are destroying a generation.pic.twitter.com/3pssEYLugS
13/13 The take home from this new study is the evidence that smart phones are destroying a generation is not any stronger than potatoes and eyeglasses are destroying a generation. The moral panic surrounding the fear of screens is simply not supported by good science.pic.twitter.com/Ox9CVlpTB6
This seems like a poor analysis. The 3 surveys analyzed each had one question that accounted for the majority of the positive specifications (TV use, Weekday TV use, and "Own a computer?"). Screens aren't inherently bad, it's how we use them, which this study explicitly ignores.
This is the same data set and questions used by past researchers who have said phones are destroying a generation. The EXACT same questions used. That’s kind of the point of the study. The past studies are not to be believed
I understand it's the same surveys used but this analysis treats all the survey questions as identical and then interprets the median coef. as though all technology use have the same effects on depression. "owning a computer" and "social media" have opposite effects so Med. = 0
Put another way, on average digital technology use has limited effects on depression, but you're averaging the effects of "owning a computer" and "Social media use", which seem like silly things to average the effects of. Especially if your hypothesis is that social media is bad.
Totally agree with you! But again, this is the exact issue used in every large scale study on this topic. For example, every study from Twenge suffers from this same issue - yet she often blames SM. The point is that the fear of SM is built on this type of research!
It is partly built on this type of research, but there is other evidence too - e.g. the experiment showing that people instructed to spend less time on facebook for a week became happier than controls.
Does any of this apply to video game use, Professor?
Not directly but video game scholars have been guilty of using very flexible assessments (eg picking and choosing how to score outcomes to fit their beliefs). Seehttp://www.flexiblemeasures.com/crtt/
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