Descriptive Statistics

Analyze → Descriptive Statistics. These four procedures summarise a dataset before you model it: how each variable is distributed, and how categorical variables relate. They are the right first stop for any new dataset.

Frequencies

When to use it. To see how often each value of a categorical (or coarse-numeric) variable occurs.

Dialog. Move one or more variables into Variables. Optional statistics (mean, median, quartiles) and a bar/pie chart can be requested.

Output. One frequency table per variable: each value, its count, percent, valid percent (excluding missing) and cumulative percent.

Example. StudyZone and SmokingStatus from the sample data each yield a table of category counts — e.g. how many participants are from each region, and the split of Never/Former/Current smokers.

Descriptives

When to use it. To get the standard summary statistics for scale variables side by side.

Dialog. Move scale variables into Variables. The Options section lets you choose which statistics appear (mean, SD, variance, range, min, max, S.E., skewness, kurtosis) and whether to save standardised (z) scores as new variables.

Output. A single Descriptive Statistics table: one row per variable with N, minimum, maximum, mean and standard deviation (plus any extras you enabled).

Example. Age, BMI, SystolicBP, FastingGlucose_mmolL → a compact table of their valid N, range, mean and SD.

Explore

When to use it. For a deeper look at a scale variable's distribution, on its own or split by a factor — including normality and outliers.

Dialog. Put the scale variable(s) in Dependent List and an optional grouping variable in Factor List.

Output.

• Descriptives with a confidence interval for the mean, trimmed mean, median, variance, skewness and kurtosis.
• Tests of Normality — Shapiro-Wilk and Kolmogorov-Smirnov (with Lilliefors significance), and a Decision column that rejects normality at your chosen α.
• Percentiles.

Example. BMI by Sex shows each group's distribution and whether each is plausibly normal — useful before choosing a t-test vs a nonparametric test.

Crosstabs

When to use it. To cross two categorical variables and test whether they are associated.

Dialog. Choose a Row and a Column variable; optionally a Layer variable for a stratified (three-way) table. Tick the tests and measures you want.

Output.

• The contingency table of counts (with optional row/column/total percents).
• Chi-square tests — Pearson, likelihood-ratio, Fisher's exact and (for 2×2) McNemar.
• Association measures — phi and Cramér's V, Goodman & Kruskal's gamma and lambda (with its significance), and Cohen's kappa for a matched table.
• With a layer: the Cochran/Mantel-Haenszel statistics — conditional-independence test, the common odds ratio, and the Breslow-Day test of homogeneity.

Example. SmokingStatus × Hypertensive tests whether smoking and hypertension are associated; adding Sex as a layer asks whether that association holds within each sex.

See also

• Compare Means — when one variable is a scale outcome.
• Graphs — histograms and boxplots for these distributions.