How to calculate multiple means if dataset is irregular

#1
For a longitudinal study I'm building a database, I cut down 24 hours measurements in 96 mean value's of 15 minutes. The raw data is is irregular, i.e. it is measured every 5 or 6 seconds. (see attached picture) Anyone has an idea how to automatically calculate 15 minute mean values of this dataset?

I prefer to use excel or SPSS. Thanks in regards.
 

Attachments

hlsmith

Less is more. Stay pure. Stay poor.
#2
Are you expecting to see a change in means across time? I would imagine if you have an increment that spans two 15 minute periods and has 3 seconds in one group and 2 seconds in the other that it would not be a huge deal in which one it ends up in correct, since it is just 1 of 60 obs used in calculation and it will be accounted for in the mean denominator.

Given this I would just break the data up into 15 min increments and calculate means accordingly. Some may have 59 or 61 obs per period, but this will be negligible.
 
#3
Are you expecting to see a change in means across time? I would imagine if you have an increment that spans two 15 minute periods and has 3 seconds in one group and 2 seconds in the other that it would not be a huge deal in which one it ends up in correct, since it is just 1 of 60 obs used in calculation and it will be accounted for in the mean denominator.

Given this I would just break the data up into 15 min increments and calculate means accordingly. Some may have 59 or 61 obs per period, but this will be negligible.
Problem is I have many measurements of 24 hours, so it is too much work to break them all into 15 minutes piece manually. I’m searching for a way to cut the data into 15 minutes pieces automatically, then it’s no no problem they are based on 59 or 61 observations per 15 minutes.

I thought of imputing the data so that there’s one observation every second. Then the data will be regular. Problem is I don’t know how to impute data when there are no empty rows.
 

hlsmith

Less is more. Stay pure. Stay poor.
#4
Got a little lost, can't you just figure out the best denominator and instead of finding say quartile you find ?? percentiles to split data into groups?
 
#5
Got a little lost, can't you just figure out the best denominator and instead of finding say quartile you find ?? percentiles to split data into groups?
The problem is that I have many different datasets with different missing observations. I still have to manually split the data