Hi all, I have the following problem which I don't find an actually satisfying statistical method for: The item discrimination index for questionnaire items (i.e. the corrected item-total-correlation) is supposed to increase with the number of similar items answered (when controlling the item content). In several papers these "item context effects" are tested by correlating (fisher-z-transformed) item discrimination with item position in the quesionnaire (e.g. Knowles, 1988*). What leaves me somehow unsatisfied about this technique is that the "sample size" used for this analysis equals the number of questionnaire items, not the number of respondents. What I'm looking for is a method to test the _trend_ in correlations of a series of variables x(1), x(2), ... x(k) with another variable y. I know how to test the null hypothesis that all k correlations are equal, but this is not exactly the question I'm trying to answer. Is there any way to test the trend in series of correlations based on the raw data, i.e. that uses the power of the original sample size? Or is the correlation between k and r(x(k),y) an adequate procedure, even if this means a "sample" size of e.g. 36 items that were originally answered by 1200 respondents? Thanks a lot for any hint, Johannes Hartig
*Knowles, E. S., (1988). Item context effects on personality scales: Measuring changes the measure. Journal of personality and social psychology, 55, 312-320. . . ================================================================= Instructions for joining and leaving this list, remarks about the problem of INAPPROPRIATE MESSAGES, and archives are available at: . http://jse.stat.ncsu.edu/ . =================================================================
