Overview (NPTESTS command)

NPTESTS is a collection of nonparametric tests. These tests make minimal assumptions about the underlying distribution of the data.

The tests that are available in NPTESTS can be grouped into three broad categories based on how the data are organized.

  • A one-sample test analyzes one field.
  • A test for related samples compares two or more fields for the same set of cases.
  • An independent-samples test analyzes one field that is grouped by categories of another field.

Options

Automatic test selection
If a test subcommand (ONESAMPLE, INDEPENDENT, or RELATED) is specified without any test specifications, then NPTESTS automatically chooses the "best" tests to perform for each field specified on the subcommand. See the individual subcommands for details.
Multiple comparisons
In addition to the "omnibus" tests for k independent or related samples, the samples can also be tested pairwise, either by all pairwise comparisions or through a stepwise stepdown procedure.
Missing value handling
Records with missing values can be handled listwise or analysis by analysis. User-missing values on categorical fields can be treated as valid or excluded from analysis.

Basic Specification

The basic specification is a single test subcommand (ONESAMPLE, INDEPENDENT, or RELATED) and a TEST keyword with a list of fields to be tested.

Syntax Rules

  • At least one test subcommand must be specified; all other subcommands are optional.
  • Subcommands may be specified in any order.
  • Only a single instance of each subcommand is allowed.
  • An error occurs if a keyword is specified more than once within a subcommand.
  • Parentheses, equals signs, and slashes shown in the syntax chart are required.
  • The command name, subcommand names, and keywords must be spelled in full.
  • Empty subcommands are not allowed.
  • Any split field defined on the SPLIT FILE command cannot be used on this command.
  • Any field specified on the WEIGHT command cannot be used on this command.

Operations

Note: Measurement level can affect the results. If any variables (fields) have an unknown measurement level, a data pass is performed to determine the measurement level before the analysis begins. For information on the determination criteria, see SET SCALEMIN.