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 @item @code{@i{area}PCT} or @code{@i{area}PCT.COUNT} (``@i{Area} %'')
1246 A percentage within the specified @var{area}.
1248 @item @code{@i{area}PCT.VALIDN} (``@i{Area} Valid N %'')
1249 A percentage of valid values within the specified @var{area}.
1251 @item @code{@i{area}PCT.TOTALN} (``@i{Area} Total N %'')
1252 A percentage of total values within the specified @var{area}.
1255 The following summary functions apply only to scalar variables:
1258 @item @code{MAXIMUM} (``Maximum'')
1261 @item @code{MEAN} (``Mean'')
1264 @item @code{MEDIAN} (``Median'')
1267 @item @code{MINIMUM} (``Minimum'')
1270 @item @code{MISSING} (``Missing'')
1271 Sum of weights of user- and system-missing values.
1273 @item @code{MODE} (``Mode'')
1274 The highest-frequency value. Ties are broken by taking the smallest mode.
1276 @item @code{@i{area}PCT.SUM} (``@i{Area} Sum %'')
1277 Percentage of the sum of the values across @var{area}.
1279 @item @code{PTILE} @i{n} (``Percentile @i{n}'')
1280 The @var{n}th percentile, where @math{0 @leq{} @var{n} @leq{} 100}.
1282 @item @code{RANGE} (``Range'')
1283 The maximum minus the minimum.
1285 @item @code{SEMEAN} (``Std Error of Mean'')
1286 The standard error of the mean.
1288 @item @code{STDDEV} (``Std Deviation'')
1289 The standard deviation.
1291 @item @code{SUM} (``Sum'')
1294 @item @code{TOTALN} (``Total N'')
1295 The sum of total count weights.
1297 @item @code{VALIDN} (``Valid N'')
1298 The sum of valid count weights.
1300 @item @code{VARIANCE} (``Variance'')
1304 If the @code{WEIGHT} subcommand specified an adjustment weight
1305 variable, then the following summary functions use its value instead
1306 of the dictionary weight variable. Otherwise, they are equivalent to
1307 the summary function without the @samp{E}-prefix:
1311 @code{ECOUNT} (``Adjusted Count'')
1314 @code{ETOTALN} (``Adjusted Total N'')
1317 @code{EVALIDN} (``Adjusted Valid N'')
1320 The following summary functions with a @samp{U}-prefix are equivalent
1321 to the same ones without the prefix, except that they use unweighted
1326 @code{UCOUNT} (``Unweighted Count'')
1329 @code{U@i{area}PCT} or @code{U@i{area}PCT.COUNT} (``Unweighted @i{Area} %'')
1332 @code{U@i{area}PCT.VALIDN} (``Unweighted @i{Area} Valid N %'')
1335 @code{U@i{area}PCT.TOTALN} (``Unweighted @i{Area} Total N %'')
1338 @code{UMEAN} (``Unweighted Mean'')
1341 @code{UMEDIAN} (``Unweighted Median'')
1344 @code{UMISSING} (``Unweighted Missing'')
1347 @code{UMODE} (``Unweight Mode'')
1350 @code{U@i{area}PCT.SUM} (``Unweighted @i{Area} Sum %'')
1353 @code{UPTILE} @i{n} (``Unweighted Percentile @i{n}'')
1356 @code{USEMEAN} (``Unweighted Std Error of Mean'')
1359 @code{USTDDEV} (``Unweighted Std Deviation'')
1362 @code{USUM} (``Unweighted Sum'')
1365 @code{UTOTALN} (``Unweighted Total N'')
1368 @code{UVALIDN} (``Unweighted Valid N'')
1371 @code{UVARIANCE} (``Unweighted Variance'')
1374 @node CTABLES Statistics Positions and Labels
1375 @subsection Statistics Positions and Labels
1379 [@t{POSITION=}@{@t{COLUMN} @math{|} @t{ROW} @math{|} @t{LAYER}@}]
1380 [@t{VISIBLE=}@{@t{YES} @math{|} @t{NO}@}]
1383 The @code{SLABELS} subcommand controls the position and visibility of
1384 summary statistics for the @code{TABLE} subcommand that it follows.
1386 @code{POSITION} sets the axis on which summary statistics appear.
1387 With @t{POSITION=COLUMN}, which is the default, each summary statistic
1388 appears in a column. For example:
1391 CTABLES /TABLE=qnd1 [MEAN, MEDIAN] BY qns3a.
1393 @psppoutput {ctables13}
1396 With @t{POSITION=ROW}, each summary statistic appears in a row, as
1400 CTABLES /TABLE=qnd1 [MEAN, MEDIAN] BY qns3a /SLABELS POSITION=ROW.
1402 @psppoutput {ctables14}
1405 @t{POSITION=LAYER} is also available to place each summary statistic in
1408 Labels for summary statistics are shown by default. Use
1409 @t{VISIBLE=NO} to suppress them. Because unlabeled data can cause
1410 confusion, it should only be considered if the meaning of the data is
1411 evident, as in a simple case like this:
1414 CTABLES /TABLE=AgeGroup [TABLEPCT] /SLABELS VISIBLE=NO.
1416 @psppoutput {ctables15}
1418 @node CTABLES Category Label Positions
1419 @subsection Category Label Positions
1422 @t{/CLABELS} @{@t{AUTO} @math{|} @{@t{ROWLABELS}@math{|}@t{COLLABELS}@}@t{=}@{@t{OPPOSITE}@math{|}@t{LAYER}@}@}
1425 The @code{CLABELS} subcommand controls the position of category labels
1426 for the @code{TABLE} subcommand that it follows. By default, or if
1427 @t{AUTO} is specified, category labels for a given variable nest
1428 inside the variable's label on the same axis. For example, the
1429 command below results in age categories nesting within the age group
1430 variable on the rows axis and gender categories within the gender
1431 variable on the columns axis:
1434 CTABLES /TABLE AgeGroup BY qns3a.
1436 @psppoutput {ctables16}
1438 @t{ROWLABELS=OPPOSITE} or @t{COLLABELS=OPPOSITE} move row or column
1439 variable category labels, respectively, to the opposite axis. The
1440 setting affects only the innermost variable on the given axis. For
1444 CTABLES /TABLE AgeGroup BY qns3a /CLABELS ROWLABELS=OPPOSITE.
1445 CTABLES /TABLE AgeGroup BY qns3a /CLABELS COLLABELS=OPPOSITE.
1447 @psppoutput {ctables17}
1449 @t{ROWLABELS=LAYER} or @t{COLLABELS=LAYER} move the innermost row or
1450 column variable category labels, respectively, to the layer axis.
1452 Only one axis's labels may be moved, whether to the opposite axis or
1455 @node CTABLES Per-Variable Category Options
1456 @subsection Per-Variable Category Options
1459 @t{/CATEGORIES} @t{VARIABLES=}@i{variables}
1460 @{@t{[}@i{value}@t{,} @i{value}@dots{}@t{]}
1461 @math{|} [@t{ORDER=}@{@t{A} @math{|} @t{D}@}]
1462 [@t{KEY=}@{@t{VALUE} @math{|} @t{LABEL} @math{|} @i{summary}@t{(}@i{variable}@t{)}@}]
1463 [@t{MISSING=}@{@t{EXCLUDE} @math{|} @t{INCLUDE}@}]@}
1464 [@t{TOTAL=}@{@t{NO} @math{|} @t{YES}@} [@t{LABEL=}@i{string}] [@t{POSITION=}@{@t{AFTER} @math{|} @t{BEFORE}@}]]
1465 [@t{EMPTY=}@{@t{INCLUDE} @math{|} @t{EXCLUDE}@}]
1468 The @code{CATEGORIES} subcommand specifies, for one or more
1469 categorical variables, the categories to include and exclude, the sort
1470 order for included categories, and treatment of missing values. It
1471 also controls the totals and subtotals to display. It may be
1472 specified any number of times, each time for a different set of
1473 variables. @code{CATEGORIES} applies to the table produced by the
1474 @code{TABLE} subcommand that it follows.
1476 @code{CATEGORIES} does not apply to scalar variables.
1478 @t{VARIABLES} is required. List the variables for the subcommand
1481 There are two way to specify the Categories to include and their sort
1485 @item Explicit categories.
1486 @anchor{CTABLE Explicit Category List}
1487 To explicitly specify categories to include, list the categories
1488 within square brackets in the desired sort order. Use spaces or
1489 commas to separate values. Categories not covered by the list are
1490 excluded from analysis.
1492 Each element of the list takes one of the following forms:
1497 A numeric or string category value, for variables that have the
1502 A date or time category value, for variables that have a date or time
1505 @item @i{min} THRU @i{max}
1506 @itemx LO THRU @i{max}
1507 @itemx @i{min} THRU HI
1508 A range of category values, where @var{min} and @var{max} each takes
1509 one of the forms above, in increasing order.
1512 All user-missing values. (To match individual user-missing values,
1513 specify their category values.)
1516 Any non-missing value not covered by any other element of the list
1517 (regardless of where @t{OTHERNM} is placed in the list).
1519 @item &@i{postcompute}
1520 A computed category name (@pxref{CTABLES Computed Categories}).
1523 Additional forms, described later, allow for subtotals.
1524 If multiple elements of the list cover a given category, the last one
1525 in the list is considered to be a match.
1527 @item Implicit categories.
1528 Without an explicit list of categories, @pspp{} sorts
1529 categories automatically.
1531 The @code{KEY} setting specifies the sort key. By default, or with
1532 @code{KEY=VALUE}, categories are sorted by default. Categories may
1533 also be sorted by value label, with @code{KEY=LABEL}, or by the value
1534 of a summary function, e.g.@: @code{KEY=COUNT}. For summary
1535 functions, a variable name may be specified in parentheses, e.g.@:
1536 @code{KEY=MAXIUM(qnd1)}, and this is required for functions that apply
1537 only to scalar variables. The @code{PTILE} function also requires a
1538 percentage argument, e.g.@: @code{KEY=PTILE(qnd1, 90)}. Only summary
1539 functions used in the table may be used, except that @code{COUNT} is
1542 By default, or with @code{ORDER=A}, categories are sorted in ascending
1543 order. Specify @code{ORDER=D} to sort in descending order.
