1 @c PSPP - a program for statistical analysis.
2 @c Copyright (C) 2017, 2020 Free Software Foundation, Inc.
3 @c Permission is granted to copy, distribute and/or modify this document
4 @c under the terms of the GNU Free Documentation License, Version 1.3
5 @c or any later version published by the Free Software Foundation;
6 @c with no Invariant Sections, no Front-Cover Texts, and no Back-Cover Texts.
7 @c A copy of the license is included in the section entitled "GNU
8 @c Free Documentation License".
13 This chapter documents the statistical procedures that @pspp{} supports so
17 * DESCRIPTIVES:: Descriptive statistics.
18 * FREQUENCIES:: Frequency tables.
19 * EXAMINE:: Testing data for normality.
21 * CORRELATIONS:: Correlation tables.
22 * CROSSTABS:: Crosstabulation tables.
23 * CTABLES:: Custom tables.
24 * FACTOR:: Factor analysis and Principal Components analysis.
25 * GLM:: Univariate Linear Models.
26 * LOGISTIC REGRESSION:: Bivariate Logistic Regression.
27 * MEANS:: Average values and other statistics.
28 * NPAR TESTS:: Nonparametric tests.
29 * T-TEST:: Test hypotheses about means.
30 * ONEWAY:: One way analysis of variance.
31 * QUICK CLUSTER:: K-Means clustering.
32 * RANK:: Compute rank scores.
33 * RELIABILITY:: Reliability analysis.
34 * ROC:: Receiver Operating Characteristic.
43 /VARIABLES=@var{var_list}
44 /MISSING=@{VARIABLE,LISTWISE@} @{INCLUDE,NOINCLUDE@}
45 /FORMAT=@{LABELS,NOLABELS@} @{NOINDEX,INDEX@} @{LINE,SERIAL@}
47 /STATISTICS=@{ALL,MEAN,SEMEAN,STDDEV,VARIANCE,KURTOSIS,
48 SKEWNESS,RANGE,MINIMUM,MAXIMUM,SUM,DEFAULT,
49 SESKEWNESS,SEKURTOSIS@}
50 /SORT=@{NONE,MEAN,SEMEAN,STDDEV,VARIANCE,KURTOSIS,SKEWNESS,
51 RANGE,MINIMUM,MAXIMUM,SUM,SESKEWNESS,SEKURTOSIS,NAME@}
55 The @cmd{DESCRIPTIVES} procedure reads the active dataset and outputs
56 linear descriptive statistics requested by the user. In addition, it can optionally
59 The @subcmd{VARIABLES} subcommand, which is required, specifies the list of
60 variables to be analyzed. Keyword @subcmd{VARIABLES} is optional.
62 All other subcommands are optional:
64 The @subcmd{MISSING} subcommand determines the handling of missing variables. If
65 @subcmd{INCLUDE} is set, then user-missing values are included in the
66 calculations. If @subcmd{NOINCLUDE} is set, which is the default, user-missing
67 values are excluded. If @subcmd{VARIABLE} is set, then missing values are
68 excluded on a variable by variable basis; if @subcmd{LISTWISE} is set, then
69 the entire case is excluded whenever any value in that case has a
70 system-missing or, if @subcmd{INCLUDE} is set, user-missing value.
72 The @subcmd{FORMAT} subcommand has no effect. It is accepted for
73 backward compatibility.
75 The @subcmd{SAVE} subcommand causes @cmd{DESCRIPTIVES} to calculate Z scores for all
76 the specified variables. The Z scores are saved to new variables.
77 Variable names are generated by trying first the original variable name
78 with Z prepended and truncated to a maximum of 8 characters, then the
79 names ZSC000 through ZSC999, STDZ00 through STDZ09, ZZZZ00 through
80 ZZZZ09, ZQZQ00 through ZQZQ09, in that sequence. In addition, Z score
81 variable names can be specified explicitly on @subcmd{VARIABLES} in the variable
82 list by enclosing them in parentheses after each variable.
83 When Z scores are calculated, @pspp{} ignores @cmd{TEMPORARY},
84 treating temporary transformations as permanent.
86 The @subcmd{STATISTICS} subcommand specifies the statistics to be displayed:
90 All of the statistics below.
94 Standard error of the mean.
97 @item @subcmd{VARIANCE}
99 @item @subcmd{KURTOSIS}
100 Kurtosis and standard error of the kurtosis.
101 @item @subcmd{SKEWNESS}
102 Skewness and standard error of the skewness.
112 Mean, standard deviation of the mean, minimum, maximum.
114 Standard error of the kurtosis.
116 Standard error of the skewness.
119 The @subcmd{SORT} subcommand specifies how the statistics should be sorted. Most
120 of the possible values should be self-explanatory. @subcmd{NAME} causes the
121 statistics to be sorted by name. By default, the statistics are listed
122 in the order that they are specified on the @subcmd{VARIABLES} subcommand.
123 The @subcmd{A} and @subcmd{D} settings request an ascending or descending
124 sort order, respectively.
126 @subsection Descriptives Example
128 The @file{physiology.sav} file contains various physiological data for a sample
129 of persons. Running the @cmd{DESCRIPTIVES} command on the variables @exvar{height}
130 and @exvar{temperature} with the default options allows one to see simple linear
131 statistics for these two variables. In @ref{descriptives:ex}, these variables
132 are specfied on the @subcmd{VARIABLES} subcommand and the @subcmd{SAVE} option
133 has been used, to request that Z scores be calculated.
135 After the command has completed, this example runs @cmd{DESCRIPTIVES} again, this
136 time on the @exvar{zheight} and @exvar{ztemperature} variables,
137 which are the two normalized (Z-score) variables generated by the
138 first @cmd{DESCRIPTIVES} command.
140 @float Example, descriptives:ex
141 @psppsyntax {descriptives.sps}
142 @caption {Running two @cmd{DESCRIPTIVES} commands, one with the @subcmd{SAVE} subcommand}
145 @float Screenshot, descriptives:scr
146 @psppimage {descriptives}
147 @caption {The Descriptives dialog box with two variables and Z-Scores option selected}
150 In @ref{descriptives:res}, we can see that there are 40 valid data for each of the variables
151 and no missing values. The mean average of the height and temperature is 16677.12
152 and 37.02 respectively. The descriptive statistics for temperature seem reasonable.
153 However there is a very high standard deviation for @exvar{height} and a suspiciously
154 low minimum. This is due to a data entry error in the
155 data (@pxref{Identifying incorrect data}).
157 In the second Descriptive Statistics command, one can see that the mean and standard
158 deviation of both Z score variables is 0 and 1 respectively. All Z score statistics
159 should have these properties since they are normalized versions of the original scores.
161 @float Result, descriptives:res
162 @psppoutput {descriptives}
163 @caption {Descriptives statistics including two normalized variables (Z-scores)}
172 /VARIABLES=@var{var_list}
173 /FORMAT=@{TABLE,NOTABLE,LIMIT(@var{limit})@}
174 @{AVALUE,DVALUE,AFREQ,DFREQ@}
175 /MISSING=@{EXCLUDE,INCLUDE@}
176 /STATISTICS=@{DEFAULT,MEAN,SEMEAN,MEDIAN,MODE,STDDEV,VARIANCE,
177 KURTOSIS,SKEWNESS,RANGE,MINIMUM,MAXIMUM,SUM,
178 SESKEWNESS,SEKURTOSIS,ALL,NONE@}
180 /PERCENTILES=percent@dots{}
181 /HISTOGRAM=[MINIMUM(@var{x_min})] [MAXIMUM(@var{x_max})]
182 [@{FREQ[(@var{y_max})],PERCENT[(@var{y_max})]@}] [@{NONORMAL,NORMAL@}]
183 /PIECHART=[MINIMUM(@var{x_min})] [MAXIMUM(@var{x_max})]
184 [@{FREQ,PERCENT@}] [@{NOMISSING,MISSING@}]
185 /BARCHART=[MINIMUM(@var{x_min})] [MAXIMUM(@var{x_max})]
187 /ORDER=@{ANALYSIS,VARIABLE@}
190 (These options are not currently implemented.)
195 The @cmd{FREQUENCIES} procedure outputs frequency tables for specified
197 @cmd{FREQUENCIES} can also calculate and display descriptive statistics
198 (including median and mode) and percentiles, and various graphical representations
199 of the frequency distribution.
201 The @subcmd{VARIABLES} subcommand is the only required subcommand. Specify the
202 variables to be analyzed.
204 The @subcmd{FORMAT} subcommand controls the output format. It has several
209 @subcmd{TABLE}, the default, causes a frequency table to be output for every
210 variable specified. @subcmd{NOTABLE} prevents them from being output. @subcmd{LIMIT}
211 with a numeric argument causes them to be output except when there are
212 more than the specified number of values in the table.
215 Normally frequency tables are sorted in ascending order by value. This
216 is @subcmd{AVALUE}. @subcmd{DVALUE} tables are sorted in descending order by value.
217 @subcmd{AFREQ} and @subcmd{DFREQ} tables are sorted in ascending and descending order,
218 respectively, by frequency count.
221 The @subcmd{MISSING} subcommand controls the handling of user-missing values.
222 When @subcmd{EXCLUDE}, the default, is set, user-missing values are not included
223 in frequency tables or statistics. When @subcmd{INCLUDE} is set, user-missing
224 are included. System-missing values are never included in statistics,
225 but are listed in frequency tables.
227 The available @subcmd{STATISTICS} are the same as available
228 in @cmd{DESCRIPTIVES} (@pxref{DESCRIPTIVES}), with the addition
229 of @subcmd{MEDIAN}, the data's median
230 value, and MODE, the mode. (If there are multiple modes, the smallest
231 value is reported.) By default, the mean, standard deviation of the
232 mean, minimum, and maximum are reported for each variable.
235 @subcmd{PERCENTILES} causes the specified percentiles to be reported.
236 The percentiles should be presented at a list of numbers between 0
238 The @subcmd{NTILES} subcommand causes the percentiles to be reported at the
239 boundaries of the data set divided into the specified number of ranges.
240 For instance, @subcmd{/NTILES=4} would cause quartiles to be reported.
243 The @subcmd{HISTOGRAM} subcommand causes the output to include a histogram for
244 each specified numeric variable. The X axis by default ranges from
245 the minimum to the maximum value observed in the data, but the @subcmd{MINIMUM}
246 and @subcmd{MAXIMUM} keywords can set an explicit range.
247 @footnote{The number of
248 bins is chosen according to the Freedman-Diaconis rule:
249 @math{2 \times IQR(x)n^{-1/3}}, where @math{IQR(x)} is the interquartile range of @math{x}
250 and @math{n} is the number of samples. Note that
251 @cmd{EXAMINE} uses a different algorithm to determine bin sizes.}
252 Histograms are not created for string variables.
254 Specify @subcmd{NORMAL} to superimpose a normal curve on the
258 The @subcmd{PIECHART} subcommand adds a pie chart for each variable to the data. Each
259 slice represents one value, with the size of the slice proportional to
260 the value's frequency. By default, all non-missing values are given
262 The @subcmd{MINIMUM} and @subcmd{MAXIMUM} keywords can be used to limit the
263 displayed slices to a given range of values.
264 The keyword @subcmd{NOMISSING} causes missing values to be omitted from the
265 piechart. This is the default.
266 If instead, @subcmd{MISSING} is specified, then the pie chart includes
267 a single slice representing all system missing and user-missing cases.
270 The @subcmd{BARCHART} subcommand produces a bar chart for each variable.
271 The @subcmd{MINIMUM} and @subcmd{MAXIMUM} keywords can be used to omit
272 categories whose counts which lie outside the specified limits.
273 The @subcmd{FREQ} option (default) causes the ordinate to display the frequency
274 of each category, whereas the @subcmd{PERCENT} option displays relative
277 The @subcmd{FREQ} and @subcmd{PERCENT} options on @subcmd{HISTOGRAM} and
278 @subcmd{PIECHART} are accepted but not currently honoured.
280 The @subcmd{ORDER} subcommand is accepted but ignored.
282 @subsection Frequencies Example
284 @ref{frequencies:ex} runs a frequency analysis on the @exvar{sex}
285 and @exvar{occupation} variables from the @file{personnel.sav} file.
286 This is useful to get an general idea of the way in which these nominal
287 variables are distributed.
289 @float Example, frequencies:ex
290 @psppsyntax {frequencies.sps}
291 @caption {Running frequencies on the @exvar{sex} and @exvar{occupation} variables}
294 If you are using the graphic user interface, the dialog box is set up such that
295 by default, several statistics are calculated. Some are not particularly useful
296 for categorical variables, so you may want to disable those.
298 @float Screenshot, frequencies:scr
299 @psppimage {frequencies}
300 @caption {The frequencies dialog box with the @exvar{sex} and @exvar{occupation} variables selected}
303 From @ref{frequencies:res} it is evident that there are 33 males, 21 females and
304 2 persons for whom their sex has not been entered.
306 One can also see how many of each occupation there are in the data.
307 When dealing with string variables used as nominal values, running a frequency
308 analysis is useful to detect data input entries. Notice that
309 one @exvar{occupation} value has been mistyped as ``Scrientist''. This entry should
310 be corrected, or marked as missing before using the data.
312 @float Result, frequencies:res
313 @psppoutput {frequencies}
314 @caption {The relative frequencies of @exvar{sex} and @exvar{occupation}}
321 @cindex Exploratory data analysis
322 @cindex normality, testing
326 VARIABLES= @var{var1} [@var{var2}] @dots{} [@var{varN}]
327 [BY @var{factor1} [BY @var{subfactor1}]
328 [ @var{factor2} [BY @var{subfactor2}]]
330 [ @var{factor3} [BY @var{subfactor3}]]
332 /STATISTICS=@{DESCRIPTIVES, EXTREME[(@var{n})], ALL, NONE@}
333 /PLOT=@{BOXPLOT, NPPLOT, HISTOGRAM, SPREADLEVEL[(@var{t})], ALL, NONE@}
335 /COMPARE=@{GROUPS,VARIABLES@}
336 /ID=@var{identity_variable}
338 /PERCENTILE=[@var{percentiles}]=@{HAVERAGE, WAVERAGE, ROUND, AEMPIRICAL, EMPIRICAL @}
339 /MISSING=@{LISTWISE, PAIRWISE@} [@{EXCLUDE, INCLUDE@}]
340 [@{NOREPORT,REPORT@}]
344 The @cmd{EXAMINE} command is used to perform exploratory data analysis.
345 In particular, it is useful for testing how closely a distribution follows a
346 normal distribution, and for finding outliers and extreme values.
348 The @subcmd{VARIABLES} subcommand is mandatory.
349 It specifies the dependent variables and optionally variables to use as
350 factors for the analysis.
351 Variables listed before the first @subcmd{BY} keyword (if any) are the
353 The dependent variables may optionally be followed by a list of
354 factors which tell @pspp{} how to break down the analysis for each
357 Following the dependent variables, factors may be specified.
358 The factors (if desired) should be preceded by a single @subcmd{BY} keyword.
359 The format for each factor is
361 @var{factorvar} [BY @var{subfactorvar}].
363 Each unique combination of the values of @var{factorvar} and
364 @var{subfactorvar} divide the dataset into @dfn{cells}.
365 Statistics are calculated for each cell
366 and for the entire dataset (unless @subcmd{NOTOTAL} is given).
368 The @subcmd{STATISTICS} subcommand specifies which statistics to show.
369 @subcmd{DESCRIPTIVES} produces a table showing some parametric and
370 non-parametrics statistics.
371 @subcmd{EXTREME} produces a table showing the extremities of each cell.
372 A number in parentheses, @var{n} determines
373 how many upper and lower extremities to show.
374 The default number is 5.
376 The subcommands @subcmd{TOTAL} and @subcmd{NOTOTAL} are mutually exclusive.
377 If @subcmd{TOTAL} appears, then statistics for the entire dataset
378 as well as for each cell are produced.
379 If @subcmd{NOTOTAL} appears, then statistics are produced only for the cells
380 (unless no factor variables have been given).
381 These subcommands have no effect if there have been no factor variables
387 @cindex spreadlevel plot
388 The @subcmd{PLOT} subcommand specifies which plots are to be produced if any.
389 Available plots are @subcmd{HISTOGRAM}, @subcmd{NPPLOT}, @subcmd{BOXPLOT} and
390 @subcmd{SPREADLEVEL}.
