@c PSPP - a program for statistical analysis.
-@c Copyright (C) 2017 Free Software Foundation, Inc.
+@c Copyright (C) 2017, 2020 Free Software Foundation, Inc.
@c Permission is granted to copy, distribute and/or modify this document
@c under the terms of the GNU Free Documentation License, Version 1.3
@c or any later version published by the Free Software Foundation;
* GRAPH:: Plot data.
* CORRELATIONS:: Correlation tables.
* CROSSTABS:: Crosstabulation tables.
+* CTABLES:: Custom tables.
* FACTOR:: Factor analysis and Principal Components analysis.
* GLM:: Univariate Linear Models.
* LOGISTIC REGRESSION:: Bivariate Logistic Regression.
* ONEWAY:: One way analysis of variance.
* QUICK CLUSTER:: K-Means clustering.
* RANK:: Compute rank scores.
-* REGRESSION:: Linear regression.
* RELIABILITY:: Reliability analysis.
* ROC:: Receiver Operating Characteristic.
@end menu
@end display
The @cmd{DESCRIPTIVES} procedure reads the active dataset and outputs
-descriptive
-statistics requested by the user. In addition, it can optionally
+linear descriptive statistics requested by the user. In addition, it can optionally
compute Z-scores.
The @subcmd{VARIABLES} subcommand, which is required, specifies the list of
the entire case is excluded whenever any value in that case has a
system-missing or, if @subcmd{INCLUDE} is set, user-missing value.
-The @subcmd{FORMAT} subcommand affects the output format. Currently the
-@subcmd{LABELS/NOLABELS} and @subcmd{NOINDEX/INDEX} settings are not used.
-When @subcmd{SERIAL} is
-set, both valid and missing number of cases are listed in the output;
-when @subcmd{NOSERIAL} is set, only valid cases are listed.
+The @subcmd{FORMAT} subcommand has no effect. It is accepted for
+backward compatibility.
The @subcmd{SAVE} subcommand causes @cmd{DESCRIPTIVES} to calculate Z scores for all
the specified variables. The Z scores are saved to new variables.
The @subcmd{A} and @subcmd{D} settings request an ascending or descending
sort order, respectively.
+@subsection Descriptives Example
+
+The @file{physiology.sav} file contains various physiological data for a sample
+of persons. Running the @cmd{DESCRIPTIVES} command on the variables @exvar{height}
+and @exvar{temperature} with the default options allows one to see simple linear
+statistics for these two variables. In @ref{descriptives:ex}, these variables
+are specfied on the @subcmd{VARIABLES} subcommand and the @subcmd{SAVE} option
+has been used, to request that Z scores be calculated.
+
+After the command has completed, this example runs @cmd{DESCRIPTIVES} again, this
+time on the @exvar{zheight} and @exvar{ztemperature} variables,
+which are the two normalized (Z-score) variables generated by the
+first @cmd{DESCRIPTIVES} command.
+
+@float Example, descriptives:ex
+@psppsyntax {descriptives.sps}
+@caption {Running two @cmd{DESCRIPTIVES} commands, one with the @subcmd{SAVE} subcommand}
+@end float
+
+@float Screenshot, descriptives:scr
+@psppimage {descriptives}
+@caption {The Descriptives dialog box with two variables and Z-Scores option selected}
+@end float
+
+In @ref{descriptives:res}, 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 @exvar{height} and a suspiciously
+low minimum. This is due to a data entry error in the
+data (@pxref{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.
+
+@float Result, descriptives:res
+@psppoutput {descriptives}
+@caption {Descriptives statistics including two normalized variables (Z-scores)}
+@end float
+
@node FREQUENCIES
@section FREQUENCIES
SESKEWNESS,SEKURTOSIS,ALL,NONE@}
/NTILES=@var{ntiles}
/PERCENTILES=percent@dots{}
- /HISTOGRAM=[MINIMUM(@var{x_min})] [MAXIMUM(@var{x_max})]
+ /HISTOGRAM=[MINIMUM(@var{x_min})] [MAXIMUM(@var{x_max})]
[@{FREQ[(@var{y_max})],PERCENT[(@var{y_max})]@}] [@{NONORMAL,NORMAL@}]
/PIECHART=[MINIMUM(@var{x_min})] [MAXIMUM(@var{x_max})]
[@{FREQ,PERCENT@}] [@{NOMISSING,MISSING@}]
variables to be analyzed.
The @subcmd{FORMAT} subcommand controls the output format. It has several
-possible settings:
+possible settings:
@itemize @subcmd{}
@item
are included. System-missing values are never included in statistics,
but are listed in frequency tables.
-The available @subcmd{STATISTICS} are the same as available
-in @cmd{DESCRIPTIVES} (@pxref{DESCRIPTIVES}), with the addition
+The available @subcmd{STATISTICS} are the same as available
+in @cmd{DESCRIPTIVES} (@pxref{DESCRIPTIVES}), with the addition
of @subcmd{MEDIAN}, the data's median
value, and MODE, the mode. (If there are multiple modes, the smallest
value is reported.) By default, the mean, standard deviation of the
@cindex percentiles
@subcmd{PERCENTILES} causes the specified percentiles to be reported.
The percentiles should be presented at a list of numbers between 0
-and 100 inclusive.
+and 100 inclusive.
The @subcmd{NTILES} subcommand causes the percentiles to be reported at the
boundaries of the data set divided into the specified number of ranges.
For instance, @subcmd{/NTILES=4} would cause quartiles to be reported.
The @subcmd{HISTOGRAM} subcommand causes the output to include a histogram for
each specified numeric variable. The X axis by default ranges from
the minimum to the maximum value observed in the data, but the @subcmd{MINIMUM}
-and @subcmd{MAXIMUM} keywords can set an explicit range.
+and @subcmd{MAXIMUM} keywords can set an explicit range.
@footnote{The number of
bins is chosen according to the Freedman-Diaconis rule:
@math{2 \times IQR(x)n^{-1/3}}, where @math{IQR(x)} is the interquartile range of @math{x}
The @subcmd{PIECHART} subcommand adds a pie chart for each variable to the data. Each
slice represents one value, with the size of the slice proportional to
the value's frequency. By default, all non-missing values are given
-slices.
+slices.
The @subcmd{MINIMUM} and @subcmd{MAXIMUM} keywords can be used to limit the
-displayed slices to a given range of values.
+displayed slices to a given range of values.
The keyword @subcmd{NOMISSING} causes missing values to be omitted from the
piechart. This is the default.
-If instead, @subcmd{MISSING} is specified, then a single slice
-will be included representing all system missing and user-missing cases.
+If instead, @subcmd{MISSING} is specified, then the pie chart includes
+a single slice representing all system missing and user-missing cases.
@cindex bar chart
The @subcmd{BARCHART} subcommand produces a bar chart for each variable.
The @subcmd{MINIMUM} and @subcmd{MAXIMUM} keywords can be used to omit
categories whose counts which lie outside the specified limits.
The @subcmd{FREQ} option (default) causes the ordinate to display the frequency
-of each category, whereas the @subcmd{PERCENT} option will display relative
+of each category, whereas the @subcmd{PERCENT} option displays relative
percentages.
-The @subcmd{FREQ} and @subcmd{PERCENT} options on @subcmd{HISTOGRAM} and
+The @subcmd{FREQ} and @subcmd{PERCENT} options on @subcmd{HISTOGRAM} and
@subcmd{PIECHART} are accepted but not currently honoured.
The @subcmd{ORDER} subcommand is accepted but ignored.
+@subsection Frequencies Example
+
+@ref{frequencies:ex} runs a frequency analysis on the @exvar{sex}
+and @exvar{occupation} variables from the @file{personnel.sav} file.
+This is useful to get an general idea of the way in which these nominal
+variables are distributed.
+
+@float Example, frequencies:ex
+@psppsyntax {frequencies.sps}
+@caption {Running frequencies on the @exvar{sex} and @exvar{occupation} variables}
+@end float
+
+If you are using the graphic user interface, the dialog box is set up such that
+by default, several statistics are calculated. Some are not particularly useful
+for categorical variables, so you may want to disable those.
+
+@float Screenshot, frequencies:scr
+@psppimage {frequencies}
+@caption {The frequencies dialog box with the @exvar{sex} and @exvar{occupation} variables selected}
+@end float
+
+From @ref{frequencies:res} it is evident that there are 33 males, 21 females and
+2 persons for whom their sex has not been entered.
+
+One can also see how many of each occupation there are in the data.
+When dealing with string variables used as nominal values, running a frequency
+analysis is useful to detect data input entries. Notice that
+one @exvar{occupation} value has been mistyped as ``Scrientist''. This entry should
+be corrected, or marked as missing before using the data.
+
+@float Result, frequencies:res
+@psppoutput {frequencies}
+@caption {The relative frequencies of @exvar{sex} and @exvar{occupation}}
+@end float
+
@node EXAMINE
@section EXAMINE
/ID=@var{identity_variable}
/@{TOTAL,NOTOTAL@}
/PERCENTILE=[@var{percentiles}]=@{HAVERAGE, WAVERAGE, ROUND, AEMPIRICAL, EMPIRICAL @}
- /MISSING=@{LISTWISE, PAIRWISE@} [@{EXCLUDE, INCLUDE@}]
+ /MISSING=@{LISTWISE, PAIRWISE@} [@{EXCLUDE, INCLUDE@}]
[@{NOREPORT,REPORT@}]
@end display
In particular, it is useful for testing how closely a distribution follows a
normal distribution, and for finding outliers and extreme values.
-The @subcmd{VARIABLES} subcommand is mandatory.
+The @subcmd{VARIABLES} subcommand is mandatory.
It specifies the dependent variables and optionally variables to use as
factors for the analysis.
-Variables listed before the first @subcmd{BY} keyword (if any) are the
+Variables listed before the first @subcmd{BY} keyword (if any) are the
dependent variables.
The dependent variables may optionally be followed by a list of
factors which tell @pspp{} how to break down the analysis for each
-dependent variable.
+dependent variable.
Following the dependent variables, factors may be specified.
The factors (if desired) should be preceded by a single @subcmd{BY} keyword.
-The format for each factor is
+The format for each factor is
@display
@var{factorvar} [BY @var{subfactorvar}].
@end display
Each unique combination of the values of @var{factorvar} and
@var{subfactorvar} divide the dataset into @dfn{cells}.
-Statistics will be calculated for each cell
+Statistics are calculated for each cell
and for the entire dataset (unless @subcmd{NOTOTAL} is given).
The @subcmd{STATISTICS} subcommand specifies which statistics to show.
-@subcmd{DESCRIPTIVES} will produce a table showing some parametric and
+@subcmd{DESCRIPTIVES} produces a table showing some parametric and
non-parametrics statistics.
@subcmd{EXTREME} produces a table showing the extremities of each cell.
A number in parentheses, @var{n} determines
The default number is 5.
The subcommands @subcmd{TOTAL} and @subcmd{NOTOTAL} are mutually exclusive.
-If @subcmd{TOTAL} appears, then statistics will be produced for the entire dataset
-as well as for each cell.
-If @subcmd{NOTOTAL} appears, then statistics will be produced only for the cells
+If @subcmd{TOTAL} appears, then statistics for the entire dataset
+as well as for each cell are produced.
+If @subcmd{NOTOTAL} appears, then statistics are produced only for the cells
(unless no factor variables have been given).
These subcommands have no effect if there have been no factor variables
specified.
The @subcmd{PLOT} subcommand specifies which plots are to be produced if any.
Available plots are @subcmd{HISTOGRAM}, @subcmd{NPPLOT}, @subcmd{BOXPLOT} and
@subcmd{SPREADLEVEL}.
-The first three can be used to visualise how closely each cell conforms to a
+The first three can be used to visualise how closely each cell conforms to a
normal distribution, whilst the spread vs.@: level plot can be useful to visualise
-how the variance of differs between factors.
-Boxplots will also show you the outliers and extreme values.
+how the variance differs between factors.
+Boxplots show you the outliers and extreme values.
@footnote{@subcmd{HISTOGRAM} uses Sturges' rule to determine the number of
bins, as approximately @math{1 + \log2(n)}, where @math{n} is the number of samples.
Note that @cmd{FREQUENCIES} uses a different algorithm to find the bin size.}
-The @subcmd{SPREADLEVEL} plot displays the interquartile range versus the
+The @subcmd{SPREADLEVEL} plot displays the interquartile range versus the
median. It takes an optional parameter @var{t}, which specifies how the data
should be transformed prior to plotting.
-The given value @var{t} is a power to which the data is raised. For example, if
-@var{t} is given as 2, then the data will be squared.
-Zero, however is a special value. If @var{t} is 0 or
-is omitted, then data will be transformed by taking its natural logarithm instead of
+The given value @var{t} is a power to which the data are raised. For example, if
+@var{t} is given as 2, then the square of the data is used.
+Zero, however is a special value. If @var{t} is 0 or
+is omitted, then data are transformed by taking its natural logarithm instead of
raising to the power of @var{t}.
-The @subcmd{COMPARE} subcommand is only relevant if producing boxplots, and it is only
+@cindex Shapiro-Wilk
+When one or more plots are requested, @subcmd{EXAMINE} also performs the
+Shapiro-Wilk test for each category.
+There are however a number of provisos:
+@itemize
+@item All weight values must be integer.
+@item The cumulative weight value must be in the range [3, 5000]
+@end itemize
+
+The @subcmd{COMPARE} subcommand is only relevant if producing boxplots, and it is only
useful there is more than one dependent variable and at least one factor.
-If
+If
@subcmd{/COMPARE=GROUPS} is specified, then one plot per dependent variable is produced,
each of which contain boxplots for all the cells.
If @subcmd{/COMPARE=VARIABLES} is specified, then one plot per cell is produced,
each containing one boxplot per dependent variable.
If the @subcmd{/COMPARE} subcommand is omitted, then @pspp{} behaves as if
@subcmd{/COMPARE=GROUPS} were given.
-
-The @subcmd{ID} subcommand is relevant only if @subcmd{/PLOT=BOXPLOT} or
+
+The @subcmd{ID} subcommand is relevant only if @subcmd{/PLOT=BOXPLOT} or
@subcmd{/STATISTICS=EXTREME} has been given.
If given, it should provide the name of a variable which is to be used
to labels extreme values and outliers.
-Numeric or string variables are permissible.
-If the @subcmd{ID} subcommand is not given, then the case number will be used for
+Numeric or string variables are permissible.
+If the @subcmd{ID} subcommand is not given, then the case number is used for
labelling.
The @subcmd{CINTERVAL} subcommand specifies the confidence interval to use in
calculation of the descriptives command. The default is 95%.
@cindex percentiles
-The @subcmd{PERCENTILES} subcommand specifies which percentiles are to be calculated,
+The @subcmd{PERCENTILES} subcommand specifies which percentiles are to be calculated,
and which algorithm to use for calculating them. The default is to
calculate the 5, 10, 25, 50, 75, 90, 95 percentiles using the
@subcmd{HAVERAGE} algorithm.