1545 User-missing values are excluded by default, or with
1546 @code{MISSING=EXCLUDE}. Specify @code{MISSING=INCLUDE} to include
1547 user-missing values. The system-missing value is always excluded.
1550 @subsubheading Totals and Subtotals
1552 @code{CATEGORIES} also controls display of totals and subtotals.
1553 Totals are not displayed by default, or with @code{TOTAL=NO}. Specify
1554 @code{TOTAL=YES} to display a total. By default, the total is labeled
1555 ``Total''; use @code{LABEL="@i{label}"} to override it.
1557 Subtotals are also not displayed by default. To add one or more
1558 subtotals, use an explicit category list and insert @code{SUBTOTAL} or
1559 @code{HSUBTOTAL} in the position or positions where the subtotal
1560 should appear. With @code{SUBTOTAL}, the subtotal becomes an extra
1561 row or column or layer; @code{HSUBTOTAL} additionally hides the
1562 categories that make up the subtotal. Either way, the default label
1563 is ``Subtotal'', use @code{SUBTOTAL="@i{label}"} or
1564 @code{HSUBTOTAL="@i{label}"} to specify a custom label.
1566 By default, or with @code{POSITION=AFTER}, totals come after the last
1567 category and subtotals apply to categories that precede them. With
1568 @code{POSITION=BEFORE}, totals come before the first category and
1569 subtotals apply to categories that follow them.
1571 Only categorical variables may have totals and subtotals. Scalar
1572 variables may be ``totaled'' indirectly by enabling totals and
1573 subtotals on a categorical variable within which the scalar variable is
1576 @subsubheading Categories Without Values
1578 Some categories might not be included in the data set being analyzed.
1579 For example, our example data set has no cases in the ``15 or
1580 younger'' age group. By default, or with @code{EMPTY=INCLUDE},
1581 @pspp{} includes these empty categories in output tables. To exclude
1582 them, specify @code{EMPTY=EXCLUDE}.
1584 For implicit categories, empty categories potentially include all the
1585 values with labels for a given variable; for explicit categories, they
1586 include all the values listed individually and all labeled values
1587 covered by ranges or @code{MISSING} or @code{OTHERNM}.
1589 @node CTABLES Titles
1594 [@t{TITLE=}@i{string}@dots{}]
1595 [@t{CAPTION=}@i{string}@dots{}]
1596 [@t{CORNER=}@i{string}@dots{}]
1599 The @code{TITLES} subcommand sets the title, caption, and corner text
1600 for the table output for the previous @code{TABLE} subcommand. The
1601 title appears above the table, the caption below the table, and the
1602 corner text appears in the table's upper left corner. By default, the
1603 title is ``Custom Tables'' and the caption and corner text are empty.
1605 @node CTABLES Table Formatting
1606 @subsection Table Formatting
1610 [@t{MINCOLWIDTH=}@{@t{DEFAULT} @math{|} @i{width}@}]
1611 [@t{MAXCOLWIDTH=}@{@t{DEFAULT} @math{|} @i{width}@}]
1612 [@t{UNITS=}@{@t{POINTS} @math{|} @t{INCHES} @math{|} @t{CM}@}]
1613 [@t{EMPTY=}@{@t{ZERO} @math{|} @t{BLANK} @math{|} @i{string}@}]
1614 [@t{MISSING=}@i{string}]
1617 The @code{FORMAT} subcommand, which must precede the first
1618 @code{TABLE} subcommand, controls formatting for all the output
1619 tables. @code{FORMAT} and all of its settings are optional.
1621 Use @code{MINCOLWIDTH} and @code{MAXCOLWIDTH} to control the minimum
1622 or maximum width of columns in output tables. By default, or with
1623 @code{DEFAULT}, column width varies based on content. Otherwise,
1624 specify a number for either or both of these settings. If both are
1625 specified, @code{MAXCOLWIDTH} must be bigger than @code{MINCOLWIDTH}.
1626 The default unit, or with @code{UNITS=POINTS}, is points (1/72 inch),
1627 but specify @code{UNITS=INCHES} to use inches or @code{UNITS=CM} for
1630 By default, or with @code{EMPTY=ZERO}, zero values are displayed in
1631 their usual format. Use @code{EMPTY=BLANK} to use an empty cell
1632 instead, or @code{EMPTY="@i{string}"} to use the specified string.
1634 By default, missing values are displayed as @samp{.}, the same as in
1635 other tables. Specify @code{MISSING="@i{string}"} to instead use a
1638 @node CTABLES Display of Variable Labels
1639 @subsection Display of Variable Labels
1643 @t{VARIABLES=}@i{variables}
1644 @t{DISPLAY}=@{@t{DEFAULT} @math{|} @t{NAME} @math{|} @t{LABEL} @math{|} @t{BOTH} @math{|} @t{NONE}@}
1647 The @code{VLABELS} subcommand, which must precede the first
1648 @code{TABLE} subcommand, controls display of variable labels in all
1649 the output tables. @code{VLABELS} is optional. It may appear
1650 multiple times to adjust settings for different variables.
1652 @code{VARIABLES} and @code{DISPLAY} are required. The value of
1653 @code{DISPLAY} controls how variable labels are displayed for the
1654 variables listed on @code{VARIABLES}. The supported values are:
1658 Uses the setting from @ref{SET TVARS}.
1661 Show only a variable name.
1664 Show only a variable label.
1667 Show variable name and label.
1673 @node CTABLES Missing Value Treatment
1674 @subsection Missing Value Treatment
1677 @t{/SMISSING} @{@t{VARIABLE} @math{|} @t{LISTWISE}@}
1680 The @code{SMISSING} subcommand, which must precede the first
1681 @code{TABLE} subcommand, controls treatment of missing values for
1682 scalar variables in producing all the output tables. @code{SMISSING}
1685 With @code{SMISSING=VARIABLE}, which is the default, missing values
1686 are excluded on a variable-by-variable basis. With
1687 @code{SMISSING=LISTWISE}, when scalar variables are stacked, a missing
1688 value for any of the scalar variables causes the case to be excluded
1691 @node CTABLES Computed Categories
1692 @subsection Computed Categories
1695 @t{/PCOMPUTE} @t{&}@i{postcompute}@t{=EXPR(}@i{expression}@t{)}
1698 @dfn{Computed categories}, also called @dfn{postcomputes}, are
1699 categories created using arithmetic on categories obtained from the
1700 data. The @code{PCOMPUTE} subcommand defines computed categories,
1701 which can then be used in two places: on @code{CATEGORIES} within an
1702 explicit category list (@pxref{CTABLE Explicit Category List}), and on
1703 the @code{PPROPERTIES} subcommand to define further properties for a
1706 @code{PCOMPUTE} must precede the first @code{TABLE} command. It is
1707 optional and it may be used any number of times to define multiple
1710 Each @code{PCOMPUTE} defines one postcompute. Its syntax consists of
1711 a name to identify the postcompute as a @pspp{} identifier prefixed by
1712 @samp{&}, followed by @samp{=} and a postcompute expression enclosed
1713 in @code{EXPR(@dots{})}. A postcompute expression consists of:
1716 @item [@i{category}]
1717 This form evaluates to the summary statistic for @i{category}, e.g.@:
1718 @code{[1]} evaluates to the value of the summary statistic associated
1719 with category 1. The @i{category} may be a number, a quoted string,
1720 or a quoted time or date value, and all of the categories for a given
1721 postcompute must have the same form.
1723 @item [@i{min} THRU @i{max}]
1724 @itemx [LO THRU @i{max}]
1725 @itemx [@i{min} THRU HI]
1728 These forms evaluate to the summary statistics for categories matching
1729 the given syntax, as described in previous sections (@pxref{CTABLE
1730 Explicit Category List}). If more than one category matches, their
1734 The summary statistic for the subtotal category. This form is allowed
1735 only for variables with exactly one subtotal.
1737 @item SUBTOTAL[@i{index}]
1738 The summary statistic for subtotal category @i{index}, where 1 is the
1739 first subtotal, 2 is the second, and so on. This form may be used for
1740 any number of subtotals.
1743 The summary statistic for the total.
1746 @itemx @i{a} - @i{b}
1747 @itemx @i{a} * @i{b}
1748 @itemx @i{a} / @i{b}
1749 @itemx @i{a} ** @i{b}
1750 These forms perform arithmetic on the values of postcompute
1751 expressions @i{a} and @i{b}. The usual operator precedence rules
1755 Numeric constants may be used in postcompute expressions.
1758 Parentheses override operator precedence.
1761 A postcompute is not associated with any particular variable.
1762 Instead, it may be referenced within @code{CATEGORIES} for any
1763 suitable variable (e.g.@: only a string variable is suitable for a
1764 postcompute expression that refers to a string category, only a
1765 variable with subtotals for an expression that refers to subtotals,
1768 Normally a named postcompute is defined only once, but if a later
1769 @code{PCOMPUTE} redefines a postcompute with the same name as an
1770 earlier one, the later one take precedence.
1772 @node CTABLES Computed Category Properties
1773 @subsection Computed Category Properties
1776 @t{/PPROPERTIES} @t{&}@i{postcompute}@dots{}
1777 [@t{LABEL=}@i{string}]
1778 [@t{FORMAT=}[@i{summary} @i{format}]@dots{}]
1779 [@t{HIDESOURCECATS=}@{@t{NO} @math{|} @t{YES}@}
1782 The @code{PPROPERTIES} subcommand, which must appear before
1783 @code{TABLE}, sets properties for one or more postcomputes defined on
1784 prior @code{PCOMPUTE} subcommands. The subcommand syntax begins with
1785 the list of postcomputes, each prefixed with @samp{&} as specified on
1788 All of the settings on @code{PPROPERTIES} are optional. Use
1789 @code{LABEL} to set the label shown for the postcomputes in table
1790 output. The default label for a postcompute is the expression used to
1793 The @code{FORMAT} setting sets summary statistics and display formats
1794 for the postcomputes.
1796 By default, or with @code{HIDESOURCECATS=NO}, categories referred to
1797 by computed categories are displayed like other categories. Use
1798 @code{HIDESOURCECATS=YES} to hide them.
