I'm dealing with a fairly large ecological data set (field data, not collected by me) of approximately 60 attributes measured for about 400 individual trees. Very few trees (~20%) have complete data (have a value for every attribute), the rest are missing one or more data points, due to field-research limitations and error.
I'm working on univariate analyses--basically just generating a bunch of summary statistics--and I'm not sure what is the most responsible approach to handling the missing values?
The conservative approach would be to restrict my analyses to the 20% of individuals with complete data, but that looses a lot of critical information--the data is batched by field sites, and only a few sites have complete information, so I'd have to throw out a bunch of sites, which would kill the overall research objectives.
Alternatively, I could just use all the data, and use a somewhat different set of individuals for each attribute--but my concern with that approach is that it makes comparison of different attributes invalid, because the data isn't paired. This is important, because some of the data is time-series (values for the same attribute in multiple years) and I want to compare different years to each other. For example, I want to look at how "attribute A" changes from year to year, and eventually I intend to correlate that change with other data, such as local temperature and rainfall.
I'm leaning toward the laborious hybrid-approach of using a different data sub-set for each comparison--for example when I look at "attribute A" over multiple years, I'll include only trees that were measured in all relevant sample periods, and exclude any with missing data. But if I want to do the same thing with "attribute B", I'll use a different set of trees, which is complete for that attribute. Before I dig too far into that approach though, I would love any advice folks have about how to handle this issue. I've tried poking around this forum and some text books, but I'm not finding any good answers.