- I don't see a public answer to this one from a couple of weeks ago - On 25 Jan 2002 10:16:45 -0800, [EMAIL PROTECTED] (Jack Eslick) wrote:
> I am working with a data that were collected at non-uniform time > intervals. I want to use regression analyses to determine if there is > a temporal trend. Common sense tells me that the data need to be > assigned some type of weight value (data collected at closer intervals > should have less ability to influence the regression line). I have > been unable to find a reference about how to assign the weights, other > than some vague references to "an appropriate" method. Can someone > recommend an approachable reference? On careful re-reading, this is not the question that I expected from its first line. Now I gather that the precise lack of uniformity does not bother you for its lack of precision. You are (possibly) quite satisfied with what you have as a time line, for whatever it says about the passage of time. But you have some individuals with many extra (or fewer) points than others, or some parts of the time lines, by individual or for the whole sample, that are not equivalent in data density. - That is fuzzy on my part, but I am not highly confident in (for instance) my basic reading, that you have 'repeated measures.' Why does common sense tell you that you need weights? How extreme is the problem? - Perhaps you are underestimating the robustness of regression. What is the design (N and repeats) and what is the approximate R-squared: Are you trying to model these data on hand with great confidence, and place accurate intervals on coefficients, or are you trying to squeeze out a statistical test that will be legitimate? There is not always a unified solution. For instance, I can imagine that you might be best served by partitioning cases (I am assuming repeated measures again) into 'sparse data' and 'heavy data'; analyzing separately; and combining results. You do have to explain, at some time, why some data are sparse, and argue that it makes no important difference, right? I don't know of particular references for what I have been talking about, but "unbalanced data" might help your search. And you could try us with more detail, if you wish. -- Rich Ulrich, [EMAIL PROTECTED] http://www.pitt.edu/~wpilib/index.html ================================================================= Instructions for joining and leaving this list, remarks about the problem of INAPPROPRIATE MESSAGES, and archives are available at http://jse.stat.ncsu.edu/ =================================================================
