SPSS® 12.0 Command Syntax Reference

Example
NPAR TESTS 1089
NPAR TESTS /J-T=V1 BY V2(0,4)
/METHOD=EXACT.
• This example performs the Jonckheere-Terpstra test for groups defined by values 0
through 4 of V2. The exact p values are calculated.
K-S Subcommand (One-Sample)
Syntax
NPAR TESTS K-S({NORMAL [mean,stddev]})=varlist
{POISSON [mean] }
{UNIFORM [min,max] }
{EXPONENTIAL [mean] }
The K-S (alias KOLMOGOROV-SMIRNOV) one-sample test compares the cumulative
distribution function for a variable with a uniform, normal, Poisson, or exponential
distribution, and it tests whether the distributions are homogeneous. The parameters of the test
distribution can be specified; the defaults are the observed parameters. The output shows the
number of valid cases, parameters of the test distribution, most-extreme absolute, positive, and
negative differences, Kolmogorov-Smirnov Z, and two-tailed probability for each variable.
Operations
The minimum specification is a distribution keyword and a list of variables. The distribution
keywords are NORMAL, POISSON, EXPONENTIAL, and UNIFORM.
• The distribution keyword and its optional parameters must be enclosed within parentheses.
• The distribution keyword must be separated from its parameters by blanks or commas.
NORMAL [mean, stdev] Normal distribution. The default parameters are the observed mean
and standard deviation.
POISSON [mean] Poisson distribution. The default parameter is the observed mean.
UNIFORM [min,max] Uniform distribution. The default parameters are the observed minimum
and maximum values.
EXPONENTIAL [mean] Exponential distribution. The default parameter is the observed mean.
• The Kolmogorov-Smirnov Z is computed from the largest difference in absolute value
between the observed and test distribution functions.
• The K-S probability levels assume that the test distribution is specified entirely in
advance. The distribution of the test statistic and resulting probabilities are different
when the parameters of the test distribution are estimated from the sample. No correction
is made.
• For a mean of 100,000 or larger, a normal approximation to the Poisson distribution is
used.