391 The first three can be used to visualise how closely each cell conforms to a
392 normal distribution, whilst the spread vs.@: level plot can be useful to visualise
393 how the variance differs between factors.
394 Boxplots show you the outliers and extreme values.
395 @footnote{@subcmd{HISTOGRAM} uses Sturges' rule to determine the number of
396 bins, as approximately @math{1 + \log2(n)}, where @math{n} is the number of samples.
397 Note that @cmd{FREQUENCIES} uses a different algorithm to find the bin size.}
399 The @subcmd{SPREADLEVEL} plot displays the interquartile range versus the
400 median. It takes an optional parameter @var{t}, which specifies how the data
401 should be transformed prior to plotting.
402 The given value @var{t} is a power to which the data are raised. For example, if
403 @var{t} is given as 2, then the square of the data is used.
404 Zero, however is a special value. If @var{t} is 0 or
405 is omitted, then data are transformed by taking its natural logarithm instead of
406 raising to the power of @var{t}.
409 When one or more plots are requested, @subcmd{EXAMINE} also performs the
410 Shapiro-Wilk test for each category.
411 There are however a number of provisos:
413 @item All weight values must be integer.
414 @item The cumulative weight value must be in the range [3, 5000]
417 The @subcmd{COMPARE} subcommand is only relevant if producing boxplots, and it is only
418 useful there is more than one dependent variable and at least one factor.
420 @subcmd{/COMPARE=GROUPS} is specified, then one plot per dependent variable is produced,
421 each of which contain boxplots for all the cells.
422 If @subcmd{/COMPARE=VARIABLES} is specified, then one plot per cell is produced,
423 each containing one boxplot per dependent variable.
424 If the @subcmd{/COMPARE} subcommand is omitted, then @pspp{} behaves as if
425 @subcmd{/COMPARE=GROUPS} were given.
427 The @subcmd{ID} subcommand is relevant only if @subcmd{/PLOT=BOXPLOT} or
428 @subcmd{/STATISTICS=EXTREME} has been given.
429 If given, it should provide the name of a variable which is to be used
430 to labels extreme values and outliers.
431 Numeric or string variables are permissible.
432 If the @subcmd{ID} subcommand is not given, then the case number is used for
435 The @subcmd{CINTERVAL} subcommand specifies the confidence interval to use in
436 calculation of the descriptives command. The default is 95%.
439 The @subcmd{PERCENTILES} subcommand specifies which percentiles are to be calculated,
440 and which algorithm to use for calculating them. The default is to
441 calculate the 5, 10, 25, 50, 75, 90, 95 percentiles using the
442 @subcmd{HAVERAGE} algorithm.
444 The @subcmd{TOTAL} and @subcmd{NOTOTAL} subcommands are mutually exclusive. If @subcmd{NOTOTAL}
445 is given and factors have been specified in the @subcmd{VARIABLES} subcommand,
446 then statistics for the unfactored dependent variables are
447 produced in addition to the factored variables. If there are no
448 factors specified then @subcmd{TOTAL} and @subcmd{NOTOTAL} have no effect.
451 The following example generates descriptive statistics and histograms for
452 two variables @var{score1} and @var{score2}.
453 Two factors are given, @i{viz}: @var{gender} and @var{gender} BY @var{culture}.
454 Therefore, the descriptives and histograms are generated for each
456 of @var{gender} @emph{and} for each distinct combination of the values
457 of @var{gender} and @var{race}.
458 Since the @subcmd{NOTOTAL} keyword is given, statistics and histograms for
459 @var{score1} and @var{score2} covering the whole dataset are not produced.
461 EXAMINE @var{score1} @var{score2} BY
463 @var{gender} BY @var{culture}
464 /STATISTICS = DESCRIPTIVES
469 Here is a second example showing how the @cmd{examine} command can be used to find extremities.
471 EXAMINE @var{height} @var{weight} BY
473 /STATISTICS = EXTREME (3)
478 In this example, we look at the height and weight of a sample of individuals and
479 how they differ between male and female.
480 A table showing the 3 largest and the 3 smallest values of @exvar{height} and
481 @exvar{weight} for each gender, and for the whole dataset as are shown.
482 In addition, the @subcmd{/PLOT} subcommand requests boxplots.
483 Because @subcmd{/COMPARE = GROUPS} was specified, boxplots for male and female are
484 shown in juxtaposed in the same graphic, allowing us to easily see the difference between
486 Since the variable @var{name} was specified on the @subcmd{ID} subcommand,
487 values of the @var{name} variable are used to label the extreme values.
490 If you specify many dependent variables or factor variables
491 for which there are many distinct values, then @cmd{EXAMINE} will produce a very
492 large quantity of output.
498 @cindex Exploratory data analysis
499 @cindex normality, testing
503 /HISTOGRAM [(NORMAL)]= @var{var}
504 /SCATTERPLOT [(BIVARIATE)] = @var{var1} WITH @var{var2} [BY @var{var3}]
505 /BAR = @{@var{summary-function}(@var{var1}) | @var{count-function}@} BY @var{var2} [BY @var{var3}]
506 [ /MISSING=@{LISTWISE, VARIABLE@} [@{EXCLUDE, INCLUDE@}] ]
507 [@{NOREPORT,REPORT@}]
511 The @cmd{GRAPH} command produces graphical plots of data. Only one of the subcommands
512 @subcmd{HISTOGRAM}, @subcmd{BAR} or @subcmd{SCATTERPLOT} can be specified, @i{i.e.} only one plot
513 can be produced per call of @cmd{GRAPH}. The @subcmd{MISSING} is optional.
516 * SCATTERPLOT:: Cartesian Plots
517 * HISTOGRAM:: Histograms
518 * BAR CHART:: Bar Charts
522 @subsection Scatterplot
525 The subcommand @subcmd{SCATTERPLOT} produces an xy plot of the
527 @cmd{GRAPH} uses the third variable @var{var3}, if specified, to determine
528 the colours and/or markers for the plot.
529 The following is an example for producing a scatterplot.
533 /SCATTERPLOT = @var{height} WITH @var{weight} BY @var{gender}.
536 This example produces a scatterplot where @var{height} is plotted versus @var{weight}. Depending
537 on the value of the @var{gender} variable, the colour of the datapoint is different. With
538 this plot it is possible to analyze gender differences for @var{height} versus @var{weight} relation.
541 @subsection Histogram
544 The subcommand @subcmd{HISTOGRAM} produces a histogram. Only one variable is allowed for
546 The keyword @subcmd{NORMAL} may be specified in parentheses, to indicate that the ideal normal curve
547 should be superimposed over the histogram.
548 For an alternative method to produce histograms @pxref{EXAMINE}. The
549 following example produces a histogram plot for the variable @var{weight}.
553 /HISTOGRAM = @var{weight}.
557 @subsection Bar Chart
560 The subcommand @subcmd{BAR} produces a bar chart.
561 This subcommand requires that a @var{count-function} be specified (with no arguments) or a @var{summary-function} with a variable @var{var1} in parentheses.
562 Following the summary or count function, the keyword @subcmd{BY} should be specified and then a catagorical variable, @var{var2}.
563 The values of the variable @var{var2} determine the labels of the bars to be plotted.
564 Optionally a second categorical variable @var{var3} may be specified in which case a clustered (grouped) bar chart is produced.
566 Valid count functions are
569 The weighted counts of the cases in each category.
571 The weighted counts of the cases in each category expressed as a percentage of the total weights of the cases.
573 The cumulative weighted counts of the cases in each category.
575 The cumulative weighted counts of the cases in each category expressed as a percentage of the total weights of the cases.
578 The summary function is applied to @var{var1} across all cases in each category.
579 The recognised summary functions are:
591 The following examples assume a dataset which is the results of a survey.
592 Each respondent has indicated annual income, their sex and city of residence.
593 One could create a bar chart showing how the mean income varies between of residents of different cities, thus:
595 GRAPH /BAR = MEAN(@var{income}) BY @var{city}.
598 This can be extended to also indicate how income in each city differs between the sexes.
600 GRAPH /BAR = MEAN(@var{income}) BY @var{city} BY @var{sex}.
603 One might also want to see how many respondents there are from each city. This can be achieved as follows:
605 GRAPH /BAR = COUNT BY @var{city}.
608 Bar charts can also be produced using the @ref{FREQUENCIES} and @ref{CROSSTABS} commands.
611 @section CORRELATIONS
616 /VARIABLES = @var{var_list} [ WITH @var{var_list} ]
621 /VARIABLES = @var{var_list} [ WITH @var{var_list} ]
622 /VARIABLES = @var{var_list} [ WITH @var{var_list} ]
625 [ /PRINT=@{TWOTAIL, ONETAIL@} @{SIG, NOSIG@} ]
626 [ /STATISTICS=DESCRIPTIVES XPROD ALL]
627 [ /MISSING=@{PAIRWISE, LISTWISE@} @{INCLUDE, EXCLUDE@} ]
631 The @cmd{CORRELATIONS} procedure produces tables of the Pearson correlation coefficient
632 for a set of variables. The significance of the coefficients are also given.
634 At least one @subcmd{VARIABLES} subcommand is required. If you specify the @subcmd{WITH}
635 keyword, then a non-square correlation table is produced.
636 The variables preceding @subcmd{WITH}, are used as the rows of the table,
637 and the variables following @subcmd{WITH} are used as the columns of the table.
638 If no @subcmd{WITH} subcommand is specified, then @cmd{CORRELATIONS} produces a
639 square, symmetrical table using all variables.
641 The @cmd{MISSING} subcommand determines the handling of missing variables.
642 If @subcmd{INCLUDE} is set, then user-missing values are included in the
643 calculations, but system-missing values are not.
644 If @subcmd{EXCLUDE} is set, which is the default, user-missing
645 values are excluded as well as system-missing values.
647 If @subcmd{LISTWISE} is set, then the entire case is excluded from analysis
648 whenever any variable specified in any @cmd{/VARIABLES} subcommand
649 contains a missing value.
650 If @subcmd{PAIRWISE} is set, then a case is considered missing only if either of the
651 values for the particular coefficient are missing.
652 The default is @subcmd{PAIRWISE}.
654 The @subcmd{PRINT} subcommand is used to control how the reported significance values are printed.
655 If the @subcmd{TWOTAIL} option is used, then a two-tailed test of significance is
656 printed. If the @subcmd{ONETAIL} option is given, then a one-tailed test is used.
657 The default is @subcmd{TWOTAIL}.
659 If the @subcmd{NOSIG} option is specified, then correlation coefficients with significance less than
660 0.05 are highlighted.
661 If @subcmd{SIG} is specified, then no highlighting is performed. This is the default.
664 The @subcmd{STATISTICS} subcommand requests additional statistics to be displayed. The keyword
665 @subcmd{DESCRIPTIVES} requests that the mean, number of non-missing cases, and the non-biased
666 estimator of the standard deviation are displayed.
667 These statistics are displayed in a separated table, for all the variables listed
668 in any @subcmd{/VARIABLES} subcommand.
669 The @subcmd{XPROD} keyword requests cross-product deviations and covariance estimators to
670 be displayed for each pair of variables.
671 The keyword @subcmd{ALL} is the union of @subcmd{DESCRIPTIVES} and @subcmd{XPROD}.
679 /TABLES=@var{var_list} BY @var{var_list} [BY @var{var_list}]@dots{}
680 /MISSING=@{TABLE,INCLUDE,REPORT@}
681 /FORMAT=@{TABLES,NOTABLES@}
683 /CELLS=@{COUNT,ROW,COLUMN,TOTAL,EXPECTED,RESIDUAL,SRESIDUAL,
684 ASRESIDUAL,ALL,NONE@}
685 /COUNT=@{ASIS,CASE,CELL@}
687 /STATISTICS=@{CHISQ,PHI,CC,LAMBDA,UC,BTAU,CTAU,RISK,GAMMA,D,
688 KAPPA,ETA,CORR,ALL,NONE@}
692 /VARIABLES=@var{var_list} (@var{low},@var{high})@dots{}
695 The @cmd{CROSSTABS} procedure displays crosstabulation
696 tables requested by the user. It can calculate several statistics for
697 each cell in the crosstabulation tables. In addition, a number of
698 statistics can be calculated for each table itself.
700 The @subcmd{TABLES} subcommand is used to specify the tables to be reported. Any
701 number of dimensions is permitted, and any number of variables per
702 dimension is allowed. The @subcmd{TABLES} subcommand may be repeated as many
703 times as needed. This is the only required subcommand in @dfn{general
706 Occasionally, one may want to invoke a special mode called @dfn{integer
707 mode}. Normally, in general mode, @pspp{} automatically determines
708 what values occur in the data. In integer mode, the user specifies the
709 range of values that the data assumes. To invoke this mode, specify the
710 @subcmd{VARIABLES} subcommand, giving a range of data values in parentheses for
711 each variable to be used on the @subcmd{TABLES} subcommand. Data values inside
712 the range are truncated to the nearest integer, then assigned to that
713 value. If values occur outside this range, they are discarded. When it
714 is present, the @subcmd{VARIABLES} subcommand must precede the @subcmd{TABLES}
717 In general mode, numeric and string variables may be specified on
718 TABLES. In integer mode, only numeric variables are allowed.
720 The @subcmd{MISSING} subcommand determines the handling of user-missing values.
721 When set to @subcmd{TABLE}, the default, missing values are dropped on a table by
722 table basis. When set to @subcmd{INCLUDE}, user-missing values are included in
723 tables and statistics. When set to @subcmd{REPORT}, which is allowed only in
724 integer mode, user-missing values are included in tables but marked with
725 a footnote and excluded from statistical calculations.
727 The @subcmd{FORMAT} subcommand controls the characteristics of the
728 crosstabulation tables to be displayed. It has a number of possible
733 @subcmd{TABLES}, the default, causes crosstabulation tables to be output.
734 @subcmd{NOTABLES}, which is equivalent to @code{CELLS=NONE}, suppresses them.
737 @subcmd{AVALUE}, the default, causes values to be sorted in ascending order.
738 @subcmd{DVALUE} asserts a descending sort order.
741 The @subcmd{CELLS} subcommand controls the contents of each cell in the displayed
742 crosstabulation table. The possible settings are:
758 Standardized residual.
760 Adjusted standardized residual.
764 Suppress cells entirely.
767 @samp{/CELLS} without any settings specified requests @subcmd{COUNT}, @subcmd{ROW},
768 @subcmd{COLUMN}, and @subcmd{TOTAL}.
769 If @subcmd{CELLS} is not specified at all then only @subcmd{COUNT}
772 By default, crosstabulation and statistics use raw case weights,
773 without rounding. Use the @subcmd{/COUNT} subcommand to perform
774 rounding: CASE rounds the weights of individual weights as cases are
775 read, CELL rounds the weights of cells within each crosstabulation
776 table after it has been constructed, and ASIS explicitly specifies the
777 default non-rounding behavior. When rounding is requested, ROUND, the
778 default, rounds to the nearest integer and TRUNCATE rounds toward
781 The @subcmd{STATISTICS} subcommand selects statistics for computation:
787 Pearson chi-square, likelihood ratio, Fisher's exact test, continuity
788 correction, linear-by-linear association.
792 Contingency coefficient.
796 Uncertainty coefficient.
812 Spearman correlation, Pearson's r.
819 Selected statistics are only calculated when appropriate for the
820 statistic. Certain statistics require tables of a particular size, and
821 some statistics are calculated only in integer mode.
823 @samp{/STATISTICS} without any settings selects CHISQ. If the
824 @subcmd{STATISTICS} subcommand is not given, no statistics are calculated.
827 The @samp{/BARCHART} subcommand produces a clustered bar chart for the first two
828 variables on each table.
829 If a table has more than two variables, the counts for the third and subsequent levels
830 are aggregated and the chart is produced as if there were only two variables.
833 @strong{Please note:} Currently the implementation of @cmd{CROSSTABS} has the
834 following limitations:
838 Significance of some symmetric and directional measures is not calculated.
840 Asymptotic standard error is not calculated for
841 Goodman and Kruskal's tau or symmetric Somers' d.
843 Approximate T is not calculated for symmetric uncertainty coefficient.
846 Fixes for any of these deficiencies would be welcomed.
848 @subsection Crosstabs Example
850 @cindex chi-square test of independence
852 A researcher wishes to know if, in an industry, a person's sex is related to
853 the person's occupation. To investigate this, she has determined that the
854 @file{personnel.sav} is a representative, randomly selected sample of persons.
