```Date: Mon, 1 Sep 2003 01:43:56 -0400 Reply-To: Richard Ristow Sender: "SPSSX(r) Discussion" From: Richard Ristow Subject: Re: ANOVA or Kruskal-Wallis ANOVA Comments: To: Christina Cutshaw In-Reply-To: <3F52A805.A61A5A0@jhem.jhmi.edu> Content-Type: text/plain; charset="us-ascii"; format=flowed At 09:59 PM 8/31/2003 -0400, Christina Cutshaw wrote: >I want to determine whether four groups: A, B, C, D (total N=73) >differ in their number of child-related policy goals. I need to >decide between using a one-way >ANOVA to compare means or the Kruskal-Wallis ANOVA to compare mean ranks: >[Appropriate statistical tests rejected normality of distribution of >the dependent variable, and failed to find heterogeneity of variance >between groups.] With this information I decided to use a >Kruskal-Wallis test and then pair-wise Mann-Whitney tests to look at >the differences between groups in their number of goals. Did I make >the correct decision? I'll step in with a rough-and-ready, aware that there are far more sophisticated mathematical statisticians on the list (Marta, are you listening?). Your decision was certainly a defensible one. However, in all probability a (parametric) one-way ANOVA would give very similar results. The parametric methods (grouped under the rubric of general linear models) tend to be quite forgiving of deviations from normality, which is good, as most real data has a distribution differing markedly from the normal. The one exception is extreme values: parametric methods are very sensitive to these, and non-parametric methods are indicated where extreme values are known or likely. (The alternative of rejecting the extreme values can lead to many biases: just what is "extreme"?) In your case, I'd doubt that the observed maximum of "number of child-related policy goals" is very large, and either method should give satisfactory results. In fact, you'll probably find the results of the two methods quite consonant. ```

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