1800 @node CTABLES Base Weight
1801 @subsection Base Weight
1804 @t{/WEIGHT VARIABLE=}@i{variable}
1807 The @code{WEIGHT} subcommand is optional and must appear before
1808 @code{TABLE}. If it appears, it must name a numeric variable, known
1809 as the @dfn{effective base weight} or @dfn{adjustment weight}. The
1810 effective base weight variable is used for the @code{ECOUNT},
1811 @code{ETOTALN}, and @code{EVALIDN} summary functions.
1813 Cases with zero, missing, or negative effective base weight are
1814 excluded from all analysis.
1816 Weights obtained from the @pspp{} dictionary are rounded to the
1817 nearest integer. Effective base weights are not rounded.
1819 @node CTABLES Hiding Small Counts
1820 @subsection Hiding Small Counts
1823 @t{/HIDESMALLCOUNTS COUNT=@i{count}}
1826 The @code{HIDESMALLCOUNTS} subcommand is optional. If it specified,
1827 then count values in output tables less than the value of @i{count}
1828 are shown as @code{<@i{count}} instead of their true values. The
1829 value of @i{count} must be an integer and must be at least 2. Case
1830 weights are considered for deciding whether to hide a count.
1836 @cindex factor analysis
1837 @cindex principal components analysis
1838 @cindex principal axis factoring
1839 @cindex data reduction
1843 VARIABLES=@var{var_list},
1844 MATRIX IN (@{CORR,COV@}=@{*,@var{file_spec}@})
1847 [ /METHOD = @{CORRELATION, COVARIANCE@} ]
1849 [ /ANALYSIS=@var{var_list} ]
1851 [ /EXTRACTION=@{PC, PAF@}]
1853 [ /ROTATION=@{VARIMAX, EQUAMAX, QUARTIMAX, PROMAX[(@var{k})], NOROTATE@}]
1855 [ /PRINT=[INITIAL] [EXTRACTION] [ROTATION] [UNIVARIATE] [CORRELATION] [COVARIANCE] [DET] [KMO] [AIC] [SIG] [ALL] [DEFAULT] ]
1859 [ /FORMAT=[SORT] [BLANK(@var{n})] [DEFAULT] ]
1861 [ /CRITERIA=[FACTORS(@var{n})] [MINEIGEN(@var{l})] [ITERATE(@var{m})] [ECONVERGE (@var{delta})] [DEFAULT] ]
1863 [ /MISSING=[@{LISTWISE, PAIRWISE@}] [@{INCLUDE, EXCLUDE@}] ]
1866 The @cmd{FACTOR} command performs Factor Analysis or Principal Axis Factoring on a dataset. It may be used to find
1867 common factors in the data or for data reduction purposes.
1869 The @subcmd{VARIABLES} subcommand is required (unless the @subcmd{MATRIX IN}
1870 subcommand is used).
1871 It lists the variables which are to partake in the analysis. (The @subcmd{ANALYSIS}
1872 subcommand may optionally further limit the variables that
1873 participate; it is useful primarily in conjunction with @subcmd{MATRIX IN}.)
1875 If @subcmd{MATRIX IN} instead of @subcmd{VARIABLES} is specified, then the analysis
1876 is performed on a pre-prepared correlation or covariance matrix file instead of on
1877 individual data cases. Typically the matrix file will have been generated by
1878 @cmd{MATRIX DATA} (@pxref{MATRIX DATA}) or provided by a third party.
1879 If specified, @subcmd{MATRIX IN} must be followed by @samp{COV} or @samp{CORR},
1880 then by @samp{=} and @var{file_spec} all in parentheses.
1881 @var{file_spec} may either be an asterisk, which indicates the currently loaded
1882 dataset, or it may be a file name to be loaded. @xref{MATRIX DATA}, for the expected
1885 The @subcmd{/EXTRACTION} subcommand is used to specify the way in which factors
1886 (components) are extracted from the data.
1887 If @subcmd{PC} is specified, then Principal Components Analysis is used.
1888 If @subcmd{PAF} is specified, then Principal Axis Factoring is
1889 used. By default Principal Components Analysis is used.
1891 The @subcmd{/ROTATION} subcommand is used to specify the method by which the
1892 extracted solution is rotated. Three orthogonal rotation methods are available:
1893 @subcmd{VARIMAX} (which is the default), @subcmd{EQUAMAX}, and @subcmd{QUARTIMAX}.
1894 There is one oblique rotation method, @i{viz}: @subcmd{PROMAX}.
1895 Optionally you may enter the power of the promax rotation @var{k}, which must be enclosed in parentheses.
1896 The default value of @var{k} is 5.
1897 If you don't want any rotation to be performed, the word @subcmd{NOROTATE}
1898 prevents the command from performing any rotation on the data.
1900 The @subcmd{/METHOD} subcommand should be used to determine whether the
1901 covariance matrix or the correlation matrix of the data is
1902 to be analysed. By default, the correlation matrix is analysed.
1904 The @subcmd{/PRINT} subcommand may be used to select which features of the analysis are reported:
1907 @item @subcmd{UNIVARIATE}
1908 A table of mean values, standard deviations and total weights are printed.
1909 @item @subcmd{INITIAL}
1910 Initial communalities and eigenvalues are printed.
1911 @item @subcmd{EXTRACTION}
1912 Extracted communalities and eigenvalues are printed.
1913 @item @subcmd{ROTATION}
1914 Rotated communalities and eigenvalues are printed.
1915 @item @subcmd{CORRELATION}
1916 The correlation matrix is printed.
1917 @item @subcmd{COVARIANCE}
1918 The covariance matrix is printed.
1920 The determinant of the correlation or covariance matrix is printed.
1922 The anti-image covariance and anti-image correlation matrices are printed.
1924 The Kaiser-Meyer-Olkin measure of sampling adequacy and the Bartlett test of sphericity is printed.
1926 The significance of the elements of correlation matrix is printed.
1928 All of the above are printed.
1929 @item @subcmd{DEFAULT}
1930 Identical to @subcmd{INITIAL} and @subcmd{EXTRACTION}.
1933 If @subcmd{/PLOT=EIGEN} is given, then a ``Scree'' plot of the eigenvalues is
1934 printed. This can be useful for visualizing the factors and deciding
1935 which factors (components) should be retained.
1937 The @subcmd{/FORMAT} subcommand determined how data are to be
1938 displayed in loading matrices. If @subcmd{SORT} is specified, then
1939 the variables are sorted in descending order of significance. If
1940 @subcmd{BLANK(@var{n})} is specified, then coefficients whose absolute
1941 value is less than @var{n} are not printed. If the keyword
1942 @subcmd{DEFAULT} is specified, or if no @subcmd{/FORMAT} subcommand is
1943 specified, then no sorting is performed, and all coefficients are printed.
1945 You can use the @subcmd{/CRITERIA} subcommand to specify how the number of
1946 extracted factors (components) are chosen. If @subcmd{FACTORS(@var{n})} is
1947 specified, where @var{n} is an integer, then @var{n} factors are
1948 extracted. Otherwise, the @subcmd{MINEIGEN} setting is used.
1949 @subcmd{MINEIGEN(@var{l})} requests that all factors whose eigenvalues
1950 are greater than or equal to @var{l} are extracted. The default value
1951 of @var{l} is 1. The @subcmd{ECONVERGE} setting has effect only when
1952 using iterative algorithms for factor extraction (such as Principal Axis
1953 Factoring). @subcmd{ECONVERGE(@var{delta})} specifies that
1954 iteration should cease when the maximum absolute value of the
1955 communality estimate between one iteration and the previous is less
1956 than @var{delta}. The default value of @var{delta} is 0.001.
1958 The @subcmd{ITERATE(@var{m})} may appear any number of times and is
1959 used for two different purposes. It is used to set the maximum number
1960 of iterations (@var{m}) for convergence and also to set the maximum
1961 number of iterations for rotation.
1962 Whether it affects convergence or rotation depends upon which
1963 subcommand follows the @subcmd{ITERATE} subcommand.
1964 If @subcmd{EXTRACTION} follows, it affects convergence.
1965 If @subcmd{ROTATION} follows, it affects rotation.
1966 If neither @subcmd{ROTATION} nor @subcmd{EXTRACTION} follow a
1967 @subcmd{ITERATE} subcommand, then the entire subcommand is ignored.
1968 The default value of @var{m} is 25.
1970 The @cmd{MISSING} subcommand determines the handling of missing
1971 variables. If @subcmd{INCLUDE} is set, then user-missing values are
1972 included in the calculations, but system-missing values are not.
1973 If @subcmd{EXCLUDE} is set, which is the default, user-missing
1974 values are excluded as well as system-missing values. This is the
1975 default. If @subcmd{LISTWISE} is set, then the entire case is excluded
1976 from analysis whenever any variable specified in the @cmd{VARIABLES}
1977 subcommand contains a missing value.
1979 If @subcmd{PAIRWISE} is set, then a case is considered missing only if
1980 either of the values for the particular coefficient are missing.
1981 The default is @subcmd{LISTWISE}.
1987 @cindex univariate analysis of variance
1988 @cindex fixed effects
1989 @cindex factorial anova
1990 @cindex analysis of variance
1995 GLM @var{dependent_vars} BY @var{fixed_factors}
1996 [/METHOD = SSTYPE(@var{type})]
1997 [/DESIGN = @var{interaction_0} [@var{interaction_1} [... @var{interaction_n}]]]
1998 [/INTERCEPT = @{INCLUDE|EXCLUDE@}]
1999 [/MISSING = @{INCLUDE|EXCLUDE@}]
2002 The @cmd{GLM} procedure can be used for fixed effects factorial Anova.
2004 The @var{dependent_vars} are the variables to be analysed.
2005 You may analyse several variables in the same command in which case they should all
2006 appear before the @code{BY} keyword.
2008 The @var{fixed_factors} list must be one or more categorical variables. Normally it
2009 does not make sense to enter a scalar variable in the @var{fixed_factors} and doing
2010 so may cause @pspp{} to do a lot of unnecessary processing.
2012 The @subcmd{METHOD} subcommand is used to change the method for producing the sums of
2013 squares. Available values of @var{type} are 1, 2 and 3. The default is type 3.
