* CORRELATIONS:: Correlation tables.
* CROSSTABS:: Crosstabulation tables.
* FACTOR:: Factor analysis and Principal Components analysis
+* MEANS:: Average values and other statistics.
* NPAR TESTS:: Nonparametric tests.
* T-TEST:: Test hypotheses about means.
* ONEWAY:: One way analysis of variance.
+* QUICK CLUSTER:: K-Means clustering.
* RANK:: Compute rank scores.
* REGRESSION:: Linear regression.
* RELIABILITY:: Reliability analysis.
@{A,D@}
@end display
-The @cmd{DESCRIPTIVES} procedure reads the active file and outputs
+The @cmd{DESCRIPTIVES} procedure reads the active dataset and outputs
descriptive
statistics requested by the user. In addition, it can optionally
compute Z-scores.
boundaries of the data set divided into the specified number of ranges.
For instance, @code{/NTILES=4} would cause quartiles to be reported.
+@cindex histogram
The 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 MINIMUM
superimpose a normal curve on the histogram. Histograms are not
created for string variables.
+@cindex piechart
The PIECHART 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
but not currently honored.
@node EXAMINE
-@comment node-name, next, previous, up
@section EXAMINE
-@vindex EXAMINE
+@vindex EXAMINE
+@cindex Exploratory data analysis
@cindex Normality, testing for
@display
EXAMINE
- VARIABLES=var_list [BY factor_list ]
- /STATISTICS=@{DESCRIPTIVES, EXTREME[(n)], ALL, NONE@}
+ VARIABLES= @var{var1} [@var{var2}] @dots{} [@var{varN}]
+ [BY @var{factor1} [BY @var{subfactor1}]
+ [ @var{factor2} [BY @var{subfactor2}]]
+ @dots{}
+ [ @var{factor3} [BY @var{subfactor3}]]
+ ]
+ /STATISTICS=@{DESCRIPTIVES, EXTREME[(@var{n})], ALL, NONE@}
/PLOT=@{BOXPLOT, NPPLOT, HISTOGRAM, ALL, NONE@}
- /CINTERVAL n
+ /CINTERVAL @var{p}
/COMPARE=@{GROUPS,VARIABLES@}
- /ID=var_name
+ /ID=@var{identity_variable}
/@{TOTAL,NOTOTAL@}
- /PERCENTILE=[value_list]=@{HAVERAGE, WAVERAGE, ROUND, AEMPIRICAL, EMPIRICAL @}
+ /PERCENTILE=[@var{percentiles}]=@{HAVERAGE, WAVERAGE, ROUND, AEMPIRICAL, EMPIRICAL @}
/MISSING=@{LISTWISE, PAIRWISE@} [@{EXCLUDE, INCLUDE@}]
[@{NOREPORT,REPORT@}]
@end display
-The @cmd{EXAMINE} command is used to test how closely a distribution is to a
-normal distribution. It also shows you outliers and extreme values.
+The @cmd{EXAMINE} command is used to perform exploratory data analysis.
+In particular, it is useful for testing how closely a distribution follows a
+normal distribution, and for finding outliers and extreme values.
-The VARIABLES subcommand specifies the dependent variables and the
-independent variable to use as factors for the analysis. Variables
-listed before the first BY keyword are the dependent variables.
+The VARIABLES subcommand is mandatory.
+It specifies the dependent variables and optionally variables to use as
+factors for the analysis.
+Variables listed before the first 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. The format for each factor is
+dependent variable.
+
+Following the dependent variables, factors may be specified.
+The factors (if desired) should be preceeded by a single BY keyword.
+The format for each factor is
@display
-var [BY var].
+@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
+and for the entire dataset (unless NOTOTAL is given).
-
-The STATISTICS subcommand specifies the analysis to be done.
+The STATISTICS subcommand specifies which statistics to show.
DESCRIPTIVES will produce a table showing some parametric and
-non-parametrics statistics. EXTREME produces a table showing extreme
-values of the dependent variable. A number in parentheses determines
-how many upper and lower extremes to show. The default number is 5.
-
-
+non-parametrics statistics.
+EXTREME produces a table showing the extremities of each cell.
+A number in parentheses, @var{n} determines
+how many upper and lower extremities to show.
+The default number is 5.