855 The researcher's null hypothesis is that a person's sex has no relation to a
856 person's occupation. She uses a chi-squared test of independence to investigate
859 @float Example, crosstabs:ex
860 @psppsyntax {crosstabs.sps}
861 @caption {Running crosstabs on the @exvar{sex} and @exvar{occupation} variables}
864 The syntax in @ref{crosstabs:ex} conducts a chi-squared test of independence.
865 The line @code{/tables = occupation by sex} indicates that @exvar{occupation}
866 and @exvar{sex} are the variables to be tabulated. To do this using the @gui{}
867 you must place these variable names respectively in the @samp{Row} and
868 @samp{Column} fields as shown in @ref{crosstabs:scr}.
870 @float Screenshot, crosstabs:scr
871 @psppimage {crosstabs}
872 @caption {The Crosstabs dialog box with the @exvar{sex} and @exvar{occupation} variables selected}
875 Similarly, the @samp{Cells} button shows a dialog box to select the @code{count}
876 and @code{expected} options. All other cell options can be deselected for this
879 You would use the @samp{Format} and @samp{Statistics} buttons to select options
880 for the @subcmd{FORMAT} and @subcmd{STATISTICS} subcommands. In this example,
881 the @samp{Statistics} requires only the @samp{Chisq} option to be checked. All
882 other options should be unchecked. No special settings are required from the
883 @samp{Format} dialog.
885 As shown in @ref{crosstabs:res} @cmd{CROSSTABS} generates a contingency table
886 containing the observed count and the expected count of each sex and each
887 occupation. The expected count is the count which would be observed if the
888 null hypothesis were true.
890 The significance of the Pearson Chi-Square value is very much larger than the
891 normally accepted value of 0.05 and so one cannot reject the null hypothesis.
892 Thus the researcher must conclude that a person's sex has no relation to the
895 @float Results, crosstabs:res
896 @psppoutput {crosstabs}
897 @caption {The results of a test of independence between @exvar{sex} and @exvar{occupation}}
904 @cindex custom tables
905 @cindex tables, custom
907 @code{CTABLES} has the following overall syntax. At least one
908 @code{TABLE} subcommand is required:
912 @dots{}@i{global subcommands}@dots{}
913 [@t{/TABLE} @i{axis} [@t{BY} @i{axis} [@t{BY} @i{axis}]]
914 @dots{}@i{per-table subcommands}@dots{}]@dots{}
918 where each @i{axis} may be empty or take one of the following forms:
922 @i{variable} @t{[}@{@t{C} @math{|} @t{S}@}@t{]}
926 @i{axis} @t{[}@i{summary} [@i{string}] [@i{format}]@t{]}
929 The following subcommands precede the first @code{TABLE} subcommand
930 and apply to all of the output tables. All of these subcommands are
935 [@t{MINCOLWIDTH=}@{@t{DEFAULT} @math{|} @i{width}@}]
936 [@t{MAXCOLWIDTH=}@{@t{DEFAULT} @math{|} @i{width}@}]
937 [@t{UNITS=}@{@t{POINTS} @math{|} @t{INCHES} @math{|} @t{CM}@}]
938 [@t{EMPTY=}@{@t{ZERO} @math{|} @t{BLANK} @math{|} @i{string}@}]
939 [@t{MISSING=}@i{string}]
941 @t{VARIABLES=}@i{variables}
942 @t{DISPLAY}=@{@t{DEFAULT} @math{|} @t{NAME} @math{|} @t{LABEL} @math{|} @t{BOTH} @math{|} @t{NONE}@}
943 @ignore @c not yet implemented
944 @t{/MRSETS COUNTDUPLICATES=}@{@t{YES} @math{|} @t{NO}@}
946 @t{/SMISSING} @{@t{VARIABLE} @math{|} @t{LISTWISE}@}
947 @t{/PCOMPUTE} @t{&}@i{postcompute}@t{=EXPR(}@i{expression}@t{)}
948 @t{/PPROPERTIES} @t{&}@i{postcompute}@dots{}
949 [@t{LABEL=}@i{string}]
950 [@t{FORMAT=}[@i{summary} @i{format}]@dots{}]
951 [@t{HIDESOURCECATS=}@{@t{NO} @math{|} @t{YES}@}
952 @t{/WEIGHT VARIABLE=}@i{variable}
953 @t{/HIDESMALLCOUNTS COUNT=@i{count}}
956 The following subcommands follow @code{TABLE} and apply only to the
957 previous @code{TABLE}. All of these subcommands are optional:
961 [@t{POSITION=}@{@t{COLUMN} @math{|} @t{ROW} @math{|} @t{LAYER}@}]
962 [@t{VISIBLE=}@{@t{YES} @math{|} @t{NO}@}]
963 @t{/CLABELS} @{@t{AUTO} @math{|} @{@t{ROWLABELS}@math{|}@t{COLLABELS}@}@t{=}@{@t{OPPOSITE}@math{|}@t{LAYER}@}@}
964 @t{/CATEGORIES} @t{VARIABLES=}@i{variables}
965 @{@t{[}@i{value}@t{,} @i{value}@dots{}@t{]}
966 @math{|} [@t{ORDER=}@{@t{A} @math{|} @t{D}@}]
967 [@t{KEY=}@{@t{VALUE} @math{|} @t{LABEL} @math{|} @i{summary}@t{(}@i{variable}@t{)}@}]
968 [@t{MISSING=}@{@t{EXCLUDE} @math{|} @t{INCLUDE}@}]@}
969 [@t{TOTAL=}@{@t{NO} @math{|} @t{YES}@} [@t{LABEL=}@i{string}] [@t{POSITION=}@{@t{AFTER} @math{|} @t{BEFORE}@}]]
970 [@t{EMPTY=}@{@t{INCLUDE} @math{|} @t{EXCLUDE}@}]
972 [@t{TITLE=}@i{string}@dots{}]
973 [@t{CAPTION=}@i{string}@dots{}]
974 [@t{CORNER=}@i{string}@dots{}]
975 @ignore @c not yet implemented
976 @t{/CRITERIA CILEVEL=}@i{percentage}
977 @t{/SIGTEST TYPE=CHISQUARE}
978 [@t{ALPHA=}@i{siglevel}]
979 [@t{INCLUDEMRSETS=}@{@t{YES} @math{|} @t{NO}@}]
980 [@t{CATEGORIES=}@{@t{ALLVISIBLE} @math{|} @t{SUBTOTALS}@}]
981 @t{/COMPARETEST TYPE=}@{@t{PROP} @math{|} @t{MEAN}@}
982 [@t{ALPHA=}@i{value}[@t{,} @i{value}]]
983 [@t{ADJUST=}@{@t{BONFERRONI} @math{|} @t{BH} @math{|} @t{NONE}@}]
984 [@t{INCLUDEMRSETS=}@{@t{YES} @math{|} @t{NO}@}]
985 [@t{MEANSVARIANCE=}@{@t{ALLCATS} @math{|} @t{TESTEDCATS}@}]
986 [@t{CATEGORIES=}@{@t{ALLVISIBLE} @math{|} @t{SUBTOTALS}@}]
987 [@t{MERGE=}@{@t{NO} @math{|} @t{YES}@}]
988 [@t{STYLE=}@{@t{APA} @math{|} @t{SIMPLE}@}]
989 [@t{SHOWSIG=}@{@t{NO} @math{|} @t{YES}@}]
993 The @code{CTABLES} (aka ``custom tables'') command produces
994 multi-dimensional tables from categorical and scale data. It offers
995 many options for data summarization and formatting.
997 This section's examples use data from the 2008 (USA) National Survey
998 of Drinking and Driving Attitudes and Behaviors, a public domain data
999 set from the (USA) National Highway Traffic Administration and
1000 available at @url{https://data.transportation.gov}. @pspp{} includes
1001 this data set, with a slightly modified dictionary, as
1002 @file{examples/nhtsa.sav}.
1004 @node CTABLES Basics
1007 The only required subcommand is @code{TABLE}, which specifies the
1008 variables to include along each axis:
1010 @t{/TABLE} @i{rows} [@t{BY} @i{columns} [@t{BY} @i{layers}]]
1013 In @code{TABLE}, each of @var{rows}, @var{columns}, and @var{layers}
1014 is either empty or an axis expression that specifies one or more
1015 variables. At least one must specify an axis expression.
1018 * CTABLES Categorical Variable Basics::
1019 * CTABLES Scalar Variable Basics::
1020 * CTABLES Overriding Measurement Level::
1023 @node CTABLES Categorical Variable Basics
1024 @subsubsection Categorical Variables
1026 An axis expression that names a categorical variable divides the data
1027 into cells according to the values of that variable. When all the
1028 variables named on @code{TABLE} are categorical, by default each cell
1029 displays the number of cases that it contains, so specifying a single
1030 variable yields a frequency table:
1033 CTABLES /TABLE=AgeGroup.
1035 @psppoutput {ctables1}
1038 Specifying a row and a column categorical variable yields a
1042 CTABLES /TABLE=AgeGroup BY qns3a.
1044 @psppoutput {ctables2}
1047 The @samp{>} ``nesting'' operator nests multiple variables on a single
1051 CTABLES /TABLE qn105ba BY AgeGroup > qns3a.
1053 @psppoutput {ctables3}
1056 The @samp{+} ``stacking'' operator allows a single output table to
1057 include multiple data analyses. With @samp{+}, @code{CTABLES} divides
1058 the output table into multiple @dfn{sections}, each of which includes
1059 an analysis of the full data set. For example, the following command
1060 separately tabulates age group and driving frequency by gender:
1063 CTABLES /TABLE AgeGroup + qn1 BY qns3a.
1065 @psppoutput {ctables4}
1068 When @samp{+} and @samp{>} are used together, @samp{>} binds more
1069 tightly. Use parentheses to override operator precedence. Thus:
1072 CTABLES /TABLE qn26 + qn27 > qns3a.
1073 CTABLES /TABLE (qn26 + qn27) > qns3a.
1075 @psppoutput {ctables5}
1077 @node CTABLES Scalar Variable Basics
1078 @subsubsection Scalar Variables
1080 For a categorical variable, @code{CTABLES} divides the table into a
1081 cell per category. For a scalar variable, @code{CTABLES} instead
1082 calculates a summary measure, by default the mean, of the values that
1083 fall into a cell. For example, if the only variable specified is a
1084 scalar variable, then the output is a single cell that holds the mean
1088 CTABLES /TABLE qnd1.
1090 @psppoutput {ctables6}
1092 A scalar variable may nest with categorical variables. The following
1093 example shows the mean age of survey respondents across gender and
1097 CTABLES /TABLE qns3a > qnd1 BY region.
1099 @psppoutput {ctables7}
1101 The order of nesting of scalar and categorical variables affects table
1102 labeling, but it does not affect the data displayed in the table. The
1103 following example shows how the output changes when the nesting order
1104 of the scalar and categorical variable are interchanged:
1107 CTABLES /TABLE qnd1 > qns3a BY region.
1109 @psppoutput {ctables8}
1111 Only a single scalar variable may appear in each section; that is, a
1112 scalar variable may not nest inside a scalar variable directly or
1113 indirectly. Scalar variables may only appear on one axis within
1116 @node CTABLES Overriding Measurement Level
1117 @subsubsection Overriding Measurement Level
1119 By default, @code{CTABLES} uses a variable's measurement level to
1120 decide whether to treat it as categorical or scalar. Variables
1121 assigned the nominal or ordinal measurement level are treated as
1122 categorical, and scalar variables are treated as scalar.
1124 Use the @code{VARIABLE LEVEL} command to change a variable's
1125 measurement level (@pxref{VARIABLE LEVEL}). To treat a variable as
1126 categorical or scalar only for one use on @code{CTABLES}, add
1127 @samp{[C]} or @samp{[S]}, respectively, after the variable name. The
1128 following example shows how to analyze the scalar variable @code{qn20}
1132 CTABLES /TABLE qn20 [C] BY qns3a.
1134 @psppoutput {ctables9}
1137 @node CTABLES Multiple Response Sets
1138 @subsubheading Multiple Response Sets
1140 The @code{CTABLES} command does not yet support multiple response
1144 @node CTABLES Data Summarization
1145 @subsection Data Summarization
1147 The @code{CTABLES} command allows the user to control how the data are
1148 summarized with summary specifications, which are enclosed in square
1149 brackets following a variable name on the @code{TABLE} subcommand.
1150 When all the variables are categorical, summary specifications can be
1151 given for the innermost nested variables on any one axis. When a
1152 scalar variable is present, only the scalar variable may have summary
1153 specifications. The following example includes a summary
1154 specification for column and row percentages for categorical
1155 variables, and mean and median for a scalar variable:
1159 /TABLE=qnd1 [MEAN, MEDIAN] BY qns3a
1160 /TABLE=AgeGroup [COLPCT, ROWPCT] BY qns3a.
1162 @psppoutput {ctables10}
1164 A summary specification may override the default label and format by
1165 appending a string or format specification or both (in that order) to
1166 the summary function name. For example:
1169 CTABLES /TABLE=AgeGroup [COLPCT 'Gender %' PCT5.0,
1170 ROWPCT 'Age Group %' PCT5.0]
1173 @psppoutput {ctables11}
1175 Parentheses provide a shorthand to apply summary specifications to
1176 multiple variables. For example, both of these commands:
1179 CTABLES /TABLE=AgeGroup[COLPCT] + qns1[COLPCT] BY qns3a.
1180 CTABLES /TABLE=(AgeGroup + qns1)[COLPCT] BY qns3a.
1184 produce the same output shown below:
1186 @psppoutput {ctables12}
1188 The following section lists the available summary functions.
1191 * CTABLES Summary Functions::
1194 @node CTABLES Summary Functions
1195 @subsubsection Summary Functions
1197 This section lists the summary functions that can be applied to cells
1198 in @code{CTABLES}. Many of these functions have an @var{area} in
1199 their names. The supported areas are:
1203 Areas that correspond to parts of @dfn{subtables}, whose contents are
1204 the cells that pair an innermost row variable and an innermost column
1209 A row within a subtable.
1212 A column within a subtable.
1215 All the cells in a subtable
1219 Areas that correspond to parts of @dfn{sections}, where stacked
1220 variables divide each section from another:
1227 A layer within a section.
1230 A row in one layer within a section.
1233 A column in one layer within a section.
1237 The following summary functions may be applied to any variable
1238 regardless of whether it is categorical or scalar. The default label
1239 for each function is listed in parentheses:
1242 @item @code{COUNT} (``Count'')
1243 The sum of weights in a cell.
1245 If @code{CATEGORIES} for one or more of the variables in a table
1246 include missing values (@pxref{CTABLES Per-Variable Category
1247 Options}), then some or all of the categories for a cell might be
1248 missing values. @code{COUNT} counts data included in a cell
1249 regardless of whether its categories are missing.
1251 @item @code{@i{area}PCT} or @code{@i{area}PCT.COUNT} (``@i{Area} %'')
1252 A percentage within the specified @var{area}.
1254 @item @code{@i{area}PCT.VALIDN} (``@i{Area} Valid N %'')
1255 A percentage of valid values within the specified @var{area}.
1257 @item @code{@i{area}PCT.TOTALN} (``@i{Area} Total N %'')
1258 A percentage of total values within the specified @var{area}.
1261 The following summary functions apply only to scalar variables or
1262 totals and subtotals for categorical variables. Be cautious about
1263 interpreting the summary value in the latter case, because it is not
1264 necessarily meaningful; however, the mean of a Likert scale, etc.@:
1265 may have a straightforward interpreation.
