DESCRIPTIVES /VARIABLES=var_list /MISSING={VARIABLE,LISTWISE} {INCLUDE,NOINCLUDE} /FORMAT={LABELS,NOLABELS} {NOINDEX,INDEX} {LINE,SERIAL} /SAVE /STATISTICS={ALL,MEAN,SEMEAN,STDDEV,VARIANCE,KURTOSIS, SKEWNESS,RANGE,MINIMUM,MAXIMUM,SUM,DEFAULT, SESKEWNESS,SEKURTOSIS} /SORT={NONE,MEAN,SEMEAN,STDDEV,VARIANCE,KURTOSIS,SKEWNESS, RANGE,MINIMUM,MAXIMUM,SUM,SESKEWNESS,SEKURTOSIS,NAME} {A,D}
The DESCRIPTIVES
procedure reads the active dataset and outputs
linear descriptive statistics requested by the user. In addition, it can optionally
compute Zscores.
The VARIABLES
subcommand, which is required, specifies the list of
variables to be analyzed. Keyword VARIABLES
is optional.
All other subcommands are optional:
The MISSING
subcommand determines the handling of missing variables. If
INCLUDE
is set, then usermissing values are included in the
calculations. If NOINCLUDE
is set, which is the default, usermissing
values are excluded. If VARIABLE
is set, then missing values are
excluded on a variable by variable basis; if LISTWISE
is set, then
the entire case is excluded whenever any value in that case has a
systemmissing or, if INCLUDE
is set, usermissing value.
The FORMAT
subcommand has no effect. It is accepted for
backward compatibility.
The SAVE
subcommand causes DESCRIPTIVES
to calculate Z scores for all
the specified variables. The Z scores are saved to new variables.
Variable names are generated by trying first the original variable name
with Z prepended and truncated to a maximum of 8 characters, then the
names ZSC000 through ZSC999, STDZ00 through STDZ09, ZZZZ00 through
ZZZZ09, ZQZQ00 through ZQZQ09, in that sequence. In addition, Z score
variable names can be specified explicitly on VARIABLES
in the variable
list by enclosing them in parentheses after each variable.
When Z scores are calculated, PSPP ignores TEMPORARY
,
treating temporary transformations as permanent.
The STATISTICS
subcommand specifies the statistics to be displayed:
ALL
All of the statistics below.
MEAN
Arithmetic mean.
SEMEAN
Standard error of the mean.
STDDEV
Standard deviation.
VARIANCE
Variance.
KURTOSIS
Kurtosis and standard error of the kurtosis.
SKEWNESS
Skewness and standard error of the skewness.
RANGE
Range.
MINIMUM
Minimum value.
MAXIMUM
Maximum value.
SUM
Sum.
DEFAULT
Mean, standard deviation of the mean, minimum, maximum.
SEKURTOSIS
Standard error of the kurtosis.
SESKEWNESS
Standard error of the skewness.
The SORT
subcommand specifies how the statistics should be sorted. Most
of the possible values should be selfexplanatory. NAME
causes the
statistics to be sorted by name. By default, the statistics are listed
in the order that they are specified on the VARIABLES
subcommand.
The A
and D
settings request an ascending or descending
sort order, respectively.
The physiology.sav file contains various physiological data for a sample
of persons. Running the DESCRIPTIVES
command on the variables height
and temperature with the default options allows one to see simple linear
statistics for these two variables. In Example 15.1, these variables
are specfied on the VARIABLES
subcommand and the SAVE
option
has been used, to request that Z scores be calculated.
After the command has completed, this example runs DESCRIPTIVES
again, this
time on the zheight and ztemperature variables,
which are the two normalized (Zscore) variables generated by the
first DESCRIPTIVES
command.
get file='physiology.sav'. descriptives /variables = height temperature /save. descriptives /variables = zheight ztemperature. 
In Result 15.1, we can see that there are 40 valid data for each of the variables and no missing values. The mean average of the height and temperature is 16677.12 and 37.02 respectively. The descriptive statistics for temperature seem reasonable. However there is a very high standard deviation for height and a suspiciously low minimum. This is due to a data entry error in the data (see Identifying incorrect data).
In the second Descriptive Statistics command, one can see that the mean and standard deviation of both Z score variables is 0 and 1 respectively. All Z score statistics should have these properties since they are normalized versions of the original scores.
