Date:         Fri, 4 Sep 1998 10:52:50 -0500
Reply-To:     "Nichols, David" <nichols@SPSS.COM>
Sender:       "SPSSX(r) Discussion" <SPSSX-L@UGA.CC.UGA.EDU>
From:         "Nichols, David" <nichols@SPSS.COM>
Subject:      Re: Non-par tests

NPAR TESTS does not have any ability to incorporate covariates.
 
Siegel's classic though now very old text indicates that the K-S test is
apparently slightly more efficient in small samples, but the M-W is better
in larger samples. No definitions of exactly what that means or the
crossover point are given. I assume much work has been done since the 1950s,
so I wouldn't take this as definitive.
 
David Nichols
Principal Support Statistician and
Manager of Statistical Support
SPSS Inc.
 
        -----Original Message-----
        From:   Alex Walker [SMTP:A.Walker@unsw.EDU.AU]
        Sent:   Thursday, September 03, 1998 12:41 AM
        To:     SPSSX-L@UGA.CC.UGA.EDU
        Subject:        Non-par tests
 
        When comparing groups using non-parametric tests, is there any way
for
        controlling for a covariate type variable, as in ANCOVA?
 
        Secondly, having found a group difference (3 levels) with a
        Kruskall-Wallis, I compared 2 pairs of groups based on a priori
        assumptions and used both Mann-Whitney and Kolmogorov-Smirnov (not
        knowing which was appropriate). The Kol-Smi gave less significant
        results on several variables than the Mann-W. On what basis should I
        make a decision about which test is more appropriate?? The help
section
        talks about location and shape with the Kol-Smi- is it better for
large
        samples? I have 30 subjects in each group.
 
        Thankying you in anticipation.
 
        Alex Walker