1268 @item @code{MAXIMUM} (``Maximum'')
1271 @item @code{MEAN} (``Mean'')
1274 @item @code{MEDIAN} (``Median'')
1277 @item @code{MINIMUM} (``Minimum'')
1280 @item @code{MISSING} (``Missing'')
1281 Sum of weights of user- and system-missing values.
1283 @item @code{MODE} (``Mode'')
1284 The highest-frequency value. Ties are broken by taking the smallest mode.
1286 @item @code{@i{area}PCT.SUM} (``@i{Area} Sum %'')
1287 Percentage of the sum of the values across @var{area}.
1289 @item @code{PTILE} @i{n} (``Percentile @i{n}'')
1290 The @var{n}th percentile, where @math{0 @leq{} @var{n} @leq{} 100}.
1292 @item @code{RANGE} (``Range'')
1293 The maximum minus the minimum.
1295 @item @code{SEMEAN} (``Std Error of Mean'')
1296 The standard error of the mean.
1298 @item @code{STDDEV} (``Std Deviation'')
1299 The standard deviation.
1301 @item @code{SUM} (``Sum'')
1304 @item @code{TOTALN} (``Total N'')
1305 The sum of weights in a cell.
1307 For scale data, @code{COUNT} and @code{TOTALN} are the same.
1309 For categorical data, @code{TOTALN} counts missing values in excluded
1310 categories, that is, user-missing values not in an explicit category
1311 list on @code{CATEGORIES} (@pxref{CTABLES Per-Variable Category
1312 Options}), or user-missing values excluded because
1313 @code{MISSING=EXCLUDE} is in effect on @code{CATEGORIES}, or
1314 system-missing values. @code{COUNT} does not count these.
1316 @item @code{VALIDN} (``Valid N'')
1317 The sum of valid count weights in included categories.
1319 @code{VALIDN} does not count missing values regardless of whether they
1320 are in included categories via @code{CATEGORIES}. @code{VALIDN} does
1321 not count valid values that are in excluded categories.
1323 @item @code{VARIANCE} (``Variance'')
1327 If the @code{WEIGHT} subcommand specified an adjustment weight
1328 variable, then the following summary functions use its value instead
1329 of the dictionary weight variable. Otherwise, they are equivalent to
1330 the summary function without the @samp{E}-prefix:
1334 @code{ECOUNT} (``Adjusted Count'')
1337 @code{ETOTALN} (``Adjusted Total N'')
1340 @code{EVALIDN} (``Adjusted Valid N'')
1343 The following summary functions with a @samp{U}-prefix are equivalent
1344 to the same ones without the prefix, except that they use unweighted
1349 @code{UCOUNT} (``Unweighted Count'')
1352 @code{U@i{area}PCT} or @code{U@i{area}PCT.COUNT} (``Unweighted @i{Area} %'')
1355 @code{U@i{area}PCT.VALIDN} (``Unweighted @i{Area} Valid N %'')
1358 @code{U@i{area}PCT.TOTALN} (``Unweighted @i{Area} Total N %'')
1361 @code{UMEAN} (``Unweighted Mean'')
1364 @code{UMEDIAN} (``Unweighted Median'')
1367 @code{UMISSING} (``Unweighted Missing'')
1370 @code{UMODE} (``Unweight Mode'')
1373 @code{U@i{area}PCT.SUM} (``Unweighted @i{Area} Sum %'')
1376 @code{UPTILE} @i{n} (``Unweighted Percentile @i{n}'')
1379 @code{USEMEAN} (``Unweighted Std Error of Mean'')
1382 @code{USTDDEV} (``Unweighted Std Deviation'')
1385 @code{USUM} (``Unweighted Sum'')
1388 @code{UTOTALN} (``Unweighted Total N'')
1391 @code{UVALIDN} (``Unweighted Valid N'')
1394 @code{UVARIANCE} (``Unweighted Variance'')
1397 @node CTABLES Statistics Positions and Labels
1398 @subsection Statistics Positions and Labels
1402 [@t{POSITION=}@{@t{COLUMN} @math{|} @t{ROW} @math{|} @t{LAYER}@}]
1403 [@t{VISIBLE=}@{@t{YES} @math{|} @t{NO}@}]
1406 The @code{SLABELS} subcommand controls the position and visibility of
1407 summary statistics for the @code{TABLE} subcommand that it follows.
1409 @code{POSITION} sets the axis on which summary statistics appear.
1410 With @t{POSITION=COLUMN}, which is the default, each summary statistic
1411 appears in a column. For example:
1414 CTABLES /TABLE=qnd1 [MEAN, MEDIAN] BY qns3a.
1416 @psppoutput {ctables13}
1419 With @t{POSITION=ROW}, each summary statistic appears in a row, as
1423 CTABLES /TABLE=qnd1 [MEAN, MEDIAN] BY qns3a /SLABELS POSITION=ROW.
1425 @psppoutput {ctables14}
1428 @t{POSITION=LAYER} is also available to place each summary statistic in
1431 Labels for summary statistics are shown by default. Use
1432 @t{VISIBLE=NO} to suppress them. Because unlabeled data can cause
1433 confusion, it should only be considered if the meaning of the data is
1434 evident, as in a simple case like this:
1437 CTABLES /TABLE=AgeGroup [TABLEPCT] /SLABELS VISIBLE=NO.
1439 @psppoutput {ctables15}
1441 @node CTABLES Category Label Positions
1442 @subsection Category Label Positions
1445 @t{/CLABELS} @{@t{AUTO} @math{|} @{@t{ROWLABELS}@math{|}@t{COLLABELS}@}@t{=}@{@t{OPPOSITE}@math{|}@t{LAYER}@}@}
1448 The @code{CLABELS} subcommand controls the position of category labels
1449 for the @code{TABLE} subcommand that it follows. By default, or if
1450 @t{AUTO} is specified, category labels for a given variable nest
1451 inside the variable's label on the same axis. For example, the
1452 command below results in age categories nesting within the age group
1453 variable on the rows axis and gender categories within the gender
1454 variable on the columns axis:
1457 CTABLES /TABLE AgeGroup BY qns3a.
1459 @psppoutput {ctables16}
1461 @t{ROWLABELS=OPPOSITE} or @t{COLLABELS=OPPOSITE} move row or column
1462 variable category labels, respectively, to the opposite axis. The
1463 setting affects only the innermost variable on the given axis. For
1467 CTABLES /TABLE AgeGroup BY qns3a /CLABELS ROWLABELS=OPPOSITE.
1468 CTABLES /TABLE AgeGroup BY qns3a /CLABELS COLLABELS=OPPOSITE.
1470 @psppoutput {ctables17}
1472 @t{ROWLABELS=LAYER} or @t{COLLABELS=LAYER} move the innermost row or
1473 column variable category labels, respectively, to the layer axis.
1475 Only one axis's labels may be moved, whether to the opposite axis or
1478 @node CTABLES Per-Variable Category Options
1479 @subsection Per-Variable Category Options
1482 @t{/CATEGORIES} @t{VARIABLES=}@i{variables}
1483 @{@t{[}@i{value}@t{,} @i{value}@dots{}@t{]}
1484 @math{|} [@t{ORDER=}@{@t{A} @math{|} @t{D}@}]
1485 [@t{KEY=}@{@t{VALUE} @math{|} @t{LABEL} @math{|} @i{summary}@t{(}@i{variable}@t{)}@}]
1486 [@t{MISSING=}@{@t{EXCLUDE} @math{|} @t{INCLUDE}@}]@}
1487 [@t{TOTAL=}@{@t{NO} @math{|} @t{YES}@} [@t{LABEL=}@i{string}] [@t{POSITION=}@{@t{AFTER} @math{|} @t{BEFORE}@}]]
1488 [@t{EMPTY=}@{@t{INCLUDE} @math{|} @t{EXCLUDE}@}]
1491 The @code{CATEGORIES} subcommand specifies, for one or more
1492 categorical variables, the categories to include and exclude, the sort
1493 order for included categories, and treatment of missing values. It
1494 also controls the totals and subtotals to display. It may be
1495 specified any number of times, each time for a different set of
1496 variables. @code{CATEGORIES} applies to the table produced by the
1497 @code{TABLE} subcommand that it follows.
1499 @code{CATEGORIES} does not apply to scalar variables.
1501 @t{VARIABLES} is required. List the variables for the subcommand
1504 There are two way to specify the Categories to include and their sort
1508 @item Explicit categories.
1509 @anchor{CTABLES Explicit Category List}
1510 To explicitly specify categories to include, list the categories
1511 within square brackets in the desired sort order. Use spaces or
1512 commas to separate values. Categories not covered by the list are
1513 excluded from analysis.
1515 Each element of the list takes one of the following forms:
1520 A numeric or string category value, for variables that have the
1525 A date or time category value, for variables that have a date or time
1528 @item @i{min} THRU @i{max}
1529 @itemx LO THRU @i{max}
1530 @itemx @i{min} THRU HI
1531 A range of category values, where @var{min} and @var{max} each takes
1532 one of the forms above, in increasing order.
1535 All user-missing values. (To match individual user-missing values,
1536 specify their category values.)
1539 Any non-missing value not covered by any other element of the list
1540 (regardless of where @t{OTHERNM} is placed in the list).
1542 @item &@i{postcompute}
1543 A computed category name (@pxref{CTABLES Computed Categories}).
1546 Additional forms, described later, allow for subtotals.
1547 If multiple elements of the list cover a given category, the last one
1548 in the list is considered to be a match.
1550 @item Implicit categories.
1551 Without an explicit list of categories, @pspp{} sorts
1552 categories automatically.
1554 The @code{KEY} setting specifies the sort key. By default, or with
1555 @code{KEY=VALUE}, categories are sorted by default. Categories may
1556 also be sorted by value label, with @code{KEY=LABEL}, or by the value
1557 of a summary function, e.g.@: @code{KEY=COUNT}. For summary
1558 functions, a variable name may be specified in parentheses, e.g.@:
1559 @code{KEY=MAXIUM(qnd1)}, and this is required for functions that apply
1560 only to scalar variables. The @code{PTILE} function also requires a
1561 percentage argument, e.g.@: @code{KEY=PTILE(qnd1, 90)}. Only summary
1562 functions used in the table may be used, except that @code{COUNT} is
1565 By default, or with @code{ORDER=A}, categories are sorted in ascending
1566 order. Specify @code{ORDER=D} to sort in descending order.
1568 User-missing values are excluded by default, or with
1569 @code{MISSING=EXCLUDE}. Specify @code{MISSING=INCLUDE} to include
1570 user-missing values. The system-missing value is always excluded.
1573 @subsubheading Totals and Subtotals
1575 @code{CATEGORIES} also controls display of totals and subtotals.
1576 Totals are not displayed by default, or with @code{TOTAL=NO}. Specify
1577 @code{TOTAL=YES} to display a total. By default, the total is labeled
1578 ``Total''; use @code{LABEL="@i{label}"} to override it.
1580 Subtotals are also not displayed by default. To add one or more
1581 subtotals, use an explicit category list and insert @code{SUBTOTAL} or
1582 @code{HSUBTOTAL} in the position or positions where the subtotal
1583 should appear. With @code{SUBTOTAL}, the subtotal becomes an extra
1584 row or column or layer; @code{HSUBTOTAL} additionally hides the
1585 categories that make up the subtotal. Either way, the default label
1586 is ``Subtotal'', use @code{SUBTOTAL="@i{label}"} or
1587 @code{HSUBTOTAL="@i{label}"} to specify a custom label.
1589 By default, or with @code{POSITION=AFTER}, totals come after the last
1590 category and subtotals apply to categories that precede them. With
1591 @code{POSITION=BEFORE}, totals come before the first category and
1592 subtotals apply to categories that follow them.
1594 Only categorical variables may have totals and subtotals. Scalar
1595 variables may be ``totaled'' indirectly by enabling totals and
1596 subtotals on a categorical variable within which the scalar variable is
1599 @subsubheading Categories Without Values
1601 Some categories might not be included in the data set being analyzed.
1602 For example, our example data set has no cases in the ``15 or
1603 younger'' age group. By default, or with @code{EMPTY=INCLUDE},
1604 @pspp{} includes these empty categories in output tables. To exclude
1605 them, specify @code{EMPTY=EXCLUDE}.
1607 For implicit categories, empty categories potentially include all the
1608 values with labels for a given variable; for explicit categories, they
1609 include all the values listed individually and all labeled values
1610 covered by ranges or @code{MISSING} or @code{OTHERNM}.
1612 @node CTABLES Titles
1617 [@t{TITLE=}@i{string}@dots{}]
1618 [@t{CAPTION=}@i{string}@dots{}]
1619 [@t{CORNER=}@i{string}@dots{}]
1622 The @code{TITLES} subcommand sets the title, caption, and corner text
1623 for the table output for the previous @code{TABLE} subcommand. The
1624 title appears above the table, the caption below the table, and the
1625 corner text appears in the table's upper left corner. By default, the
1626 title is ``Custom Tables'' and the caption and corner text are empty.
1628 @node CTABLES Table Formatting
1629 @subsection Table Formatting
1633 [@t{MINCOLWIDTH=}@{@t{DEFAULT} @math{|} @i{width}@}]
1634 [@t{MAXCOLWIDTH=}@{@t{DEFAULT} @math{|} @i{width}@}]
1635 [@t{UNITS=}@{@t{POINTS} @math{|} @t{INCHES} @math{|} @t{CM}@}]
1636 [@t{EMPTY=}@{@t{ZERO} @math{|} @t{BLANK} @math{|} @i{string}@}]
1637 [@t{MISSING=}@i{string}]
1640 The @code{FORMAT} subcommand, which must precede the first
1641 @code{TABLE} subcommand, controls formatting for all the output
1642 tables. @code{FORMAT} and all of its settings are optional.
1644 Use @code{MINCOLWIDTH} and @code{MAXCOLWIDTH} to control the minimum
1645 or maximum width of columns in output tables. By default, or with
1646 @code{DEFAULT}, column width varies based on content. Otherwise,
1647 specify a number for either or both of these settings. If both are
1648 specified, @code{MAXCOLWIDTH} must be bigger than @code{MINCOLWIDTH}.
1649 The default unit, or with @code{UNITS=POINTS}, is points (1/72 inch),
1650 but specify @code{UNITS=INCHES} to use inches or @code{UNITS=CM} for
1653 By default, or with @code{EMPTY=ZERO}, zero values are displayed in
1654 their usual format. Use @code{EMPTY=BLANK} to use an empty cell
1655 instead, or @code{EMPTY="@i{string}"} to use the specified string.
1657 By default, missing values are displayed as @samp{.}, the same as in
1658 other tables. Specify @code{MISSING="@i{string}"} to instead use a
1661 @node CTABLES Display of Variable Labels
1662 @subsection Display of Variable Labels
1666 @t{VARIABLES=}@i{variables}
1667 @t{DISPLAY}=@{@t{DEFAULT} @math{|} @t{NAME} @math{|} @t{LABEL} @math{|} @t{BOTH} @math{|} @t{NONE}@}
1670 The @code{VLABELS} subcommand, which must precede the first
1671 @code{TABLE} subcommand, controls display of variable labels in all
1672 the output tables. @code{VLABELS} is optional. It may appear
1673 multiple times to adjust settings for different variables.
1675 @code{VARIABLES} and @code{DISPLAY} are required. The value of
1676 @code{DISPLAY} controls how variable labels are displayed for the
1677 variables listed on @code{VARIABLES}. The supported values are:
1681 Uses the setting from @ref{SET TVARS}.
1684 Show only a variable name.
1687 Show only a variable label.
1690 Show variable name and label.
1696 @node CTABLES Missing Value Treatment
1697 @subsection Missing Value Treatment
1700 @t{/SMISSING} @{@t{VARIABLE} @math{|} @t{LISTWISE}@}
1703 The @code{SMISSING} subcommand, which must precede the first
1704 @code{TABLE} subcommand, controls treatment of missing values for
1705 scalar variables in producing all the output tables. @code{SMISSING}
1708 With @code{SMISSING=VARIABLE}, which is the default, missing values
1709 are excluded on a variable-by-variable basis. With
1710 @code{SMISSING=LISTWISE}, when stacked scalar variables are nested
1711 together with a categorical variable, a missing value for any of the
1712 scalar variables causes the case to be excluded for all of them.