+
+The subcommands TOTAL and NOTOTAL are mutually exclusive.
+If TOTAL appears, then statistics will be produced for the entire dataset
+as well as for each cell.
+If NOTOTAL appears, then statistics will be 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.
+
+@cindex boxplot
+@cindex histogram
+@cindex npplot
The PLOT subcommand specifies which plots are to be produced if any.
+Available plots are HISTOGRAM, NPPLOT and BOXPLOT.
+They can all be used to visualise how closely each cell conforms to a
+normal distribution.
+Boxplots will also show you the outliers and extreme values.
The 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
+useful there is more than one dependent variable and at least one factor.
+If
/COMPARE=GROUPS is specified, then one plot per dependent variable is produced,
-containing boxplots for all the factors.
-If /COMPARE=VARIABLES is specified, then one plot per factor is produced, each
+each of which contain boxplots for all the cells.
+If /COMPARE=VARIABLES is specified, then one plot per cell is produced,
each containing one boxplot per dependent variable.
-If the /COMPARE subcommand is ommitted, then PSPP uses the default value of
-/COMPARE=GROUPS.
+If the /COMPARE subcommand is omitted, then PSPP behaves as if
+/COMPARE=GROUPS were given.
-The ID subcommand also pertains to boxplots. If given, it must
-specify a variable name. Outliers and extreme cases plotted in
-boxplots will be labelled with the case from that variable. Numeric or
-string variables are permissible. If the ID subcommand is not given,
-then the casenumber will be used for labelling.
+The ID subcommand is relevant only if /PLOT=BOXPLOT or
+/STATISTICS=EXTREME has been given.
+If given, it shoule 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 ID subcommand is not given, then the casenumber will be used for
+labelling.
The CINTERVAL subcommand specifies the confidence interval to use in
-calculation of the descriptives command. The default it 95%.
+calculation of the descriptives command. The default is 95%.
@cindex percentiles
The PERCENTILES subcommand specifies which percentiles are to be calculated,
produced in addition to the factored variables. If there are no
factors specified then TOTAL and NOTOTAL have no effect.
+
+The following example will generate 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
+distinct value
+of @var{gender} @emph{and} for each distinct combination of the values
+of @var{gender} and @var{race}.
+Since the 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
+ @var{gender}
+ @var{gender} BY @var{culture}
+ /STATISTICS = DESCRIPTIVES
+ /PLOT = HISTOGRAM
+ /NOTOTAL.
+@end example
+
+Here is a second example showing how the @cmd{examine} command can be used to find extremities.
+@example
+EXAMINE @var{height} @var{weight} BY
+ @var{gender}
+ /STATISTICS = EXTREME (3)
+ /PLOT = BOXPLOT
+ /COMPARE = GROUPS
+ /ID = @var{name}.
+@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 /COMPARE = GROUPS was given, boxplots for male and female will be
+shown in the same graphic, allowing us to easily see the difference between
+the genders.
+Since the variable @var{name} was specified on the ID subcommand, this will be
+used to label the extreme values.
+
@strong{Warning!}
-If many dependent variable are given, or factors are given for which
+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
large quantity of output.
/MISSING=@{TABLE,INCLUDE,REPORT@}
/WRITE=@{NONE,CELLS,ALL@}
/FORMAT=@{TABLES,NOTABLES@}
- @{LABELS,NOLABELS,NOVALLABS@}
@{PIVOT,NOPIVOT@}
@{AVALUE,DVALUE@}
@{NOINDEX,INDEX@}
TABLES, the default, causes crosstabulation tables to be output.
NOTABLES suppresses them.
-@item
-LABELS, the default, allows variable labels and value labels to appear
-in the output. NOLABELS suppresses them. NOVALLABS displays variable
-labels but suppresses value labels.
-
@item
PIVOT, the default, causes each TABLES subcommand to be displayed in a
pivot table format. NOPIVOT causes the old-style crosstabulation format
[ /ROTATION=@{VARIMAX, EQUAMAX, QUARTIMAX, NOROTATE@}]
- [ /PRINT=[INITIAL] [EXTRACTION] [ROTATION] [UNIVARIATE] [CORRELATION] [COVARIANCE] [DET] [SIG] [ALL] [DEFAULT] ]
+ [ /PRINT=[INITIAL] [EXTRACTION] [ROTATION] [UNIVARIATE] [CORRELATION] [COVARIANCE] [DET] [KMO] [SIG] [ALL] [DEFAULT] ]
[ /PLOT=[EIGEN] ]
The covariance matrix is printed.