2015 You may specify a custom design using the @subcmd{DESIGN} subcommand.
2016 The design comprises a list of interactions where each interaction is a
2017 list of variables separated by a @samp{*}. For example the command
2019 GLM subject BY sex age_group race
2020 /DESIGN = age_group sex group age_group*sex age_group*race
2022 @noindent specifies the model @math{subject = age_group + sex + race + age_group*sex + age_group*race}.
2023 If no @subcmd{DESIGN} subcommand is specified, then the default is all possible combinations
2024 of the fixed factors. That is to say
2026 GLM subject BY sex age_group race
2029 @math{subject = age_group + sex + race + age_group*sex + age_group*race + sex*race + age_group*sex*race}.
2032 The @subcmd{MISSING} subcommand determines the handling of missing
2034 If @subcmd{INCLUDE} is set then, for the purposes of GLM analysis,
2035 only system-missing values are considered
2036 to be missing; user-missing values are not regarded as missing.
2037 If @subcmd{EXCLUDE} is set, which is the default, then user-missing
2038 values are considered to be missing as well as system-missing values.
2039 A case for which any dependent variable or any factor
2040 variable has a missing value is excluded from the analysis.
2042 @node LOGISTIC REGRESSION
2043 @section LOGISTIC REGRESSION
2045 @vindex LOGISTIC REGRESSION
2046 @cindex logistic regression
2047 @cindex bivariate logistic regression
2050 LOGISTIC REGRESSION [VARIABLES =] @var{dependent_var} WITH @var{predictors}
2052 [/CATEGORICAL = @var{categorical_predictors}]
2054 [@{/NOCONST | /ORIGIN | /NOORIGIN @}]
2056 [/PRINT = [SUMMARY] [DEFAULT] [CI(@var{confidence})] [ALL]]
2058 [/CRITERIA = [BCON(@var{min_delta})] [ITERATE(@var{max_interations})]
2059 [LCON(@var{min_likelihood_delta})] [EPS(@var{min_epsilon})]
2060 [CUT(@var{cut_point})]]
2062 [/MISSING = @{INCLUDE|EXCLUDE@}]
2065 Bivariate Logistic Regression is used when you want to explain a dichotomous dependent
2066 variable in terms of one or more predictor variables.
2068 The minimum command is
2070 LOGISTIC REGRESSION @var{y} WITH @var{x1} @var{x2} @dots{} @var{xn}.
2072 Here, @var{y} is the dependent variable, which must be dichotomous and @var{x1} @dots{} @var{xn}
2073 are the predictor variables whose coefficients the procedure estimates.
2075 By default, a constant term is included in the model.
2076 Hence, the full model is
2079 = b_0 + b_1 {\bf x_1}
2085 Predictor variables which are categorical in nature should be listed on the @subcmd{/CATEGORICAL} subcommand.
2086 Simple variables as well as interactions between variables may be listed here.
2088 If you want a model without the constant term @math{b_0}, use the keyword @subcmd{/ORIGIN}.
2089 @subcmd{/NOCONST} is a synonym for @subcmd{/ORIGIN}.
2091 An iterative Newton-Raphson procedure is used to fit the model.
2092 The @subcmd{/CRITERIA} subcommand is used to specify the stopping criteria of the procedure,
2093 and other parameters.
2094 The value of @var{cut_point} is used in the classification table. It is the
2095 threshold above which predicted values are considered to be 1. Values
2096 of @var{cut_point} must lie in the range [0,1].
2097 During iterations, if any one of the stopping criteria are satisfied, the procedure is
2098 considered complete.
2099 The stopping criteria are:
2101 @item The number of iterations exceeds @var{max_iterations}.
2102 The default value of @var{max_iterations} is 20.
2103 @item The change in the all coefficient estimates are less than @var{min_delta}.
2104 The default value of @var{min_delta} is 0.001.
2105 @item The magnitude of change in the likelihood estimate is less than @var{min_likelihood_delta}.
2106 The default value of @var{min_delta} is zero.
2107 This means that this criterion is disabled.
2108 @item The differential of the estimated probability for all cases is less than @var{min_epsilon}.
2109 In other words, the probabilities are close to zero or one.
2110 The default value of @var{min_epsilon} is 0.00000001.
2114 The @subcmd{PRINT} subcommand controls the display of optional statistics.
2115 Currently there is one such option, @subcmd{CI}, which indicates that the
2116 confidence interval of the odds ratio should be displayed as well as its value.
2117 @subcmd{CI} should be followed by an integer in parentheses, to indicate the
2118 confidence level of the desired confidence interval.
2120 The @subcmd{MISSING} subcommand determines the handling of missing
2122 If @subcmd{INCLUDE} is set, then user-missing values are included in the
2123 calculations, but system-missing values are not.
2124 If @subcmd{EXCLUDE} is set, which is the default, user-missing
2125 values are excluded as well as system-missing values.
2126 This is the default.
2137 [ BY @{@var{var_list}@} [BY @{@var{var_list}@} [BY @{@var{var_list}@} @dots{} ]]]
2139 [ /@{@var{var_list}@}
2140 [ BY @{@var{var_list}@} [BY @{@var{var_list}@} [BY @{@var{var_list}@} @dots{} ]]] ]
2142 [/CELLS = [MEAN] [COUNT] [STDDEV] [SEMEAN] [SUM] [MIN] [MAX] [RANGE]
2143 [VARIANCE] [KURT] [SEKURT]
2144 [SKEW] [SESKEW] [FIRST] [LAST]
2145 [HARMONIC] [GEOMETRIC]
2150 [/MISSING = [INCLUDE] [DEPENDENT]]
2153 You can use the @cmd{MEANS} command to calculate the arithmetic mean and similar
2154 statistics, either for the dataset as a whole or for categories of data.
2156 The simplest form of the command is
2160 @noindent which calculates the mean, count and standard deviation for @var{v}.
2161 If you specify a grouping variable, for example
2163 MEANS @var{v} BY @var{g}.
2165 @noindent then the means, counts and standard deviations for @var{v} after having
2166 been grouped by @var{g} are calculated.
2167 Instead of the mean, count and standard deviation, you could specify the statistics
2168 in which you are interested:
2170 MEANS @var{x} @var{y} BY @var{g}
2171 /CELLS = HARMONIC SUM MIN.
2173 This example calculates the harmonic mean, the sum and the minimum values of @var{x} and @var{y}
2176 The @subcmd{CELLS} subcommand specifies which statistics to calculate. The available statistics
2180 @cindex arithmetic mean
2181 The arithmetic mean.
2182 @item @subcmd{COUNT}
2183 The count of the values.
2184 @item @subcmd{STDDEV}
2185 The standard deviation.
2186 @item @subcmd{SEMEAN}
2187 The standard error of the mean.
2189 The sum of the values.
2194 @item @subcmd{RANGE}
2195 The difference between the maximum and minimum values.
2196 @item @subcmd{VARIANCE}
2198 @item @subcmd{FIRST}
2199 The first value in the category.
2201 The last value in the category.
2204 @item @subcmd{SESKEW}
2205 The standard error of the skewness.
2208 @item @subcmd{SEKURT}
2209 The standard error of the kurtosis.
2210 @item @subcmd{HARMONIC}
2211 @cindex harmonic mean
2213 @item @subcmd{GEOMETRIC}
2214 @cindex geometric mean
2218 In addition, three special keywords are recognized:
2220 @item @subcmd{DEFAULT}
2221 This is the same as @subcmd{MEAN} @subcmd{COUNT} @subcmd{STDDEV}.
2223 All of the above statistics are calculated.
2225 No statistics are calculated (only a summary is shown).
2229 More than one @dfn{table} can be specified in a single command.
2230 Each table is separated by a @samp{/}. For
2234 @var{c} @var{d} @var{e} BY @var{x}
2235 /@var{a} @var{b} BY @var{x} @var{y}
2236 /@var{f} BY @var{y} BY @var{z}.
2238 has three tables (the @samp{TABLE =} is optional).
2239 The first table has three dependent variables @var{c}, @var{d} and @var{e}
2240 and a single categorical variable @var{x}.
2241 The second table has two dependent variables @var{a} and @var{b},
2242 and two categorical variables @var{x} and @var{y}.
2243 The third table has a single dependent variables @var{f}
2244 and a categorical variable formed by the combination of @var{y} and @var{z}.
2247 By default values are omitted from the analysis only if missing values
2248 (either system missing or user missing)
2249 for any of the variables directly involved in their calculation are
2251 This behaviour can be modified with the @subcmd{/MISSING} subcommand.
2252 Three options are possible: @subcmd{TABLE}, @subcmd{INCLUDE} and @subcmd{DEPENDENT}.
2254 @subcmd{/MISSING = INCLUDE} says that user missing values, either in the dependent
2255 variables or in the categorical variables should be taken at their face
2256 value, and not excluded.
2258 @subcmd{/MISSING = DEPENDENT} says that user missing values, in the dependent
2259 variables should be taken at their face value, however cases which
2260 have user missing values for the categorical variables should be omitted
2261 from the calculation.
2263 @subsection Example Means
2265 The dataset in @file{repairs.sav} contains the mean time between failures (@exvar{mtbf})
2266 for a sample of artifacts produced by different factories and trialed under
2267 different operating conditions.
2268 Since there are four combinations of categorical variables, by simply looking
2269 at the list of data, it would be hard to how the scores vary for each category.
2270 @ref{means:ex} shows one way of tabulating the @exvar{mtbf} in a way which is
2271 easier to understand.
2273 @float Example, means:ex
2274 @psppsyntax {means.sps}
2275 @caption {Running @cmd{MEANS} on the @exvar{mtbf} score with categories @exvar{factory} and @exvar{environment}}
2278 The results are shown in @ref{means:res}. The figures shown indicate the mean,
2279 standard deviation and number of samples in each category.
2280 These figures however do not indicate whether the results are statistically
2281 significant. For that, you would need to use the procedures @cmd{ONEWAY}, @cmd{GLM} or
2282 @cmd{T-TEST} depending on the hypothesis being tested.
2284 @float Result, means:res
2286 @caption {The @exvar{mtbf} categorised by @exvar{factory} and @exvar{environment}}
2289 Note that there is no limit to the number of variables for which you can calculate
2290 statistics, nor to the number of categorical variables per layer, nor the number
2292 However, running @cmd{MEANS} on a large numbers of variables, or with categorical variables
2293 containing a large number of distinct values may result in an extremely large output, which
2294 will not be easy to interpret.