1714 As an example, consider the following dataset, in which @samp{x} is a
1715 categorical variable and @samp{y} and @samp{z} are scale:
1717 @psppoutput{ctables18}
1720 With the default missing-value treatment, @samp{x}'s mean is 20, based
1721 on the values 10, 20, and 30, and @samp{y}'s mean is 50, based on 40,
1725 CTABLES /TABLE (y + z) > x.
1727 @psppoutput{ctables19}
1730 By adding @code{SMISSING=LISTWISE}, only cases where @samp{y} and
1731 @samp{z} are both non-missing are considered, so @samp{x}'s mean
1732 becomes 15, as the average of 10 and 20, and @samp{y}'s mean becomes
1733 55, the average of 50 and 60:
1736 CTABLES /SMISSING LISTWISE /TABLE (y + z) > x.
1738 @psppoutput{ctables20}
1741 Even with @code{SMISSING=LISTWISE}, if @samp{y} and @samp{z} are
1742 separately nested with @samp{x}, instead of using a single @samp{>}
1743 operator, missing values revert to being considered on a
1744 variable-by-variable basis:
1747 CTABLES /SMISSING LISTWISE /TABLE (y > x) + (z > x).
1749 @psppoutput{ctables21}
1751 @node CTABLES Computed Categories
1752 @subsection Computed Categories
1755 @t{/PCOMPUTE} @t{&}@i{postcompute}@t{=EXPR(}@i{expression}@t{)}
1758 @dfn{Computed categories}, also called @dfn{postcomputes}, are
1759 categories created using arithmetic on categories obtained from the
1760 data. The @code{PCOMPUTE} subcommand defines computed categories,
1761 which can then be used in two places: on @code{CATEGORIES} within an
1762 explicit category list (@pxref{CTABLES Explicit Category List}), and on
1763 the @code{PPROPERTIES} subcommand to define further properties for a
1766 @code{PCOMPUTE} must precede the first @code{TABLE} command. It is
1767 optional and it may be used any number of times to define multiple
1770 Each @code{PCOMPUTE} defines one postcompute. Its syntax consists of
1771 a name to identify the postcompute as a @pspp{} identifier prefixed by
1772 @samp{&}, followed by @samp{=} and a postcompute expression enclosed
1773 in @code{EXPR(@dots{})}. A postcompute expression consists of:
1776 @item [@i{category}]
1777 This form evaluates to the summary statistic for @i{category}, e.g.@:
1778 @code{[1]} evaluates to the value of the summary statistic associated
1779 with category 1. The @i{category} may be a number, a quoted string,
1780 or a quoted time or date value, and all of the categories for a given
1781 postcompute must have the same form.
1783 @item [@i{min} THRU @i{max}]
1784 @itemx [LO THRU @i{max}]
1785 @itemx [@i{min} THRU HI]
1788 These forms evaluate to the summary statistics for categories matching
1789 the given syntax, as described in previous sections (@pxref{CTABLES
1790 Explicit Category List}). If more than one category matches, their
1794 The summary statistic for the subtotal category. This form is allowed
1795 only for variables with exactly one subtotal.
1797 @item SUBTOTAL[@i{index}]
1798 The summary statistic for subtotal category @i{index}, where 1 is the
1799 first subtotal, 2 is the second, and so on. This form may be used for
1800 any number of subtotals.
1803 The summary statistic for the total.
1806 @itemx @i{a} - @i{b}
1807 @itemx @i{a} * @i{b}
1808 @itemx @i{a} / @i{b}
1809 @itemx @i{a} ** @i{b}
1810 These forms perform arithmetic on the values of postcompute
1811 expressions @i{a} and @i{b}. The usual operator precedence rules
1815 Numeric constants may be used in postcompute expressions.
1818 Parentheses override operator precedence.
1821 A postcompute is not associated with any particular variable.
1822 Instead, it may be referenced within @code{CATEGORIES} for any
1823 suitable variable (e.g.@: only a string variable is suitable for a
1824 postcompute expression that refers to a string category, only a
1825 variable with subtotals for an expression that refers to subtotals,
1828 Normally a named postcompute is defined only once, but if a later
1829 @code{PCOMPUTE} redefines a postcompute with the same name as an
1830 earlier one, the later one take precedence.
1832 @node CTABLES Computed Category Properties
1833 @subsection Computed Category Properties
1836 @t{/PPROPERTIES} @t{&}@i{postcompute}@dots{}
1837 [@t{LABEL=}@i{string}]
1838 [@t{FORMAT=}[@i{summary} @i{format}]@dots{}]
1839 [@t{HIDESOURCECATS=}@{@t{NO} @math{|} @t{YES}@}
1842 The @code{PPROPERTIES} subcommand, which must appear before
1843 @code{TABLE}, sets properties for one or more postcomputes defined on
1844 prior @code{PCOMPUTE} subcommands. The subcommand syntax begins with
1845 the list of postcomputes, each prefixed with @samp{&} as specified on
1848 All of the settings on @code{PPROPERTIES} are optional. Use
1849 @code{LABEL} to set the label shown for the postcomputes in table
1850 output. The default label for a postcompute is the expression used to
1853 The @code{FORMAT} setting sets summary statistics and display formats
1854 for the postcomputes.
1856 By default, or with @code{HIDESOURCECATS=NO}, categories referred to
1857 by computed categories are displayed like other categories. Use
1858 @code{HIDESOURCECATS=YES} to hide them.
1860 @node CTABLES Base Weight
1861 @subsection Base Weight
1864 @t{/WEIGHT VARIABLE=}@i{variable}
1867 The @code{WEIGHT} subcommand is optional and must appear before
1868 @code{TABLE}. If it appears, it must name a numeric variable, known
1869 as the @dfn{effective base weight} or @dfn{adjustment weight}. The
1870 effective base weight variable is used for the @code{ECOUNT},
1871 @code{ETOTALN}, and @code{EVALIDN} summary functions.
1873 Cases with zero, missing, or negative effective base weight are
1874 excluded from all analysis.
1876 Weights obtained from the @pspp{} dictionary are rounded to the
1877 nearest integer. Effective base weights are not rounded.
1879 @node CTABLES Hiding Small Counts
1880 @subsection Hiding Small Counts
1883 @t{/HIDESMALLCOUNTS COUNT=@i{count}}
1886 The @code{HIDESMALLCOUNTS} subcommand is optional. If it specified,
1887 then count values in output tables less than the value of @i{count}
1888 are shown as @code{<@i{count}} instead of their true values. The
1889 value of @i{count} must be an integer and must be at least 2. Case
1890 weights are considered for deciding whether to hide a count.
1896 @cindex factor analysis
1897 @cindex principal components analysis
1898 @cindex principal axis factoring
1899 @cindex data reduction
1903 VARIABLES=@var{var_list},
1904 MATRIX IN (@{CORR,COV@}=@{*,@var{file_spec}@})
1907 [ /METHOD = @{CORRELATION, COVARIANCE@} ]
1909 [ /ANALYSIS=@var{var_list} ]
1911 [ /EXTRACTION=@{PC, PAF@}]
1913 [ /ROTATION=@{VARIMAX, EQUAMAX, QUARTIMAX, PROMAX[(@var{k})], NOROTATE@}]
1915 [ /PRINT=[INITIAL] [EXTRACTION] [ROTATION] [UNIVARIATE] [CORRELATION] [COVARIANCE] [DET] [KMO] [AIC] [SIG] [ALL] [DEFAULT] ]
1919 [ /FORMAT=[SORT] [BLANK(@var{n})] [DEFAULT] ]
1921 [ /CRITERIA=[FACTORS(@var{n})] [MINEIGEN(@var{l})] [ITERATE(@var{m})] [ECONVERGE (@var{delta})] [DEFAULT] ]
1923 [ /MISSING=[@{LISTWISE, PAIRWISE@}] [@{INCLUDE, EXCLUDE@}] ]
1926 The @cmd{FACTOR} command performs Factor Analysis or Principal Axis Factoring on a dataset. It may be used to find
1927 common factors in the data or for data reduction purposes.
1929 The @subcmd{VARIABLES} subcommand is required (unless the @subcmd{MATRIX IN}
1930 subcommand is used).
1931 It lists the variables which are to partake in the analysis. (The @subcmd{ANALYSIS}
1932 subcommand may optionally further limit the variables that
1933 participate; it is useful primarily in conjunction with @subcmd{MATRIX IN}.)
1935 If @subcmd{MATRIX IN} instead of @subcmd{VARIABLES} is specified, then the analysis
1936 is performed on a pre-prepared correlation or covariance matrix file instead of on
1937 individual data cases. Typically the matrix file will have been generated by
1938 @cmd{MATRIX DATA} (@pxref{MATRIX DATA}) or provided by a third party.
1939 If specified, @subcmd{MATRIX IN} must be followed by @samp{COV} or @samp{CORR},
1940 then by @samp{=} and @var{file_spec} all in parentheses.
1941 @var{file_spec} may either be an asterisk, which indicates the currently loaded
1942 dataset, or it may be a file name to be loaded. @xref{MATRIX DATA}, for the expected
1945 The @subcmd{/EXTRACTION} subcommand is used to specify the way in which factors
1946 (components) are extracted from the data.
1947 If @subcmd{PC} is specified, then Principal Components Analysis is used.
1948 If @subcmd{PAF} is specified, then Principal Axis Factoring is
1949 used. By default Principal Components Analysis is used.
1951 The @subcmd{/ROTATION} subcommand is used to specify the method by which the
1952 extracted solution is rotated. Three orthogonal rotation methods are available:
1953 @subcmd{VARIMAX} (which is the default), @subcmd{EQUAMAX}, and @subcmd{QUARTIMAX}.
1954 There is one oblique rotation method, @i{viz}: @subcmd{PROMAX}.
1955 Optionally you may enter the power of the promax rotation @var{k}, which must be enclosed in parentheses.
1956 The default value of @var{k} is 5.
1957 If you don't want any rotation to be performed, the word @subcmd{NOROTATE}
1958 prevents the command from performing any rotation on the data.
1960 The @subcmd{/METHOD} subcommand should be used to determine whether the
1961 covariance matrix or the correlation matrix of the data is
1962 to be analysed. By default, the correlation matrix is analysed.
1964 The @subcmd{/PRINT} subcommand may be used to select which features of the analysis are reported:
1967 @item @subcmd{UNIVARIATE}
1968 A table of mean values, standard deviations and total weights are printed.
1969 @item @subcmd{INITIAL}
1970 Initial communalities and eigenvalues are printed.
1971 @item @subcmd{EXTRACTION}
1972 Extracted communalities and eigenvalues are printed.
1973 @item @subcmd{ROTATION}
1974 Rotated communalities and eigenvalues are printed.
1975 @item @subcmd{CORRELATION}
1976 The correlation matrix is printed.
1977 @item @subcmd{COVARIANCE}
1978 The covariance matrix is printed.
1980 The determinant of the correlation or covariance matrix is printed.
1982 The anti-image covariance and anti-image correlation matrices are printed.
1984 The Kaiser-Meyer-Olkin measure of sampling adequacy and the Bartlett test of sphericity is printed.
1986 The significance of the elements of correlation matrix is printed.
1988 All of the above are printed.
1989 @item @subcmd{DEFAULT}
1990 Identical to @subcmd{INITIAL} and @subcmd{EXTRACTION}.
1993 If @subcmd{/PLOT=EIGEN} is given, then a ``Scree'' plot of the eigenvalues is
1994 printed. This can be useful for visualizing the factors and deciding
1995 which factors (components) should be retained.
1997 The @subcmd{/FORMAT} subcommand determined how data are to be
1998 displayed in loading matrices. If @subcmd{SORT} is specified, then
1999 the variables are sorted in descending order of significance. If
2000 @subcmd{BLANK(@var{n})} is specified, then coefficients whose absolute
2001 value is less than @var{n} are not printed. If the keyword
2002 @subcmd{DEFAULT} is specified, or if no @subcmd{/FORMAT} subcommand is
2003 specified, then no sorting is performed, and all coefficients are printed.
2005 You can use the @subcmd{/CRITERIA} subcommand to specify how the number of
2006 extracted factors (components) are chosen. If @subcmd{FACTORS(@var{n})} is
2007 specified, where @var{n} is an integer, then @var{n} factors are
2008 extracted. Otherwise, the @subcmd{MINEIGEN} setting is used.
2009 @subcmd{MINEIGEN(@var{l})} requests that all factors whose eigenvalues
2010 are greater than or equal to @var{l} are extracted. The default value
2011 of @var{l} is 1. The @subcmd{ECONVERGE} setting has effect only when
2012 using iterative algorithms for factor extraction (such as Principal Axis
2013 Factoring). @subcmd{ECONVERGE(@var{delta})} specifies that
2014 iteration should cease when the maximum absolute value of the
2015 communality estimate between one iteration and the previous is less
2016 than @var{delta}. The default value of @var{delta} is 0.001.
2018 The @subcmd{ITERATE(@var{m})} may appear any number of times and is
2019 used for two different purposes. It is used to set the maximum number
2020 of iterations (@var{m}) for convergence and also to set the maximum
2021 number of iterations for rotation.
2022 Whether it affects convergence or rotation depends upon which
2023 subcommand follows the @subcmd{ITERATE} subcommand.
2024 If @subcmd{EXTRACTION} follows, it affects convergence.
2025 If @subcmd{ROTATION} follows, it affects rotation.
2026 If neither @subcmd{ROTATION} nor @subcmd{EXTRACTION} follow a
2027 @subcmd{ITERATE} subcommand, then the entire subcommand is ignored.
2028 The default value of @var{m} is 25.
2030 The @cmd{MISSING} subcommand determines the handling of missing
2031 variables. If @subcmd{INCLUDE} is set, then user-missing values are
2032 included in the calculations, but system-missing values are not.
2033 If @subcmd{EXCLUDE} is set, which is the default, user-missing
2034 values are excluded as well as system-missing values. This is the
2035 default. If @subcmd{LISTWISE} is set, then the entire case is excluded
2036 from analysis whenever any variable specified in the @cmd{VARIABLES}
2037 subcommand contains a missing value.
2039 If @subcmd{PAIRWISE} is set, then a case is considered missing only if
2040 either of the values for the particular coefficient are missing.
2041 The default is @subcmd{LISTWISE}.
2047 @cindex univariate analysis of variance
2048 @cindex fixed effects
2049 @cindex factorial anova
2050 @cindex analysis of variance
2055 GLM @var{dependent_vars} BY @var{fixed_factors}
2056 [/METHOD = SSTYPE(@var{type})]
2057 [/DESIGN = @var{interaction_0} [@var{interaction_1} [... @var{interaction_n}]]]
2058 [/INTERCEPT = @{INCLUDE|EXCLUDE@}]
2059 [/MISSING = @{INCLUDE|EXCLUDE@}]
2062 The @cmd{GLM} procedure can be used for fixed effects factorial Anova.
2064 The @var{dependent_vars} are the variables to be analysed.
2065 You may analyse several variables in the same command in which case they should all
2066 appear before the @code{BY} keyword.
2068 The @var{fixed_factors} list must be one or more categorical variables. Normally it
2069 does not make sense to enter a scalar variable in the @var{fixed_factors} and doing
2070 so may cause @pspp{} to do a lot of unnecessary processing.
2072 The @subcmd{METHOD} subcommand is used to change the method for producing the sums of
2073 squares. Available values of @var{type} are 1, 2 and 3. The default is type 3.
2075 You may specify a custom design using the @subcmd{DESIGN} subcommand.
2076 The design comprises a list of interactions where each interaction is a
2077 list of variables separated by a @samp{*}. For example the command
2079 GLM subject BY sex age_group race
2080 /DESIGN = age_group sex group age_group*sex age_group*race
2082 @noindent specifies the model @math{subject = age_group + sex + race + age_group*sex + age_group*race}.
2083 If no @subcmd{DESIGN} subcommand is specified, then the default is all possible combinations
2084 of the fixed factors. That is to say
2086 GLM subject BY sex age_group race
2089 @math{subject = age_group + sex + race + age_group*sex + age_group*race + sex*race + age_group*sex*race}.
2092 The @subcmd{MISSING} subcommand determines the handling of missing
2094 If @subcmd{INCLUDE} is set then, for the purposes of GLM analysis,
2095 only system-missing values are considered
2096 to be missing; user-missing values are not regarded as missing.
2097 If @subcmd{EXCLUDE} is set, which is the default, then user-missing
2098 values are considered to be missing as well as system-missing values.
2099 A case for which any dependent variable or any factor
2100 variable has a missing value is excluded from the analysis.
2102 @node LOGISTIC REGRESSION
2103 @section LOGISTIC REGRESSION
2105 @vindex LOGISTIC REGRESSION
2106 @cindex logistic regression
2107 @cindex bivariate logistic regression
2110 LOGISTIC REGRESSION [VARIABLES =] @var{dependent_var} WITH @var{predictors}
2112 [/CATEGORICAL = @var{categorical_predictors}]
2114 [@{/NOCONST | /ORIGIN | /NOORIGIN @}]
2116 [/PRINT = [SUMMARY] [DEFAULT] [CI(@var{confidence})] [ALL]]
2118 [/CRITERIA = [BCON(@var{min_delta})] [ITERATE(@var{max_interations})]
2119 [LCON(@var{min_likelihood_delta})] [EPS(@var{min_epsilon})]
2120 [CUT(@var{cut_point})]]
2122 [/MISSING = @{INCLUDE|EXCLUDE@}]
2125 Bivariate Logistic Regression is used when you want to explain a dichotomous dependent
2126 variable in terms of one or more predictor variables.