@item DET
The determinant of the correlation or covariance matrix is printed.
+@item KMO
+ The Kaiser-Meyer-Olkin measure of sampling adequacy and the Bartlett test of sphericity is printed.
@item SIG
The significance of the elements of correlation matrix is printed.
@item ALL
Identical to INITIAL and EXTRACTION.
@end itemize
-If /PLOT=EIGEN is given, then a ``Scree'' plot of the eigenvalues will be printed. This can be useful for visualising
+If /PLOT=EIGEN is given, then a ``Scree'' plot of the eigenvalues will be printed. This can be useful for visualizing
which factors (components) should be retained.
The /FORMAT subcommand determined how data are to be displayed in loading matrices. If SORT is specified, then the variables
If PAIRWISE is set, then a case is considered missing only if either of the
values for the particular coefficient are missing.
The default is LISTWISE.
-
+
+@node MEANS
+@section MEANS
+
+@vindex MEANS
+@cindex means
+
+@display
+MEANS [TABLES =]
+ @{varlist@}
+ [ BY @{varlist@} [BY @{varlist@} [BY @{varlist@} @dots{} ]]]
+
+ [ /@{varlist@}
+ [ BY @{varlist@} [BY @{varlist@} [BY @{varlist@} @dots{} ]]] ]
+
+ [/CELLS = [MEAN] [COUNT] [STDDEV] [SEMEAN] [SUM] [MIN] [MAX] [RANGE]
+ [VARIANCE] [KURT] [SEKURT]
+ [SKEW] [SESKEW] [FIRST] [LAST]
+ [HARMONIC] [GEOMETRIC]
+ [DEFAULT]
+ [ALL]
+ [NONE] ]
+
+ [/MISSING = [TABLE] [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.
+
+The simplest form of the command is
+@example
+MEANS @var{v}.
+@end example
+@noindent which calculates the mean, count and standard deviation for @var{v}.
+If you specify a grouping variable, for example
+@example
+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.
+Instead of the mean, count and standard deviation, you could specify the statistics
+in which you are interested:
+@example
+MEANS @var{x} @var{y} BY @var{g}
+ /CELLS = HARMONIC SUM MIN.
+@end example
+This example calculates the harmonic mean, the sum and the minimum values of @var{x} and @var{y}
+grouped by @var{g}.
+
+The CELLS subcommand specifies which statistics to calculate. The available statistics
+are:
+@itemize
+@item MEAN
+@cindex arithmetic mean
+ The arithmetic mean.
+@item COUNT
+ The count of the values.
+@item STDDEV
+ The standard deviation.
+@item SEMEAN
+ The standard error of the mean.
+@item SUM
+ The sum of the values.
+@item MIN
+ The minimum value.
+@item MAX
+ The maximum value.
+@item RANGE
+ The difference between the maximum and minimum values.
+@item VARIANCE
+ The variance.
+@item FIRST
+ The first value in the category.
+@item LAST
+ The last value in the category.
+@item SKEW
+ The skewness.
+@item SESKEW
+ The standard error of the skewness.
+@item KURT
+ The kurtosis
+@item SEKURT
+ The standard error of the kurtosis.
+@item HARMONIC
+@cindex harmonic mean
+ The harmonic mean.
+@item GEOMETRIC
+@cindex geometric mean
+ The geometric mean.
+@end itemize
+
+In addition, three special keywords are recognized:
+@itemize
+@item DEFAULT
+ This is the same as MEAN COUNT STDDEV
+@item ALL
+ All of the above statistics will be calculated.
+@item NONE
+ No statistics will be calculated (only a summary will be shown).
+@end itemize
+
+
+More than one @dfn{table} can be specified in a single command.
+Each table is separated by a @samp{/}. For
+example
+@example
+MEANS TABLES =
+ @var{c} @var{d} @var{e} BY @var{x}
+ /@var{a} @var{b} BY @var{x} @var{y}
+ /@var{f} BY @var{y} BY @var{z}.
