How To Report Skewness And Kurtosis Spss

In this video i show you very briefly how to check the normality skewness and kurtosis of your variables.

How to report skewness and kurtosis spss. Skewness 0 71 kurtosis 0 75. I wouldn t italicise skewness or kurtosis though since they aren t abbreviations. We ll add the resulting syntax as well. N represents the number of observations.

Because it is the fourth moment kurtosis is always positive. Clicking on options gives you the ability to select kurtosis and skewness in the options menu. The screenshots below guide you through running a shapiro wilk test correctly in spss. I can t find a guideline for skewness kurtosis in specific but i d assume that one can report these similarily to how you d report a mean or standard deviation.

If skewness is between 1 and 0 5 or between 0 5 and 1 the distribution is moderately skewed. Skewness and kurtosis are closer to zero for trials 1 and 4. It quietly assumes that your data hold a sample rather than an entire population. Kurtosis is sensitive to departures from normality on the tails.

Skewness and kurtosis assignment help. Depending on the certain procedure of kurtosis that is utilized there are numerous analyses of kurtosis and of how certain steps ought to be analyzed. There s plenty of options for obtaining it. You should now be able to calculate statistics for skewness and kurtosis in spss.

Kurtosis tells you the height and sharpness of the central peak relative to that of a standard bell curve. For skewness if the value is greater than 1 0 the distribution is right skewed. First off skewness in spss always refers to sample skewness. You can also see that spss has calculated the mean 46 93 metres and the standard deviation 21 122 metres.

So now that we ve a basic idea what our data look like let s proceed with the actual test. 2 dp s probably best. The skewness statistic is 719 and kurtosis is 125 see above. Hit ok and check for any skew values over 2 or under 2 and any kurtosis values over 7 or under 7 in the output.

Just like skewness kurtosis is a moment based measure and it is a central standardized moment. If skewness is between 0 5 and 0 5 the distribution is approximately symmetric. Kurtosis is a criterion that explains the shape of a random variable s probability circulation. In spss the skewness and kurtosis statistic values should be less than 1 0 to be considered normal.

Those values might indicate that a variable may be non normal. Here x is the sample mean. Because of the 4th power smaller values of centralized values y i µ in the above equation are greatly de emphasized. Okay that s it for this quick tutorial.