On 20 May 2004 03:37:10 -0700, [EMAIL PROTECTED] (G.S.Clarke) wrote: [snip, about educational level and marital status >
> OK � two questions. > > First � is there any good reason why kruskal-wallis (which I would have > taken as being the more powerful technique) doesn�t show significance > when the chi-squared tests do (in other words, am I correct in my > assumptions as to which is the more powerful technique). > I will go further than Thom's reply did. The contingency table is more powerful against the so-called non-ordered alternatives. The KW is more powerful against ordered alternatives. If two tests are of equal size (honest alpha) and obtain different p-values, then they are testing different hypotheses - e.g., here - or they are weighting the errors differently. An example of the latter would be the Pearson contingency chi-squared versus the likelihood chi-squared: Pearson is more powerful against large deviations by cell, the (O-E) term, and the other is more powerful against multiple small deviations. This might be considered a more subtle form of "testing a different hypothesis". > Secondly chi-squared provides a �symmetrical� analysis � it says (at > least I think that it does) that having information about one of the > variables provides information about the other variable � and vice > versa. However is Kruskal-Wallis �symmetrical� in this sense. Does not > KW say (had it been significant) that the marital status of the subjects > (the factor) informs us somewhat on their educational status (the > dependent variable). Now logically if this is true then the educational > status should inform us somewhat on the marital status, however is this > also the case �statistically� i.e. will any significance level generated > by the KW using education (dependent) v marital status (independent) > also apply to phrasing the question the �other way around�? Yes, these tests give us a report on "relation", not causation. That is one reason why I don't like those terms very much, Dependent and Independent. The causation may run opposite to what we expect, or both things might be caused by something else. A regression gives us coefficients that are asymmetrical, but the ordinary regression of A on B has the same test as B on A, which is the test on the correlation. There may be something more to be said about symmetry, but nothing comes to mind. -- 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/ . =================================================================