The @subcmd{TOTAL} and @subcmd{NOTOTAL} subcommands are mutually exclusive. If @subcmd{NOTOTAL}
is given and factors have been specified in the @subcmd{VARIABLES} subcommand,
-then then statistics for the unfactored dependent variables are
+then statistics for the unfactored dependent variables are
produced in addition to the factored variables. If there are no
factors specified then @subcmd{TOTAL} and @subcmd{NOTOTAL} have no effect.
-The following example will generate descriptive statistics and histograms for
+The following example generates descriptive statistics and histograms for
two variables @var{score1} and @var{score2}.
Two factors are given, @i{viz}: @var{gender} and @var{gender} BY @var{culture}.
-Therefore, the descriptives and histograms will be generated for each
+Therefore, the descriptives and histograms are generated for each
distinct value
of @var{gender} @emph{and} for each distinct combination of the values
of @var{gender} and @var{race}.
-Since the @subcmd{NOTOTAL} keyword is given, statistics and histograms for
+Since the @subcmd{NOTOTAL} keyword is given, statistics and histograms for
@var{score1} and @var{score2} covering the whole dataset are not produced.
@example
-EXAMINE @var{score1} @var{score2} BY
+EXAMINE @var{score1} @var{score2} BY
@var{gender}
@var{gender} BY @var{culture}
/STATISTICS = DESCRIPTIVES
Here is a second example showing how the @cmd{examine} command can be used to find extremities.
@example
-EXAMINE @var{height} @var{weight} BY
+EXAMINE @var{height} @var{weight} BY
@var{gender}
/STATISTICS = EXTREME (3)
/PLOT = BOXPLOT
@end example
In this example, we look at the height and weight of a sample of individuals and
how they differ between male and female.
-A table showing the 3 largest and the 3 smallest values of @var{height} and
-@var{weight} for each gender, and for the whole dataset will be shown.
-Boxplots will also be produced.
-Because @subcmd{/COMPARE = GROUPS} was given, boxplots for male and female will be
-shown in the same graphic, allowing us to easily see the difference between
+A table showing the 3 largest and the 3 smallest values of @exvar{height} and
+@exvar{weight} for each gender, and for the whole dataset as are shown.
+In addition, the @subcmd{/PLOT} subcommand requests boxplots.
+Because @subcmd{/COMPARE = GROUPS} was specified, boxplots for male and female are
+shown in juxtaposed in the same graphic, allowing us to easily see the difference between
the genders.
-Since the variable @var{name} was specified on the @subcmd{ID} subcommand, this will be
-used to label the extreme values.
+Since the variable @var{name} was specified on the @subcmd{ID} subcommand,
+values of the @var{name} variable are used to label the extreme values.
@strong{Warning!}
-If many dependent variables are specified, or if factor variables are
-specified for which
-there are many distinct values, then @cmd{EXAMINE} will produce a very
+If you specify many dependent variables or factor variables
+for which there are many distinct values, then @cmd{EXAMINE} will produce a very
large quantity of output.
@node GRAPH
GRAPH
/HISTOGRAM [(NORMAL)]= @var{var}
/SCATTERPLOT [(BIVARIATE)] = @var{var1} WITH @var{var2} [BY @var{var3}]
- /BAR = @{@var{summary-function}(@var{var1}) | @var{count-function}@} BY @var{var2} [BY @var{var3}]
- [ /MISSING=@{LISTWISE, VARIABLE@} [@{EXCLUDE, INCLUDE@}] ]
+ /BAR = @{@var{summary-function}(@var{var1}) | @var{count-function}@} BY @var{var2} [BY @var{var3}]
+ [ /MISSING=@{LISTWISE, VARIABLE@} [@{EXCLUDE, INCLUDE@}] ]
[@{NOREPORT,REPORT@}]
@end display
-The @cmd{GRAPH} produces graphical plots of data. Only one of the subcommands
-@subcmd{HISTOGRAM} or @subcmd{SCATTERPLOT} can be specified, i.e. only one plot
-can be produced per call of @cmd{GRAPH}. The @subcmd{MISSING} is optional.
+The @cmd{GRAPH} command produces graphical plots of data. Only one of the subcommands
+@subcmd{HISTOGRAM}, @subcmd{BAR} or @subcmd{SCATTERPLOT} can be specified, @i{i.e.} only one plot
+can be produced per call of @cmd{GRAPH}. The @subcmd{MISSING} is optional.
@menu
* SCATTERPLOT:: Cartesian Plots
@cindex scatterplot
The subcommand @subcmd{SCATTERPLOT} produces an xy plot of the
-data. The different values of the optional third variable @var{var3}
-will result in different colours and/or markers for the plot. The
-following is an example for producing a scatterplot.
+data.
+@cmd{GRAPH} uses the third variable @var{var3}, if specified, to determine
+the colours and/or markers for the plot.
+The following is an example for producing a scatterplot.
@example
-GRAPH
+GRAPH
/SCATTERPLOT = @var{height} WITH @var{weight} BY @var{gender}.
@end example
-This example will produce a scatterplot where @var{height} is plotted versus @var{weight}. Depending
+This example produces a scatterplot where @var{height} is plotted versus @var{weight}. Depending
on the value of the @var{gender} variable, the colour of the datapoint is different. With
-this plot it is possible to analyze gender differences for @var{height} vs.@: @var{weight} relation.
+this plot it is possible to analyze gender differences for @var{height} versus @var{weight} relation.
@node HISTOGRAM
@subsection Histogram
following example produces a histogram plot for the variable @var{weight}.
@example
-GRAPH
+GRAPH
/HISTOGRAM = @var{weight}.
@end example
[ /PRINT=@{TWOTAIL, ONETAIL@} @{SIG, NOSIG@} ]
[ /STATISTICS=DESCRIPTIVES XPROD ALL]
[ /MISSING=@{PAIRWISE, LISTWISE@} @{INCLUDE, EXCLUDE@} ]
-@end display
+@end display
@cindex correlation
The @cmd{CORRELATIONS} procedure produces tables of the Pearson correlation coefficient
for a set of variables. The significance of the coefficients are also given.
-At least one @subcmd{VARIABLES} subcommand is required. If the @subcmd{WITH}
-keyword is used, then a non-square correlation table will be produced.
-The variables preceding @subcmd{WITH}, will be used as the rows of the table,
-and the variables following will be the columns of the table.
-If no @subcmd{WITH} subcommand is given, then a square, symmetrical table using all variables is produced.
-
+At least one @subcmd{VARIABLES} subcommand is required. If you specify the @subcmd{WITH}
+keyword, then a non-square correlation table is produced.
+The variables preceding @subcmd{WITH}, are used as the rows of the table,
+and the variables following @subcmd{WITH} are used as the columns of the table.
+If no @subcmd{WITH} subcommand is specified, then @cmd{CORRELATIONS} produces a
+square, symmetrical table using all variables.
-The @cmd{MISSING} subcommand determines the handling of missing variables.
+The @cmd{MISSING} subcommand determines the handling of missing variables.
If @subcmd{INCLUDE} is set, then user-missing values are included in the
calculations, but system-missing values are not.
If @subcmd{EXCLUDE} is set, which is the default, user-missing
-values are excluded as well as system-missing values.
+values are excluded as well as system-missing values.
If @subcmd{LISTWISE} is set, then the entire case is excluded from analysis
whenever any variable specified in any @cmd{/VARIABLES} subcommand
-contains a missing value.
+contains a missing value.
If @subcmd{PAIRWISE} is set, then a case is considered missing only if either of the
values for the particular coefficient are missing.
The default is @subcmd{PAIRWISE}.
The @subcmd{PRINT} subcommand is used to control how the reported significance values are printed.
-If the @subcmd{TWOTAIL} option is used, then a two-tailed test of significance is
+If the @subcmd{TWOTAIL} option is used, then a two-tailed test of significance is
printed. If the @subcmd{ONETAIL} option is given, then a one-tailed test is used.
The default is @subcmd{TWOTAIL}.
If @subcmd{SIG} is specified, then no highlighting is performed. This is the default.
@cindex covariance
-The @subcmd{STATISTICS} subcommand requests additional statistics to be displayed. The keyword
+The @subcmd{STATISTICS} subcommand requests additional statistics to be displayed. The keyword
@subcmd{DESCRIPTIVES} requests that the mean, number of non-missing cases, and the non-biased
estimator of the standard deviation are displayed.
-These statistics will be displayed in a separated table, for all the variables listed
+These statistics are displayed in a separated table, for all the variables listed
in any @subcmd{/VARIABLES} subcommand.
-The @subcmd{XPROD} keyword requests cross-product deviations and covariance estimators to
+The @subcmd{XPROD} keyword requests cross-product deviations and covariance estimators to
be displayed for each pair of variables.
The keyword @subcmd{ALL} is the union of @subcmd{DESCRIPTIVES} and @subcmd{XPROD}.
CROSSTABS
/TABLES=@var{var_list} BY @var{var_list} [BY @var{var_list}]@dots{}
/MISSING=@{TABLE,INCLUDE,REPORT@}
- /WRITE=@{NONE,CELLS,ALL@}
/FORMAT=@{TABLES,NOTABLES@}
- @{PIVOT,NOPIVOT@}
@{AVALUE,DVALUE@}
- @{NOINDEX,INDEX@}
- @{BOX,NOBOX@}
/CELLS=@{COUNT,ROW,COLUMN,TOTAL,EXPECTED,RESIDUAL,SRESIDUAL,
ASRESIDUAL,ALL,NONE@}
/COUNT=@{ASIS,CASE,CELL@}
/STATISTICS=@{CHISQ,PHI,CC,LAMBDA,UC,BTAU,CTAU,RISK,GAMMA,D,
KAPPA,ETA,CORR,ALL,NONE@}
/BARCHART
-
+
(Integer mode.)
/VARIABLES=@var{var_list} (@var{low},@var{high})@dots{}
@end display
number of dimensions is permitted, and any number of variables per
dimension is allowed. The @subcmd{TABLES} subcommand may be repeated as many
times as needed. This is the only required subcommand in @dfn{general
-mode}.
+mode}.
Occasionally, one may want to invoke a special mode called @dfn{integer
mode}. Normally, in general mode, @pspp{} automatically determines
table basis. When set to @subcmd{INCLUDE}, user-missing values are included in
tables and statistics. When set to @subcmd{REPORT}, which is allowed only in
integer mode, user-missing values are included in tables but marked with
-an @samp{M} (for ``missing'') and excluded from statistical
-calculations.
-
-Currently the @subcmd{WRITE} subcommand is ignored.
+a footnote and excluded from statistical calculations.
The @subcmd{FORMAT} subcommand controls the characteristics of the
crosstabulation tables to be displayed. It has a number of possible
@itemize @w{}
@item
@subcmd{TABLES}, the default, causes crosstabulation tables to be output.
-@subcmd{NOTABLES} suppresses them.
-
-@item
-@subcmd{PIVOT}, the default, causes each @subcmd{TABLES} subcommand to be displayed in a
-pivot table format. @subcmd{NOPIVOT} causes the old-style crosstabulation format
-to be used.
+@subcmd{NOTABLES}, which is equivalent to @code{CELLS=NONE}, suppresses them.
@item
@subcmd{AVALUE}, the default, causes values to be sorted in ascending order.
@subcmd{DVALUE} asserts a descending sort order.
-
-@item
-@subcmd{INDEX} and @subcmd{NOINDEX} are currently ignored.
-
-@item
-@subcmd{BOX} and @subcmd{NOBOX} is currently ignored.
@end itemize
The @subcmd{CELLS} subcommand controls the contents of each cell in the displayed
Table percent.
@item EXPECTED
Expected value.
-@item RESIDUAL
+@item RESIDUAL
Residual.
@item SRESIDUAL
Standardized residual.
@end table
@samp{/CELLS} without any settings specified requests @subcmd{COUNT}, @subcmd{ROW},
-@subcmd{COLUMN}, and @subcmd{TOTAL}.
+@subcmd{COLUMN}, and @subcmd{TOTAL}.
If @subcmd{CELLS} is not specified at all then only @subcmd{COUNT}
-will be selected.
+is selected.
By default, crosstabulation and statistics use raw case weights,
without rounding. Use the @subcmd{/COUNT} subcommand to perform
@table @asis
@item CHISQ
-@cindex chisquare
@cindex chi-square
Pearson chi-square, likelihood ratio, Fisher's exact test, continuity
@cindex bar chart
The @samp{/BARCHART} subcommand produces a clustered bar chart for the first two
variables on each table.
-If a table has more than two variables, the counts for the third and subsequent levels
-will be aggregated and the chart will be produces as if there were only two variables.
+If a table has more than two variables, the counts for the third and subsequent levels
+are aggregated and the chart is produced as if there were only two variables.
@strong{Please note:} Currently the implementation of @cmd{CROSSTABS} has the
Fixes for any of these deficiencies would be welcomed.
+@subsection Crosstabs Example
+
+@cindex chi-square test of independence
+
+A researcher wishes to know if, in an industry, a person's sex is related to
+the person's occupation. To investigate this, she has determined that the
+@file{personnel.sav} is a representative, randomly selected sample of persons.
+The researcher's null hypothesis is that a person's sex has no relation to a
+person's occupation. She uses a chi-squared test of independence to investigate
+the hypothesis.
+
+@float Example, crosstabs:ex
+@psppsyntax {crosstabs.sps}
+@caption {Running crosstabs on the @exvar{sex} and @exvar{occupation} variables}
+@end float
+
+The syntax in @ref{crosstabs:ex} conducts a chi-squared test of independence.
+The line @code{/tables = occupation by sex} indicates that @exvar{occupation}
+and @exvar{sex} are the variables to be tabulated. To do this using the @gui{}
+you must place these variable names respectively in the @samp{Row} and
+@samp{Column} fields as shown in @ref{crosstabs:scr}.
+
+@float Screenshot, crosstabs:scr
+@psppimage {crosstabs}
+@caption {The Crosstabs dialog box with the @exvar{sex} and @exvar{occupation} variables selected}
+@end float
+
+Similarly, the @samp{Cells} button shows a dialog box to select the @code{count}
+and @code{expected} options. All other cell options can be deselected for this
+test.
+
+You would use the @samp{Format} and @samp{Statistics} buttons to select options
+for the @subcmd{FORMAT} and @subcmd{STATISTICS} subcommands. In this example,
+the @samp{Statistics} requires only the @samp{Chisq} option to be checked. All
+other options should be unchecked. No special settings are required from the
+@samp{Format} dialog.
+
+As shown in @ref{crosstabs:res} @cmd{CROSSTABS} generates a contingency table
+containing the observed count and the expected count of each sex and each
+occupation. The expected count is the count which would be observed if the
+null hypothesis were true.