+@end 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},
+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
+encountered.
+This behaviour can be modified with the /MISSING subcommand.
+Three options are possible: TABLE, INCLUDE and DEPENDENT.
+
+/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.
+
+/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.
+
+/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
+from the calculation.
@node NPAR TESTS
@section NPAR TESTS
@menu
* BINOMIAL:: Binomial Test
* CHISQUARE:: Chisquare Test
-* WILCOXON:: Wilcoxon Signed Ranks Test
+* COCHRAN:: Cochran Q Test
+* FRIEDMAN:: Friedman Test
+* KENDALL:: Kendall's W Test
+* KOLMOGOROV-SMIRNOV:: Kolmogorov Smirnov Test
+* KRUSKAL-WALLIS:: Kruskal-Wallis Test
+* MANN-WHITNEY:: Mann Whitney U Test
+* MCNEMAR:: McNemar Test
+* MEDIAN:: Median Test
+* RUNS:: Runs Test
* SIGN:: The Sign Test
+* WILCOXON:: Wilcoxon Signed Ranks Test
@end menu
If no /EXPECTED subcommand is given, then then equal frequencies
are expected.
-@node WILCOXON
-@subsection Wilcoxon Matched Pairs Signed Ranks Test
-@comment node-name, next, previous, up
-@vindex WILCOXON
-@cindex wilcoxon matched pairs signed ranks test
+
+@node COCHRAN
+@subsection Cochran Q Test
+@vindex Cochran
+@cindex Cochran Q test
+@cindex Q, Cochran Q
@display
- [ /WILCOXON varlist [ WITH varlist [ (PAIRED) ]]]
+ [ /COCHRAN = varlist ]
@end display
-The /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 symetrical.
+The Cochran Q test is used to test for differences between three or more groups.
+The data for @var{varlist} 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.
+
+@node FRIEDMAN
+@subsection Friedman Test
+@vindex FRIEDMAN
+@cindex Friedman test
+
+@display
+ [ /FRIEDMAN = varlist ]
+@end display
+
+The Friedman test is used to test for differences between repeated measures when there is no indication that the distributions are normally distributed.
+
+A list of variables which contain the measured data must be given. The procedure prints the sum of ranks for each variable, the test statistic and its significance.
+
+@node KENDALL
+@subsection Kendall's W Test
+@vindex KENDALL
+@cindex Kendall's W test
+@cindex coefficient of concordance
+
+@display
+ [ /KENDALL = varlist ]
+@end display
+
+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
+unity indicates complete agreement.
+
+
+@node KOLMOGOROV-SMIRNOV
+@subsection Kolmogorov-Smirnov Test
+@vindex KOLMOGOROV-SMIRNOV
+@vindex K-S
+@cindex Kolmogorov-Smirnov test
+
+@display
+ [ /KOLMOGOROV-SMIRNOV (@{NORMAL [@var{mu}, @var{sigma}], UNIFORM [@var{min}, @var{max}], POISSON [@var{lambda}], EXPONENTIAL [@var{scale}] @}) = varlist ]
+@end display
+
+The one sample Kolmogorov-Smirnov subcommand is used to test whether or not a dataset is
+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.
+@example
+ NPAR TESTS
+ /KOLMOGOROV-SMIRNOV (normal 3.5 2.0) = @var{score} @var{age}.
+@end example
+If the variables need to be tested against different distributions, then a separate
+subcommand must be used. For example the following syntax tests @var{score} against
+a normal distribution with mean of 3.5 and standard deviation of 2.0 whilst @var{age}
+is tested against a normal distribution of mean 40 and standard deviation 1.5.
+@example
+ NPAR TESTS
+ /KOLMOGOROV-SMIRNOV (normal 3.5 2.0) = @var{score}
+ /KOLMOGOROV-SMIRNOV (normal 40 1.5) = @var{age}.
+@end example
+
+The abbreviated subcommand K-S may be used in place of KOLMOGOROV-SMIRNOV.
+
+@node KRUSKAL-WALLIS
+@subsection Kruskal-Wallis Test
+@vindex KRUSKAL-WALLIS
+@vindex K-W
+@cindex Kruskal-Wallis test
+
+@display
+ [ /KRUSKAL-WALLIS = varlist BY var (lower, upper) ]
+@end display
+
+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{varlist}.