2128 The minimum command is
2130 LOGISTIC REGRESSION @var{y} WITH @var{x1} @var{x2} @dots{} @var{xn}.
2132 Here, @var{y} is the dependent variable, which must be dichotomous and @var{x1} @dots{} @var{xn}
2133 are the predictor variables whose coefficients the procedure estimates.
2135 By default, a constant term is included in the model.
2136 Hence, the full model is
2139 = b_0 + b_1 {\bf x_1}
2145 Predictor variables which are categorical in nature should be listed on the @subcmd{/CATEGORICAL} subcommand.
2146 Simple variables as well as interactions between variables may be listed here.
2148 If you want a model without the constant term @math{b_0}, use the keyword @subcmd{/ORIGIN}.
2149 @subcmd{/NOCONST} is a synonym for @subcmd{/ORIGIN}.
2151 An iterative Newton-Raphson procedure is used to fit the model.
2152 The @subcmd{/CRITERIA} subcommand is used to specify the stopping criteria of the procedure,
2153 and other parameters.
2154 The value of @var{cut_point} is used in the classification table. It is the
2155 threshold above which predicted values are considered to be 1. Values
2156 of @var{cut_point} must lie in the range [0,1].
2157 During iterations, if any one of the stopping criteria are satisfied, the procedure is
2158 considered complete.
2159 The stopping criteria are:
2161 @item The number of iterations exceeds @var{max_iterations}.
2162 The default value of @var{max_iterations} is 20.
2163 @item The change in the all coefficient estimates are less than @var{min_delta}.
2164 The default value of @var{min_delta} is 0.001.
2165 @item The magnitude of change in the likelihood estimate is less than @var{min_likelihood_delta}.
2166 The default value of @var{min_delta} is zero.
2167 This means that this criterion is disabled.
2168 @item The differential of the estimated probability for all cases is less than @var{min_epsilon}.
2169 In other words, the probabilities are close to zero or one.
2170 The default value of @var{min_epsilon} is 0.00000001.
2174 The @subcmd{PRINT} subcommand controls the display of optional statistics.
2175 Currently there is one such option, @subcmd{CI}, which indicates that the
2176 confidence interval of the odds ratio should be displayed as well as its value.
2177 @subcmd{CI} should be followed by an integer in parentheses, to indicate the
2178 confidence level of the desired confidence interval.
2180 The @subcmd{MISSING} subcommand determines the handling of missing
2182 If @subcmd{INCLUDE} is set, then user-missing values are included in the
2183 calculations, but system-missing values are not.
2184 If @subcmd{EXCLUDE} is set, which is the default, user-missing
2185 values are excluded as well as system-missing values.
2186 This is the default.
2197 [ BY @{@var{var_list}@} [BY @{@var{var_list}@} [BY @{@var{var_list}@} @dots{} ]]]
2199 [ /@{@var{var_list}@}
2200 [ BY @{@var{var_list}@} [BY @{@var{var_list}@} [BY @{@var{var_list}@} @dots{} ]]] ]
2202 [/CELLS = [MEAN] [COUNT] [STDDEV] [SEMEAN] [SUM] [MIN] [MAX] [RANGE]
2203 [VARIANCE] [KURT] [SEKURT]
2204 [SKEW] [SESKEW] [FIRST] [LAST]
2205 [HARMONIC] [GEOMETRIC]
2210 [/MISSING = [INCLUDE] [DEPENDENT]]
2213 You can use the @cmd{MEANS} command to calculate the arithmetic mean and similar
2214 statistics, either for the dataset as a whole or for categories of data.
2216 The simplest form of the command is
2220 @noindent which calculates the mean, count and standard deviation for @var{v}.
2221 If you specify a grouping variable, for example
2223 MEANS @var{v} BY @var{g}.
2225 @noindent then the means, counts and standard deviations for @var{v} after having
2226 been grouped by @var{g} are calculated.
2227 Instead of the mean, count and standard deviation, you could specify the statistics
2228 in which you are interested:
2230 MEANS @var{x} @var{y} BY @var{g}
2231 /CELLS = HARMONIC SUM MIN.
2233 This example calculates the harmonic mean, the sum and the minimum values of @var{x} and @var{y}
2236 The @subcmd{CELLS} subcommand specifies which statistics to calculate. The available statistics
2240 @cindex arithmetic mean
2241 The arithmetic mean.
2242 @item @subcmd{COUNT}
2243 The count of the values.
2244 @item @subcmd{STDDEV}
2245 The standard deviation.
2246 @item @subcmd{SEMEAN}
2247 The standard error of the mean.
2249 The sum of the values.
2254 @item @subcmd{RANGE}
2255 The difference between the maximum and minimum values.
2256 @item @subcmd{VARIANCE}
2258 @item @subcmd{FIRST}
2259 The first value in the category.
2261 The last value in the category.
2264 @item @subcmd{SESKEW}
2265 The standard error of the skewness.
2268 @item @subcmd{SEKURT}
2269 The standard error of the kurtosis.
2270 @item @subcmd{HARMONIC}
2271 @cindex harmonic mean
2273 @item @subcmd{GEOMETRIC}
2274 @cindex geometric mean
2278 In addition, three special keywords are recognized:
2280 @item @subcmd{DEFAULT}
2281 This is the same as @subcmd{MEAN} @subcmd{COUNT} @subcmd{STDDEV}.
2283 All of the above statistics are calculated.
2285 No statistics are calculated (only a summary is shown).
2289 More than one @dfn{table} can be specified in a single command.
2290 Each table is separated by a @samp{/}. For
2294 @var{c} @var{d} @var{e} BY @var{x}
2295 /@var{a} @var{b} BY @var{x} @var{y}
2296 /@var{f} BY @var{y} BY @var{z}.
2298 has three tables (the @samp{TABLE =} is optional).
2299 The first table has three dependent variables @var{c}, @var{d} and @var{e}
2300 and a single categorical variable @var{x}.
2301 The second table has two dependent variables @var{a} and @var{b},
2302 and two categorical variables @var{x} and @var{y}.
2303 The third table has a single dependent variables @var{f}
2304 and a categorical variable formed by the combination of @var{y} and @var{z}.
2307 By default values are omitted from the analysis only if missing values
2308 (either system missing or user missing)
2309 for any of the variables directly involved in their calculation are
2311 This behaviour can be modified with the @subcmd{/MISSING} subcommand.
2312 Three options are possible: @subcmd{TABLE}, @subcmd{INCLUDE} and @subcmd{DEPENDENT}.
2314 @subcmd{/MISSING = INCLUDE} says that user missing values, either in the dependent
2315 variables or in the categorical variables should be taken at their face
2316 value, and not excluded.
2318 @subcmd{/MISSING = DEPENDENT} says that user missing values, in the dependent
2319 variables should be taken at their face value, however cases which
2320 have user missing values for the categorical variables should be omitted
2321 from the calculation.
2323 @subsection Example Means
2325 The dataset in @file{repairs.sav} contains the mean time between failures (@exvar{mtbf})
2326 for a sample of artifacts produced by different factories and trialed under
2327 different operating conditions.
2328 Since there are four combinations of categorical variables, by simply looking
2329 at the list of data, it would be hard to how the scores vary for each category.
2330 @ref{means:ex} shows one way of tabulating the @exvar{mtbf} in a way which is
2331 easier to understand.
2333 @float Example, means:ex
2334 @psppsyntax {means.sps}
2335 @caption {Running @cmd{MEANS} on the @exvar{mtbf} score with categories @exvar{factory} and @exvar{environment}}
2338 The results are shown in @ref{means:res}. The figures shown indicate the mean,
2339 standard deviation and number of samples in each category.
2340 These figures however do not indicate whether the results are statistically
2341 significant. For that, you would need to use the procedures @cmd{ONEWAY}, @cmd{GLM} or
2342 @cmd{T-TEST} depending on the hypothesis being tested.
2344 @float Result, means:res
2346 @caption {The @exvar{mtbf} categorised by @exvar{factory} and @exvar{environment}}
2349 Note that there is no limit to the number of variables for which you can calculate
2350 statistics, nor to the number of categorical variables per layer, nor the number
2352 However, running @cmd{MEANS} on a large numbers of variables, or with categorical variables
2353 containing a large number of distinct values may result in an extremely large output, which
2354 will not be easy to interpret.
2355 So you should consider carefully which variables to select for participation in the analysis.
2361 @cindex nonparametric tests
2366 nonparametric test subcommands
2371 [ /STATISTICS=@{DESCRIPTIVES@} ]
2373 [ /MISSING=@{ANALYSIS, LISTWISE@} @{INCLUDE, EXCLUDE@} ]
2375 [ /METHOD=EXACT [ TIMER [(@var{n})] ] ]
2378 @cmd{NPAR TESTS} performs nonparametric tests.
2379 Non parametric tests make very few assumptions about the distribution of the
2381 One or more tests may be specified by using the corresponding subcommand.
2382 If the @subcmd{/STATISTICS} subcommand is also specified, then summary statistics are
2383 produces for each variable that is the subject of any test.
2385 Certain tests may take a long time to execute, if an exact figure is required.
2386 Therefore, by default asymptotic approximations are used unless the
2387 subcommand @subcmd{/METHOD=EXACT} is specified.
2388 Exact tests give more accurate results, but may take an unacceptably long
2389 time to perform. If the @subcmd{TIMER} keyword is used, it sets a maximum time,
2390 after which the test is abandoned, and a warning message printed.
2391 The time, in minutes, should be specified in parentheses after the @subcmd{TIMER} keyword.
2392 If the @subcmd{TIMER} keyword is given without this figure, then a default value of 5 minutes
2397 * BINOMIAL:: Binomial Test
2398 * CHISQUARE:: Chi-square Test
2399 * COCHRAN:: Cochran Q Test
2400 * FRIEDMAN:: Friedman Test
2401 * KENDALL:: Kendall's W Test
2402 * KOLMOGOROV-SMIRNOV:: Kolmogorov Smirnov Test
2403 * KRUSKAL-WALLIS:: Kruskal-Wallis Test
2404 * MANN-WHITNEY:: Mann Whitney U Test
2405 * MCNEMAR:: McNemar Test
2406 * MEDIAN:: Median Test
2408 * SIGN:: The Sign Test
2409 * WILCOXON:: Wilcoxon Signed Ranks Test
2414 @subsection Binomial test
2416 @cindex binomial test
2419 [ /BINOMIAL[(@var{p})]=@var{var_list}[(@var{value1}[, @var{value2})] ] ]
2422 The @subcmd{/BINOMIAL} subcommand compares the observed distribution of a dichotomous
2423 variable with that of a binomial distribution.
2424 The variable @var{p} specifies the test proportion of the binomial
2426 The default value of 0.5 is assumed if @var{p} is omitted.
2428 If a single value appears after the variable list, then that value is
2429 used as the threshold to partition the observed values. Values less
2430 than or equal to the threshold value form the first category. Values
2431 greater than the threshold form the second category.
2433 If two values appear after the variable list, then they are used
2434 as the values which a variable must take to be in the respective
2436 Cases for which a variable takes a value equal to neither of the specified
2437 values, take no part in the test for that variable.
2439 If no values appear, then the variable must assume dichotomous
2441 If more than two distinct, non-missing values for a variable
2442 under test are encountered then an error occurs.
2444 If the test proportion is equal to 0.5, then a two tailed test is
2445 reported. For any other test proportion, a one tailed test is
2447 For one tailed tests, if the test proportion is less than
2448 or equal to the observed proportion, then the significance of
2449 observing the observed proportion or more is reported.
2450 If the test proportion is more than the observed proportion, then the
2451 significance of observing the observed proportion or less is reported.
2452 That is to say, the test is always performed in the observed
2455 @pspp{} uses a very precise approximation to the gamma function to
2456 compute the binomial significance. Thus, exact results are reported
2457 even for very large sample sizes.
2461 @subsection Chi-square Test
2463 @cindex chi-square test
2467 [ /CHISQUARE=@var{var_list}[(@var{lo},@var{hi})] [/EXPECTED=@{EQUAL|@var{f1}, @var{f2} @dots{} @var{fn}@}] ]
2471 The @subcmd{/CHISQUARE} subcommand produces a chi-square statistic for the differences
2472 between the expected and observed frequencies of the categories of a variable.
2473 Optionally, a range of values may appear after the variable list.
2474 If a range is given, then non integer values are truncated, and values
2475 outside the specified range are excluded from the analysis.
2477 The @subcmd{/EXPECTED} subcommand specifies the expected values of each
2479 There must be exactly one non-zero expected value, for each observed
2480 category, or the @subcmd{EQUAL} keyword must be specified.
2481 You may use the notation @subcmd{@var{n}*@var{f}} to specify @var{n}
2482 consecutive expected categories all taking a frequency of @var{f}.
2483 The frequencies given are proportions, not absolute frequencies. The
2484 sum of the frequencies need not be 1.
2485 If no @subcmd{/EXPECTED} subcommand is given, then equal frequencies
2488 @subsubsection Chi-square Example
2490 A researcher wishes to investigate whether there are an equal number of
2491 persons of each sex in a population. The sample chosen for invesigation
2492 is that from the @file {physiology.sav} dataset. The null hypothesis for
2493 the test is that the population comprises an equal number of males and females.
2494 The analysis is performed as shown in @ref{chisquare:ex}.
2496 @float Example, chisquare:ex
2497 @psppsyntax {chisquare.sps}
2498 @caption {Performing a chi-square test to check for equal distribution of sexes}
2501 There is only one test variable, @i{viz:} @exvar{sex}. The other variables in the dataset
2504 @float Screenshot, chisquare:scr
2505 @psppimage {chisquare}
2506 @caption {Performing a chi-square test using the graphic user interface}
2509 In @ref{chisquare:res} the summary box shows that in the sample, there are more males
2510 than females. However the significance of chi-square result is greater than 0.05
2511 --- the most commonly accepted p-value --- and therefore
2512 there is not enough evidence to reject the null hypothesis and one must conclude
2513 that the evidence does not indicate that there is an imbalance of the sexes
2516 @float Result, chisquare:res
2517 @psppoutput {chisquare}
2518 @caption {The results of running a chi-square test on @exvar{sex}}
2523 @subsection Cochran Q Test
2525 @cindex Cochran Q test
2526 @cindex Q, Cochran Q
2529 [ /COCHRAN = @var{var_list} ]
2532 The Cochran Q test is used to test for differences between three or more groups.
2533 The data for @var{var_list} in all cases must assume exactly two
2534 distinct values (other than missing values).
2536 The value of Q is displayed along with its Asymptotic significance
2537 based on a chi-square distribution.
2540 @subsection Friedman Test
2542 @cindex Friedman test
2545 [ /FRIEDMAN = @var{var_list} ]
2548 The Friedman test is used to test for differences between repeated measures when
2549 there is no indication that the distributions are normally distributed.
2551 A list of variables which contain the measured data must be given. The procedure
2552 prints the sum of ranks for each variable, the test statistic and its significance.
2555 @subsection Kendall's W Test
2557 @cindex Kendall's W test
2558 @cindex coefficient of concordance
2561 [ /KENDALL = @var{var_list} ]
2564 The Kendall test investigates whether an arbitrary number of related samples come from the
2566 It is identical to the Friedman test except that the additional statistic W, Kendall's Coefficient of Concordance is printed.
2567 It has the range [0,1] --- a value of zero indicates no agreement between the samples whereas a value of
2568 unity indicates complete agreement.
2571 @node KOLMOGOROV-SMIRNOV
2572 @subsection Kolmogorov-Smirnov Test
2573 @vindex KOLMOGOROV-SMIRNOV
2575 @cindex Kolmogorov-Smirnov test
2578 [ /KOLMOGOROV-SMIRNOV (@{NORMAL [@var{mu}, @var{sigma}], UNIFORM [@var{min}, @var{max}], POISSON [@var{lambda}], EXPONENTIAL [@var{scale}] @}) = @var{var_list} ]
2581 The one sample Kolmogorov-Smirnov subcommand is used to test whether or not a dataset is
2582 drawn from a particular distribution. Four distributions are supported, @i{viz:}
2583 Normal, Uniform, Poisson and Exponential.
2585 Ideally you should provide the parameters of the distribution against
2586 which you wish to test the data. For example, with the normal
2587 distribution the mean (@var{mu})and standard deviation (@var{sigma})
2588 should be given; with the uniform distribution, the minimum
2589 (@var{min})and maximum (@var{max}) value should be provided.