+
+The significance of the Pearson Chi-Square value is very much larger than the
+normally accepted value of 0.05 and so one cannot reject the null hypothesis.
+Thus the researcher must conclude that a person's sex has no relation to the
+person's occupation.
+
+@float Results, crosstabs:res
+@psppoutput {crosstabs}
+@caption {The results of a test of independence between @exvar{sex} and @exvar{occupation}}
+@end float
+
+@node CTABLES
+@section CTABLES
+
+@vindex CTABLES
+@cindex custom tables
+@cindex tables, custom
+
+@code{CTABLES} has the following overall syntax. At least one
+@code{TABLE} subcommand is required:
+
+@display
+@t{CTABLES}
+ @dots{}@i{global subcommands}@dots{}
+ [@t{/TABLE} @i{axis} [@t{BY} @i{axis} [@t{BY} @i{axis}]]
+ @dots{}@i{per-table subcommands}@dots{}]@dots{}
+@end display
+
+@noindent
+where each @i{axis} may be empty or take one of the following forms:
+
+@display
+@i{variable}
+@i{variable} @t{[}@{@t{C} @math{|} @t{S}@}@t{]}
+@i{axis} + @i{axis}
+@i{axis} > @i{axis}
+(@i{axis})
+@i{axis} @t{[}@i{summary} [@i{string}] [@i{format}]@t{]}
+@end display
+
+The following subcommands precede the first @code{TABLE} subcommand
+and apply to all of the output tables. All of these subcommands are
+optional:
+
+@display
+@t{/FORMAT}
+ [@t{MINCOLWIDTH=}@{@t{DEFAULT} @math{|} @i{width}@}]
+ [@t{MAXCOLWIDTH=}@{@t{DEFAULT} @math{|} @i{width}@}]
+ [@t{UNITS=}@{@t{POINTS} @math{|} @t{INCHES} @math{|} @t{CM}@}]
+ [@t{EMPTY=}@{@t{ZERO} @math{|} @t{BLANK} @math{|} @i{string}@}]
+ [@t{MISSING=}@i{string}]
+@t{/VLABELS}
+ @t{VARIABLES=}@i{variables}
+ @t{DISPLAY}=@{@t{DEFAULT} @math{|} @t{NAME} @math{|} @t{LABEL} @math{|} @t{BOTH} @math{|} @t{NONE}@}
+@ignore @c not yet implemented
+@t{/MRSETS COUNTDUPLICATES=}@{@t{YES} @math{|} @t{NO}@}
+@end ignore
+@t{/SMISSING} @{@t{VARIABLE} @math{|} @t{LISTWISE}@}
+@t{/PCOMPUTE} @t{&}@i{postcompute}@t{=EXPR(}@i{expression}@t{)}
+@t{/PPROPERTIES} @t{&}@i{postcompute}@dots{}
+ [@t{LABEL=}@i{string}]
+ [@t{FORMAT=}[@i{summary} @i{format}]@dots{}]
+ [@t{HIDESOURCECATS=}@{@t{NO} @math{|} @t{YES}@}
+@t{/WEIGHT VARIABLE=}@i{variable}
+@t{/HIDESMALLCOUNTS COUNT=@i{count}}
+@end display
+
+The following subcommands follow @code{TABLE} and apply only to the
+previous @code{TABLE}. All of these subcommands are optional:
+
+@display
+@t{/SLABELS}
+ [@t{POSITION=}@{@t{COLUMN} @math{|} @t{ROW} @math{|} @t{LAYER}@}]
+ [@t{VISIBLE=}@{@t{YES} @math{|} @t{NO}@}]
+@t{/CLABELS} @{@t{AUTO} @math{|} @{@t{ROWLABELS}@math{|}@t{COLLABELS}@}@t{=}@{@t{OPPOSITE}@math{|}@t{LAYER}@}@}
+@t{/CATEGORIES} @t{VARIABLES=}@i{variables}
+ @{@t{[}@i{value}@t{,} @i{value}@dots{}@t{]}
+ @math{|} [@t{ORDER=}@{@t{A} @math{|} @t{D}@}]
+ [@t{KEY=}@{@t{VALUE} @math{|} @t{LABEL} @math{|} @i{summary}@t{(}@i{variable}@t{)}@}]
+ [@t{MISSING=}@{@t{EXCLUDE} @math{|} @t{INCLUDE}@}]@}
+ [@t{TOTAL=}@{@t{NO} @math{|} @t{YES}@} [@t{LABEL=}@i{string}] [@t{POSITION=}@{@t{AFTER} @math{|} @t{BEFORE}@}]]
+ [@t{EMPTY=}@{@t{INCLUDE} @math{|} @t{EXCLUDE}@}]
+@t{/TITLES}
+ [@t{TITLE=}@i{string}@dots{}]
+ [@t{CAPTION=}@i{string}@dots{}]
+ [@t{CORNER=}@i{string}@dots{}]
+@ignore @c not yet implemented
+@t{/CRITERIA CILEVEL=}@i{percentage}
+@t{/SIGTEST TYPE=CHISQUARE}
+ [@t{ALPHA=}@i{siglevel}]
+ [@t{INCLUDEMRSETS=}@{@t{YES} @math{|} @t{NO}@}]
+ [@t{CATEGORIES=}@{@t{ALLVISIBLE} @math{|} @t{SUBTOTALS}@}]
+@t{/COMPARETEST TYPE=}@{@t{PROP} @math{|} @t{MEAN}@}
+ [@t{ALPHA=}@i{value}[@t{,} @i{value}]]
+ [@t{ADJUST=}@{@t{BONFERRONI} @math{|} @t{BH} @math{|} @t{NONE}@}]
+ [@t{INCLUDEMRSETS=}@{@t{YES} @math{|} @t{NO}@}]
+ [@t{MEANSVARIANCE=}@{@t{ALLCATS} @math{|} @t{TESTEDCATS}@}]
+ [@t{CATEGORIES=}@{@t{ALLVISIBLE} @math{|} @t{SUBTOTALS}@}]
+ [@t{MERGE=}@{@t{NO} @math{|} @t{YES}@}]
+ [@t{STYLE=}@{@t{APA} @math{|} @t{SIMPLE}@}]
+ [@t{SHOWSIG=}@{@t{NO} @math{|} @t{YES}@}]
+@end ignore
+@end display
+
+The @code{CTABLES} (aka ``custom tables'') command produces
+multi-dimensional tables from categorical and scale data. It offers
+many options for data summarization and formatting.
+
+This section's examples use data from the 2008 (USA) National Survey
+of Drinking and Driving Attitudes and Behaviors, a public domain data
+set from the (USA) National Highway Traffic Administration and
+available at @url{https://data.transportation.gov}. @pspp{} includes
+this data set, with a slightly modified dictionary, as
+@file{examples/nhtsa.sav}.
+
+@node CTABLES Basics
+@subsection Basics
+
+The only required subcommand is @code{TABLE}, which specifies the
+variables to include along each axis:
+@display
+@t{/TABLE} @i{rows} [@t{BY} @i{columns} [@t{BY} @i{layers}]]
+@end display
+@noindent
+In @code{TABLE}, each of @var{rows}, @var{columns}, and @var{layers}
+is either empty or an axis expression that specifies one or more
+variables. At least one must specify an axis expression.
+
+@menu
+* CTABLES Categorical Variable Basics::
+* CTABLES Scalar Variable Basics::
+* CTABLES Overriding Measurement Level::
+@end menu
+
+@node CTABLES Categorical Variable Basics
+@subsubsection Categorical Variables
+
+An axis expression that names a categorical variable divides the data
+into cells according to the values of that variable. When all the
+variables named on @code{TABLE} are categorical, by default each cell
+displays the number of cases that it contains, so specifying a single
+variable yields a frequency table:
+
+@example
+CTABLES /TABLE=AgeGroup.
+@end example
+@psppoutput {ctables1}
+
+@noindent
+Specifying a row and a column categorical variable yields a
+crosstabulation:
+
+@example
+CTABLES /TABLE=AgeGroup BY qns3a.
+@end example
+@psppoutput {ctables2}
+
+@noindent
+The @samp{>} ``nesting'' operator nests multiple variables on a single
+axis, e.g.:
+
+@example
+CTABLES /TABLE qn105ba BY AgeGroup > qns3a.
+@end example
+@psppoutput {ctables3}
+
+@noindent
+The @samp{+} ``stacking'' operator allows a single output table to
+include multiple data analyses. With @samp{+}, @code{CTABLES} divides
+the output table into multiple @dfn{sections}, each of which includes
+an analysis of the full data set. For example, the following command
+separately tabulates age group and driving frequency by gender:
+
+@example
+CTABLES /TABLE AgeGroup + qn1 BY qns3a.
+@end example
+@psppoutput {ctables4}
+
+@noindent
+When @samp{+} and @samp{>} are used together, @samp{>} binds more
+tightly. Use parentheses to override operator precedence. Thus:
+
+@example
+CTABLES /TABLE qn26 + qn27 > qns3a.
+CTABLES /TABLE (qn26 + qn27) > qns3a.
+@end example
+@psppoutput {ctables5}
+
+@node CTABLES Scalar Variable Basics
+@subsubsection Scalar Variables
+
+For a categorical variable, @code{CTABLES} divides the table into a
+cell per category. For a scalar variable, @code{CTABLES} instead
+calculates a summary measure, by default the mean, of the values that
+fall into a cell. For example, if the only variable specified is a
+scalar variable, then the output is a single cell that holds the mean
+of all of the data:
+
+@example
+CTABLES /TABLE qnd1.
+@end example
+@psppoutput {ctables6}
+
+A scalar variable may nest with categorical variables. The following
+example shows the mean age of survey respondents across gender and
+language groups:
+
+@example
+CTABLES /TABLE qns3a > qnd1 BY region.
+@end example
+@psppoutput {ctables7}
+
+The order of nesting of scalar and categorical variables affects table
+labeling, but it does not affect the data displayed in the table. The
+following example shows how the output changes when the nesting order
+of the scalar and categorical variable are interchanged:
+
+@example
+CTABLES /TABLE qnd1 > qns3a BY region.
+@end example
+@psppoutput {ctables8}
+
+Only a single scalar variable may appear in each section; that is, a
+scalar variable may not nest inside a scalar variable directly or
+indirectly. Scalar variables may only appear on one axis within
+@code{TABLE}.
+
+@node CTABLES Overriding Measurement Level
+@subsubsection Overriding Measurement Level
+
+By default, @code{CTABLES} uses a variable's measurement level to
+decide whether to treat it as categorical or scalar. Variables
+assigned the nominal or ordinal measurement level are treated as
+categorical, and scalar variables are treated as scalar.
+
+Use the @code{VARIABLE LEVEL} command to change a variable's
+measurement level (@pxref{VARIABLE LEVEL}). To treat a variable as
+categorical or scalar only for one use on @code{CTABLES}, add
+@samp{[C]} or @samp{[S]}, respectively, after the variable name. The
+following example shows how to analyze the scalar variable @code{qn20}
+as categorical:
+
+@example
+CTABLES /TABLE qn20 [C] BY qns3a.
+@end example
+@psppoutput {ctables9}
+
+@ignore
+@node CTABLES Multiple Response Sets
+@subsubheading Multiple Response Sets
+
+The @code{CTABLES} command does not yet support multiple response
+sets.
+@end ignore
+
+@node CTABLES Data Summarization
+@subsection Data Summarization
+
+The @code{CTABLES} command allows the user to control how the data are
+summarized with summary specifications, which are enclosed in square
+brackets following a variable name on the @code{TABLE} subcommand.
+When all the variables are categorical, summary specifications can be
+given for the innermost nested variables on any one axis. When a
+scalar variable is present, only the scalar variable may have summary
+specifications. The following example includes a summary
+specification for column and row percentages for categorical
+variables, and mean and median for a scalar variable:
+
+@example
+CTABLES
+ /TABLE=qnd1 [MEAN, MEDIAN] BY qns3a
+ /TABLE=AgeGroup [COLPCT, ROWPCT] BY qns3a.
+@end example
+@psppoutput {ctables10}
+
+A summary specification may override the default label and format by
+appending a string or format specification or both (in that order) to
+the summary function name. For example:
+
+@example
+CTABLES /TABLE=AgeGroup [COLPCT 'Gender %' PCT5.0,
+ ROWPCT 'Age Group %' PCT5.0]
+ BY qns3a.
+@end example
+@psppoutput {ctables11}
+
+Parentheses provide a shorthand to apply summary specifications to
+multiple variables. For example, both of these commands:
+
+@example
+CTABLES /TABLE=AgeGroup[COLPCT] + qns1[COLPCT] BY qns3a.
+CTABLES /TABLE=(AgeGroup + qns1)[COLPCT] BY qns3a.
+@end example
+
+@noindent
+produce the same output shown below:
+
+@psppoutput {ctables12}
+
+The following sections list the available summary functions.
+
+@menu
+* CTABLES Summary Functions for Individual Cells::
+* CTABLES Summary Functions for Groups of Cells::
+* CTABLES Summary Functions for Adjusted Weights::
+* CTABLES Unweighted Summary Functions::
+@end menu
+
+@node CTABLES Summary Functions for Individual Cells
+@subsubsection Summary Functions for Individual Cells
+
+This section lists the summary functions that consider only an
+individual cell in @code{CTABLES}. Only one such summary function,
+@code{COUNT}, may be applied to both categorical and scale variables:
+
+@table @asis
+@item @code{COUNT} (``Count'')
+The sum of weights in a cell.
+
+If @code{CATEGORIES} for one or more of the variables in a table
+include missing values (@pxref{CTABLES Per-Variable Category
+Options}), then some or all of the categories for a cell might be
+missing values. @code{COUNT} counts data included in a cell
+regardless of whether its categories are missing.
+@end table
+
+The following summary functions apply only to scale variables or
+totals and subtotals for categorical variables. Be cautious about
+interpreting the summary value in the latter case, because it is not
+necessarily meaningful; however, the mean of a Likert scale, etc.@:
+may have a straightforward interpreation.
+
+@table @asis
+@item @code{MAXIMUM} (``Maximum'')
+The largest value.
+
+@item @code{MEAN} (``Mean'')
+The mean.
+
+@item @code{MEDIAN} (``Median'')
+The median value.
+
+@item @code{MINIMUM} (``Minimum'')
+The smallest value.
+
+@item @code{MISSING} (``Missing'')
+Sum of weights of user- and system-missing values.
+
+@item @code{MODE} (``Mode'')
+The highest-frequency value. Ties are broken by taking the smallest mode.
+
+@item @code{PTILE} @i{n} (``Percentile @i{n}'')
+The @var{n}th percentile, where @math{0 @leq{} @var{n} @leq{} 100}.
+
+@item @code{RANGE} (``Range'')
+The maximum minus the minimum.
+
+@item @code{SEMEAN} (``Std Error of Mean'')
+The standard error of the mean.
+
+@item @code{STDDEV} (``Std Deviation'')
+The standard deviation.
