MONITORING THE FUTURE : M.T.F. which asks adolescents about drug and alcohol use as well as other things, including more recently, vaping and digital technology.

In 2019, more than 40,000 students from nearly 400 schools responded.

Gathering data on so many behaviours also means that means that respondents aren't always asked about topics in detail.

This is particularly problematic when studying tech use. In past decades, if researchers asked how much time a person spent with a device - TV, say - they know what happened basically during that window.

But screen time today can range from texting friends, to using social media to passively watching videos to memorizing notes for class - all very different experiences and potentially very different effects.

Still, these limitations are the same for everyone who accesses the raw data. What makes one study that draws on the data distinct from another is a series of choices researchers make about how to analyze those numbers.

TO examine the relationship between digital technology use and well being, a researcher has to define ''well being''. The M.T.F. survey, as the Nature paper notes has 13 questions concerning depression, happiness and self-esteem.

Any one of those could serve as a measure of well-being, or any combination of two, or all 13. 

A researcher must decide on one before running the numbers; testing them all, and then choosing the one that generates the strongest association between depression and screen use, would be bad science.

But suppose five ways produce results that are strong enough to be considered meaningful, while five don't. Unconscious bias [or pure luck] could lead a researcher to pick one of the meaningful ways and find a link between screen time and depression without acknowledging the five equal probable outcomes that show no such link.

''Even just a couple of years ago, we as researchers still considered statistics king of like a magnifying glass, something you would hold to the data and you would then see what's inside, and it just helped you extract the truth.'' Orben, at the University of Cambridge, says.

''We now know that statistics actually can change what you see.''

To show how many legitimate outcomes a large data set can generate, Orben and Pryzbylski used a method called ''specification curve analysis'' to look for relationship between digital technology use and adolescent well-being in three ongoing surveys of adolescents in the United States and the United Kingdom  including the M.T.F.

The honor and serving of the latest operational research on Screen Time, continues. The World Students Society thanks author Kim Tingley.


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