YouвЂ™ll notice that weвЂ™ve also instructed SPSS to calculate the mean and standard deviation. To calculate skewness and kurtosis, just select the options (as above). This will bring up the Descriptives: Options dialog box, within which it is possible to choose a number of descriptive measures. Once youвЂ™ve got your variable into the right hand column, click on the Options button. You can drag and drop, or use the arrow button, as shown below. You need to get the variable for which you wish to calculate skewness and kurtosis into the box on the right. This will bring up the Descriptives dialog box. To begin the calculation, click on Analyze -> Descriptive Statistics -> Descriptives. WeвЂ™re going to use the Descriptives menu option. There are a number of different ways to calculate skewness and kurtosis in SPSS. The usual reason to do this is to get an idea of whether the data is normally distributed.

WeвЂ™re going to calculate the skewness and kurtosis of the data that represents the Frisbee Throwing Distance in Metres variable (see above). Kurtosis measures the tail-heaviness of the distribution. Skewness is a measure of the symmetry, or lack thereof, of a distribution.

Result will appear in the SPSS output viewerДЄs is the norm with these quick tutorials, we start from the assumption that you have already imported your data into SPSS, and your data view looks something a bit like this.

Click on Options, and select Skewness and Kurtosis.

Drag and drop the variable for which you wish to calculate skewness and kurtosis into the box on the right.

Click on Analyze -> Descriptive Statistics -> Descriptives.

Leeper for permission to adapt and distribute this page from our site. This page was adapted from ChoosingВ the Correct Statistic developed by James D. Additionally, many of these models produce estimates that are robust to violation of the assumption of normality, particularly in large samples. These errors are unobservable, since we usually do not know the true values, but we can estimate them with residuals, the deviation of the observed values from the model-predicted values. Statistical errors are the deviations of the observed values of the dependent variable from their true or expected values. *Technically, assumptions of normality concern the errors rather than the dependent variable itself. Number of Dependent VariablesДЎ IV with 2 or more levels (independent groups)ДЎ IV with 2 levels (dependent/matched groups)ДЎ IV with 2 or more levels (dependent/matched groups)ДЎ or more interval IVs and/or 1 or more categorical IVs Necessarily the only type of test that could be used) and links showing how toДo such tests using SAS, Stata and SPSS. Statistical tests commonly used given these types of variables (but not Variable, and whether it is normally distributed (see What is the difference between categorical, ordinal and interval variables?ДЇor more information on this). Variable, namely whether it is an interval variable, ordinal or categorical You also want to consider the nature of your dependent Nature of your independent variables (sometimes referred to as Number of dependent variables (sometimes referred to as outcome variables), the The table belowД¬overs a number of common analyses and helps you choose among them based on the Multiple ways, each of which could yield legitimate answers. We emphasize that these are general guidelines and should not beД¬onstrued as hard and fast rules. The following table shows general guidelines for choosing a statisticalДЄnalysis.

Statistical calculations in spss