+
+@item @code{SUM} (``Sum'')
+The sum.
+
+@item @code{TOTALN} (``Total N'')
+The sum of weights in a cell.
+
+For scale data, @code{COUNT} and @code{TOTALN} are the same.
+
+For categorical data, @code{TOTALN} counts missing values in excluded
+categories, that is, user-missing values not in an explicit category
+list on @code{CATEGORIES} (@pxref{CTABLES Per-Variable Category
+Options}), or user-missing values excluded because
+@code{MISSING=EXCLUDE} is in effect on @code{CATEGORIES}, or
+system-missing values. @code{COUNT} does not count these.
+
+@item @code{VALIDN} (``Valid N'')
+The sum of valid count weights in included categories.
+
+@code{VALIDN} does not count missing values regardless of whether they
+are in included categories via @code{CATEGORIES}. @code{VALIDN} does
+not count valid values that are in excluded categories.
+
+@item @code{VARIANCE} (``Variance'')
+The variance.
+@end table
+
+@node CTABLES Summary Functions for Groups of Cells
+@subsubsection Summary Functions for Groups of Cells
+
+These summary functions summarize over multiple cells within an area
+of the output chosen by the user and specified as part of the function
+name. The following basic @var{area}s are supported, in decreasing
+order of size:
+
+@table @code
+@item TABLE
+A @dfn{section}. Stacked variables divide sections of the output from
+each other. sections may span multiple layers.
+
+@item LAYER
+A section within a single layer.
+
+@item SUBTABLE
+A @dfn{subtable}, whose contents are the cells that pair an innermost
+row variable and an innermost column variable within a single layer.
+@end table
+
+The following shows how the output for the table expression @code{qn61
+> qn57 BY qnd7a > qn86 + qn64b BY qns3a}@footnote{This is not
+necessarily a meaningful table, so for clarity variable labels are
+omitted.} is divided up into @code{TABLE}, @code{LAYER}, and
+@code{SUBTABLE} areas. Each unique value for Table ID is one section,
+and similarly for Layer ID and Subtable ID. Thus, this output has two
+@code{TABLE} areas (one for @code{qnd7a} and one for @code{qn64b}),
+four @code{LAYER} areas (for those two variables, per layer), and 12
+@code{SUBTABLE} areas.
+@psppoutput {ctables22}
+
+@code{CTABLES} also supports the following @var{area}s that further
+divide a subtable or a layer within a section:
+
+@table @code
+@item LAYERROW
+@itemx LAYERCOL
+A row or column, respectively, in one layer of a section.
+
+@item ROW
+@itemx COL
+A row or column, respectively, in a subtable.
+@end table
+
+The following summary functions for groups of cells are available for
+each @var{area} described above, for both categorical and scale
+variables:
+
+@table @asis
+@item @code{@i{area}PCT} or @code{@i{area}PCT.COUNT} (``@i{Area} %'')
+A percentage of total counts within @var{area}.
+
+@item @code{@i{area}PCT.VALIDN} (``@i{Area} Valid N %'')
+A percentage of total counts for valid values within @var{area}.
+
+@item @code{@i{area}PCT.TOTALN} (``@i{Area} Total N %'')
+A percentage of total counts for all values within @var{area}.
+@end table
+
+Scale variables and totals and subtotals for categorical variables may
+use the following additional group cell summary function:
+
+@table @asis
+@item @code{@i{area}PCT.SUM} (``@i{Area} Sum %'')
+Percentage of the sum of the values within @var{area}.
+@end table
+
+@node CTABLES Summary Functions for Adjusted Weights
+@subsubsection Summary Functions for Adjusted Weights
+
+If the @code{WEIGHT} subcommand specified an adjustment weight
+variable, then the following summary functions use its value instead
+of the dictionary weight variable. Otherwise, they are equivalent to
+the summary function without the @samp{E}-prefix:
+
+@itemize @bullet
+@item
+@code{ECOUNT} (``Adjusted Count'')
+
+@item
+@code{ETOTALN} (``Adjusted Total N'')
+
+@item
+@code{EVALIDN} (``Adjusted Valid N'')
+@end itemize
+
+@node CTABLES Unweighted Summary Functions
+@subsubsection Unweighted Summary Functions
+
+The following summary functions with a @samp{U}-prefix are equivalent
+to the same ones without the prefix, except that they use unweighted
+counts:
+
+@itemize @bullet
+@item
+@code{UCOUNT} (``Unweighted Count'')
+
+@item
+@code{U@i{area}PCT} or @code{U@i{area}PCT.COUNT} (``Unweighted @i{Area} %'')
+
+@item
+@code{U@i{area}PCT.VALIDN} (``Unweighted @i{Area} Valid N %'')
+
+@item
+@code{U@i{area}PCT.TOTALN} (``Unweighted @i{Area} Total N %'')
+
+@item
+@code{UMEAN} (``Unweighted Mean'')
+
+@item
+@code{UMEDIAN} (``Unweighted Median'')
+
+@item
+@code{UMISSING} (``Unweighted Missing'')
+
+@item
+@code{UMODE} (``Unweight Mode'')
+
+@item
+@code{U@i{area}PCT.SUM} (``Unweighted @i{Area} Sum %'')
+
+@item
+@code{UPTILE} @i{n} (``Unweighted Percentile @i{n}'')
+
+@item
+@code{USEMEAN} (``Unweighted Std Error of Mean'')
+
+@item
+@code{USTDDEV} (``Unweighted Std Deviation'')
+
+@item
+@code{USUM} (``Unweighted Sum'')
+
+@item
+@code{UTOTALN} (``Unweighted Total N'')
+
+@item
+@code{UVALIDN} (``Unweighted Valid N'')
+
+@item
+@code{UVARIANCE} (``Unweighted Variance'')
+@end itemize
+
+@node CTABLES Statistics Positions and Labels
+@subsection Statistics Positions and Labels
+
+@display
+@t{/SLABELS}
+ [@t{POSITION=}@{@t{COLUMN} @math{|} @t{ROW} @math{|} @t{LAYER}@}]
+ [@t{VISIBLE=}@{@t{YES} @math{|} @t{NO}@}]
+@end display
+
+The @code{SLABELS} subcommand controls the position and visibility of
+summary statistics for the @code{TABLE} subcommand that it follows.
+
+@code{POSITION} sets the axis on which summary statistics appear.
+With @t{POSITION=COLUMN}, which is the default, each summary statistic
+appears in a column. For example:
+
+@example
+CTABLES /TABLE=qnd1 [MEAN, MEDIAN] BY qns3a.
+@end example
+@psppoutput {ctables13}
+
+@noindent
+With @t{POSITION=ROW}, each summary statistic appears in a row, as
+shown below:
+
+@example
+CTABLES /TABLE=qnd1 [MEAN, MEDIAN] BY qns3a /SLABELS POSITION=ROW.
+@end example
+@psppoutput {ctables14}
+
+@noindent
+@t{POSITION=LAYER} is also available to place each summary statistic in
+a separate layer.
+
+Labels for summary statistics are shown by default. Use
+@t{VISIBLE=NO} to suppress them. Because unlabeled data can cause
+confusion, it should only be considered if the meaning of the data is
+evident, as in a simple case like this:
+
+@example
+CTABLES /TABLE=AgeGroup [TABLEPCT] /SLABELS VISIBLE=NO.
+@end example
+@psppoutput {ctables15}
+
+@node CTABLES Category Label Positions
+@subsection Category Label Positions
+
+@display
+@t{/CLABELS} @{@t{AUTO} @math{|} @{@t{ROWLABELS}@math{|}@t{COLLABELS}@}@t{=}@{@t{OPPOSITE}@math{|}@t{LAYER}@}@}
+@end display
+
+The @code{CLABELS} subcommand controls the position of category labels
+for the @code{TABLE} subcommand that it follows. By default, or if
+@t{AUTO} is specified, category labels for a given variable nest
+inside the variable's label on the same axis. For example, the
+command below results in age categories nesting within the age group
+variable on the rows axis and gender categories within the gender
+variable on the columns axis:
+
+@example
+CTABLES /TABLE AgeGroup BY qns3a.
+@end example
+@psppoutput {ctables16}
+
+@t{ROWLABELS=OPPOSITE} or @t{COLLABELS=OPPOSITE} move row or column
+variable category labels, respectively, to the opposite axis. The
+setting affects only the innermost variable on the given axis. For
+example:
+
+@example
+CTABLES /TABLE AgeGroup BY qns3a /CLABELS ROWLABELS=OPPOSITE.
+CTABLES /TABLE AgeGroup BY qns3a /CLABELS COLLABELS=OPPOSITE.
+@end example
+@psppoutput {ctables17}
+
+@t{ROWLABELS=LAYER} or @t{COLLABELS=LAYER} move the innermost row or
+column variable category labels, respectively, to the layer axis.
+
+Only one axis's labels may be moved, whether to the opposite axis or
+to the layer axis.
+
+@subsubheading Effect on Summary Statistics
+
+@code{CLABELS} primarily affects the appearance of tables, not the
+data displayed in them. However, @code{CTABLES} can affect the values
+displayed for statistics that summarize areas of a table, since it can
+change the definitions of these areas.
+
+For example, consider the following syntax and output:
+
+@example
+CTABLES /TABLE AgeGroup BY qns3a [ROWPCT, COLPCT].
+@end example
+@psppoutput {ctables23}
+
+@noindent
+Using @code{COLLABELS=OPPOSITE} changes the definitions of rows and
+columns, so that column percentages display what were previously row
+percentages and the new row percentages become meaningless (because
+there is only one cell per row):
+
+@example
+CTABLES
+ /TABLE AgeGroup BY qns3a [ROWPCT, COLPCT]
+ /CLABELS COLLABELS=OPPOSITE.
+@end example
+@psppoutput {ctables24}
+
+@node CTABLES Per-Variable Category Options
+@subsection Per-Variable Category Options
+
+@display
+@t{/CATEGORIES} @t{VARIABLES=}@i{variables}
+ @{@t{[}@i{value}@t{,} @i{value}@dots{}@t{]}
+ @math{|} [@t{ORDER=}@{@t{A} @math{|} @t{D}@}]
+ [@t{KEY=}@{@t{VALUE} @math{|} @t{LABEL} @math{|} @i{summary}@t{(}@i{variable}@t{)}@}]
+ [@t{MISSING=}@{@t{EXCLUDE} @math{|} @t{INCLUDE}@}]@}
+ [@t{TOTAL=}@{@t{NO} @math{|} @t{YES}@} [@t{LABEL=}@i{string}] [@t{POSITION=}@{@t{AFTER} @math{|} @t{BEFORE}@}]]
+ [@t{EMPTY=}@{@t{INCLUDE} @math{|} @t{EXCLUDE}@}]
+@end display
+
+The @code{CATEGORIES} subcommand specifies, for one or more
+categorical variables, the categories to include and exclude, the sort
+order for included categories, and treatment of missing values. It
+also controls the totals and subtotals to display. It may be
+specified any number of times, each time for a different set of
+variables. @code{CATEGORIES} applies to the table produced by the
+@code{TABLE} subcommand that it follows.
+
+@code{CATEGORIES} does not apply to scalar variables.
+
+@t{VARIABLES} is required. List the variables for the subcommand
+to affect.
+
+There are two way to specify the Categories to include and their sort
+order:
+
+@table @asis
+@item Explicit categories.
+@anchor{CTABLES Explicit Category List}
+To explicitly specify categories to include, list the categories
+within square brackets in the desired sort order. Use spaces or
+commas to separate values. Categories not covered by the list are
+excluded from analysis.
+
+Each element of the list takes one of the following forms:
+
+@table @t
+@item @i{number}
+@itemx '@i{string}'
+A numeric or string category value, for variables that have the
+corresponding type.
+
+@item '@i{date}'
+@itemx '@i{time}'
+A date or time category value, for variables that have a date or time
+print format.
+
+@item @i{min} THRU @i{max}
+@itemx LO THRU @i{max}
+@itemx @i{min} THRU HI
+A range of category values, where @var{min} and @var{max} each takes
+one of the forms above, in increasing order.
+
+@item MISSING
+All user-missing values. (To match individual user-missing values,
+specify their category values.)
+
+@item OTHERNM
+Any non-missing value not covered by any other element of the list
+(regardless of where @t{OTHERNM} is placed in the list).
+
+@item &@i{postcompute}
+A computed category name (@pxref{CTABLES Computed Categories}).
+@end table
+
+Additional forms, described later, allow for subtotals.
+If multiple elements of the list cover a given category, the last one
+in the list is considered to be a match.
+
+@item Implicit categories.
+Without an explicit list of categories, @pspp{} sorts
+categories automatically.
+
+The @code{KEY} setting specifies the sort key. By default, or with
+@code{KEY=VALUE}, categories are sorted by default. Categories may
+also be sorted by value label, with @code{KEY=LABEL}, or by the value
+of a summary function, e.g.@: @code{KEY=COUNT}. For summary
+functions, a variable name may be specified in parentheses, e.g.@:
+@code{KEY=MAXIUM(qnd1)}, and this is required for functions that apply
+only to scalar variables. The @code{PTILE} function also requires a
+percentage argument, e.g.@: @code{KEY=PTILE(qnd1, 90)}. Only summary
+functions used in the table may be used, except that @code{COUNT} is
+always allowed.
+
+By default, or with @code{ORDER=A}, categories are sorted in ascending
+order. Specify @code{ORDER=D} to sort in descending order.
+
+User-missing values are excluded by default, or with
+@code{MISSING=EXCLUDE}. Specify @code{MISSING=INCLUDE} to include
+user-missing values. The system-missing value is always excluded.
+@end table
+
+@subsubheading Totals and Subtotals
+
+@code{CATEGORIES} also controls display of totals and subtotals.
+Totals are not displayed by default, or with @code{TOTAL=NO}. Specify
+@code{TOTAL=YES} to display a total. By default, the total is labeled
+``Total''; use @code{LABEL="@i{label}"} to override it.
+
+Subtotals are also not displayed by default. To add one or more
+subtotals, use an explicit category list and insert @code{SUBTOTAL} or
+@code{HSUBTOTAL} in the position or positions where the subtotal
+should appear. With @code{SUBTOTAL}, the subtotal becomes an extra
+row or column or layer; @code{HSUBTOTAL} additionally hides the
+categories that make up the subtotal. Either way, the default label
+is ``Subtotal'', use @code{SUBTOTAL="@i{label}"} or
+@code{HSUBTOTAL="@i{label}"} to specify a custom label.
+
+By default, or with @code{POSITION=AFTER}, totals come after the last
+category and subtotals apply to categories that precede them. With
+@code{POSITION=BEFORE}, totals come before the first category and
+subtotals apply to categories that follow them.
+
+Only categorical variables may have totals and subtotals. Scalar
+variables may be ``totaled'' indirectly by enabling totals and
+subtotals on a categorical variable within which the scalar variable is
+summarized.
