If we compare k groups, we have k - 1 degrees of freedom, denoted by df in our output. We therefore usually approximate the p-value with a chi-square distribution. However, it turns out that if each group contains 4 or more cases, this exact sampling distribution is almost identical to the (much simpler) chi-square distribution. uses the exact (but very complex) sampling distribution of H. We need to know its sampling distribution for evaluating whether this is unusually large. A larger value indicates larger differences between the groups we're comparing.
Our test statistic -incorrectly labeled as “ Chi-Square” by SPSS- is known as Kruskal-Wallis H. We'll skip the “RANKS” table and head over to the “Test Statistics” shown below. NPAR TESTS /K-W=gain BY group(1 3) /MISSING ANALYSIS. SPSS Kruskal-Wallis Test Syntaxįollowing the previous screenshots results in the syntax below. They are “independent” because our groups don't overlap (each case belongs to only one creatine condition).ĭepending on your license, your SPSS version may or may have the E xact option shown below.
We use K Independent Samples if we compare 3 or more groups of cases. We'll run it by following the screenshots below. It basically replaces the weight gain scores with their rank numbers and tests whether these are equal over groups. Well, a test that was designed for precisely this situation is the Kruskal-Wallis test which doesn't require these assumptions. So what should we do now? We'd like to use an ANOVA but our data seriously violates its assumptions. This is a second violation of the ANOVA assumptions. The assumption of equal population standard deviations for all groups is known as homoscedasticity. This suggests that creatine does make a real difference.īut don't overlook the standard deviations for our groups: they are very different but ANOVA requires them to be equal. SPSS MEANS Outputįirst, note that our evening creatine group (4 participants) gained an average of 961 grams as opposed to 120 grams for “no creatine”. The fastest way for doing so is by running the syntax below. Some basic checks will tell us that these assumptions aren't satisfied by our data at hand.Ī very efficient data check is to run histograms on all metric variables. The most likely test for this scenario is a one-way ANOVA but using it requires some assumptions. That is, we'll test if three means -each calculated on a different group of people- are equal. The creatine condition to which people were assigned? After doing so for a month, their weight gains were measured.
These were divided into 3 groups: some didn't take any creatine, others took it in the morning and still others took it in the evening. Our data contain the result of a small experiment regarding creatine, a supplement that's popular among body builders. But let's first take a quick look at what's in the data anyway. We'll show in a minute why that's the case with creatine.sav, the data we'll use in this tutorial. The Kruskal-Wallis test is an alternative for a one-way ANOVA if the assumptions of the latter are violated.
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How to Run a Kruskal-Wallis Test in SPSS? By Ruben Geert van den Berg under Nonparametric Tests
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