I’d start by playing around with the data, trying to find different ways to organize it, identifying and defining relevant variables, and making graphs to look for patterns. Then you can start to look at relatively simple tests of relationships, and once you have a better idea of how the data are structured and what sorts of relationships seem to be present then you can try fancier math (if necessary).
For instance, you linked to your data organized by sleep cycle, starting a new row of data each time you fell asleep. I made a spreadsheet here using that organization (but slightly different formatting which was easier for me to work with) and used it to make this graph, which shows how much time you slept in blue and then the following amount of time you spent awake in orange, and then starts the next sleep period on the row below it. Two things that jump out from the graph are that you have lots of short cycles (under 5 hours, even) and that most of the cycles are under 24 hours. If you tend to find yourself on a cycle of over 24 hours (going to bed later and later each day), that must be from sleeping multiple times in a single calendar day.
Another way to organize the data is by calendar day. You could have one row for each calendar day (possibly starting each day at a time other than midnight, like the time when you are most often awake or when you’re most often asleep) and make a two-color graph of when you’re asleep & awake, like the one I linked to (except this time it would be a rectangle). With the data organized that way, you could look at questions like: how often am I awake at each time of day? If I am awake at a certain moment, how likely am I to be awake 24 hours later? If I am asleep? Or x hours later—you could make a graph where the x-axis is the number of hours later, and the y-axis is the probability of being awake that long later (give that you’re awake/asleep at t0). How does this change depending on time of day?
There are also various correlations that you could look at, or graphs that you can plot of 2 variables, like bedtime, length of sleep, wakeup time, length of awakenness, sleep debt (percent of time asleep during previous 72 hours? during previous 4 sleep cycles?). You might need to do something about the short cycles (leave them out? combine them with adjacent sleep/wake periods?). Here is a graph of amount of sleep vs. bedtime, which seems to show a pattern (you sleep longer when you fall asleep earlier) if we ignore the short sleeps.
Or, come up with some definition of a “normal” sleep cycle (e.g. one lasting close to 24 hours, which includes close to 8 hours of sleep) or a normal day (e.g. sleeping at least 6 hours from 10pm-10am and then being awake for at least 9 hours from 10am-10pm). How common are these “normal” cycles/days, are there any patterns to when they occur, do they come in streaks, and what seems to happen to precipitate the end of a streak of normality?
There are a bunch of other things like these that you could try. If you’ve already done some, you could share them here.
I’d start by playing around with the data, trying to find different ways to organize it, identifying and defining relevant variables, and making graphs to look for patterns. Then you can start to look at relatively simple tests of relationships, and once you have a better idea of how the data are structured and what sorts of relationships seem to be present then you can try fancier math (if necessary).
For instance, you linked to your data organized by sleep cycle, starting a new row of data each time you fell asleep. I made a spreadsheet here using that organization (but slightly different formatting which was easier for me to work with) and used it to make this graph, which shows how much time you slept in blue and then the following amount of time you spent awake in orange, and then starts the next sleep period on the row below it. Two things that jump out from the graph are that you have lots of short cycles (under 5 hours, even) and that most of the cycles are under 24 hours. If you tend to find yourself on a cycle of over 24 hours (going to bed later and later each day), that must be from sleeping multiple times in a single calendar day.
Another way to organize the data is by calendar day. You could have one row for each calendar day (possibly starting each day at a time other than midnight, like the time when you are most often awake or when you’re most often asleep) and make a two-color graph of when you’re asleep & awake, like the one I linked to (except this time it would be a rectangle). With the data organized that way, you could look at questions like: how often am I awake at each time of day? If I am awake at a certain moment, how likely am I to be awake 24 hours later? If I am asleep? Or x hours later—you could make a graph where the x-axis is the number of hours later, and the y-axis is the probability of being awake that long later (give that you’re awake/asleep at t0). How does this change depending on time of day?
There are also various correlations that you could look at, or graphs that you can plot of 2 variables, like bedtime, length of sleep, wakeup time, length of awakenness, sleep debt (percent of time asleep during previous 72 hours? during previous 4 sleep cycles?). You might need to do something about the short cycles (leave them out? combine them with adjacent sleep/wake periods?). Here is a graph of amount of sleep vs. bedtime, which seems to show a pattern (you sleep longer when you fall asleep earlier) if we ignore the short sleeps.
Or, come up with some definition of a “normal” sleep cycle (e.g. one lasting close to 24 hours, which includes close to 8 hours of sleep) or a normal day (e.g. sleeping at least 6 hours from 10pm-10am and then being awake for at least 9 hours from 10am-10pm). How common are these “normal” cycles/days, are there any patterns to when they occur, do they come in streaks, and what seems to happen to precipitate the end of a streak of normality?
There are a bunch of other things like these that you could try. If you’ve already done some, you could share them here.