+
+@subsubheading Categories Without Values
+
+Some categories might not be included in the data set being analyzed.
+For example, our example data set has no cases in the ``15 or
+younger'' age group. By default, or with @code{EMPTY=INCLUDE},
+@pspp{} includes these empty categories in output tables. To exclude
+them, specify @code{EMPTY=EXCLUDE}.
+
+For implicit categories, empty categories potentially include all the
+values with labels for a given variable; for explicit categories, they
+include all the values listed individually and all labeled values
+covered by ranges or @code{MISSING} or @code{OTHERNM}.
+
+@node CTABLES Titles
+@subsection Titles
+
+@display
+@t{/TITLES}
+ [@t{TITLE=}@i{string}@dots{}]
+ [@t{CAPTION=}@i{string}@dots{}]
+ [@t{CORNER=}@i{string}@dots{}]
+@end display
+
+The @code{TITLES} subcommand sets the title, caption, and corner text
+for the table output for the previous @code{TABLE} subcommand. The
+title appears above the table, the caption below the table, and the
+corner text appears in the table's upper left corner. By default, the
+title is ``Custom Tables'' and the caption and corner text are empty.
+
+@node CTABLES Table Formatting
+@subsection Table Formatting
+
+@display
+@t{/FORMAT}
+ [@t{MINCOLWIDTH=}@{@t{DEFAULT} @math{|} @i{width}@}]
+ [@t{MAXCOLWIDTH=}@{@t{DEFAULT} @math{|} @i{width}@}]
+ [@t{UNITS=}@{@t{POINTS} @math{|} @t{INCHES} @math{|} @t{CM}@}]
+ [@t{EMPTY=}@{@t{ZERO} @math{|} @t{BLANK} @math{|} @i{string}@}]
+ [@t{MISSING=}@i{string}]
+@end display
+
+The @code{FORMAT} subcommand, which must precede the first
+@code{TABLE} subcommand, controls formatting for all the output
+tables. @code{FORMAT} and all of its settings are optional.
+
+Use @code{MINCOLWIDTH} and @code{MAXCOLWIDTH} to control the minimum
+or maximum width of columns in output tables. By default, or with
+@code{DEFAULT}, column width varies based on content. Otherwise,
+specify a number for either or both of these settings. If both are
+specified, @code{MAXCOLWIDTH} must be bigger than @code{MINCOLWIDTH}.
+The default unit, or with @code{UNITS=POINTS}, is points (1/72 inch),
+but specify @code{UNITS=INCHES} to use inches or @code{UNITS=CM} for
+centimeters.
+
+By default, or with @code{EMPTY=ZERO}, zero values are displayed in
+their usual format. Use @code{EMPTY=BLANK} to use an empty cell
+instead, or @code{EMPTY="@i{string}"} to use the specified string.
+
+By default, missing values are displayed as @samp{.}, the same as in
+other tables. Specify @code{MISSING="@i{string}"} to instead use a
+custom string.
+
+@node CTABLES Display of Variable Labels
+@subsection Display of Variable Labels
+
+@display
+@t{/VLABELS}
+ @t{VARIABLES=}@i{variables}
+ @t{DISPLAY}=@{@t{DEFAULT} @math{|} @t{NAME} @math{|} @t{LABEL} @math{|} @t{BOTH} @math{|} @t{NONE}@}
+@end display
+
+The @code{VLABELS} subcommand, which must precede the first
+@code{TABLE} subcommand, controls display of variable labels in all
+the output tables. @code{VLABELS} is optional. It may appear
+multiple times to adjust settings for different variables.
+
+@code{VARIABLES} and @code{DISPLAY} are required. The value of
+@code{DISPLAY} controls how variable labels are displayed for the
+variables listed on @code{VARIABLES}. The supported values are:
+
+@table @code
+@item DEFAULT
+Uses the setting from @ref{SET TVARS}.
+
+@item NAME
+Show only a variable name.
+
+@item LABEL
+Show only a variable label.
+
+@item BOTH
+Show variable name and label.
+
+@item NONE
+Show nothing.
+@end table
+
+@node CTABLES Missing Value Treatment
+@subsection Missing Value Treatment
+
+@display
+@t{/SMISSING} @{@t{VARIABLE} @math{|} @t{LISTWISE}@}
+@end display
+
+The @code{SMISSING} subcommand, which must precede the first
+@code{TABLE} subcommand, controls treatment of missing values for
+scalar variables in producing all the output tables. @code{SMISSING}
+is optional.
+
+With @code{SMISSING=VARIABLE}, which is the default, missing values
+are excluded on a variable-by-variable basis. With
+@code{SMISSING=LISTWISE}, when stacked scalar variables are nested
+together with a categorical variable, a missing value for any of the
+scalar variables causes the case to be excluded for all of them.
+
+As an example, consider the following dataset, in which @samp{x} is a
+categorical variable and @samp{y} and @samp{z} are scale:
+
+@psppoutput{ctables18}
+
+@noindent
+With the default missing-value treatment, @samp{x}'s mean is 20, based
+on the values 10, 20, and 30, and @samp{y}'s mean is 50, based on 40,
+50, and 60:
+
+@example
+CTABLES /TABLE (y + z) > x.
+@end example
+@psppoutput{ctables19}
+
+@noindent
+By adding @code{SMISSING=LISTWISE}, only cases where @samp{y} and
+@samp{z} are both non-missing are considered, so @samp{x}'s mean
+becomes 15, as the average of 10 and 20, and @samp{y}'s mean becomes
+55, the average of 50 and 60:
+
+@example
+CTABLES /SMISSING LISTWISE /TABLE (y + z) > x.
+@end example
+@psppoutput{ctables20}
+
+@noindent
+Even with @code{SMISSING=LISTWISE}, if @samp{y} and @samp{z} are
+separately nested with @samp{x}, instead of using a single @samp{>}
+operator, missing values revert to being considered on a
+variable-by-variable basis:
+
+@example
+CTABLES /SMISSING LISTWISE /TABLE (y > x) + (z > x).
+@end example
+@psppoutput{ctables21}
+
+@node CTABLES Computed Categories
+@subsection Computed Categories
+
+@display
+@t{/PCOMPUTE} @t{&}@i{postcompute}@t{=EXPR(}@i{expression}@t{)}
+@end display
+
+@dfn{Computed categories}, also called @dfn{postcomputes}, are
+categories created using arithmetic on categories obtained from the
+data. The @code{PCOMPUTE} subcommand defines computed categories,
+which can then be used in two places: on @code{CATEGORIES} within an
+explicit category list (@pxref{CTABLES Explicit Category List}), and on
+the @code{PPROPERTIES} subcommand to define further properties for a
+given postcompute.
+
+@code{PCOMPUTE} must precede the first @code{TABLE} command. It is
+optional and it may be used any number of times to define multiple
+postcomputes.
+
+Each @code{PCOMPUTE} defines one postcompute. Its syntax consists of
+a name to identify the postcompute as a @pspp{} identifier prefixed by
+@samp{&}, followed by @samp{=} and a postcompute expression enclosed
+in @code{EXPR(@dots{})}. A postcompute expression consists of:
+
+@table @t
+@item [@i{category}]
+This form evaluates to the summary statistic for @i{category}, e.g.@:
+@code{[1]} evaluates to the value of the summary statistic associated
+with category 1. The @i{category} may be a number, a quoted string,
+or a quoted time or date value, and all of the categories for a given
+postcompute must have the same form.
+
+@item [@i{min} THRU @i{max}]
+@itemx [LO THRU @i{max}]
+@itemx [@i{min} THRU HI]
+@itemx MISSING
+@itemx OTHERNM
+These forms evaluate to the summary statistics for categories matching
+the given syntax, as described in previous sections (@pxref{CTABLES
+Explicit Category List}). If more than one category matches, their
+values are summed.
+
+@item SUBTOTAL
+The summary statistic for the subtotal category. This form is allowed
+only for variables with exactly one subtotal.
+
+@item SUBTOTAL[@i{index}]
+The summary statistic for subtotal category @i{index}, where 1 is the
+first subtotal, 2 is the second, and so on. This form may be used for
+any number of subtotals.
+
+@item TOTAL
+The summary statistic for the total.
+
+@item @i{a} + @i{b}
+@itemx @i{a} - @i{b}
+@itemx @i{a} * @i{b}
+@itemx @i{a} / @i{b}
+@itemx @i{a} ** @i{b}
+These forms perform arithmetic on the values of postcompute
+expressions @i{a} and @i{b}. The usual operator precedence rules
+apply.
+
+@item @i{number}
+Numeric constants may be used in postcompute expressions.
+
+@item (@i{a})
+Parentheses override operator precedence.
+@end table
+
+A postcompute is not associated with any particular variable.
+Instead, it may be referenced within @code{CATEGORIES} for any
+suitable variable (e.g.@: only a string variable is suitable for a
+postcompute expression that refers to a string category, only a
+variable with subtotals for an expression that refers to subtotals,
+@dots{}).
+
+Normally a named postcompute is defined only once, but if a later
+@code{PCOMPUTE} redefines a postcompute with the same name as an
+earlier one, the later one take precedence.
+
+@node CTABLES Computed Category Properties
+@subsection Computed Category Properties
+
+@display
+@t{/PPROPERTIES} @t{&}@i{postcompute}@dots{}
+ [@t{LABEL=}@i{string}]
+ [@t{FORMAT=}[@i{summary} @i{format}]@dots{}]
+ [@t{HIDESOURCECATS=}@{@t{NO} @math{|} @t{YES}@}
+@end display
+
+The @code{PPROPERTIES} subcommand, which must appear before
+@code{TABLE}, sets properties for one or more postcomputes defined on
+prior @code{PCOMPUTE} subcommands. The subcommand syntax begins with
+the list of postcomputes, each prefixed with @samp{&} as specified on
+@code{PCOMPUTE}.
+
+All of the settings on @code{PPROPERTIES} are optional. Use
+@code{LABEL} to set the label shown for the postcomputes in table
+output. The default label for a postcompute is the expression used to
+define it.
+
+The @code{FORMAT} setting sets summary statistics and display formats
+for the postcomputes.
+
+By default, or with @code{HIDESOURCECATS=NO}, categories referred to
+by computed categories are displayed like other categories. Use
+@code{HIDESOURCECATS=YES} to hide them.
+
+@node CTABLES Base Weight
+@subsection Base Weight
+
+@display
+@t{/WEIGHT VARIABLE=}@i{variable}
+@end display
+
+The @code{WEIGHT} subcommand is optional and must appear before
+@code{TABLE}. If it appears, it must name a numeric variable, known
+as the @dfn{effective base weight} or @dfn{adjustment weight}. The
+effective base weight variable is used for the @code{ECOUNT},
+@code{ETOTALN}, and @code{EVALIDN} summary functions.
+
+Cases with zero, missing, or negative effective base weight are
+excluded from all analysis.
+
+Weights obtained from the @pspp{} dictionary are rounded to the
+nearest integer. Effective base weights are not rounded.
+
+@node CTABLES Hiding Small Counts
+@subsection Hiding Small Counts
+
+@display
+@t{/HIDESMALLCOUNTS COUNT=@i{count}}
+@end display
+
+The @code{HIDESMALLCOUNTS} subcommand is optional. If it specified,
+then count values in output tables less than the value of @i{count}
+are shown as @code{<@i{count}} instead of their true values. The
+value of @i{count} must be an integer and must be at least 2. Case
+weights are considered for deciding whether to hide a count.
+
@node FACTOR
@section FACTOR
[ /ANALYSIS=@var{var_list} ]
- [ /EXTRACTION=@{PC, PAF@}]
+ [ /EXTRACTION=@{PC, PAF@}]
[ /ROTATION=@{VARIMAX, EQUAMAX, QUARTIMAX, PROMAX[(@var{k})], NOROTATE@}]
If specified, @subcmd{MATRIX IN} must be followed by @samp{COV} or @samp{CORR},
then by @samp{=} and @var{file_spec} all in parentheses.
@var{file_spec} may either be an asterisk, which indicates the currently loaded
-dataset, or it may be a filename to be loaded. @xref{MATRIX DATA}, for the expected
+dataset, or it may be a file name to be loaded. @xref{MATRIX DATA}, for the expected
format of the file.
-The @subcmd{/EXTRACTION} subcommand is used to specify the way in which factors (components) are extracted from the data.
-If @subcmd{PC} is specified, then Principal Components Analysis is used.
+The @subcmd{/EXTRACTION} subcommand is used to specify the way in which factors
+(components) are extracted from the data.
+If @subcmd{PC} is specified, then Principal Components Analysis is used.
If @subcmd{PAF} is specified, then Principal Axis Factoring is
-used. By default Principal Components Analysis will be used.
+used. By default Principal Components Analysis is used.
-The @subcmd{/ROTATION} subcommand is used to specify the method by which the extracted solution will be rotated.
-Three orthogonal rotation methods are available:
+The @subcmd{/ROTATION} subcommand is used to specify the method by which the
+extracted solution is rotated. Three orthogonal rotation methods are available:
@subcmd{VARIMAX} (which is the default), @subcmd{EQUAMAX}, and @subcmd{QUARTIMAX}.
There is one oblique rotation method, @i{viz}: @subcmd{PROMAX}.
Optionally you may enter the power of the promax rotation @var{k}, which must be enclosed in parentheses.
The default value of @var{k} is 5.
-If you don't want any rotation to be performed, the word @subcmd{NOROTATE} will prevent the command from performing any
-rotation on the data.
+If you don't want any rotation to be performed, the word @subcmd{NOROTATE}
+prevents the command from performing any rotation on the data.
-The @subcmd{/METHOD} subcommand should be used to determine whether the covariance matrix or the correlation matrix of the data is
+The @subcmd{/METHOD} subcommand should be used to determine whether the
+covariance matrix or the correlation matrix of the data is
to be analysed. By default, the correlation matrix is analysed.
The @subcmd{/PRINT} subcommand may be used to select which features of the analysis are reported:
-@itemize
+@itemize
@item @subcmd{UNIVARIATE}
A table of mean values, standard deviations and total weights are printed.
@item @subcmd{INITIAL}
Identical to @subcmd{INITIAL} and @subcmd{EXTRACTION}.
@end itemize
-If @subcmd{/PLOT=EIGEN} is given, then a ``Scree'' plot of the eigenvalues will be printed. This can be useful for visualizing
+If @subcmd{/PLOT=EIGEN} is given, then a ``Scree'' plot of the eigenvalues is
+printed. This can be useful for visualizing the factors and deciding
which factors (components) should be retained.
-The @subcmd{/FORMAT} subcommand determined how data are to be displayed in loading matrices. If @subcmd{SORT} is specified, then the variables
-are sorted in descending order of significance. If @subcmd{BLANK(@var{n})} is specified, then coefficients whose absolute value is less
-than @var{n} will not be printed. If the keyword @subcmd{DEFAULT} is given, or if no @subcmd{/FORMAT} subcommand is given, then no sorting is
-performed, and all coefficients will be printed.