2590 However, if the parameters are omitted they are imputed from the
2591 data. Imputing the parameters reduces the power of the test so should
2592 be avoided if possible.
2594 In the following example, two variables @var{score} and @var{age} are
2595 tested to see if they follow a normal distribution with a mean of 3.5
2596 and a standard deviation of 2.0.
2599 /KOLMOGOROV-SMIRNOV (normal 3.5 2.0) = @var{score} @var{age}.
2601 If the variables need to be tested against different distributions, then a separate
2602 subcommand must be used. For example the following syntax tests @var{score} against
2603 a normal distribution with mean of 3.5 and standard deviation of 2.0 whilst @var{age}
2604 is tested against a normal distribution of mean 40 and standard deviation 1.5.
2607 /KOLMOGOROV-SMIRNOV (normal 3.5 2.0) = @var{score}
2608 /KOLMOGOROV-SMIRNOV (normal 40 1.5) = @var{age}.
2611 The abbreviated subcommand @subcmd{K-S} may be used in place of @subcmd{KOLMOGOROV-SMIRNOV}.
2613 @node KRUSKAL-WALLIS
2614 @subsection Kruskal-Wallis Test
2615 @vindex KRUSKAL-WALLIS
2617 @cindex Kruskal-Wallis test
2620 [ /KRUSKAL-WALLIS = @var{var_list} BY var (@var{lower}, @var{upper}) ]
2623 The Kruskal-Wallis test is used to compare data from an
2624 arbitrary number of populations. It does not assume normality.
2625 The data to be compared are specified by @var{var_list}.
2626 The categorical variable determining the groups to which the
2627 data belongs is given by @var{var}. The limits @var{lower} and
2628 @var{upper} specify the valid range of @var{var}.
2629 If @var{upper} is smaller than @var{lower}, the PSPP will assume their values
2630 to be reversed. Any cases for which @var{var} falls outside
2631 [@var{lower}, @var{upper}] are ignored.
2633 The mean rank of each group as well as the chi-squared value and
2634 significance of the test are printed.
2635 The abbreviated subcommand @subcmd{K-W} may be used in place of
2636 @subcmd{KRUSKAL-WALLIS}.
2640 @subsection Mann-Whitney U Test
2641 @vindex MANN-WHITNEY
2643 @cindex Mann-Whitney U test
2644 @cindex U, Mann-Whitney U
2647 [ /MANN-WHITNEY = @var{var_list} BY var (@var{group1}, @var{group2}) ]
2650 The Mann-Whitney subcommand is used to test whether two groups of data
2651 come from different populations. The variables to be tested should be
2652 specified in @var{var_list} and the grouping variable, that determines
2653 to which group the test variables belong, in @var{var}.
2654 @var{Var} may be either a string or an alpha variable.
2655 @var{Group1} and @var{group2} specify the
2656 two values of @var{var} which determine the groups of the test data.
2657 Cases for which the @var{var} value is neither @var{group1} or
2658 @var{group2} are ignored.
2660 The value of the Mann-Whitney U statistic, the Wilcoxon W, and the
2661 significance are printed.
2662 You may abbreviated the subcommand @subcmd{MANN-WHITNEY} to
2667 @subsection McNemar Test
2669 @cindex McNemar test
2672 [ /MCNEMAR @var{var_list} [ WITH @var{var_list} [ (PAIRED) ]]]
2675 Use McNemar's test to analyse the significance of the difference between
2676 pairs of correlated proportions.
2678 If the @code{WITH} keyword is omitted, then tests for all
2679 combinations of the listed variables are performed.
2680 If the @code{WITH} keyword is given, and the @code{(PAIRED)} keyword
2681 is also given, then the number of variables preceding @code{WITH}
2682 must be the same as the number following it.
2683 In this case, tests for each respective pair of variables are
2685 If the @code{WITH} keyword is given, but the
2686 @code{(PAIRED)} keyword is omitted, then tests for each combination
2687 of variable preceding @code{WITH} against variable following
2688 @code{WITH} are performed.
2690 The data in each variable must be dichotomous. If there are more
2691 than two distinct variables an error will occur and the test will
2695 @subsection Median Test
2700 [ /MEDIAN [(@var{value})] = @var{var_list} BY @var{variable} (@var{value1}, @var{value2}) ]
2703 The median test is used to test whether independent samples come from
2704 populations with a common median.
2705 The median of the populations against which the samples are to be tested
2706 may be given in parentheses immediately after the
2707 @subcmd{/MEDIAN} subcommand. If it is not given, the median is imputed from the
2708 union of all the samples.
2710 The variables of the samples to be tested should immediately follow the @samp{=} sign. The
2711 keyword @code{BY} must come next, and then the grouping variable. Two values
2712 in parentheses should follow. If the first value is greater than the second,
2713 then a 2 sample test is performed using these two values to determine the groups.
2714 If however, the first variable is less than the second, then a @i{k} sample test is
2715 conducted and the group values used are all values encountered which lie in the
2716 range [@var{value1},@var{value2}].
2720 @subsection Runs Test
2725 [ /RUNS (@{MEAN, MEDIAN, MODE, @var{value}@}) = @var{var_list} ]
2728 The @subcmd{/RUNS} subcommand tests whether a data sequence is randomly ordered.
2730 It works by examining the number of times a variable's value crosses a given threshold.
2731 The desired threshold must be specified within parentheses.
2732 It may either be specified as a number or as one of @subcmd{MEAN}, @subcmd{MEDIAN} or @subcmd{MODE}.
2733 Following the threshold specification comes the list of variables whose values are to be
2736 The subcommand shows the number of runs, the asymptotic significance based on the
2740 @subsection Sign Test
2745 [ /SIGN @var{var_list} [ WITH @var{var_list} [ (PAIRED) ]]]
2748 The @subcmd{/SIGN} subcommand tests for differences between medians of the
2750 The test does not make any assumptions about the
2751 distribution of the data.
2753 If the @code{WITH} keyword is omitted, then tests for all
2754 combinations of the listed variables are performed.
2755 If the @code{WITH} keyword is given, and the @code{(PAIRED)} keyword
2756 is also given, then the number of variables preceding @code{WITH}
2757 must be the same as the number following it.
2758 In this case, tests for each respective pair of variables are
2760 If the @code{WITH} keyword is given, but the
2761 @code{(PAIRED)} keyword is omitted, then tests for each combination
2762 of variable preceding @code{WITH} against variable following
2763 @code{WITH} are performed.
2766 @subsection Wilcoxon Matched Pairs Signed Ranks Test
2768 @cindex wilcoxon matched pairs signed ranks test
2771 [ /WILCOXON @var{var_list} [ WITH @var{var_list} [ (PAIRED) ]]]
2774 The @subcmd{/WILCOXON} subcommand tests for differences between medians of the
2776 The test does not make any assumptions about the variances of the samples.
2777 It does however assume that the distribution is symmetrical.
2779 If the @subcmd{WITH} keyword is omitted, then tests for all
2780 combinations of the listed variables are performed.
2781 If the @subcmd{WITH} keyword is given, and the @subcmd{(PAIRED)} keyword
2782 is also given, then the number of variables preceding @subcmd{WITH}
2783 must be the same as the number following it.
2784 In this case, tests for each respective pair of variables are
2786 If the @subcmd{WITH} keyword is given, but the
2787 @subcmd{(PAIRED)} keyword is omitted, then tests for each combination
2788 of variable preceding @subcmd{WITH} against variable following
2789 @subcmd{WITH} are performed.
2798 /MISSING=@{ANALYSIS,LISTWISE@} @{EXCLUDE,INCLUDE@}
2799 /CRITERIA=CI(@var{confidence})
2803 TESTVAL=@var{test_value}
2804 /VARIABLES=@var{var_list}
2807 (Independent Samples mode.)
2808 GROUPS=var(@var{value1} [, @var{value2}])
2809 /VARIABLES=@var{var_list}
2812 (Paired Samples mode.)
2813 PAIRS=@var{var_list} [WITH @var{var_list} [(PAIRED)] ]
2818 The @cmd{T-TEST} procedure outputs tables used in testing hypotheses about
2820 It operates in one of three modes:
2822 @item One Sample mode.
2823 @item Independent Groups mode.
2828 Each of these modes are described in more detail below.
2829 There are two optional subcommands which are common to all modes.
2831 The @cmd{/CRITERIA} subcommand tells @pspp{} the confidence interval used
2832 in the tests. The default value is 0.95.
2835 The @cmd{MISSING} subcommand determines the handling of missing
2837 If @subcmd{INCLUDE} is set, then user-missing values are included in the
2838 calculations, but system-missing values are not.
2839 If @subcmd{EXCLUDE} is set, which is the default, user-missing
2840 values are excluded as well as system-missing values.
2841 This is the default.
2843 If @subcmd{LISTWISE} is set, then the entire case is excluded from analysis
2844 whenever any variable specified in the @subcmd{/VARIABLES}, @subcmd{/PAIRS} or
2845 @subcmd{/GROUPS} subcommands contains a missing value.
2846 If @subcmd{ANALYSIS} is set, then missing values are excluded only in the analysis for
2847 which they would be needed. This is the default.
2851 * One Sample Mode:: Testing against a hypothesized mean
2852 * Independent Samples Mode:: Testing two independent groups for equal mean
2853 * Paired Samples Mode:: Testing two interdependent groups for equal mean
2856 @node One Sample Mode
2857 @subsection One Sample Mode
2859 The @subcmd{TESTVAL} subcommand invokes the One Sample mode.
2860 This mode is used to test a population mean against a hypothesized
2862 The value given to the @subcmd{TESTVAL} subcommand is the value against
2863 which you wish to test.
2864 In this mode, you must also use the @subcmd{/VARIABLES} subcommand to
2865 tell @pspp{} which variables you wish to test.
2867 @subsubsection Example - One Sample T-test
2869 A researcher wishes to know whether the weight of persons in a population
2870 is different from the national average.
2871 The samples are drawn from the population under investigation and recorded
2872 in the file @file{physiology.sav}.
2873 From the Department of Health, she
2874 knows that the national average weight of healthy adults is 76.8kg.
2875 Accordingly the @subcmd{TESTVAL} is set to 76.8.
2876 The null hypothesis therefore is that the mean average weight of the
2877 population from which the sample was drawn is 76.8kg.
2879 As previously noted (@pxref{Identifying incorrect data}), one
2880 sample in the dataset contains a weight value
2881 which is clearly incorrect. So this is excluded from the analysis
2882 using the @cmd{SELECT} command.
2884 @float Example, one-sample-t:ex
2885 @psppsyntax {one-sample-t.sps}
2886 @caption {Running a one sample T-Test after excluding all non-positive values}
2889 @float Screenshot, one-sample-t:scr
2890 @psppimage {one-sample-t}
2891 @caption {Using the One Sample T-Test dialog box to test @exvar{weight} for a mean of 76.8kg}
2895 @ref{one-sample-t:res} shows that the mean of our sample differs from the test value
2896 by -1.40kg. However the significance is very high (0.610). So one cannot
2897 reject the null hypothesis, and must conclude there is not enough evidence
2898 to suggest that the mean weight of the persons in our population is different
2901 @float Results, one-sample-t:res
2902 @psppoutput {one-sample-t}
2903 @caption {The results of a one sample T-test of @exvar{weight} using a test value of 76.8kg}
2906 @node Independent Samples Mode
2907 @subsection Independent Samples Mode
2909 The @subcmd{GROUPS} subcommand invokes Independent Samples mode or
2911 This mode is used to test whether two groups of values have the
2912 same population mean.
2913 In this mode, you must also use the @subcmd{/VARIABLES} subcommand to
2914 tell @pspp{} the dependent variables you wish to test.
2916 The variable given in the @subcmd{GROUPS} subcommand is the independent
2917 variable which determines to which group the samples belong.
2918 The values in parentheses are the specific values of the independent
2919 variable for each group.
2920 If the parentheses are omitted and no values are given, the default values
2921 of 1.0 and 2.0 are assumed.
2923 If the independent variable is numeric,
2924 it is acceptable to specify only one value inside the parentheses.
2925 If you do this, cases where the independent variable is
2926 greater than or equal to this value belong to the first group, and cases
2927 less than this value belong to the second group.
2928 When using this form of the @subcmd{GROUPS} subcommand, missing values in
2929 the independent variable are excluded on a listwise basis, regardless
2930 of whether @subcmd{/MISSING=LISTWISE} was specified.
2932 @subsubsection Example - Independent Samples T-test
2934 A researcher wishes to know whether within a population, adult males
2935 are taller than adult females.
2936 The samples are drawn from the population under investigation and recorded
2937 in the file @file{physiology.sav}.
2939 As previously noted (@pxref{Identifying incorrect data}), one
2940 sample in the dataset contains a height value
2941 which is clearly incorrect. So this is excluded from the analysis
2942 using the @cmd{SELECT} command.
2945 @float Example, indepdendent-samples-t:ex
2946 @psppsyntax {independent-samples-t.sps}
2947 @caption {Running a independent samples T-Test after excluding all observations less than 200kg}
2951 The null hypothesis is that both males and females are on average
2954 @float Screenshot, independent-samples-t:scr
2955 @psppimage {independent-samples-t}
2956 @caption {Using the Independent Sample T-test dialog, to test for differences of @exvar{height} between values of @exvar{sex}}
2960 In this case, the grouping variable is @exvar{sex}, so this is entered
2961 as the variable for the @subcmd{GROUP} subcommand. The group values are 0 (male) and
2964 If you are running the proceedure using syntax, then you need to enter
2965 the values corresponding to each group within parentheses.
2966 If you are using the graphic user interface, then you have to open
2967 the ``Define Groups'' dialog box and enter the values corresponding
2968 to each group as shown in @ref{define-groups-t:scr}. If, as in this case, the dataset has defined value
2969 labels for the group variable, then you can enter them by label
2972 @float Screenshot, define-groups-t:scr
2973 @psppimage {define-groups-t}
2974 @caption {Setting the values of the grouping variable for an Independent Samples T-test}
2977 From @ref{independent-samples-t:res}, one can clearly see that the @emph{sample} mean height
2978 is greater for males than for females. However in order to see if this
2979 is a significant result, one must consult the T-Test table.
2981 The T-Test table contains two rows; one for use if the variance of the samples
2982 in each group may be safely assumed to be equal, and the second row
2983 if the variances in each group may not be safely assumed to be equal.
2985 In this case however, both rows show a 2-tailed significance less than 0.001 and
2986 one must therefore reject the null hypothesis and conclude that within
2987 the population the mean height of males and of females are unequal.
2989 @float Result, independent-samples-t:res
2990 @psppoutput {independent-samples-t}
2991 @caption {The results of an independent samples T-test of @exvar{height} by @exvar{sex}}
2994 @node Paired Samples Mode
2995 @subsection Paired Samples Mode
2997 The @cmd{PAIRS} subcommand introduces Paired Samples mode.
2998 Use this mode when repeated measures have been taken from the same
3000 If the @subcmd{WITH} keyword is omitted, then tables for all
3001 combinations of variables given in the @cmd{PAIRS} subcommand are
3003 If the @subcmd{WITH} keyword is given, and the @subcmd{(PAIRED)} keyword
3004 is also given, then the number of variables preceding @subcmd{WITH}
3005 must be the same as the number following it.
3006 In this case, tables for each respective pair of variables are
3008 In the event that the @subcmd{WITH} keyword is given, but the
3009 @subcmd{(PAIRED)} keyword is omitted, then tables for each combination
3010 of variable preceding @subcmd{WITH} against variable following
3011 @subcmd{WITH} are generated.
3018 @cindex analysis of variance
3023 [/VARIABLES = ] @var{var_list} BY @var{var}
3024 /MISSING=@{ANALYSIS,LISTWISE@} @{EXCLUDE,INCLUDE@}
3025 /CONTRAST= @var{value1} [, @var{value2}] ... [,@var{valueN}]
3026 /STATISTICS=@{DESCRIPTIVES,HOMOGENEITY@}
3027 /POSTHOC=@{BONFERRONI, GH, LSD, SCHEFFE, SIDAK, TUKEY, ALPHA ([@var{value}])@}
3030 The @cmd{ONEWAY} procedure performs a one-way analysis of variance of
3031 variables factored by a single independent variable.