2295 So you should consider carefully which variables to select for participation in the analysis.
2301 @cindex nonparametric tests
2306 nonparametric test subcommands
2311 [ /STATISTICS=@{DESCRIPTIVES@} ]
2313 [ /MISSING=@{ANALYSIS, LISTWISE@} @{INCLUDE, EXCLUDE@} ]
2315 [ /METHOD=EXACT [ TIMER [(@var{n})] ] ]
2318 @cmd{NPAR TESTS} performs nonparametric tests.
2319 Non parametric tests make very few assumptions about the distribution of the
2321 One or more tests may be specified by using the corresponding subcommand.
2322 If the @subcmd{/STATISTICS} subcommand is also specified, then summary statistics are
2323 produces for each variable that is the subject of any test.
2325 Certain tests may take a long time to execute, if an exact figure is required.
2326 Therefore, by default asymptotic approximations are used unless the
2327 subcommand @subcmd{/METHOD=EXACT} is specified.
2328 Exact tests give more accurate results, but may take an unacceptably long
2329 time to perform. If the @subcmd{TIMER} keyword is used, it sets a maximum time,
2330 after which the test is abandoned, and a warning message printed.
2331 The time, in minutes, should be specified in parentheses after the @subcmd{TIMER} keyword.
2332 If the @subcmd{TIMER} keyword is given without this figure, then a default value of 5 minutes
2337 * BINOMIAL:: Binomial Test
2338 * CHISQUARE:: Chi-square Test
2339 * COCHRAN:: Cochran Q Test
2340 * FRIEDMAN:: Friedman Test
2341 * KENDALL:: Kendall's W Test
2342 * KOLMOGOROV-SMIRNOV:: Kolmogorov Smirnov Test
2343 * KRUSKAL-WALLIS:: Kruskal-Wallis Test
2344 * MANN-WHITNEY:: Mann Whitney U Test
2345 * MCNEMAR:: McNemar Test
2346 * MEDIAN:: Median Test
2348 * SIGN:: The Sign Test
2349 * WILCOXON:: Wilcoxon Signed Ranks Test
2354 @subsection Binomial test
2356 @cindex binomial test
2359 [ /BINOMIAL[(@var{p})]=@var{var_list}[(@var{value1}[, @var{value2})] ] ]
2362 The @subcmd{/BINOMIAL} subcommand compares the observed distribution of a dichotomous
2363 variable with that of a binomial distribution.
2364 The variable @var{p} specifies the test proportion of the binomial
2366 The default value of 0.5 is assumed if @var{p} is omitted.
2368 If a single value appears after the variable list, then that value is
2369 used as the threshold to partition the observed values. Values less
2370 than or equal to the threshold value form the first category. Values
2371 greater than the threshold form the second category.
2373 If two values appear after the variable list, then they are used
2374 as the values which a variable must take to be in the respective
2376 Cases for which a variable takes a value equal to neither of the specified
2377 values, take no part in the test for that variable.
2379 If no values appear, then the variable must assume dichotomous
2381 If more than two distinct, non-missing values for a variable
2382 under test are encountered then an error occurs.
2384 If the test proportion is equal to 0.5, then a two tailed test is
2385 reported. For any other test proportion, a one tailed test is
2387 For one tailed tests, if the test proportion is less than
2388 or equal to the observed proportion, then the significance of
2389 observing the observed proportion or more is reported.
2390 If the test proportion is more than the observed proportion, then the
2391 significance of observing the observed proportion or less is reported.
2392 That is to say, the test is always performed in the observed
2395 @pspp{} uses a very precise approximation to the gamma function to
2396 compute the binomial significance. Thus, exact results are reported
2397 even for very large sample sizes.
2401 @subsection Chi-square Test
2403 @cindex chi-square test
2407 [ /CHISQUARE=@var{var_list}[(@var{lo},@var{hi})] [/EXPECTED=@{EQUAL|@var{f1}, @var{f2} @dots{} @var{fn}@}] ]
2411 The @subcmd{/CHISQUARE} subcommand produces a chi-square statistic for the differences
2412 between the expected and observed frequencies of the categories of a variable.
2413 Optionally, a range of values may appear after the variable list.
2414 If a range is given, then non integer values are truncated, and values
2415 outside the specified range are excluded from the analysis.
2417 The @subcmd{/EXPECTED} subcommand specifies the expected values of each
2419 There must be exactly one non-zero expected value, for each observed
2420 category, or the @subcmd{EQUAL} keyword must be specified.
2421 You may use the notation @subcmd{@var{n}*@var{f}} to specify @var{n}
2422 consecutive expected categories all taking a frequency of @var{f}.
2423 The frequencies given are proportions, not absolute frequencies. The
2424 sum of the frequencies need not be 1.
2425 If no @subcmd{/EXPECTED} subcommand is given, then equal frequencies
2428 @subsubsection Chi-square Example
2430 A researcher wishes to investigate whether there are an equal number of
2431 persons of each sex in a population. The sample chosen for invesigation
2432 is that from the @file {physiology.sav} dataset. The null hypothesis for
2433 the test is that the population comprises an equal number of males and females.
2434 The analysis is performed as shown in @ref{chisquare:ex}.
2436 @float Example, chisquare:ex
2437 @psppsyntax {chisquare.sps}
2438 @caption {Performing a chi-square test to check for equal distribution of sexes}
2441 There is only one test variable, @i{viz:} @exvar{sex}. The other variables in the dataset
2444 @float Screenshot, chisquare:scr
2445 @psppimage {chisquare}
2446 @caption {Performing a chi-square test using the graphic user interface}
2449 In @ref{chisquare:res} the summary box shows that in the sample, there are more males
2450 than females. However the significance of chi-square result is greater than 0.05
2451 --- the most commonly accepted p-value --- and therefore
2452 there is not enough evidence to reject the null hypothesis and one must conclude
2453 that the evidence does not indicate that there is an imbalance of the sexes
2456 @float Result, chisquare:res
2457 @psppoutput {chisquare}
2458 @caption {The results of running a chi-square test on @exvar{sex}}
2463 @subsection Cochran Q Test
2465 @cindex Cochran Q test
2466 @cindex Q, Cochran Q
2469 [ /COCHRAN = @var{var_list} ]
2472 The Cochran Q test is used to test for differences between three or more groups.
2473 The data for @var{var_list} in all cases must assume exactly two
2474 distinct values (other than missing values).
2476 The value of Q is displayed along with its Asymptotic significance
2477 based on a chi-square distribution.
2480 @subsection Friedman Test
2482 @cindex Friedman test
2485 [ /FRIEDMAN = @var{var_list} ]
2488 The Friedman test is used to test for differences between repeated measures when
2489 there is no indication that the distributions are normally distributed.
2491 A list of variables which contain the measured data must be given. The procedure
2492 prints the sum of ranks for each variable, the test statistic and its significance.
2495 @subsection Kendall's W Test
2497 @cindex Kendall's W test
2498 @cindex coefficient of concordance
2501 [ /KENDALL = @var{var_list} ]
2504 The Kendall test investigates whether an arbitrary number of related samples come from the
2506 It is identical to the Friedman test except that the additional statistic W, Kendall's Coefficient of Concordance is printed.
2507 It has the range [0,1] --- a value of zero indicates no agreement between the samples whereas a value of
2508 unity indicates complete agreement.
2511 @node KOLMOGOROV-SMIRNOV
2512 @subsection Kolmogorov-Smirnov Test
2513 @vindex KOLMOGOROV-SMIRNOV
2515 @cindex Kolmogorov-Smirnov test
2518 [ /KOLMOGOROV-SMIRNOV (@{NORMAL [@var{mu}, @var{sigma}], UNIFORM [@var{min}, @var{max}], POISSON [@var{lambda}], EXPONENTIAL [@var{scale}] @}) = @var{var_list} ]
2521 The one sample Kolmogorov-Smirnov subcommand is used to test whether or not a dataset is
2522 drawn from a particular distribution. Four distributions are supported, @i{viz:}
2523 Normal, Uniform, Poisson and Exponential.
2525 Ideally you should provide the parameters of the distribution against
2526 which you wish to test the data. For example, with the normal
2527 distribution the mean (@var{mu})and standard deviation (@var{sigma})
2528 should be given; with the uniform distribution, the minimum
2529 (@var{min})and maximum (@var{max}) value should be provided.
2530 However, if the parameters are omitted they are imputed from the
2531 data. Imputing the parameters reduces the power of the test so should
2532 be avoided if possible.
2534 In the following example, two variables @var{score} and @var{age} are
2535 tested to see if they follow a normal distribution with a mean of 3.5
2536 and a standard deviation of 2.0.
2539 /KOLMOGOROV-SMIRNOV (normal 3.5 2.0) = @var{score} @var{age}.
2541 If the variables need to be tested against different distributions, then a separate
2542 subcommand must be used. For example the following syntax tests @var{score} against
2543 a normal distribution with mean of 3.5 and standard deviation of 2.0 whilst @var{age}
2544 is tested against a normal distribution of mean 40 and standard deviation 1.5.
2547 /KOLMOGOROV-SMIRNOV (normal 3.5 2.0) = @var{score}
2548 /KOLMOGOROV-SMIRNOV (normal 40 1.5) = @var{age}.
2551 The abbreviated subcommand @subcmd{K-S} may be used in place of @subcmd{KOLMOGOROV-SMIRNOV}.
2553 @node KRUSKAL-WALLIS
2554 @subsection Kruskal-Wallis Test
2555 @vindex KRUSKAL-WALLIS
2557 @cindex Kruskal-Wallis test
2560 [ /KRUSKAL-WALLIS = @var{var_list} BY var (@var{lower}, @var{upper}) ]
2563 The Kruskal-Wallis test is used to compare data from an
2564 arbitrary number of populations. It does not assume normality.
2565 The data to be compared are specified by @var{var_list}.
2566 The categorical variable determining the groups to which the
2567 data belongs is given by @var{var}. The limits @var{lower} and
2568 @var{upper} specify the valid range of @var{var}.
2569 If @var{upper} is smaller than @var{lower}, the PSPP will assume their values
2570 to be reversed. Any cases for which @var{var} falls outside
2571 [@var{lower}, @var{upper}] are ignored.
