SPSS command: SPSS DESCRIPTIVES

Descriptive statistics for many variables at once

DESCRIPTIVES puts the core summary statistics of several numeric variables into a single table: N, minimum, maximum, mean with its standard error, standard deviation, variance, and skewness and kurtosis with their standard errors.

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Free · No account · Files stay on your device · Updated 18 October 2026

Worked example result

Descriptive Statistics · age

Mean = 37.47

SD = 10.37 · N = 30

Item	N	Minimum	Maximum
age	30	18.00	59.00
spend	28	14.72	103.87
sessions	30	1.00	10.00
Valid N (listwise)	28

Descriptive Statistics

See the full worked example

What it tells you

Crosstabs computes it in the browser with SPSS's variable-by-variable missing-value rule and a Valid N (listwise) row.

The typical value and spread of each variable in one comparable row.
Whether a variable is skewed or heavy-tailed, judged against the standard errors of skewness and kurtosis.
How many cases each variable has, and how many are complete on all of them (Valid N listwise).
Out-of-range values, through the minimum and maximum.

When to use it

Use it when

The descriptive table at the start of a report or thesis: age, income, scores and other scale variables side by side.
Screening many numeric variables quickly before modelling.
Comparing the scale of variables you intend to combine or standardise.

Use something else when

You need the median, percentiles or normality tests: use Explore, or Frequencies for percentiles.
You want statistics within groups: use Means or Explore with a factor.
You need z-score variables saved to the data: this panel reports the table only.
Categorical codes such as region 1–5: a mean of codes is meaningless, use Frequencies.

How to run it in Crosstabs

01
Open the dataset
Load your file in the workspace and switch to Statistics mode; numeric variables are detected automatically.
02
Choose Descriptives
In the Describe group, pick Descriptives (DESCRIPTIVES).
03
Tick the numeric variables
Select every variable you want in the table from the Numeric variables list.
04
Choose missing values and order
Keep Exclude cases variable by variable (the SPSS default) or pick Exclude cases listwise; set Display order to the variable list, alphabetic, or ascending or descending means.
05
Run
Click Run. If the workspace has a weight, declare it as a frequency or sampling weight first; under a sampling weight standard errors are withheld.

Run Descriptives in your browser

Worked example

Computed by the Crosstabs engine from the synthetic data below when this page was built — not typed in.

Try with example data

Data

30 synthetic customers with age in years, monthly spend in dollars (log-normal, so right-skewed; two values are blank) and app sessions per week. Generated with a fixed seed.

Descriptive Statistics

Item	N	Minimum	Maximum	Mean	Std. Deviation	Skewness	Kurtosis
age	30	18.00	59.00	37.47	10.37	0.179	−0.756
spend	28	14.72	103.87	47.84	27.12	0.975	0.014
sessions	30	1.00	10.00	5.17	2.34	0.220	−0.089
Valid N (listwise)	28

Missing values excluded variable by variable (the default), so N differs between rows.

How to read it

Spend has N = 28 because two values are blank; Valid N (listwise), the cases complete on every listed variable, is 28. Its skewness of 0.97 (standard error 0.44) flags a long right tail: the mean of 47.84 is pulled up by a few large spenders, so the median from Frequencies or Explore is the better summary. Age (skewness 0.18) is close to symmetric.

Output tables explained

Summary sentence: A plain-language line above the table: the mean, SD and N for a single variable, or which variable has the highest and the lowest mean.
Descriptive Statistics: One row per variable with N, Minimum, Maximum, Mean, Std. Error of Mean, Std. Deviation, Variance, Skewness, Kurtosis and their standard errors, followed by Valid N (listwise). A footnote states the missing-value rule and any weight.

Assumptions and notes

The variables are measured on an interval or ratio scale, so means and standard deviations are meaningful.
Skewness and kurtosis standard errors assume a random sample; they are a rough screen, not a formal normality test.
With variable-by-variable exclusion, rows can rest on different cases; choose listwise when the rows must describe the same people.

How the results are validated

On 13 reference samples, including weighted ones, the moments match SciPy 1.18 and NumPy 2.5 (skewness and kurtosis with bias=False, as SPSS reports them): mean within 1e-12, variance, SD and standard error within 1e-11, skewness and kurtosis within 1e-10, and N, minimum and maximum exactly. Unit tests check listwise and variable-by-variable exclusion, sorting and the rejection of string variables. Validation evidence

Questions

Why does N differ between rows?

By default each variable uses every case that is valid for it. Choose Exclude cases listwise to base every row on the cases complete on all listed variables; that count is shown as Valid N (listwise) either way.

How do I judge skewness from this table?

Divide skewness by its standard error; values beyond about ±2 suggest marked asymmetry in moderate samples. For a formal test and a median, run Explore.

Is the standard deviation the sample or population version?

The sample version with N − 1 in the denominator, as in SPSS. With a frequency weight, N is the sum of the weights.

Can I get the median here?

No — DESCRIPTIVES does not compute medians. Use Explore or Frequencies, which both report the median and percentiles.

Run Descriptives on your own data

Open a .sav, CSV or Excel file. Everything runs in your browser; the file never leaves your device.

Open the workspace

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