-
-The @subcmd{/CRITERIA} subcommand is used to specify how the number of extracted factors (components) are chosen.
-If @subcmd{FACTORS(@var{n})} is
-specified, where @var{n} is an integer, then @var{n} factors will be extracted. Otherwise, the @subcmd{MINEIGEN} setting will
-be used.
-@subcmd{MINEIGEN(@var{l})} requests that all factors whose eigenvalues are greater than or equal to @var{l} are extracted.
-The default value of @var{l} is 1.
-The @subcmd{ECONVERGE} setting has effect only when iterative algorithms for factor
-extraction (such as Principal Axis Factoring) are used.
-@subcmd{ECONVERGE(@var{delta})} specifies that
-iteration should cease when
-the maximum absolute value of the communality estimate between one iteration and the previous is less than @var{delta}. The
-default value of @var{delta} is 0.001.
-The @subcmd{ITERATE(@var{m})} may appear any number of times and is used for two different purposes.
-It is used to set the maximum number of iterations (@var{m}) for convergence and also to set the maximum number of iterations
-for rotation.
-Whether it affects convergence or rotation depends upon which subcommand follows the @subcmd{ITERATE} subcommand.
-If @subcmd{EXTRACTION} follows, it affects convergence.
-If @subcmd{ROTATION} follows, it affects rotation.
-If neither @subcmd{ROTATION} nor @subcmd{EXTRACTION} follow a @subcmd{ITERATE} subcommand it will be ignored.
+The @subcmd{/FORMAT} subcommand determined how data are to be
+displayed in loading matrices. If @subcmd{SORT} is specified, then
+the variables are sorted in descending order of significance. If
+@subcmd{BLANK(@var{n})} is specified, then coefficients whose absolute
+value is less than @var{n} are not printed. If the keyword
+@subcmd{DEFAULT} is specified, or if no @subcmd{/FORMAT} subcommand is
+specified, then no sorting is performed, and all coefficients are printed.
+
+You can use the @subcmd{/CRITERIA} subcommand to specify how the number of
+extracted factors (components) are chosen. If @subcmd{FACTORS(@var{n})} is
+specified, where @var{n} is an integer, then @var{n} factors are
+extracted. Otherwise, the @subcmd{MINEIGEN} setting is used.
+@subcmd{MINEIGEN(@var{l})} requests that all factors whose eigenvalues
+are greater than or equal to @var{l} are extracted. The default value
+of @var{l} is 1. The @subcmd{ECONVERGE} setting has effect only when
+using iterative algorithms for factor extraction (such as Principal Axis
+Factoring). @subcmd{ECONVERGE(@var{delta})} specifies that
+iteration should cease when the maximum absolute value of the
+communality estimate between one iteration and the previous is less
+than @var{delta}. The default value of @var{delta} is 0.001.
+
+The @subcmd{ITERATE(@var{m})} may appear any number of times and is
+used for two different purposes. It is used to set the maximum number
+of iterations (@var{m}) for convergence and also to set the maximum
+number of iterations for rotation.
+Whether it affects convergence or rotation depends upon which
+subcommand follows the @subcmd{ITERATE} subcommand.
+If @subcmd{EXTRACTION} follows, it affects convergence.
+If @subcmd{ROTATION} follows, it affects rotation.
+If neither @subcmd{ROTATION} nor @subcmd{EXTRACTION} follow a
+@subcmd{ITERATE} subcommand, then the entire subcommand is ignored.
The default value of @var{m} is 25.
-The @cmd{MISSING} subcommand determines the handling of missing variables.
-If @subcmd{INCLUDE} is set, then user-missing values are included in the
-calculations, but system-missing values are not.
+The @cmd{MISSING} subcommand determines the handling of missing
+variables. If @subcmd{INCLUDE} is set, then user-missing values are
+included in the calculations, but system-missing values are not.
If @subcmd{EXCLUDE} is set, which is the default, user-missing
-values are excluded as well as system-missing values.
-This is the default.
-If @subcmd{LISTWISE} is set, then the entire case is excluded from analysis
-whenever any variable specified in the @cmd{VARIABLES} subcommand
-contains a missing value.
-If @subcmd{PAIRWISE} is set, then a case is considered missing only if either of the
-values for the particular coefficient are missing.
+values are excluded as well as system-missing values. This is the
+default. If @subcmd{LISTWISE} is set, then the entire case is excluded
+from analysis whenever any variable specified in the @cmd{VARIABLES}
+subcommand contains a missing value.
+
+If @subcmd{PAIRWISE} is set, then a case is considered missing only if
+either of the values for the particular coefficient are missing.
The default is @subcmd{LISTWISE}.
@node GLM
appear before the @code{BY} keyword.
The @var{fixed_factors} list must be one or more categorical variables. Normally it
-will not make sense to enter a scalar variable in the @var{fixed_factors} and doing
+does not make sense to enter a scalar variable in the @var{fixed_factors} and doing
so may cause @pspp{} to do a lot of unnecessary processing.
The @subcmd{METHOD} subcommand is used to change the method for producing the sums of
squares. Available values of @var{type} are 1, 2 and 3. The default is type 3.
You may specify a custom design using the @subcmd{DESIGN} subcommand.
-The design comprises a list of interactions where each interaction is a
+The design comprises a list of interactions where each interaction is a
list of variables separated by a @samp{*}. For example the command
@display
GLM subject BY sex age_group race
The @subcmd{MISSING} subcommand determines the handling of missing
-variables.
+variables.
If @subcmd{INCLUDE} is set then, for the purposes of GLM analysis,
only system-missing values are considered
to be missing; user-missing values are not regarded as missing.
If @subcmd{EXCLUDE} is set, which is the default, then user-missing
-values are considered to be missing as well as system-missing values.
+values are considered to be missing as well as system-missing values.
A case for which any dependent variable or any factor
variable has a missing value is excluded from the analysis.
By default, a constant term is included in the model.
Hence, the full model is
@math{
-{\bf y}
-= b_0 + b_1 {\bf x_1}
-+ b_2 {\bf x_2}
+{\bf y}
+= b_0 + b_1 {\bf x_1}
++ b_2 {\bf x_2}
+ \dots
+ b_n {\bf x_n}
}
An iterative Newton-Raphson procedure is used to fit the model.
The @subcmd{/CRITERIA} subcommand is used to specify the stopping criteria of the procedure,
and other parameters.
-The value of @var{cut_point} is used in the classification table. It is the
+The value of @var{cut_point} is used in the classification table. It is the
threshold above which predicted values are considered to be 1. Values
of @var{cut_point} must lie in the range [0,1].
During iterations, if any one of the stopping criteria are satisfied, the procedure is
considered complete.
The stopping criteria are:
@itemize
-@item The number of iterations exceeds @var{max_iterations}.
+@item The number of iterations exceeds @var{max_iterations}.
The default value of @var{max_iterations} is 20.
@item The change in the all coefficient estimates are less than @var{min_delta}.
The default value of @var{min_delta} is 0.001.
The @subcmd{PRINT} subcommand controls the display of optional statistics.
-Currently there is one such option, @subcmd{CI}, which indicates that the
+Currently there is one such option, @subcmd{CI}, which indicates that the
confidence interval of the odds ratio should be displayed as well as its value.
@subcmd{CI} should be followed by an integer in parentheses, to indicate the
confidence level of the desired confidence interval.
The @subcmd{MISSING} subcommand determines the handling of missing
-variables.
+variables.
If @subcmd{INCLUDE} is set, then user-missing values are included in the
calculations, but system-missing values are not.
If @subcmd{EXCLUDE} is set, which is the default, user-missing
-values are excluded as well as system-missing values.
+values are excluded as well as system-missing values.
This is the default.
@node MEANS
@vindex MEANS
@cindex means
-@display
-MEANS [TABLES =]
- @{@var{var_list}@}
+@display
+MEANS [TABLES =]
+ @{@var{var_list}@}
[ BY @{@var{var_list}@} [BY @{@var{var_list}@} [BY @{@var{var_list}@} @dots{} ]]]
- [ /@{@var{var_list}@}
+ [ /@{@var{var_list}@}
[ BY @{@var{var_list}@} [BY @{@var{var_list}@} [BY @{@var{var_list}@} @dots{} ]]] ]
[/CELLS = [MEAN] [COUNT] [STDDEV] [SEMEAN] [SUM] [MIN] [MAX] [RANGE]
- [VARIANCE] [KURT] [SEKURT]
- [SKEW] [SESKEW] [FIRST] [LAST]
- [HARMONIC] [GEOMETRIC]
+ [VARIANCE] [KURT] [SEKURT]
+ [SKEW] [SESKEW] [FIRST] [LAST]
+ [HARMONIC] [GEOMETRIC]
[DEFAULT]
[ALL]
[NONE] ]
- [/MISSING = [TABLE] [INCLUDE] [DEPENDENT]]
-@end display
+ [/MISSING = [INCLUDE] [DEPENDENT]]
+@end display
You can use the @cmd{MEANS} command to calculate the arithmetic mean and similar
statistics, either for the dataset as a whole or for categories of data.
MEANS @var{v} BY @var{g}.
@end example
@noindent then the means, counts and standard deviations for @var{v} after having
-been grouped by @var{g} will be calculated.
+been grouped by @var{g} are calculated.
Instead of the mean, count and standard deviation, you could specify the statistics
in which you are interested:
@example
@item @subcmd{DEFAULT}
This is the same as @subcmd{MEAN} @subcmd{COUNT} @subcmd{STDDEV}.
@item @subcmd{ALL}
- All of the above statistics will be calculated.
+ All of the above statistics are calculated.
@item @subcmd{NONE}
- No statistics will be calculated (only a summary will be shown).
+ No statistics are calculated (only a summary is shown).
@end itemize
-More than one @dfn{table} can be specified in a single command.
+More than one @dfn{table} can be specified in a single command.
Each table is separated by a @samp{/}. For
example
@example
has three tables (the @samp{TABLE =} is optional).
The first table has three dependent variables @var{c}, @var{d} and @var{e}
and a single categorical variable @var{x}.
-The second table has two dependent variables @var{a} and @var{b},
+The second table has two dependent variables @var{a} and @var{b},
and two categorical variables @var{x} and @var{y}.
The third table has a single dependent variables @var{f}
and a categorical variable formed by the combination of @var{y} and @var{z}.
By default values are omitted from the analysis only if missing values
(either system missing or user missing)
-for any of the variables directly involved in their calculation are
+for any of the variables directly involved in their calculation are
encountered.
This behaviour can be modified with the @subcmd{/MISSING} subcommand.
Three options are possible: @subcmd{TABLE}, @subcmd{INCLUDE} and @subcmd{DEPENDENT}.
-@subcmd{/MISSING = TABLE} causes cases to be dropped if any variable is missing
-in the table specification currently being processed, regardless of
-whether it is needed to calculate the statistic.
-
@subcmd{/MISSING = INCLUDE} says that user missing values, either in the dependent
variables or in the categorical variables should be taken at their face
value, and not excluded.
@subcmd{/MISSING = DEPENDENT} says that user missing values, in the dependent
-variables should be taken at their face value, however cases which
-have user missing values for the categorical variables should be omitted
+variables should be taken at their face value, however cases which
+have user missing values for the categorical variables should be omitted
from the calculation.
+@subsection Example Means
+
+The dataset in @file{repairs.sav} contains the mean time between failures (@exvar{mtbf})
+for a sample of artifacts produced by different factories and trialed under
+different operating conditions.
+Since there are four combinations of categorical variables, by simply looking
+at the list of data, it would be hard to how the scores vary for each category.
+@ref{means:ex} shows one way of tabulating the @exvar{mtbf} in a way which is
+easier to understand.
+
+@float Example, means:ex
+@psppsyntax {means.sps}
+@caption {Running @cmd{MEANS} on the @exvar{mtbf} score with categories @exvar{factory} and @exvar{environment}}
+@end float
+
+The results are shown in @ref{means:res}. The figures shown indicate the mean,
+standard deviation and number of samples in each category.
+These figures however do not indicate whether the results are statistically
+significant. For that, you would need to use the procedures @cmd{ONEWAY}, @cmd{GLM} or
+@cmd{T-TEST} depending on the hypothesis being tested.
+
+@float Result, means:res
+@psppoutput {means}
+@caption {The @exvar{mtbf} categorised by @exvar{factory} and @exvar{environment}}
+@end float
+
+Note that there is no limit to the number of variables for which you can calculate
+statistics, nor to the number of categorical variables per layer, nor the number
+of layers.
+However, running @cmd{MEANS} on a large numbers of variables, or with categorical variables
+containing a large number of distinct values may result in an extremely large output, which
+will not be easy to interpret.
+So you should consider carefully which variables to select for participation in the analysis.
+
@node NPAR TESTS
@section NPAR TESTS
@vindex NPAR TESTS
@cindex nonparametric tests
-@display
+@display
NPAR TESTS
-
+
nonparametric test subcommands
.
.
.
-
+
[ /STATISTICS=@{DESCRIPTIVES@} ]
[ /MISSING=@{ANALYSIS, LISTWISE@} @{INCLUDE, EXCLUDE@} ]
[ /METHOD=EXACT [ TIMER [(@var{n})] ] ]
@end display
-@cmd{NPAR TESTS} performs nonparametric tests.
-Non parametric tests make very few assumptions about the distribution of the
+@cmd{NPAR TESTS} performs nonparametric tests.
+Non parametric tests make very few assumptions about the distribution of the
data.
One or more tests may be specified by using the corresponding subcommand.
-If the @subcmd{/STATISTICS} subcommand is also specified, then summary statistics are
+If the @subcmd{/STATISTICS} subcommand is also specified, then summary statistics are
produces for each variable that is the subject of any test.
Certain tests may take a long time to execute, if an exact figure is required.
Therefore, by default asymptotic approximations are used unless the
-subcommand @subcmd{/METHOD=EXACT} is specified.
-Exact tests give more accurate results, but may take an unacceptably long
+subcommand @subcmd{/METHOD=EXACT} is specified.
+Exact tests give more accurate results, but may take an unacceptably long
time to perform. If the @subcmd{TIMER} keyword is used, it sets a maximum time,
-after which the test will be abandoned, and a warning message printed.
+after which the test is abandoned, and a warning message printed.
The time, in minutes, should be specified in parentheses after the @subcmd{TIMER} keyword.
-If the @subcmd{TIMER} keyword is given without this figure, then a default value of 5 minutes
+If the @subcmd{TIMER} keyword is given without this figure, then a default value of 5 minutes
is used.