2573 The mean rank of each group as well as the chi-squared value and
2574 significance of the test are printed.
2575 The abbreviated subcommand @subcmd{K-W} may be used in place of
2576 @subcmd{KRUSKAL-WALLIS}.
2580 @subsection Mann-Whitney U Test
2581 @vindex MANN-WHITNEY
2583 @cindex Mann-Whitney U test
2584 @cindex U, Mann-Whitney U
2587 [ /MANN-WHITNEY = @var{var_list} BY var (@var{group1}, @var{group2}) ]
2590 The Mann-Whitney subcommand is used to test whether two groups of data
2591 come from different populations. The variables to be tested should be
2592 specified in @var{var_list} and the grouping variable, that determines
2593 to which group the test variables belong, in @var{var}.
2594 @var{Var} may be either a string or an alpha variable.
2595 @var{Group1} and @var{group2} specify the
2596 two values of @var{var} which determine the groups of the test data.
2597 Cases for which the @var{var} value is neither @var{group1} or
2598 @var{group2} are ignored.
2600 The value of the Mann-Whitney U statistic, the Wilcoxon W, and the
2601 significance are printed.
2602 You may abbreviated the subcommand @subcmd{MANN-WHITNEY} to
2607 @subsection McNemar Test
2609 @cindex McNemar test
2612 [ /MCNEMAR @var{var_list} [ WITH @var{var_list} [ (PAIRED) ]]]
2615 Use McNemar's test to analyse the significance of the difference between
2616 pairs of correlated proportions.
2618 If the @code{WITH} keyword is omitted, then tests for all
2619 combinations of the listed variables are performed.
2620 If the @code{WITH} keyword is given, and the @code{(PAIRED)} keyword
2621 is also given, then the number of variables preceding @code{WITH}
2622 must be the same as the number following it.
2623 In this case, tests for each respective pair of variables are
2625 If the @code{WITH} keyword is given, but the
2626 @code{(PAIRED)} keyword is omitted, then tests for each combination
2627 of variable preceding @code{WITH} against variable following
2628 @code{WITH} are performed.
2630 The data in each variable must be dichotomous. If there are more
2631 than two distinct variables an error will occur and the test will
2635 @subsection Median Test
2640 [ /MEDIAN [(@var{value})] = @var{var_list} BY @var{variable} (@var{value1}, @var{value2}) ]
2643 The median test is used to test whether independent samples come from
2644 populations with a common median.
2645 The median of the populations against which the samples are to be tested
2646 may be given in parentheses immediately after the
2647 @subcmd{/MEDIAN} subcommand. If it is not given, the median is imputed from the
2648 union of all the samples.
2650 The variables of the samples to be tested should immediately follow the @samp{=} sign. The
2651 keyword @code{BY} must come next, and then the grouping variable. Two values
2652 in parentheses should follow. If the first value is greater than the second,
2653 then a 2 sample test is performed using these two values to determine the groups.
2654 If however, the first variable is less than the second, then a @i{k} sample test is
2655 conducted and the group values used are all values encountered which lie in the
2656 range [@var{value1},@var{value2}].
2660 @subsection Runs Test
2665 [ /RUNS (@{MEAN, MEDIAN, MODE, @var{value}@}) = @var{var_list} ]
2668 The @subcmd{/RUNS} subcommand tests whether a data sequence is randomly ordered.
2670 It works by examining the number of times a variable's value crosses a given threshold.
2671 The desired threshold must be specified within parentheses.
2672 It may either be specified as a number or as one of @subcmd{MEAN}, @subcmd{MEDIAN} or @subcmd{MODE}.
2673 Following the threshold specification comes the list of variables whose values are to be
2676 The subcommand shows the number of runs, the asymptotic significance based on the
2680 @subsection Sign Test
2685 [ /SIGN @var{var_list} [ WITH @var{var_list} [ (PAIRED) ]]]
2688 The @subcmd{/SIGN} subcommand tests for differences between medians of the
2690 The test does not make any assumptions about the
2691 distribution of the data.
2693 If the @code{WITH} keyword is omitted, then tests for all
2694 combinations of the listed variables are performed.
2695 If the @code{WITH} keyword is given, and the @code{(PAIRED)} keyword
2696 is also given, then the number of variables preceding @code{WITH}
2697 must be the same as the number following it.
2698 In this case, tests for each respective pair of variables are
2700 If the @code{WITH} keyword is given, but the
2701 @code{(PAIRED)} keyword is omitted, then tests for each combination
2702 of variable preceding @code{WITH} against variable following
2703 @code{WITH} are performed.
2706 @subsection Wilcoxon Matched Pairs Signed Ranks Test
2708 @cindex wilcoxon matched pairs signed ranks test
2711 [ /WILCOXON @var{var_list} [ WITH @var{var_list} [ (PAIRED) ]]]
2714 The @subcmd{/WILCOXON} subcommand tests for differences between medians of the
2716 The test does not make any assumptions about the variances of the samples.
2717 It does however assume that the distribution is symmetrical.
2719 If the @subcmd{WITH} keyword is omitted, then tests for all
2720 combinations of the listed variables are performed.
2721 If the @subcmd{WITH} keyword is given, and the @subcmd{(PAIRED)} keyword
2722 is also given, then the number of variables preceding @subcmd{WITH}
2723 must be the same as the number following it.
2724 In this case, tests for each respective pair of variables are
2726 If the @subcmd{WITH} keyword is given, but the
2727 @subcmd{(PAIRED)} keyword is omitted, then tests for each combination
2728 of variable preceding @subcmd{WITH} against variable following
2729 @subcmd{WITH} are performed.
2738 /MISSING=@{ANALYSIS,LISTWISE@} @{EXCLUDE,INCLUDE@}
2739 /CRITERIA=CI(@var{confidence})
2743 TESTVAL=@var{test_value}
2744 /VARIABLES=@var{var_list}
2747 (Independent Samples mode.)
2748 GROUPS=var(@var{value1} [, @var{value2}])
2749 /VARIABLES=@var{var_list}
2752 (Paired Samples mode.)
2753 PAIRS=@var{var_list} [WITH @var{var_list} [(PAIRED)] ]
2758 The @cmd{T-TEST} procedure outputs tables used in testing hypotheses about
2760 It operates in one of three modes:
2762 @item One Sample mode.
2763 @item Independent Groups mode.
2768 Each of these modes are described in more detail below.
2769 There are two optional subcommands which are common to all modes.
2771 The @cmd{/CRITERIA} subcommand tells @pspp{} the confidence interval used
2772 in the tests. The default value is 0.95.
2775 The @cmd{MISSING} subcommand determines the handling of missing
2777 If @subcmd{INCLUDE} is set, then user-missing values are included in the
2778 calculations, but system-missing values are not.
2779 If @subcmd{EXCLUDE} is set, which is the default, user-missing
2780 values are excluded as well as system-missing values.
2781 This is the default.
2783 If @subcmd{LISTWISE} is set, then the entire case is excluded from analysis
2784 whenever any variable specified in the @subcmd{/VARIABLES}, @subcmd{/PAIRS} or
2785 @subcmd{/GROUPS} subcommands contains a missing value.
2786 If @subcmd{ANALYSIS} is set, then missing values are excluded only in the analysis for
2787 which they would be needed. This is the default.
2791 * One Sample Mode:: Testing against a hypothesized mean
2792 * Independent Samples Mode:: Testing two independent groups for equal mean
2793 * Paired Samples Mode:: Testing two interdependent groups for equal mean
2796 @node One Sample Mode
2797 @subsection One Sample Mode
2799 The @subcmd{TESTVAL} subcommand invokes the One Sample mode.
2800 This mode is used to test a population mean against a hypothesized
2802 The value given to the @subcmd{TESTVAL} subcommand is the value against
2803 which you wish to test.
2804 In this mode, you must also use the @subcmd{/VARIABLES} subcommand to
2805 tell @pspp{} which variables you wish to test.
2807 @subsubsection Example - One Sample T-test
2809 A researcher wishes to know whether the weight of persons in a population
2810 is different from the national average.
2811 The samples are drawn from the population under investigation and recorded
2812 in the file @file{physiology.sav}.
2813 From the Department of Health, she
2814 knows that the national average weight of healthy adults is 76.8kg.
2815 Accordingly the @subcmd{TESTVAL} is set to 76.8.
2816 The null hypothesis therefore is that the mean average weight of the
2817 population from which the sample was drawn is 76.8kg.
2819 As previously noted (@pxref{Identifying incorrect data}), one
2820 sample in the dataset contains a weight value
2821 which is clearly incorrect. So this is excluded from the analysis
2822 using the @cmd{SELECT} command.
2824 @float Example, one-sample-t:ex
2825 @psppsyntax {one-sample-t.sps}
2826 @caption {Running a one sample T-Test after excluding all non-positive values}
2829 @float Screenshot, one-sample-t:scr
2830 @psppimage {one-sample-t}
2831 @caption {Using the One Sample T-Test dialog box to test @exvar{weight} for a mean of 76.8kg}
2835 @ref{one-sample-t:res} shows that the mean of our sample differs from the test value
2836 by -1.40kg. However the significance is very high (0.610). So one cannot
2837 reject the null hypothesis, and must conclude there is not enough evidence
2838 to suggest that the mean weight of the persons in our population is different
2841 @float Results, one-sample-t:res
2842 @psppoutput {one-sample-t}
2843 @caption {The results of a one sample T-test of @exvar{weight} using a test value of 76.8kg}
2846 @node Independent Samples Mode
2847 @subsection Independent Samples Mode
2849 The @subcmd{GROUPS} subcommand invokes Independent Samples mode or
2851 This mode is used to test whether two groups of values have the
2852 same population mean.
2853 In this mode, you must also use the @subcmd{/VARIABLES} subcommand to
2854 tell @pspp{} the dependent variables you wish to test.
2856 The variable given in the @subcmd{GROUPS} subcommand is the independent
2857 variable which determines to which group the samples belong.
2858 The values in parentheses are the specific values of the independent
2859 variable for each group.
2860 If the parentheses are omitted and no values are given, the default values
2861 of 1.0 and 2.0 are assumed.
2863 If the independent variable is numeric,
2864 it is acceptable to specify only one value inside the parentheses.
