This tutorial shows how to compute means over both variables and cases in a simple but solid way. We encourage you follow along by downloading and opening restaurant.sav, part of which is shown below.

Quick Data Check

Before computing anything whatsoever, we always need to know what's in our data in the first place. Skipping this step often results in ending up with wrong results as we'll see in a minute. Let's first inspect some frequencies by running the syntax below.

Result

Right, now there's two things we need to ensure before proceeding. Firstly, do all variables have similar coding schemes? For the food rating, higher numbers (4 or 5) reflect more positive attitudes (“Good” and “Very good”) but does this hold for all variables? If we take a quick peek at our 5 tables, we see this holds.
Second, do we have any user missing values? That is, do we want to include all data values in our computations? In this case, we don't. We need to exclude 6 (“No answer”) from all computations. We'll do so with the syntax below.

Setting Missing Values

Result

Computing Means over Variables

Right, the simplest way for computing means over variables is shown in the syntax below. Note that we can usually specify variable names separated by spaces but for some odd reason we need to use commas in this case.

If our target variables are adjacent in our data, we don't need to spell out all variable names. Instead, we'll enter only the first and last variable names (which can be copy-pasted from variable view into our syntax window) separated by TO.

Computing Means - Dealing with Missing Values

If we take a good look at our data, we see that some respondents have a lot of missing values on v1 to v5. By default, the mean over v1 to v5 is computed for any case who has at least one none missing value on those variables. If all five values are (system or user) missing, a mean can't be computed so it will be a system missing value as we see in our data. It's quite common to exclude cases with many missings from computations. In this case, the easiest option is using the dot operator. For example mean.3(v1 to v5) means “compute means over v1 to v5 but only for cases having at least 3 non missing values on those variables”. Let's try it.

Computing Means - Exclude Cases with Many Missings

Result

A more general way that'll work for more complex computations as well is by using IF as shown below.

SPSS - Compute Means over Cases

So far we computed horizontal means: means over variables for each case separately. Let's now compute vertical means: means over cases for each variable separately. We'll first create output tables with means and we'll then add such means to our data.
Means over all cases are easily obtained with DESCRIPTIVES as in descriptives v1 v2.

Result

Means for Groups Separately

So what if we want means for male and female respondents separately? One option is SPLIT FILE but this is way more work than necessary. A simple MEANS command will do as shown below.

Result

SPSS - Add Means to Dataset

Finally, you may sometimes want means over cases as new variables in your data. The way to go here is AGGREGATE as shown below.

If you'd like means for groups of cases separately, add one or more BREAK variables as shown below. This example also shows how to add means for multiple variables in one go, again by using TO.

Result

Note that we already saw these means (over v2, for genders separately) in our output after running means v2 by gender.
Right. That's about all we could think of regarding means in SPSS. If you've any questions or remarks, please feel free to throw in a comment below.