SPSS DESCRIPTIVES generates a single table with descriptive statistics for one or more variables. It can also add z-scores to your data. We'll walk through its major options using freelancers.sav. The screenshot below shows part of its data view.

User Missing Values

Before running any descriptives, we first need to specify some user missing values for income_2010 through income_2014. We'll do so by running the syntax below. The FORMATS command suppresses excessive decimal places for output tables that we'll generate later on.

SPSS DESCRIPTIVES - Basic Use

The most basic way to run a descriptives table is simply “DESCRIPTIVES” followed by one ore more variable names (possibly using TO or ALL) and a period. The syntax below gives an example.

Note that “Valid N (listwise)” denotes the number of cases that don't have any missing values on any of the variables shown in the table.

SPSS DESCRIPTIVES - STATISTICS Subcommand

Statistics and the order in which they'll appear in the table can be specified by adding a STATISTICS subcommand. However, the first column for descriptives is always N, even if not specified.
Note that some statistics (such as skewness and kurtosis) will always be followed by their standard error, which can't be specified separately.If this is not to your liking, try MEANS instead. Note that for MEANS, statistics are specified on a CELLS subcommand; STATISTICS refers to test statistics for significance tests here.

SPSS DESCRIPTIVES - SORT Subcommand

By default, table rows (representing variables) are sorted by the order in which these variables are specified in DESCRIPTIVES. This can be changed by specifying a statistic (or NAME for variable names) on a SORT subcommand. Add “(d)” for sorting descendingly.

SPSS DESCRIPTIVES - MISSING Subcommand

By default, DESCRIPTIVES uses pairwise deletion of missing values: for each variable, all cases having a valid value on this variable are used.Since “deletion” doesn't actually delete anything, “exclusion” would be more appropriate here. This is why N may differ for different variables.
Specifying LISTWISE on a MISSING subcommand implies listwise deletion of missing values: for all variables, only cases are used that don't have any missing value on any of these variables.

SPSS DESCRIPTIVES - Z-Scores

Standardizing variables mean rescaling them so that they have a mean of 0 and a standard deviation of 1. This is done by subtracting a variable's mean from each separate value and dividing the remainder by the variable's standard deviation. The resulting values are called z-scores.
DESCRIPTIVES offers two ways for adding z-scores to your data. First, adding a SAVE subcommand standardizes all variables on the DESCRIPTIVES command. The names for these new variables are the original variable names prefixed by “Z”. The screenshot below shows the result in data view.

A second option here is adding variable names for the new (standardized) variables behind the original variable names, enclosed by parentheses.

Note that either option for standardizing variables leaves the original variables intact. Second, DESCRIPTIVES automatically adds variable labels to the newly added standardized variables.

Z-Scores - Cautionary Note

Whenever adding z-scores to your data with DESCRIPTIVES, keep in mind that the result may be affected by the missing subcommand. Also, FILTER, SPLIT FILE or WEIGHT being in effect may influence the calculation of z-scores. This may or may not be your intention.
Recent SPSS versions will show in the status bar whether these are in effect. However, if you want to be really sure, simply run show filter split weight. just before standardizing variables to stay on the safe side.