2865 If you do this, cases where the independent variable is
2866 greater than or equal to this value belong to the first group, and cases
2867 less than this value belong to the second group.
2868 When using this form of the @subcmd{GROUPS} subcommand, missing values in
2869 the independent variable are excluded on a listwise basis, regardless
2870 of whether @subcmd{/MISSING=LISTWISE} was specified.
2872 @subsubsection Example - Independent Samples T-test
2874 A researcher wishes to know whether within a population, adult males
2875 are taller than adult females.
2876 The samples are drawn from the population under investigation and recorded
2877 in the file @file{physiology.sav}.
2879 As previously noted (@pxref{Identifying incorrect data}), one
2880 sample in the dataset contains a height value
2881 which is clearly incorrect. So this is excluded from the analysis
2882 using the @cmd{SELECT} command.
2885 @float Example, indepdendent-samples-t:ex
2886 @psppsyntax {independent-samples-t.sps}
2887 @caption {Running a independent samples T-Test after excluding all observations less than 200kg}
2891 The null hypothesis is that both males and females are on average
2894 @float Screenshot, independent-samples-t:scr
2895 @psppimage {independent-samples-t}
2896 @caption {Using the Independent Sample T-test dialog, to test for differences of @exvar{height} between values of @exvar{sex}}
2900 In this case, the grouping variable is @exvar{sex}, so this is entered
2901 as the variable for the @subcmd{GROUP} subcommand. The group values are 0 (male) and
2904 If you are running the proceedure using syntax, then you need to enter
2905 the values corresponding to each group within parentheses.
2906 If you are using the graphic user interface, then you have to open
2907 the ``Define Groups'' dialog box and enter the values corresponding
2908 to each group as shown in @ref{define-groups-t:scr}. If, as in this case, the dataset has defined value
2909 labels for the group variable, then you can enter them by label
2912 @float Screenshot, define-groups-t:scr
2913 @psppimage {define-groups-t}
2914 @caption {Setting the values of the grouping variable for an Independent Samples T-test}
2917 From @ref{independent-samples-t:res}, one can clearly see that the @emph{sample} mean height
2918 is greater for males than for females. However in order to see if this
2919 is a significant result, one must consult the T-Test table.
2921 The T-Test table contains two rows; one for use if the variance of the samples
2922 in each group may be safely assumed to be equal, and the second row
2923 if the variances in each group may not be safely assumed to be equal.
2925 In this case however, both rows show a 2-tailed significance less than 0.001 and
2926 one must therefore reject the null hypothesis and conclude that within
2927 the population the mean height of males and of females are unequal.
2929 @float Result, independent-samples-t:res
2930 @psppoutput {independent-samples-t}
2931 @caption {The results of an independent samples T-test of @exvar{height} by @exvar{sex}}
2934 @node Paired Samples Mode
2935 @subsection Paired Samples Mode
2937 The @cmd{PAIRS} subcommand introduces Paired Samples mode.
2938 Use this mode when repeated measures have been taken from the same
2940 If the @subcmd{WITH} keyword is omitted, then tables for all
2941 combinations of variables given in the @cmd{PAIRS} subcommand are
2943 If the @subcmd{WITH} keyword is given, and the @subcmd{(PAIRED)} keyword
2944 is also given, then the number of variables preceding @subcmd{WITH}
2945 must be the same as the number following it.
2946 In this case, tables for each respective pair of variables are
2948 In the event that the @subcmd{WITH} keyword is given, but the
2949 @subcmd{(PAIRED)} keyword is omitted, then tables for each combination
2950 of variable preceding @subcmd{WITH} against variable following
2951 @subcmd{WITH} are generated.
2958 @cindex analysis of variance
2963 [/VARIABLES = ] @var{var_list} BY @var{var}
2964 /MISSING=@{ANALYSIS,LISTWISE@} @{EXCLUDE,INCLUDE@}
2965 /CONTRAST= @var{value1} [, @var{value2}] ... [,@var{valueN}]
2966 /STATISTICS=@{DESCRIPTIVES,HOMOGENEITY@}
2967 /POSTHOC=@{BONFERRONI, GH, LSD, SCHEFFE, SIDAK, TUKEY, ALPHA ([@var{value}])@}
2970 The @cmd{ONEWAY} procedure performs a one-way analysis of variance of
2971 variables factored by a single independent variable.
2972 It is used to compare the means of a population
2973 divided into more than two groups.
2975 The dependent variables to be analysed should be given in the @subcmd{VARIABLES}
2977 The list of variables must be followed by the @subcmd{BY} keyword and
2978 the name of the independent (or factor) variable.
2980 You can use the @subcmd{STATISTICS} subcommand to tell @pspp{} to display
2981 ancillary information. The options accepted are:
2984 Displays descriptive statistics about the groups factored by the independent
2987 Displays the Levene test of Homogeneity of Variance for the
2988 variables and their groups.
2991 The @subcmd{CONTRAST} subcommand is used when you anticipate certain
2992 differences between the groups.
2993 The subcommand must be followed by a list of numerals which are the
2994 coefficients of the groups to be tested.
2995 The number of coefficients must correspond to the number of distinct
2996 groups (or values of the independent variable).
2997 If the total sum of the coefficients are not zero, then @pspp{} will
2998 display a warning, but will proceed with the analysis.
2999 The @subcmd{CONTRAST} subcommand may be given up to 10 times in order
3000 to specify different contrast tests.
3001 The @subcmd{MISSING} subcommand defines how missing values are handled.
3002 If @subcmd{LISTWISE} is specified then cases which have missing values for
3003 the independent variable or any dependent variable are ignored.
3004 If @subcmd{ANALYSIS} is specified, then cases are ignored if the independent
3005 variable is missing or if the dependent variable currently being
3006 analysed is missing. The default is @subcmd{ANALYSIS}.
3007 A setting of @subcmd{EXCLUDE} means that variables whose values are
3008 user-missing are to be excluded from the analysis. A setting of
3009 @subcmd{INCLUDE} means they are to be included. The default is @subcmd{EXCLUDE}.
3011 Using the @code{POSTHOC} subcommand you can perform multiple
3012 pairwise comparisons on the data. The following comparison methods
3016 Least Significant Difference.
3017 @item @subcmd{TUKEY}
3018 Tukey Honestly Significant Difference.
3019 @item @subcmd{BONFERRONI}
3021 @item @subcmd{SCHEFFE}
3023 @item @subcmd{SIDAK}
3026 The Games-Howell test.
3030 Use the optional syntax @code{ALPHA(@var{value})} to indicate that
3031 @cmd{ONEWAY} should perform the posthoc tests at a confidence level of
3032 @var{value}. If @code{ALPHA(@var{value})} is not specified, then the
3033 confidence level used is 0.05.
3036 @section QUICK CLUSTER
3037 @vindex QUICK CLUSTER
3039 @cindex K-means clustering
3043 QUICK CLUSTER @var{var_list}
3044 [/CRITERIA=CLUSTERS(@var{k}) [MXITER(@var{max_iter})] CONVERGE(@var{epsilon}) [NOINITIAL]]
3045 [/MISSING=@{EXCLUDE,INCLUDE@} @{LISTWISE, PAIRWISE@}]
3046 [/PRINT=@{INITIAL@} @{CLUSTER@}]
3047 [/SAVE[=[CLUSTER[(@var{membership_var})]] [DISTANCE[(@var{distance_var})]]]
3050 The @cmd{QUICK CLUSTER} command performs k-means clustering on the
3051 dataset. This is useful when you wish to allocate cases into clusters
3052 of similar values and you already know the number of clusters.
3054 The minimum specification is @samp{QUICK CLUSTER} followed by the names
3055 of the variables which contain the cluster data. Normally you will also
3056 want to specify @subcmd{/CRITERIA=CLUSTERS(@var{k})} where @var{k} is the
3057 number of clusters. If this is not specified, then @var{k} defaults to 2.
3059 If you use @subcmd{/CRITERIA=NOINITIAL} then a naive algorithm to select
3060 the initial clusters is used. This will provide for faster execution but
3061 less well separated initial clusters and hence possibly an inferior final
3065 @cmd{QUICK CLUSTER} uses an iterative algorithm to select the clusters centers.
3066 The subcommand @subcmd{/CRITERIA=MXITER(@var{max_iter})} sets the maximum number of iterations.
3067 During classification, @pspp{} will continue iterating until until @var{max_iter}
3068 iterations have been done or the convergence criterion (see below) is fulfilled.
3069 The default value of @var{max_iter} is 2.
3071 If however, you specify @subcmd{/CRITERIA=NOUPDATE} then after selecting the initial centers,
3072 no further update to the cluster centers is done. In this case, @var{max_iter}, if specified.
3075 The subcommand @subcmd{/CRITERIA=CONVERGE(@var{epsilon})} is used
3076 to set the convergence criterion. The value of convergence criterion is @var{epsilon}
3077 times the minimum distance between the @emph{initial} cluster centers. Iteration stops when
3078 the mean cluster distance between one iteration and the next
3079 is less than the convergence criterion. The default value of @var{epsilon} is zero.
3081 The @subcmd{MISSING} subcommand determines the handling of missing variables.
3082 If @subcmd{INCLUDE} is set, then user-missing values are considered at their face
3083 value and not as missing values.
3084 If @subcmd{EXCLUDE} is set, which is the default, user-missing
3085 values are excluded as well as system-missing values.
3087 If @subcmd{LISTWISE} is set, then the entire case is excluded from the analysis
3088 whenever any of the clustering variables contains a missing value.
3089 If @subcmd{PAIRWISE} is set, then a case is considered missing only if all the
3090 clustering variables contain missing values. Otherwise it is clustered
3091 on the basis of the non-missing values.
3092 The default is @subcmd{LISTWISE}.
3094 The @subcmd{PRINT} subcommand requests additional output to be printed.
3095 If @subcmd{INITIAL} is set, then the initial cluster memberships will
3097 If @subcmd{CLUSTER} is set, the cluster memberships of the individual
3098 cases are displayed (potentially generating lengthy output).
3100 You can specify the subcommand @subcmd{SAVE} to ask that each case's cluster membership
3101 and the euclidean distance between the case and its cluster center be saved to
3102 a new variable in the active dataset. To save the cluster membership use the
3103 @subcmd{CLUSTER} keyword and to save the distance use the @subcmd{DISTANCE} keyword.