+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.
+
+The mean rank of each group as well as the chi-squared value and significance
+of the test will be printed.
+The abbreviated subcommand K-W may be used in place of KRUSKAL-WALLIS.
+
+
+@node MANN-WHITNEY
+@subsection Mann-Whitney U Test
+@vindex MANN-WHITNEY
+@vindex M-W
+@cindex Mann-Whitney U test
+@cindex U, Mann-Whitney U
+
+@display
+ [ /MANN-WHITNEY = varlist BY var (group1, 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{varlist} 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.
+
+The value of the Mann-Whitney U statistic, the Wilcoxon W, and the significance will be printed.
+The abbreviated subcommand M-W may be used in place of MANN-WHITNEY.
+
+@node MCNEMAR
+@subsection McNemar Test
+@vindex MCNEMAR
+@cindex McNemar test
+
+@display
+ [ /MCNEMAR varlist [ WITH varlist [ (PAIRED) ]]]
+@end display
+
+Use McNemar's test to analyse the significance of the difference between
+pairs of correlated proportions.
If the @code{WITH} keyword is omitted, then tests for all
combinations of the listed variables are performed.
of variable preceding @code{WITH} against variable following
@code{WITH} are performed.
+The data in each variable must be dichotomous. If there are more
+than two distinct variables an error will occur and the test will
+not be run.
+
+@node MEDIAN
+@subsection Median Test
+@vindex MEDIAN
+@cindex Median test
+
+@display
+ [ /MEDIAN [(value)] = varlist BY variable (value1, value2) ]
+@end display
+
+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
+/MEDIAN subcommand. If it is not given, the median will be imputed from the
+union of all the samples.
+
+The variables of the samples to be tested should immediately follow the @samp{=} sign. The
+keyword @code{BY} must come next, and then the grouping variable. Two values
+in parentheses should follow. If the first value is greater than the second,
+then a 2 sample test is performed using these two values to determine the groups.
+If however, the first variable is less than the second, then a @i{k} sample test is
+conducted and the group values used are all values encountered which lie in the
+range [@var{value1},@var{value2}].
+
+
+@node RUNS
+@subsection Runs Test
+@vindex RUNS
+@cindex runs test
+
+@display
+ [ /RUNS (@{MEAN, MEDIAN, MODE, value@}) = varlist ]
+@end display
+
+The /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.
+The desired threshold must be specified within parentheses.
+It may either be specified as a number or as one of MEAN, MEDIAN or MODE.
+Following the threshold specification comes the list of variables whose values are to be
+tested.
+
+The subcommand shows the number of runs, the asymptotic significance based on the
+length of the data.
@node SIGN
@subsection Sign Test
of variable preceding @code{WITH} against variable following
@code{WITH} are performed.
+@node WILCOXON
+@subsection Wilcoxon Matched Pairs Signed Ranks Test
+@comment node-name, next, previous, up
+@vindex WILCOXON
+@cindex wilcoxon matched pairs signed ranks test
+
+@display
+ [ /WILCOXON varlist [ WITH varlist [ (PAIRED) ]]]
+@end display
+
+The /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 symetrical.
+
+If the @code{WITH} keyword is omitted, then tests for all
+combinations of the listed variables are performed.
+If the @code{WITH} keyword is given, and the @code{(PAIRED)} keyword
+is also given, then the number of variables preceding @code{WITH}
+must be the same as the number following it.
+In this case, tests for each respective pair of variables are
+performed.
+If the @code{WITH} keyword is given, but the
+@code{(PAIRED)} keyword is omitted, then tests for each combination
+of variable preceding @code{WITH} against variable following
+@code{WITH} are performed.
+
@node T-TEST
@comment node-name, next, previous, up
@section T-TEST
@menu
-* One Sample Mode:: Testing against a hypothesised mean
+* One Sample Mode:: Testing against a hypothesized mean
* Independent Samples Mode:: Testing two independent groups for equal mean
* Paired Samples Mode:: Testing two interdependent groups for equal mean
@end menu
@subsection One Sample Mode
The @cmd{TESTVAL} subcommand invokes the One Sample mode.
-This mode is used to test a population mean against a hypothesised
+This mode is used to test a population mean against a hypothesized
mean.
