> Not a psychiatrist, but I'm pretty skeptical of the approach of trying to sort things according to a few dimensions. ... I don't see why it should be likely to cleave reality at its joints.
One thing to understand about this is that they're not making up these dimensions from thin air. The dimensions are found in a data-driven manner using one of several techniques, most of which fall under the umbrella of factor analysis[1]. If you're familiar with principal component analysis or singular value decomposition, it's a pretty similar idea: you find the dimensions that explain as much variance as possible.
Then, once you have the dimensions, you try to make sense of them in some way. You may do something called factor rotation, which will change what these dimensions are, but you always have the same information among those five factors. Next, you have to give the factors arbitrary names. The names are usually given for the purpose of making it easy to discuss the factors. (This is the same way that the "Big 5" personality scale [2] was developed.)
For instance, if you had a factor which included the survey questions "Do you have trouble sleeping at night?" "Does it take you a long time to fall asleep?" and "Do you wake up not feeling refreshed?", you might name this an "insomnia" dimension. Really, it's just making it easier to discuss the math and data (though the names do end up having a big impact on how people think about the factors).
You can do this with survey data, brain imaging data, genetic data, etc. There are many different ways to find these. That means you can look at how they line up with each other. There are many theories and structures, but a popular one is about the "p factor" mentioned in the article which boils it down to a single dimension. I haven't read the literature for a few years, though, so I can't comment on current thinking about factor structures beyond what the article discusses.
But in the end, the idea is trying to find statistics which are useful and meaningful, either in the clinic or else for guiding future basic research. So when they say "how many dimensions are there?", what they really mean is "how many dimensions can we find that are useful?". It's not a perfect solution, but it is a good start.
One thing to understand about this is that they're not making up these dimensions from thin air. The dimensions are found in a data-driven manner using one of several techniques, most of which fall under the umbrella of factor analysis[1]. If you're familiar with principal component analysis or singular value decomposition, it's a pretty similar idea: you find the dimensions that explain as much variance as possible.
Then, once you have the dimensions, you try to make sense of them in some way. You may do something called factor rotation, which will change what these dimensions are, but you always have the same information among those five factors. Next, you have to give the factors arbitrary names. The names are usually given for the purpose of making it easy to discuss the factors. (This is the same way that the "Big 5" personality scale [2] was developed.)
For instance, if you had a factor which included the survey questions "Do you have trouble sleeping at night?" "Does it take you a long time to fall asleep?" and "Do you wake up not feeling refreshed?", you might name this an "insomnia" dimension. Really, it's just making it easier to discuss the math and data (though the names do end up having a big impact on how people think about the factors).
You can do this with survey data, brain imaging data, genetic data, etc. There are many different ways to find these. That means you can look at how they line up with each other. There are many theories and structures, but a popular one is about the "p factor" mentioned in the article which boils it down to a single dimension. I haven't read the literature for a few years, though, so I can't comment on current thinking about factor structures beyond what the article discusses.
But in the end, the idea is trying to find statistics which are useful and meaningful, either in the clinic or else for guiding future basic research. So when they say "how many dimensions are there?", what they really mean is "how many dimensions can we find that are useful?". It's not a perfect solution, but it is a good start.
[1] https://en.wikipedia.org/wiki/Factor_analysis
[2] https://en.wikipedia.org/wiki/Big_Five_personality_traits