@menu
* BINOMIAL:: Binomial Test
-* CHISQUARE:: Chisquare Test
+* CHISQUARE:: Chi-square Test
* COCHRAN:: Cochran Q Test
* FRIEDMAN:: Friedman Test
* KENDALL:: Kendall's W Test
@vindex BINOMIAL
@cindex binomial test
-@display
+@display
[ /BINOMIAL[(@var{p})]=@var{var_list}[(@var{value1}[, @var{value2})] ] ]
-@end display
+@end display
-The @subcmd{/BINOMIAL} subcommand compares the observed distribution of a dichotomous
+The @subcmd{/BINOMIAL} subcommand compares the observed distribution of a dichotomous
variable with that of a binomial distribution.
-The variable @var{p} specifies the test proportion of the binomial
-distribution.
+The variable @var{p} specifies the test proportion of the binomial
+distribution.
The default value of 0.5 is assumed if @var{p} is omitted.
If a single value appears after the variable list, then that value is
used as the threshold to partition the observed values. Values less
than or equal to the threshold value form the first category. Values
-greater than the threshold form the second category.
+greater than the threshold form the second category.
-If two values appear after the variable list, then they will be used
+If two values appear after the variable list, then they are used
as the values which a variable must take to be in the respective
-category.
-Cases for which a variable takes a value equal to neither of the specified
+category.
+Cases for which a variable takes a value equal to neither of the specified
values, take no part in the test for that variable.
If no values appear, then the variable must assume dichotomous
If the test proportion is equal to 0.5, then a two tailed test is
reported. For any other test proportion, a one tailed test is
-reported.
+reported.
For one tailed tests, if the test proportion is less than
or equal to the observed proportion, then the significance of
observing the observed proportion or more is reported.
If the test proportion is more than the observed proportion, then the
significance of observing the observed proportion or less is reported.
That is to say, the test is always performed in the observed
-direction.
+direction.
@pspp{} uses a very precise approximation to the gamma function to
compute the binomial significance. Thus, exact results are reported
even for very large sample sizes.
-
@node CHISQUARE
-@subsection Chisquare Test
+@subsection Chi-square Test
@vindex CHISQUARE
-@cindex chisquare test
+@cindex chi-square test
@display
[ /CHISQUARE=@var{var_list}[(@var{lo},@var{hi})] [/EXPECTED=@{EQUAL|@var{f1}, @var{f2} @dots{} @var{fn}@}] ]
-@end display
+@end display
-The @subcmd{/CHISQUARE} subcommand produces a chi-square statistic for the differences
-between the expected and observed frequencies of the categories of a variable.
-Optionally, a range of values may appear after the variable list.
+The @subcmd{/CHISQUARE} subcommand produces a chi-square statistic for the differences
+between the expected and observed frequencies of the categories of a variable.
+Optionally, a range of values may appear after the variable list.
If a range is given, then non integer values are truncated, and values
outside the specified range are excluded from the analysis.
The @subcmd{/EXPECTED} subcommand specifies the expected values of each
-category.
+category.
There must be exactly one non-zero expected value, for each observed
category, or the @subcmd{EQUAL} keyword must be specified.
You may use the notation @subcmd{@var{n}*@var{f}} to specify @var{n}
consecutive expected categories all taking a frequency of @var{f}.
The frequencies given are proportions, not absolute frequencies. The
sum of the frequencies need not be 1.
-If no @subcmd{/EXPECTED} subcommand is given, then then equal frequencies
+If no @subcmd{/EXPECTED} subcommand is given, then equal frequencies
are expected.
+@subsubsection Chi-square Example
+
+A researcher wishes to investigate whether there are an equal number of
+persons of each sex in a population. The sample chosen for invesigation
+is that from the @file {physiology.sav} dataset. The null hypothesis for
+the test is that the population comprises an equal number of males and females.
+The analysis is performed as shown in @ref{chisquare:ex}.
+
+@float Example, chisquare:ex
+@psppsyntax {chisquare.sps}
+@caption {Performing a chi-square test to check for equal distribution of sexes}
+@end float
+
+There is only one test variable, @i{viz:} @exvar{sex}. The other variables in the dataset
+are ignored.
+
+@float Screenshot, chisquare:scr
+@psppimage {chisquare}
+@caption {Performing a chi-square test using the graphic user interface}
+@end float
+
+In @ref{chisquare:res} the summary box shows that in the sample, there are more males
+than females. However the significance of chi-square result is greater than 0.05
+--- the most commonly accepted p-value --- and therefore
+there is not enough evidence to reject the null hypothesis and one must conclude
+that the evidence does not indicate that there is an imbalance of the sexes
+in the population.
+
+@float Result, chisquare:res
+@psppoutput {chisquare}
+@caption {The results of running a chi-square test on @exvar{sex}}
+@end float
+
@node COCHRAN
@subsection Cochran Q Test
@end display
The Cochran Q test is used to test for differences between three or more groups.
-The data for @var{var_list} in all cases must assume exactly two distinct values (other than missing values).
+The data for @var{var_list} in all cases must assume exactly two
+distinct values (other than missing values).
-The value of Q will be displayed and its Asymptotic significance based on a chi-square distribution.
+The value of Q is displayed along with its Asymptotic significance
+based on a chi-square distribution.
@node FRIEDMAN
@subsection Friedman Test
[ /KENDALL = @var{var_list} ]
@end display
-The Kendall test investigates whether an arbitrary number of related samples come from the
+The Kendall test investigates whether an arbitrary number of related samples come from the
same population.
It is identical to the Friedman test except that the additional statistic W, Kendall's Coefficient of Concordance is printed.
It has the range [0,1] --- a value of zero indicates no agreement between the samples whereas a value of
drawn from a particular distribution. Four distributions are supported, @i{viz:}
Normal, Uniform, Poisson and Exponential.
-Ideally you should provide the parameters of the distribution against which you wish to test
-the data. For example, with the normal distribution the mean (@var{mu})and standard deviation (@var{sigma})
-should be given; with the uniform distribution, the minimum (@var{min})and maximum (@var{max}) value should
-be provided.
-However, if the parameters are omitted they will be imputed from the data. Imputing the
-parameters reduces the power of the test so should be avoided if possible.
-
-In the following example, two variables @var{score} and @var{age} are tested to see if
-they follow a normal distribution with a mean of 3.5 and a standard deviation of 2.0.
+Ideally you should provide the parameters of the distribution against
+which you wish to test the data. For example, with the normal
+distribution the mean (@var{mu})and standard deviation (@var{sigma})
+should be given; with the uniform distribution, the minimum
+(@var{min})and maximum (@var{max}) value should be provided.
+However, if the parameters are omitted they are imputed from the
+data. Imputing the parameters reduces the power of the test so should
+be avoided if possible.
+
+In the following example, two variables @var{score} and @var{age} are
+tested to see if they follow a normal distribution with a mean of 3.5
+and a standard deviation of 2.0.
@example
NPAR TESTS
/KOLMOGOROV-SMIRNOV (normal 3.5 2.0) = @var{score} @var{age}.
[ /KRUSKAL-WALLIS = @var{var_list} BY var (@var{lower}, @var{upper}) ]
@end display
-The Kruskal-Wallis test is used to compare data from an
+The Kruskal-Wallis test is used to compare data from an
arbitrary number of populations. It does not assume normality.
The data to be compared are specified by @var{var_list}.
The categorical variable determining the groups to which the
data belongs is given by @var{var}. The limits @var{lower} and
-@var{upper} specify the valid range of @var{var}. Any cases for
-which @var{var} falls outside [@var{lower}, @var{upper}] will be
-ignored.
+@var{upper} specify the valid range of @var{var}.
+If @var{upper} is smaller than @var{lower}, the PSPP will assume their values
+to be reversed. Any cases for which @var{var} falls outside
+[@var{lower}, @var{upper}] are ignored.
-The mean rank of each group as well as the chi-squared value and significance
-of the test will be printed.
-The abbreviated subcommand @subcmd{K-W} may be used in place of @subcmd{KRUSKAL-WALLIS}.
+The mean rank of each group as well as the chi-squared value and
+significance of the test are printed.
+The abbreviated subcommand @subcmd{K-W} may be used in place of
+@subcmd{KRUSKAL-WALLIS}.
@node MANN-WHITNEY
[ /MANN-WHITNEY = @var{var_list} BY var (@var{group1}, @var{group2}) ]
@end display
-The Mann-Whitney subcommand is used to test whether two groups of data come from different populations.
-The variables to be tested should be specified in @var{var_list} and the grouping variable, that determines to which group the test variables belong, in @var{var}.
+The Mann-Whitney subcommand is used to test whether two groups of data
+come from different populations. The variables to be tested should be
+specified in @var{var_list} and the grouping variable, that determines
+to which group the test variables belong, in @var{var}.
@var{Var} may be either a string or an alpha variable.
@var{Group1} and @var{group2} specify the
two values of @var{var} which determine the groups of the test data.
-Cases for which the @var{var} value is neither @var{group1} or @var{group2} will be ignored.
+Cases for which the @var{var} value is neither @var{group1} or
+@var{group2} are ignored.
+
+The value of the Mann-Whitney U statistic, the Wilcoxon W, and the
+significance are printed.
+You may abbreviated the subcommand @subcmd{MANN-WHITNEY} to
+@subcmd{M-W}.
-The value of the Mann-Whitney U statistic, the Wilcoxon W, and the significance will be printed.
-The abbreviated subcommand @subcmd{M-W} may be used in place of @subcmd{MANN-WHITNEY}.
@node MCNEMAR
@subsection McNemar Test
[ /MEDIAN [(@var{value})] = @var{var_list} BY @var{variable} (@var{value1}, @var{value2}) ]
@end display
-The median test is used to test whether independent samples come from
+The median test is used to test whether independent samples come from
populations with a common median.
The median of the populations against which the samples are to be tested
-may be given in parentheses immediately after the
-@subcmd{/MEDIAN} subcommand. If it is not given, the median will be imputed from the
+may be given in parentheses immediately after the
+@subcmd{/MEDIAN} subcommand. If it is not given, the median is imputed from the
union of all the samples.
The variables of the samples to be tested should immediately follow the @samp{=} sign. The
@vindex RUNS
@cindex runs test
-@display
+@display
[ /RUNS (@{MEAN, MEDIAN, MODE, @var{value}@}) = @var{var_list} ]
@end display
The @subcmd{/RUNS} subcommand tests whether a data sequence is randomly ordered.
-It works by examining the number of times a variable's value crosses a given threshold.
+It works by examining the number of times a variable's value crosses a given threshold.
The desired threshold must be specified within parentheses.
It may either be specified as a number or as one of @subcmd{MEAN}, @subcmd{MEDIAN} or @subcmd{MODE}.
Following the threshold specification comes the list of variables whose values are to be
[ /SIGN @var{var_list} [ WITH @var{var_list} [ (PAIRED) ]]]
@end display
-The @subcmd{/SIGN} subcommand tests for differences between medians of the
+The @subcmd{/SIGN} subcommand tests for differences between medians of the
variables listed.
The test does not make any assumptions about the
distribution of the data.
[ /WILCOXON @var{var_list} [ WITH @var{var_list} [ (PAIRED) ]]]
@end display
-The @subcmd{/WILCOXON} subcommand tests for differences between medians of the
+The @subcmd{/WILCOXON} subcommand tests for differences between medians of the
variables listed.
The test does not make any assumptions about the variances of the samples.
It does however assume that the distribution is symmetrical.
@end display
-The @cmd{T-TEST} procedure outputs tables used in testing hypotheses about
-means.
+The @cmd{T-TEST} procedure outputs tables used in testing hypotheses about
+means.
It operates in one of three modes:
@itemize
@item One Sample mode.
The @cmd{MISSING} subcommand determines the handling of missing
-variables.
+variables.
If @subcmd{INCLUDE} is set, then user-missing values are included in the
calculations, but system-missing values are not.
If @subcmd{EXCLUDE} is set, which is the default, user-missing
-values are excluded as well as system-missing values.
+values are excluded as well as system-missing values.
This is the default.
If @subcmd{LISTWISE} is set, then the entire case is excluded from analysis
-whenever any variable specified in the @subcmd{/VARIABLES}, @subcmd{/PAIRS} or
-@subcmd{/GROUPS} subcommands contains a missing value.
+whenever any variable specified in the @subcmd{/VARIABLES}, @subcmd{/PAIRS} or
+@subcmd{/GROUPS} subcommands contains a missing value.
If @subcmd{ANALYSIS} is set, then missing values are excluded only in the analysis for
which they would be needed. This is the default.
The @subcmd{TESTVAL} subcommand invokes the One Sample mode.
This mode is used to test a population mean against a hypothesized
-mean.
+mean.
The value given to the @subcmd{TESTVAL} subcommand is the value against
which you wish to test.
In this mode, you must also use the @subcmd{/VARIABLES} subcommand to
tell @pspp{} which variables you wish to test.
+@subsubsection Example - One Sample T-test
+
+A researcher wishes to know whether the weight of persons in a population
+is different from the national average.
+The samples are drawn from the population under investigation and recorded
+in the file @file{physiology.sav}.
+From the Department of Health, she
+knows that the national average weight of healthy adults is 76.8kg.
+Accordingly the @subcmd{TESTVAL} is set to 76.8.
+The null hypothesis therefore is that the mean average weight of the
+population from which the sample was drawn is 76.8kg.
+
+As previously noted (@pxref{Identifying incorrect data}), one
+sample in the dataset contains a weight value
+which is clearly incorrect. So this is excluded from the analysis
+using the @cmd{SELECT} command.
+
+@float Example, one-sample-t:ex
+@psppsyntax {one-sample-t.sps}
+@caption {Running a one sample T-Test after excluding all non-positive values}
+@end float
+
+@float Screenshot, one-sample-t:scr
+@psppimage {one-sample-t}
+@caption {Using the One Sample T-Test dialog box to test @exvar{weight} for a mean of 76.8kg}
+@end float
+
+
+@ref{one-sample-t:res} shows that the mean of our sample differs from the test value
+by -1.40kg. However the significance is very high (0.610). So one cannot
+reject the null hypothesis, and must conclude there is not enough evidence
+to suggest that the mean weight of the persons in our population is different
+from 76.8kg.
+
+@float Results, one-sample-t:res
+@psppoutput {one-sample-t}
+@caption {The results of a one sample T-test of @exvar{weight} using a test value of 76.8kg}
+@end float
+
@node Independent Samples Mode
@subsection Independent Samples Mode
The @subcmd{GROUPS} subcommand invokes Independent Samples mode or
-`Groups' mode.
+`Groups' mode.
This mode is used to test whether two groups of values have the
same population mean.
In this mode, you must also use the @subcmd{/VARIABLES} subcommand to
variable which determines to which group the samples belong.
The values in parentheses are the specific values of the independent
variable for each group.
-If the parentheses are omitted and no values are given, the default values
+If the parentheses are omitted and no values are given, the default values
of 1.0 and 2.0 are assumed.
-If the independent variable is numeric,
+If the independent variable is numeric,
it is acceptable to specify only one value inside the parentheses.
