"You won't understand fuzzy until you've seen sharp."
In your case, you won't understand 'good' communication, until you have seen 'poor.' It takes both to understand what correlates with what.
2) Suggest that you consciously work out (based on correlations perhaps) those factors & levels that indeed are likely to influence 'communication.' Set up a proper orthogonal array of these factors (i.e., a designed experiment - it ain't hard), and then select from your data stream those conditions that fit your design. Make sure you collect data for all the selected conditions, and do a proper DoE analysis for the factor effects.
3) Oh, and I forgot to put this first - I _assume_ that your measure of 'communication' is treated as a continuous variable, on a ratio scale. If it is not, then what you measure would be a first item to resolve.
Cheers, Jay
meredith wrote:
Summary of research project: The basic idea is that on a computer network you have machines called servers and machines called clients that talk to each other. The data flow is of interest to the research team I'm working with. We're measuring the data flow and calculating a correlation value as a standard measure of "how well" a server and client talk to each other. Naturally we only want to see servers and clients that talk to each other well, so we use a correlation threshold to determine which servers and clients talk to each other well and which don't.
Currently our calculations are done after the fact, it's more a history report than a real time diagnostic tool. I'm looking for a way to guess the correlation value (within 90% - 99%) based on the number of machines that passed the correlation threshold. I have four possible variables to work with and I'm currently testing for interactions between the variables.
Any tips or guidance is appreciated. . . ================================================================= Instructions for joining and leaving this list, remarks about the problem of INAPPROPRIATE MESSAGES, and archives are available at: . http://jse.stat.ncsu.edu/ . =================================================================
. . ================================================================= Instructions for joining and leaving this list, remarks about the problem of INAPPROPRIATE MESSAGES, and archives are available at: . http://jse.stat.ncsu.edu/ . =================================================================