3104 Each keyword may optionally be followed by a variable name in parentheses to specify
3105 the new variable which is to contain the saved parameter. If no variable name is specified,
3106 then PSPP will create one.
3114 [VARIABLES=] @var{var_list} [@{A,D@}] [BY @var{var_list}]
3115 /TIES=@{MEAN,LOW,HIGH,CONDENSE@}
3116 /FRACTION=@{BLOM,TUKEY,VW,RANKIT@}
3118 /MISSING=@{EXCLUDE,INCLUDE@}
3120 /RANK [INTO @var{var_list}]
3121 /NTILES(k) [INTO @var{var_list}]
3122 /NORMAL [INTO @var{var_list}]
3123 /PERCENT [INTO @var{var_list}]
3124 /RFRACTION [INTO @var{var_list}]
3125 /PROPORTION [INTO @var{var_list}]
3126 /N [INTO @var{var_list}]
3127 /SAVAGE [INTO @var{var_list}]
3130 The @cmd{RANK} command ranks variables and stores the results into new
3133 The @subcmd{VARIABLES} subcommand, which is mandatory, specifies one or
3134 more variables whose values are to be ranked.
3135 After each variable, @samp{A} or @samp{D} may appear, indicating that
3136 the variable is to be ranked in ascending or descending order.
3137 Ascending is the default.
3138 If a @subcmd{BY} keyword appears, it should be followed by a list of variables
3139 which are to serve as group variables.
3140 In this case, the cases are gathered into groups, and ranks calculated
3143 The @subcmd{TIES} subcommand specifies how tied values are to be treated. The
3144 default is to take the mean value of all the tied cases.
3146 The @subcmd{FRACTION} subcommand specifies how proportional ranks are to be
3147 calculated. This only has any effect if @subcmd{NORMAL} or @subcmd{PROPORTIONAL} rank
3148 functions are requested.
3150 The @subcmd{PRINT} subcommand may be used to specify that a summary of the rank
3151 variables created should appear in the output.
3153 The function subcommands are @subcmd{RANK}, @subcmd{NTILES}, @subcmd{NORMAL}, @subcmd{PERCENT}, @subcmd{RFRACTION},
3154 @subcmd{PROPORTION} and @subcmd{SAVAGE}. Any number of function subcommands may appear.
3155 If none are given, then the default is RANK.
3156 The @subcmd{NTILES} subcommand must take an integer specifying the number of
3157 partitions into which values should be ranked.
3158 Each subcommand may be followed by the @subcmd{INTO} keyword and a list of
3159 variables which are the variables to be created and receive the rank
3160 scores. There may be as many variables specified as there are
3161 variables named on the @subcmd{VARIABLES} subcommand. If fewer are specified,
3162 then the variable names are automatically created.
3164 The @subcmd{MISSING} subcommand determines how user missing values are to be
3165 treated. A setting of @subcmd{EXCLUDE} means that variables whose values are
3166 user-missing are to be excluded from the rank scores. A setting of
3167 @subcmd{INCLUDE} means they are to be included. The default is @subcmd{EXCLUDE}.
3169 @include regression.texi
3173 @section RELIABILITY
3178 /VARIABLES=@var{var_list}
3179 /SCALE (@var{name}) = @{@var{var_list}, ALL@}
3180 /MODEL=@{ALPHA, SPLIT[(@var{n})]@}
3181 /SUMMARY=@{TOTAL,ALL@}
3182 /MISSING=@{EXCLUDE,INCLUDE@}
3185 @cindex Cronbach's Alpha
3186 The @cmd{RELIABILITY} command performs reliability analysis on the data.
3188 The @subcmd{VARIABLES} subcommand is required. It determines the set of variables
3189 upon which analysis is to be performed.
3191 The @subcmd{SCALE} subcommand determines the variables for which
3192 reliability is to be calculated. If @subcmd{SCALE} is omitted, then analysis for
3193 all variables named in the @subcmd{VARIABLES} subcommand are used.
3194 Optionally, the @var{name} parameter may be specified to set a string name
3197 The @subcmd{MODEL} subcommand determines the type of analysis. If @subcmd{ALPHA} is specified,
3198 then Cronbach's Alpha is calculated for the scale. If the model is @subcmd{SPLIT},
3199 then the variables are divided into 2 subsets. An optional parameter
3200 @var{n} may be given, to specify how many variables to be in the first subset.
3201 If @var{n} is omitted, then it defaults to one half of the variables in the
3202 scale, or one half minus one if there are an odd number of variables.
3203 The default model is @subcmd{ALPHA}.
3205 By default, any cases with user missing, or system missing values for
3206 any variables given in the @subcmd{VARIABLES} subcommand are omitted
3207 from the analysis. The @subcmd{MISSING} subcommand determines whether
3208 user missing values are included or excluded in the analysis.
3210 The @subcmd{SUMMARY} subcommand determines the type of summary analysis to be performed.
3211 Currently there is only one type: @subcmd{SUMMARY=TOTAL}, which displays per-item
3212 analysis tested against the totals.
3214 @subsection Example - Reliability
3216 Before analysing the results of a survey -- particularly for a multiple choice survey --
3217 it is desireable to know whether the respondents have considered their answers
3218 or simply provided random answers.
3220 In the following example the survey results from the file @file{hotel.sav} are used.
3221 All five survey questions are included in the reliability analysis.
3222 However, before running the analysis, the data must be preprocessed.
3223 An examination of the survey questions reveals that two questions, @i{viz:} v3 and v5
3224 are negatively worded, whereas the others are positively worded.
3225 All questions must be based upon the same scale for the analysis to be meaningful.
3226 One could use the @cmd{RECODE} command (@pxref{RECODE}), however a simpler way is
3227 to use @cmd{COMPUTE} (@pxref{COMPUTE}) and this is what is done in @ref{reliability:ex}.
3229 @float Example, reliability:ex
3230 @psppsyntax {reliability.sps}
3231 @caption {Investigating the reliability of survey responses}
3234 In this case, all variables in the data set are used. So we can use the special
3235 keyword @samp{ALL} (@pxref{BNF}).
3237 @float Screenshot, reliability:src
3238 @psppimage {reliability}
3239 @caption {Reliability dialog box with all variables selected}
3242 @ref{reliability:res} shows that Cronbach's Alpha is 0.11 which is a value normally considered too
3243 low to indicate consistency within the data. This is possibly due to the small number of
3244 survey questions. The survey should be redesigned before serious use of the results are
3247 @float Result, reliability:res
3248 @psppoutput {reliability}
3249 @caption {The results of the reliability command on @file{hotel.sav}}
3257 @cindex Receiver Operating Characteristic
3258 @cindex Area under curve
3261 ROC @var{var_list} BY @var{state_var} (@var{state_value})
3262 /PLOT = @{ CURVE [(REFERENCE)], NONE @}
3263 /PRINT = [ SE ] [ COORDINATES ]
3264 /CRITERIA = [ CUTOFF(@{INCLUDE,EXCLUDE@}) ]
3265 [ TESTPOS (@{LARGE,SMALL@}) ]
3266 [ CI (@var{confidence}) ]
3267 [ DISTRIBUTION (@{FREE, NEGEXPO @}) ]
3268 /MISSING=@{EXCLUDE,INCLUDE@}
3272 The @cmd{ROC} command is used to plot the receiver operating characteristic curve
3273 of a dataset, and to estimate the area under the curve.
3274 This is useful for analysing the efficacy of a variable as a predictor of a state of nature.
3276 The mandatory @var{var_list} is the list of predictor variables.
3277 The variable @var{state_var} is the variable whose values represent the actual states,
3278 and @var{state_value} is the value of this variable which represents the positive state.
3280 The optional subcommand @subcmd{PLOT} is used to determine if and how the @subcmd{ROC} curve is drawn.
3281 The keyword @subcmd{CURVE} means that the @subcmd{ROC} curve should be drawn, and the optional keyword @subcmd{REFERENCE},
3282 which should be enclosed in parentheses, says that the diagonal reference line should be drawn.
3283 If the keyword @subcmd{NONE} is given, then no @subcmd{ROC} curve is drawn.
3284 By default, the curve is drawn with no reference line.
3286 The optional subcommand @subcmd{PRINT} determines which additional
3287 tables should be printed. Two additional tables are available. The
3288 @subcmd{SE} keyword says that standard error of the area under the
3289 curve should be printed as well as the area itself. In addition, a
3290 p-value for the null hypothesis that the area under the curve equals
3291 0.5 is printed. The @subcmd{COORDINATES} keyword says that a
3292 table of coordinates of the @subcmd{ROC} curve should be printed.
3294 The @subcmd{CRITERIA} subcommand has four optional parameters:
3296 @item The @subcmd{TESTPOS} parameter may be @subcmd{LARGE} or @subcmd{SMALL}.
3297 @subcmd{LARGE} is the default, and says that larger values in the predictor variables are to be
3298 considered positive. @subcmd{SMALL} indicates that smaller values should be considered positive.
3300 @item The @subcmd{CI} parameter specifies the confidence interval that should be printed.
3301 It has no effect if the @subcmd{SE} keyword in the @subcmd{PRINT} subcommand has not been given.
3303 @item The @subcmd{DISTRIBUTION} parameter determines the method to be used when estimating the area
3305 There are two possibilities, @i{viz}: @subcmd{FREE} and @subcmd{NEGEXPO}.
3306 The @subcmd{FREE} method uses a non-parametric estimate, and the @subcmd{NEGEXPO} method a bi-negative
3307 exponential distribution estimate.
3308 The @subcmd{NEGEXPO} method should only be used when the number of positive actual states is
3309 equal to the number of negative actual states.
3310 The default is @subcmd{FREE}.
3312 @item The @subcmd{CUTOFF} parameter is for compatibility and is ignored.
3315 The @subcmd{MISSING} subcommand determines whether user missing values are to
3316 be included or excluded in the analysis. The default behaviour is to
3318 Cases are excluded on a listwise basis; if any of the variables in @var{var_list}
3319 or if the variable @var{state_var} is missing, then the entire case is
3322 @c LocalWords: subcmd subcommand