3032 It is used to compare the means of a population
3033 divided into more than two groups.
3035 The dependent variables to be analysed should be given in the @subcmd{VARIABLES}
3037 The list of variables must be followed by the @subcmd{BY} keyword and
3038 the name of the independent (or factor) variable.
3040 You can use the @subcmd{STATISTICS} subcommand to tell @pspp{} to display
3041 ancillary information. The options accepted are:
3044 Displays descriptive statistics about the groups factored by the independent
3047 Displays the Levene test of Homogeneity of Variance for the
3048 variables and their groups.
3051 The @subcmd{CONTRAST} subcommand is used when you anticipate certain
3052 differences between the groups.
3053 The subcommand must be followed by a list of numerals which are the
3054 coefficients of the groups to be tested.
3055 The number of coefficients must correspond to the number of distinct
3056 groups (or values of the independent variable).
3057 If the total sum of the coefficients are not zero, then @pspp{} will
3058 display a warning, but will proceed with the analysis.
3059 The @subcmd{CONTRAST} subcommand may be given up to 10 times in order
3060 to specify different contrast tests.
3061 The @subcmd{MISSING} subcommand defines how missing values are handled.
3062 If @subcmd{LISTWISE} is specified then cases which have missing values for
3063 the independent variable or any dependent variable are ignored.
3064 If @subcmd{ANALYSIS} is specified, then cases are ignored if the independent
3065 variable is missing or if the dependent variable currently being
3066 analysed is missing. The default is @subcmd{ANALYSIS}.
3067 A setting of @subcmd{EXCLUDE} means that variables whose values are
3068 user-missing are to be excluded from the analysis. A setting of
3069 @subcmd{INCLUDE} means they are to be included. The default is @subcmd{EXCLUDE}.
3071 Using the @code{POSTHOC} subcommand you can perform multiple
3072 pairwise comparisons on the data. The following comparison methods
3076 Least Significant Difference.
3077 @item @subcmd{TUKEY}
3078 Tukey Honestly Significant Difference.
3079 @item @subcmd{BONFERRONI}
3081 @item @subcmd{SCHEFFE}
3083 @item @subcmd{SIDAK}
3086 The Games-Howell test.
3090 Use the optional syntax @code{ALPHA(@var{value})} to indicate that
3091 @cmd{ONEWAY} should perform the posthoc tests at a confidence level of
3092 @var{value}. If @code{ALPHA(@var{value})} is not specified, then the
3093 confidence level used is 0.05.
3096 @section QUICK CLUSTER
3097 @vindex QUICK CLUSTER
3099 @cindex K-means clustering
3103 QUICK CLUSTER @var{var_list}
3104 [/CRITERIA=CLUSTERS(@var{k}) [MXITER(@var{max_iter})] CONVERGE(@var{epsilon}) [NOINITIAL]]
3105 [/MISSING=@{EXCLUDE,INCLUDE@} @{LISTWISE, PAIRWISE@}]
3106 [/PRINT=@{INITIAL@} @{CLUSTER@}]
3107 [/SAVE[=[CLUSTER[(@var{membership_var})]] [DISTANCE[(@var{distance_var})]]]
3110 The @cmd{QUICK CLUSTER} command performs k-means clustering on the
3111 dataset. This is useful when you wish to allocate cases into clusters
3112 of similar values and you already know the number of clusters.
3114 The minimum specification is @samp{QUICK CLUSTER} followed by the names
3115 of the variables which contain the cluster data. Normally you will also
3116 want to specify @subcmd{/CRITERIA=CLUSTERS(@var{k})} where @var{k} is the
3117 number of clusters. If this is not specified, then @var{k} defaults to 2.
3119 If you use @subcmd{/CRITERIA=NOINITIAL} then a naive algorithm to select
3120 the initial clusters is used. This will provide for faster execution but
3121 less well separated initial clusters and hence possibly an inferior final
3125 @cmd{QUICK CLUSTER} uses an iterative algorithm to select the clusters centers.
3126 The subcommand @subcmd{/CRITERIA=MXITER(@var{max_iter})} sets the maximum number of iterations.
3127 During classification, @pspp{} will continue iterating until until @var{max_iter}
3128 iterations have been done or the convergence criterion (see below) is fulfilled.
3129 The default value of @var{max_iter} is 2.
3131 If however, you specify @subcmd{/CRITERIA=NOUPDATE} then after selecting the initial centers,
3132 no further update to the cluster centers is done. In this case, @var{max_iter}, if specified.
3135 The subcommand @subcmd{/CRITERIA=CONVERGE(@var{epsilon})} is used
3136 to set the convergence criterion. The value of convergence criterion is @var{epsilon}
3137 times the minimum distance between the @emph{initial} cluster centers. Iteration stops when
3138 the mean cluster distance between one iteration and the next
3139 is less than the convergence criterion. The default value of @var{epsilon} is zero.
3141 The @subcmd{MISSING} subcommand determines the handling of missing variables.
3142 If @subcmd{INCLUDE} is set, then user-missing values are considered at their face
3143 value and not as missing values.
3144 If @subcmd{EXCLUDE} is set, which is the default, user-missing
3145 values are excluded as well as system-missing values.
3147 If @subcmd{LISTWISE} is set, then the entire case is excluded from the analysis
3148 whenever any of the clustering variables contains a missing value.
3149 If @subcmd{PAIRWISE} is set, then a case is considered missing only if all the
3150 clustering variables contain missing values. Otherwise it is clustered
3151 on the basis of the non-missing values.
3152 The default is @subcmd{LISTWISE}.
3154 The @subcmd{PRINT} subcommand requests additional output to be printed.
3155 If @subcmd{INITIAL} is set, then the initial cluster memberships will
3157 If @subcmd{CLUSTER} is set, the cluster memberships of the individual
3158 cases are displayed (potentially generating lengthy output).
3160 You can specify the subcommand @subcmd{SAVE} to ask that each case's cluster membership
3161 and the euclidean distance between the case and its cluster center be saved to
3162 a new variable in the active dataset. To save the cluster membership use the
3163 @subcmd{CLUSTER} keyword and to save the distance use the @subcmd{DISTANCE} keyword.
3164 Each keyword may optionally be followed by a variable name in parentheses to specify
3165 the new variable which is to contain the saved parameter. If no variable name is specified,
3166 then PSPP will create one.
3174 [VARIABLES=] @var{var_list} [@{A,D@}] [BY @var{var_list}]
3175 /TIES=@{MEAN,LOW,HIGH,CONDENSE@}
3176 /FRACTION=@{BLOM,TUKEY,VW,RANKIT@}
3178 /MISSING=@{EXCLUDE,INCLUDE@}
3180 /RANK [INTO @var{var_list}]
3181 /NTILES(k) [INTO @var{var_list}]
3182 /NORMAL [INTO @var{var_list}]
3183 /PERCENT [INTO @var{var_list}]
3184 /RFRACTION [INTO @var{var_list}]
3185 /PROPORTION [INTO @var{var_list}]
3186 /N [INTO @var{var_list}]
3187 /SAVAGE [INTO @var{var_list}]
3190 The @cmd{RANK} command ranks variables and stores the results into new
3193 The @subcmd{VARIABLES} subcommand, which is mandatory, specifies one or
3194 more variables whose values are to be ranked.
3195 After each variable, @samp{A} or @samp{D} may appear, indicating that
3196 the variable is to be ranked in ascending or descending order.
3197 Ascending is the default.
3198 If a @subcmd{BY} keyword appears, it should be followed by a list of variables
3199 which are to serve as group variables.
3200 In this case, the cases are gathered into groups, and ranks calculated
3203 The @subcmd{TIES} subcommand specifies how tied values are to be treated. The
3204 default is to take the mean value of all the tied cases.
3206 The @subcmd{FRACTION} subcommand specifies how proportional ranks are to be
3207 calculated. This only has any effect if @subcmd{NORMAL} or @subcmd{PROPORTIONAL} rank
3208 functions are requested.
3210 The @subcmd{PRINT} subcommand may be used to specify that a summary of the rank
3211 variables created should appear in the output.
3213 The function subcommands are @subcmd{RANK}, @subcmd{NTILES}, @subcmd{NORMAL}, @subcmd{PERCENT}, @subcmd{RFRACTION},
3214 @subcmd{PROPORTION} and @subcmd{SAVAGE}. Any number of function subcommands may appear.
3215 If none are given, then the default is RANK.
3216 The @subcmd{NTILES} subcommand must take an integer specifying the number of
3217 partitions into which values should be ranked.
3218 Each subcommand may be followed by the @subcmd{INTO} keyword and a list of
3219 variables which are the variables to be created and receive the rank
3220 scores. There may be as many variables specified as there are
3221 variables named on the @subcmd{VARIABLES} subcommand. If fewer are specified,
3222 then the variable names are automatically created.
3224 The @subcmd{MISSING} subcommand determines how user missing values are to be
3225 treated. A setting of @subcmd{EXCLUDE} means that variables whose values are
3226 user-missing are to be excluded from the rank scores. A setting of
3227 @subcmd{INCLUDE} means they are to be included. The default is @subcmd{EXCLUDE}.
3229 @include regression.texi
3233 @section RELIABILITY
3238 /VARIABLES=@var{var_list}
3239 /SCALE (@var{name}) = @{@var{var_list}, ALL@}
3240 /MODEL=@{ALPHA, SPLIT[(@var{n})]@}
3241 /SUMMARY=@{TOTAL,ALL@}
3242 /MISSING=@{EXCLUDE,INCLUDE@}
3245 @cindex Cronbach's Alpha
3246 The @cmd{RELIABILITY} command performs reliability analysis on the data.
3248 The @subcmd{VARIABLES} subcommand is required. It determines the set of variables
3249 upon which analysis is to be performed.
3251 The @subcmd{SCALE} subcommand determines the variables for which
3252 reliability is to be calculated. If @subcmd{SCALE} is omitted, then analysis for
3253 all variables named in the @subcmd{VARIABLES} subcommand are used.
3254 Optionally, the @var{name} parameter may be specified to set a string name
3257 The @subcmd{MODEL} subcommand determines the type of analysis. If @subcmd{ALPHA} is specified,
3258 then Cronbach's Alpha is calculated for the scale. If the model is @subcmd{SPLIT},
3259 then the variables are divided into 2 subsets. An optional parameter
3260 @var{n} may be given, to specify how many variables to be in the first subset.
3261 If @var{n} is omitted, then it defaults to one half of the variables in the
3262 scale, or one half minus one if there are an odd number of variables.
3263 The default model is @subcmd{ALPHA}.
3265 By default, any cases with user missing, or system missing values for
3266 any variables given in the @subcmd{VARIABLES} subcommand are omitted
3267 from the analysis. The @subcmd{MISSING} subcommand determines whether
3268 user missing values are included or excluded in the analysis.
3270 The @subcmd{SUMMARY} subcommand determines the type of summary analysis to be performed.
3271 Currently there is only one type: @subcmd{SUMMARY=TOTAL}, which displays per-item
3272 analysis tested against the totals.
3274 @subsection Example - Reliability
3276 Before analysing the results of a survey -- particularly for a multiple choice survey --
3277 it is desireable to know whether the respondents have considered their answers
3278 or simply provided random answers.
3280 In the following example the survey results from the file @file{hotel.sav} are used.
3281 All five survey questions are included in the reliability analysis.
3282 However, before running the analysis, the data must be preprocessed.
3283 An examination of the survey questions reveals that two questions, @i{viz:} v3 and v5
3284 are negatively worded, whereas the others are positively worded.
3285 All questions must be based upon the same scale for the analysis to be meaningful.
3286 One could use the @cmd{RECODE} command (@pxref{RECODE}), however a simpler way is
3287 to use @cmd{COMPUTE} (@pxref{COMPUTE}) and this is what is done in @ref{reliability:ex}.
3289 @float Example, reliability:ex
3290 @psppsyntax {reliability.sps}
3291 @caption {Investigating the reliability of survey responses}
3294 In this case, all variables in the data set are used. So we can use the special
3295 keyword @samp{ALL} (@pxref{BNF}).
3297 @float Screenshot, reliability:src
3298 @psppimage {reliability}
3299 @caption {Reliability dialog box with all variables selected}
3302 @ref{reliability:res} shows that Cronbach's Alpha is 0.11 which is a value normally considered too
3303 low to indicate consistency within the data. This is possibly due to the small number of
3304 survey questions. The survey should be redesigned before serious use of the results are
3307 @float Result, reliability:res
3308 @psppoutput {reliability}
3309 @caption {The results of the reliability command on @file{hotel.sav}}
3317 @cindex Receiver Operating Characteristic
3318 @cindex Area under curve
3321 ROC @var{var_list} BY @var{state_var} (@var{state_value})
3322 /PLOT = @{ CURVE [(REFERENCE)], NONE @}
3323 /PRINT = [ SE ] [ COORDINATES ]
3324 /CRITERIA = [ CUTOFF(@{INCLUDE,EXCLUDE@}) ]
3325 [ TESTPOS (@{LARGE,SMALL@}) ]
3326 [ CI (@var{confidence}) ]
3327 [ DISTRIBUTION (@{FREE, NEGEXPO @}) ]
3328 /MISSING=@{EXCLUDE,INCLUDE@}
3332 The @cmd{ROC} command is used to plot the receiver operating characteristic curve
3333 of a dataset, and to estimate the area under the curve.
3334 This is useful for analysing the efficacy of a variable as a predictor of a state of nature.
3336 The mandatory @var{var_list} is the list of predictor variables.
3337 The variable @var{state_var} is the variable whose values represent the actual states,
3338 and @var{state_value} is the value of this variable which represents the positive state.
3340 The optional subcommand @subcmd{PLOT} is used to determine if and how the @subcmd{ROC} curve is drawn.
3341 The keyword @subcmd{CURVE} means that the @subcmd{ROC} curve should be drawn, and the optional keyword @subcmd{REFERENCE},
3342 which should be enclosed in parentheses, says that the diagonal reference line should be drawn.
3343 If the keyword @subcmd{NONE} is given, then no @subcmd{ROC} curve is drawn.
3344 By default, the curve is drawn with no reference line.
3346 The optional subcommand @subcmd{PRINT} determines which additional
3347 tables should be printed. Two additional tables are available. The
3348 @subcmd{SE} keyword says that standard error of the area under the
3349 curve should be printed as well as the area itself. In addition, a
3350 p-value for the null hypothesis that the area under the curve equals
3351 0.5 is printed. The @subcmd{COORDINATES} keyword says that a
3352 table of coordinates of the @subcmd{ROC} curve should be printed.
3354 The @subcmd{CRITERIA} subcommand has four optional parameters:
3356 @item The @subcmd{TESTPOS} parameter may be @subcmd{LARGE} or @subcmd{SMALL}.
3357 @subcmd{LARGE} is the default, and says that larger values in the predictor variables are to be
3358 considered positive. @subcmd{SMALL} indicates that smaller values should be considered positive.
3360 @item The @subcmd{CI} parameter specifies the confidence interval that should be printed.
3361 It has no effect if the @subcmd{SE} keyword in the @subcmd{PRINT} subcommand has not been given.
3363 @item The @subcmd{DISTRIBUTION} parameter determines the method to be used when estimating the area
3365 There are two possibilities, @i{viz}: @subcmd{FREE} and @subcmd{NEGEXPO}.
3366 The @subcmd{FREE} method uses a non-parametric estimate, and the @subcmd{NEGEXPO} method a bi-negative
3367 exponential distribution estimate.
3368 The @subcmd{NEGEXPO} method should only be used when the number of positive actual states is
3369 equal to the number of negative actual states.
3370 The default is @subcmd{FREE}.
3372 @item The @subcmd{CUTOFF} parameter is for compatibility and is ignored.
3375 The @subcmd{MISSING} subcommand determines whether user missing values are to
3376 be included or excluded in the analysis. The default behaviour is to
3378 Cases are excluded on a listwise basis; if any of the variables in @var{var_list}
3379 or if the variable @var{state_var} is missing, then the entire case is
3382 @c LocalWords: subcmd subcommand