If you do this, cases where the independent variable is
greater than or equal to this value belong to the first group, and cases
the independent variable are excluded on a listwise basis, regardless
of whether @subcmd{/MISSING=LISTWISE} was specified.
+@subsubsection Example - Independent Samples T-test
+
+A researcher wishes to know whether within a population, adult males
+are taller than adult females.
+The samples are drawn from the population under investigation and recorded
+in the file @file{physiology.sav}.
+
+As previously noted (@pxref{Identifying incorrect data}), one
+sample in the dataset contains a height value
+which is clearly incorrect. So this is excluded from the analysis
+using the @cmd{SELECT} command.
+
+
+@float Example, indepdendent-samples-t:ex
+@psppsyntax {independent-samples-t.sps}
+@caption {Running a independent samples T-Test after excluding all observations less than 200kg}
+@end float
+
+
+The null hypothesis is that both males and females are on average
+of equal height.
+
+@float Screenshot, independent-samples-t:scr
+@psppimage {independent-samples-t}
+@caption {Using the Independent Sample T-test dialog, to test for differences of @exvar{height} between values of @exvar{sex}}
+@end float
+
+
+In this case, the grouping variable is @exvar{sex}, so this is entered
+as the variable for the @subcmd{GROUP} subcommand. The group values are 0 (male) and
+1 (female).
+
+If you are running the proceedure using syntax, then you need to enter
+the values corresponding to each group within parentheses.
+If you are using the graphic user interface, then you have to open
+the ``Define Groups'' dialog box and enter the values corresponding
+to each group as shown in @ref{define-groups-t:scr}. If, as in this case, the dataset has defined value
+labels for the group variable, then you can enter them by label
+or by value.
+
+@float Screenshot, define-groups-t:scr
+@psppimage {define-groups-t}
+@caption {Setting the values of the grouping variable for an Independent Samples T-test}
+@end float
+
+From @ref{independent-samples-t:res}, one can clearly see that the @emph{sample} mean height
+is greater for males than for females. However in order to see if this
+is a significant result, one must consult the T-Test table.
+
+The T-Test table contains two rows; one for use if the variance of the samples
+in each group may be safely assumed to be equal, and the second row
+if the variances in each group may not be safely assumed to be equal.
+
+In this case however, both rows show a 2-tailed significance less than 0.001 and
+one must therefore reject the null hypothesis and conclude that within
+the population the mean height of males and of females are unequal.
+
+@float Result, independent-samples-t:res
+@psppoutput {independent-samples-t}
+@caption {The results of an independent samples T-test of @exvar{height} by @exvar{sex}}
+@end float
@node Paired Samples Mode
@subsection Paired Samples Mode
samples.
If the @subcmd{WITH} keyword is omitted, then tables for all
combinations of variables given in the @cmd{PAIRS} subcommand are
-generated.
+generated.
If the @subcmd{WITH} keyword is given, and the @subcmd{(PAIRED)} keyword
is also given, then the number of variables preceding @subcmd{WITH}
must be the same as the number following it.
The @cmd{ONEWAY} procedure performs a one-way analysis of variance of
variables factored by a single independent variable.
It is used to compare the means of a population
-divided into more than two groups.
+divided into more than two groups.
The dependent variables to be analysed should be given in the @subcmd{VARIABLES}
-subcommand.
+subcommand.
The list of variables must be followed by the @subcmd{BY} keyword and
the name of the independent (or factor) variable.
The @subcmd{CONTRAST} subcommand may be given up to 10 times in order
to specify different contrast tests.
The @subcmd{MISSING} subcommand defines how missing values are handled.
-If @subcmd{LISTWISE} is specified then cases which have missing values for
-the independent variable or any dependent variable will be ignored.
-If @subcmd{ANALYSIS} is specified, then cases will be ignored if the independent
-variable is missing or if the dependent variable currently being
+If @subcmd{LISTWISE} is specified then cases which have missing values for
+the independent variable or any dependent variable are ignored.
+If @subcmd{ANALYSIS} is specified, then cases are ignored if the independent
+variable is missing or if the dependent variable currently being
analysed is missing. The default is @subcmd{ANALYSIS}.
A setting of @subcmd{EXCLUDE} means that variables whose values are
user-missing are to be excluded from the analysis. A setting of
@end itemize
@noindent
-The optional syntax @code{ALPHA(@var{value})} is used to indicate
-that @var{value} should be used as the
-confidence level for which the posthoc tests will be performed.
-The default is 0.05.
+Use the optional syntax @code{ALPHA(@var{value})} to indicate that
+@cmd{ONEWAY} should perform the posthoc tests at a confidence level of
+@var{value}. If @code{ALPHA(@var{value})} is not specified, then the
+confidence level used is 0.05.
@node QUICK CLUSTER
@section QUICK CLUSTER
[/CRITERIA=CLUSTERS(@var{k}) [MXITER(@var{max_iter})] CONVERGE(@var{epsilon}) [NOINITIAL]]
[/MISSING=@{EXCLUDE,INCLUDE@} @{LISTWISE, PAIRWISE@}]
[/PRINT=@{INITIAL@} @{CLUSTER@}]
+ [/SAVE[=[CLUSTER[(@var{membership_var})]] [DISTANCE[(@var{distance_var})]]]
@end display
The @cmd{QUICK CLUSTER} command performs k-means clustering on the
The subcommand @subcmd{/CRITERIA=CONVERGE(@var{epsilon})} is used
to set the convergence criterion. The value of convergence criterion is @var{epsilon}
times the minimum distance between the @emph{initial} cluster centers. Iteration stops when
-the mean cluster distance between one iteration and the next
+the mean cluster distance between one iteration and the next
is less than the convergence criterion. The default value of @var{epsilon} is zero.
-The @subcmd{MISSING} subcommand determines the handling of missing variables.
+The @subcmd{MISSING} subcommand determines the handling of missing variables.
If @subcmd{INCLUDE} is set, then user-missing values are considered at their face
value and not as missing values.
If @subcmd{EXCLUDE} is set, which is the default, user-missing
-values are excluded as well as system-missing values.
+values are excluded as well as system-missing values.
If @subcmd{LISTWISE} is set, then the entire case is excluded from the analysis
-whenever any of the clustering variables contains a missing value.
+whenever any of the clustering variables contains a missing value.
If @subcmd{PAIRWISE} is set, then a case is considered missing only if all the
clustering variables contain missing values. Otherwise it is clustered
on the basis of the non-missing values.
If @subcmd{INITIAL} is set, then the initial cluster memberships will
be printed.
If @subcmd{CLUSTER} is set, the cluster memberships of the individual
-cases will be displayed (potentially generating lengthy output).
+cases are displayed (potentially generating lengthy output).
+You can specify the subcommand @subcmd{SAVE} to ask that each case's cluster membership
+and the euclidean distance between the case and its cluster center be saved to
+a new variable in the active dataset. To save the cluster membership use the
+@subcmd{CLUSTER} keyword and to save the distance use the @subcmd{DISTANCE} keyword.
+Each keyword may optionally be followed by a variable name in parentheses to specify
+the new variable which is to contain the saved parameter. If no variable name is specified,
+then PSPP will create one.
@node RANK
@section RANK
@end display
The @cmd{RANK} command ranks variables and stores the results into new
-variables.
+variables.
The @subcmd{VARIABLES} subcommand, which is mandatory, specifies one or
-more variables whose values are to be ranked.
+more variables whose values are to be ranked.
After each variable, @samp{A} or @samp{D} may appear, indicating that
the variable is to be ranked in ascending or descending order.
Ascending is the default.
If a @subcmd{BY} keyword appears, it should be followed by a list of variables
-which are to serve as group variables.
+which are to serve as group variables.
In this case, the cases are gathered into groups, and ranks calculated
for each group.
@cindex Cronbach's Alpha
The @cmd{RELIABILITY} command performs reliability analysis on the data.
-The @subcmd{VARIABLES} subcommand is required. It determines the set of variables
+The @subcmd{VARIABLES} subcommand is required. It determines the set of variables
upon which analysis is to be performed.
-The @subcmd{SCALE} subcommand determines which variables reliability is to be
-calculated for. If it is omitted, then analysis for all variables named
-in the @subcmd{VARIABLES} subcommand will be used.
-Optionally, the @var{name} parameter may be specified to set a string name
+The @subcmd{SCALE} subcommand determines the variables for which
+reliability is to be calculated. If @subcmd{SCALE} is omitted, then analysis for
+all variables named in the @subcmd{VARIABLES} subcommand are used.
+Optionally, the @var{name} parameter may be specified to set a string name
for the scale.
-The @subcmd{MODEL} subcommand determines the type of analysis. If @subcmd{ALPHA} is specified,
-then Cronbach's Alpha is calculated for the scale. If the model is @subcmd{SPLIT},
-then the variables are divided into 2 subsets. An optional parameter
+The @subcmd{MODEL} subcommand determines the type of analysis. If @subcmd{ALPHA} is specified,
+then Cronbach's Alpha is calculated for the scale. If the model is @subcmd{SPLIT},
+then the variables are divided into 2 subsets. An optional parameter
@var{n} may be given, to specify how many variables to be in the first subset.
-If @var{n} is omitted, then it defaults to one half of the variables in the
+If @var{n} is omitted, then it defaults to one half of the variables in the
scale, or one half minus one if there are an odd number of variables.
The default model is @subcmd{ALPHA}.
-By default, any cases with user missing, or system missing values for
-any variables given
-in the @subcmd{VARIABLES} subcommand will be omitted from analysis.
-The @subcmd{MISSING} subcommand determines whether user missing values are to
-be included or excluded in the analysis.
+By default, any cases with user missing, or system missing values for
+any variables given in the @subcmd{VARIABLES} subcommand are omitted
+from the analysis. The @subcmd{MISSING} subcommand determines whether
+user missing values are included or excluded in the analysis.
The @subcmd{SUMMARY} subcommand determines the type of summary analysis to be performed.
Currently there is only one type: @subcmd{SUMMARY=TOTAL}, which displays per-item
analysis tested against the totals.
+@subsection Example - Reliability
+
+Before analysing the results of a survey -- particularly for a multiple choice survey --
+it is desireable to know whether the respondents have considered their answers
+or simply provided random answers.
+
+In the following example the survey results from the file @file{hotel.sav} are used.
+All five survey questions are included in the reliability analysis.
+However, before running the analysis, the data must be preprocessed.
+An examination of the survey questions reveals that two questions, @i{viz:} v3 and v5
+are negatively worded, whereas the others are positively worded.
+All questions must be based upon the same scale for the analysis to be meaningful.
+One could use the @cmd{RECODE} command (@pxref{RECODE}), however a simpler way is
+to use @cmd{COMPUTE} (@pxref{COMPUTE}) and this is what is done in @ref{reliability:ex}.
+
+@float Example, reliability:ex
+@psppsyntax {reliability.sps}
+@caption {Investigating the reliability of survey responses}
+@end float
+
+In this case, all variables in the data set are used. So we can use the special
+keyword @samp{ALL} (@pxref{BNF}).
+
+@float Screenshot, reliability:src
+@psppimage {reliability}
+@caption {Reliability dialog box with all variables selected}
+@end float
+
+@ref{reliability:res} shows that Cronbach's Alpha is 0.11 which is a value normally considered too
+low to indicate consistency within the data. This is possibly due to the small number of
+survey questions. The survey should be redesigned before serious use of the results are
+applied.
+
+@float Result, reliability:res
+@psppoutput {reliability}
+@caption {The results of the reliability command on @file{hotel.sav}}
+@end float
@node ROC
@end display
-The @cmd{ROC} command is used to plot the receiver operating characteristic curve
+The @cmd{ROC} command is used to plot the receiver operating characteristic curve
of a dataset, and to estimate the area under the curve.
This is useful for analysing the efficacy of a variable as a predictor of a state of nature.
The mandatory @var{var_list} is the list of predictor variables.
-The variable @var{state_var} is the variable whose values represent the actual states,
+The variable @var{state_var} is the variable whose values represent the actual states,
and @var{state_value} is the value of this variable which represents the positive state.
The optional subcommand @subcmd{PLOT} is used to determine if and how the @subcmd{ROC} curve is drawn.
If the keyword @subcmd{NONE} is given, then no @subcmd{ROC} curve is drawn.
By default, the curve is drawn with no reference line.
-The optional subcommand @subcmd{PRINT} determines which additional tables should be printed.
-Two additional tables are available.
-The @subcmd{SE} keyword says that standard error of the area under the curve should be printed as well as
-the area itself.
-In addition, a p-value under the null hypothesis that the area under the curve equals 0.5 will be
-printed.
-The @subcmd{COORDINATES} keyword says that a table of coordinates of the @subcmd{ROC} curve should be printed.
+The optional subcommand @subcmd{PRINT} determines which additional
+tables should be printed. Two additional tables are available. The
+@subcmd{SE} keyword says that standard error of the area under the
+curve should be printed as well as the area itself. In addition, a
+p-value for the null hypothesis that the area under the curve equals
+0.5 is printed. The @subcmd{COORDINATES} keyword says that a
+table of coordinates of the @subcmd{ROC} curve should be printed.
The @subcmd{CRITERIA} subcommand has four optional parameters:
@itemize @bullet
@item The @subcmd{TESTPOS} parameter may be @subcmd{LARGE} or @subcmd{SMALL}.
-@subcmd{LARGE} is the default, and says that larger values in the predictor variables are to be
+@subcmd{LARGE} is the default, and says that larger values in the predictor variables are to be
considered positive. @subcmd{SMALL} indicates that smaller values should be considered positive.
@item The @subcmd{CI} parameter specifies the confidence interval that should be printed.
It has no effect if the @subcmd{SE} keyword in the @subcmd{PRINT} subcommand has not been given.
@item The @subcmd{DISTRIBUTION} parameter determines the method to be used when estimating the area
-under the curve.
+under the curve.
There are two possibilities, @i{viz}: @subcmd{FREE} and @subcmd{NEGEXPO}.
-The @subcmd{FREE} method uses a non-parametric estimate, and the @subcmd{NEGEXPO} method a bi-negative
+The @subcmd{FREE} method uses a non-parametric estimate, and the @subcmd{NEGEXPO} method a bi-negative
exponential distribution estimate.
The @subcmd{NEGEXPO} method should only be used when the number of positive actual states is
equal to the number of negative actual states.
@item The @subcmd{CUTOFF} parameter is for compatibility and is ignored.
@end itemize
-The @subcmd{MISSING} subcommand determines whether user missing values are to
+The @subcmd{MISSING} subcommand determines whether user missing values are to
be included or excluded in the analysis. The default behaviour is to
exclude them.
-Cases are excluded on a listwise basis; if any of the variables in @var{var_list}
-or if the variable @var{state_var} is missing, then the entire case will be
+Cases are excluded on a listwise basis; if any of the variables in @var{var_list}
+or if the variable @var{state_var} is missing, then the entire case is
excluded.
@c LocalWords: subcmd subcommand
projects / pspp / blobdiff