On 14 Sep 2003 14:57:43 -0700, [EMAIL PROTECTED] (Bastian) wrote: > does it make any sense to do a factor analysis when there are lots of > missing values (i.e. 320 from 378 values)?
It sounds as if you have a per-item response rate of less than 20%. And it sounds as if you want to do a factor analysis on 58 scores, split among some unstated number of variables and number of individuals. I hope that is not right. But that is how I read it. Technically, that could be done if there were complete cases, but I imagine that you should be able to derive a better data reduction from examining the available scatter-charts or cross-tabulations. But you don't provide enough information, anyway. Why are they missing? Is it missing-at-random? If not, your best hope of making progress might be an analysis of missing versus non-missing. > After doing the principal > component analysis I want to use the factors for a regression analysis > and I assume it is not very useful to mix-up the variables with lots > of missing values with my "correct" data without missing values. > Should I omit the variables? Is there any "rule of thumb" or something > else to pay attention? If a variable is not there, you cannot predict from its score. Do you have hopes that it will be present, in the future? Can you predict from "Missing, Yes versus No?" Hope this helps. -- Rich Ulrich, [EMAIL PROTECTED] http://www.pitt.edu/~wpilib/index.html "Taxes are the price we pay for civilization." . . ================================================================= Instructions for joining and leaving this list, remarks about the problem of INAPPROPRIATE MESSAGES, and archives are available at: . http://jse.stat.ncsu.edu/ . =================================================================
