* 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.
containing boxplots for all the factors.
If /COMPARE=VARIABLES is specified, then one plot per factor is produced, each
each containing one boxplot per dependent variable.
-If the /COMPARE subcommand is ommitted, then PSPP uses the default value of
+If the /COMPARE subcommand is omitted, then PSPP uses the default value of
/COMPARE=GROUPS.
The ID subcommand also pertains to boxplots. If given, it must
/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 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-builds.git / blobdiff