The value given to the @cmd{TESTVAL} subcommand is the value against
which you wish to test.
/MISSING=@{ANALYSIS,LISTWISE@} @{EXCLUDE,INCLUDE@}
/CONTRAST= value1 [, value2] ... [,valueN]
/STATISTICS=@{DESCRIPTIVES,HOMOGENEITY@}
-
+ /POSTHOC=@{BONFERRONI, GH, LSD, SCHEFFE, SIDAK, TUKEY, ALPHA ([value])@}
@end display
The @cmd{ONEWAY} procedure performs a one-way analysis of variance of
It is used to compare the means of a population
divided into more than two groups.
-The variables to be analysed should be given in the @code{VARIABLES}
+The dependent variables to be analysed should be given in the @code{VARIABLES}
subcommand.
The list of variables must be followed by the @code{BY} keyword and
the name of the independent (or factor) variable.
display a warning, but will proceed with the analysis.
The @code{CONTRAST} subcommand may be given up to 10 times in order
to specify different contrast tests.
+The @code{MISSING} subcommand defines how missing values are handled.
+If LISTWISE is specified then cases which have missing values for
+the independent variable or any dependent variable will be ignored.
+If ANALYSIS is specified, then cases will be ignored if the independent
+variable is missing or if the dependent variable currently being
+analysed is missing. The default is ANALYSIS.
+A setting of EXCLUDE means that variables whose values are
+user-missing are to be excluded from the analysis. A setting of
+INCLUDE means they are to be included. The default is EXCLUDE.
+
+Using the @code{POSTHOC} subcommand you can perform multiple
+pairwise comparisons on the data. The following comparison methods
+are available:
+@itemize
+@item LSD
+Least Significant Difference.
+@item TUKEY
+Tukey Honestly Significant Difference.
+@item BONFERRONI
+Bonferroni test.
+@item SCHEFFE
+Scheff@'e's test.
+@item SIDAK
+Sidak test.
+@item GH
+The Games-Howell test.
+@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.
+
+@node QUICK CLUSTER
+@comment node-name, next, previous, up
+@section QUICK CLUSTER
+@vindex QUICK CLUSTER
+
+@cindex K-means clustering
+@cindex clustering
+
+@display
+QUICK CLUSTER var_list
+ [/CRITERIA=CLUSTERS(@var{k}) [MXITER(@var{max_iter})]]
+ [/MISSING=@{EXCLUDE,INCLUDE@} @{LISTWISE, PAIRWISE@}]
+@end display
+
+The @cmd{QUICK CLUSTER} command performs k-means clustering on the
+dataset. This is useful when you wish to allocate cases into clusters
+of similar values and you already know the number of clusters.
+
+The minimum specification is @samp{QUICK CLUSTER} followed by the names
+of the variables which contain the cluster data. Normally you will also
+want to specify @samp{/CRITERIA=CLUSTERS(@var{k})} where @var{k} is the
+number of clusters. If this is not given, then @var{k} defaults to 2.
+
+The command uses an iterative algorithm to determine the clusters for
+each case. It will continue iterating until convergence, or until @var{max_iter}
+iterations have been done. The default value of @var{max_iter} is 2.
+
+The @cmd{MISSING} subcommand determines the handling of missing variables.
+If INCLUDE is set, then user-missing values are considered at their face
+value and not as missing values.
+If EXCLUDE is set, which is the default, user-missing
+values are excluded as well as system-missing values.
+
+If LISTWISE is set, then the entire case is excluded from the analysis
+whenever any of the clustering variables contains a missing value.
+If 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.
+The default is LISTWISE.
+
@node RANK
@comment node-name, next, previous, up
@section RANK
+
@vindex RANK
@display
RANK
@end display
@cindex Cronbach's Alpha
-The @cmd{RELIABILTY} command performs reliablity analysis on the data.
+The @cmd{RELIABILTY} command performs reliability analysis on the data.
The VARIABLES subcommand is required. It determines the set of variables
upon which analysis is to be performed.
@section ROC
@vindex ROC
-@cindex Receiver Operating Characterstic
+@cindex Receiver Operating Characteristic
@cindex Area under curve
@display
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
excluded.
-
-
projects / pspp / blobdiff