This analysis suffers from a fairly clear confounder: since you are basing the data on which days you actually listened to music, there might be a common antecedent that both improves your mood and causes you to listen to music. As a silly example, maybe you love shopping for jeans, and clothing stores tend to play music, so your mood will, on average, be better on the days you hear music for this reason alone.
An intention-to-treat approach where you make the random booleans the explainatory variable would be better, as in less biased and suffer less from confounding. It would also give you less statistical power, but such is the cost of avoiding false conclusions. You may need to run the experiment for longer to counterbalance.
It appears that listening to music, in the short-term: [...] makes earworms play in my mind for slightly less of the time
Whenever I suffer from an earworm, my solution has for a long time been to just play and listen to that song once, sometimes twice. For some reason, this satisfies my brain and it drops it. Still counter-intuitive, but you might want to try it.
On a completely separate note:
Both response variables were queried by surprise, 0 to 23 times per day (median 6), constrained by convenience.
How was this accomplished, technically? I’ve long wanted to do similar things but never bothered to look up a good way of doing it.
there might be a common antecedent that both improves your mood and causes you to listen to music. As a silly example, maybe you love shopping for jeans, and clothing stores tend to play music, so your mood will, on average, be better on the days you hear music for this reason alone.
There might be a common antecedent that both worsens my mood and causes me to listen to music. As a silly example, maybe I hate shopping for jeans, but clothing stores tend to play music, which actually improves my mood enough to outweigh the shopping. That is, confounding could go both ways here; the effect could be greater than it appears, rather than less.
An intention-to-treat approach where you make the random booleans the explainatory variable would be better, as in less biased and suffer less from confounding.
I’ll reanalyse that way and post results, if I remember.
How was this accomplished, technically?
I made a script run in the background on my PC, something like
while true:
qt = random(0, INTERVAL)
while time() % INTERVAL < qt:
sleep(1)
announce_interruption()
mood = popup_input("mood (-1 to 1):")
earworm = popup_input("song in head (N/D/R/O):")
save_to_log(time(), mood, earworm)
sleep(INTERVAL - time() % INTERVAL)
The “constrained by convenience” part means that I recorded data when and only when I was at my PC. More reliable would be to run such a script on a device that’s with you most of the time, like a smartphone or smartwatch, but I’ve no such device.
That is, confounding could go both ways here; the effect could be greater than it appears, rather than less.
Absolutely, but if we assume the null hypothesis until proven otherwise, we will prefer to think of confounding as creating effect that is not there, rather than subduing an even stronger effect.
I’ll reanalyse that way and post results, if I remember.
Yes, please do! I suspect (60 % confident maybe?) the effect will still be at least a standard error, but it would be nice to know.
I made a script run in the background on my PC, something lik
Ah, bummer! I also have this problem solved for computer time, and I was hoping you had done something for smartphone carriage.
(Note, by the way, that a uniformly random delay is not as surprising as an exponentially distributed delay. Probably does not matter for your usecase, and you might already know all of that...)
This analysis suffers from a fairly clear confounder: since you are basing the data on which days you actually listened to music, there might be a common antecedent that both improves your mood and causes you to listen to music. As a silly example, maybe you love shopping for jeans, and clothing stores tend to play music, so your mood will, on average, be better on the days you hear music for this reason alone.
An intention-to-treat approach where you make the random booleans the explainatory variable would be better, as in less biased and suffer less from confounding. It would also give you less statistical power, but such is the cost of avoiding false conclusions. You may need to run the experiment for longer to counterbalance.
Whenever I suffer from an earworm, my solution has for a long time been to just play and listen to that song once, sometimes twice. For some reason, this satisfies my brain and it drops it. Still counter-intuitive, but you might want to try it.
On a completely separate note:
How was this accomplished, technically? I’ve long wanted to do similar things but never bothered to look up a good way of doing it.
There might be a common antecedent that both worsens my mood and causes me to listen to music. As a silly example, maybe I hate shopping for jeans, but clothing stores tend to play music, which actually improves my mood enough to outweigh the shopping. That is, confounding could go both ways here; the effect could be greater than it appears, rather than less.
I’ll reanalyse that way and post results, if I remember.
I made a script run in the background on my PC, something like
The “constrained by convenience” part means that I recorded data when and only when I was at my PC. More reliable would be to run such a script on a device that’s with you most of the time, like a smartphone or smartwatch, but I’ve no such device.
Absolutely, but if we assume the null hypothesis until proven otherwise, we will prefer to think of confounding as creating effect that is not there, rather than subduing an even stronger effect.
Yes, please do! I suspect (60 % confident maybe?) the effect will still be at least a standard error, but it would be nice to know.
Ah, bummer! I also have this problem solved for computer time, and I was hoping you had done something for smartphone carriage.
(Note, by the way, that a uniformly random delay is not as surprising as an exponentially distributed delay. Probably does not matter for your usecase, and you might already know all of that...)
I added intention-to-treat statistics in an addendum.