SPSS® 12.0 Command Syntax Reference

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702 GRAPH Figure 28 LINE(DIFFERENCE)=MEAN(SALBEG SALNOW) BY TIME Figure 29 LINE(DIFFERENCE)=MEAN(SALNOW) BY TIME BY SEX Specification: sum_function (varlist) BY var Legend title: fn name Legend labels: fn var labels y-axis title: none y-axis labels: fn value scale x-axis title: BY var label x-axis labels: BY var value labels Specification: sum_function(var) BY var BY var Legend title: 2nd BY var label Legend labels: 2nd BY var value labels y-axis title: fn name [+var label] y-axis labels: fn value scale x-axis title: 1st BY var label x-axis labels: 1st BY var value labels The second BY variable must be dichotomous. Figure 30 LINE(DIFFERENCE)=COUNT BY TIME BY SEX The second BY variable must be dichotomous. Specification: count_function BY var BY var Legend title: 2nd BY var label Legend labels: 2nd BY var value labels y-axis title: fn name y-axis labels: fn value scale x-axis title: 1st BY var label x-axis labels: 1st BY var value labels
Figure 31 LINE(DIFFERENCE)=VALUE(SALBEG SALNOW) BY TIME PIE Subcommand GRAPH 703 Specification: value_function(var1 var2 [BY var] Legend title: 2nd BY var label Legend labels: 2nd BY var value labels y-axis title: fn name y-axis labels: fn value scale x-axis title: 1st BY var label x-axis labels: 1st BY var value labels If no BY variable is specified, the x-axis title is Case Number and x-axis labels are the case numbers. PIE creates pie charts. A pie chart can be defined by a single function and a single BY variable or by multiple summary functions and no BY variable. A pie chart divides a circle into slices. The size of each slice indicates the value of the category relative to the whole (see Figure 32 to Figure 34). Cumulative functions (CUPCT, CUFREQ, and CUSUM) are inappropriate for pie charts but are not prohibited. When specified, all cases except those in the last category are counted more than once in the resulting pie. Figure 32 /PIE=COUNT BY JOBCAT Specification: count_function BY var Labels: BY var value labels Each pie slice shows proportionally the number of cases in each category defined by the BY variable.
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SPSS® 12.0 Command Syntax Referenc
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Universals This part of the SPSS Sy
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Commands 5 before processing. All u
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Commands 7 • Subcommands begin wi
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Commands 9 • Transformation comma
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Files SPSS reads, creates, and writ
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SPSS-Format Data File Files 13 An S
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SPSS Matrix Data Files Files 15 An
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Files 17 4. VARNAME_ variable (or F
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Figure 6 Dictionary of a matrix sys
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Variables To prepare data for proce
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Keyword TO Variables 23 You can est
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Variable Formats Variables 25 SPSS
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Format type Description Ew.d Scient
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Table 4 Numeric formats with punctu
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Variables 31 PIBHEXw (hexadecimal o
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Variables 33 RBw (real binary): The
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Variables 35 is read as two separat
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Transformation Expressions Transfor
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SQRT(arg) Square root. SQRT(SIBS) i
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The following are suffixes for cont
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Transformation Expressions 43 0≤p
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Domain Errors Transformation Expres
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Transformation Expressions 47 origi
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Transformation Expressions 49 • L
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NOT Logical Operator Transformation
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and COMPUTE lagvar=LAG(var1). EXECU
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Date and Time SPSS reads and writes
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Input Data Specification Date and T
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The LIST output from these commands
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Date and Time 61 • A time interva
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Aggregation Functions Date and Time
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Conversion Functions Date and Time
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Date and Time 67 XDATE.HOUR(arg) Re
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Precautions with Date and Time Vari
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72 ACF and DIFF subcommands. With s
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74 ACF SDIFF Subcommand If the seri
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76 ACF SERROR Subcommand SERROR spe
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ADD DOCUMENT ADD DOCUMENT ’text
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ADD FILES ADD FILES FILE={file} {*
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82 ADD FILES • The resulting file
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84 ADD FILES • Input data files a
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86 ADD FILES FIRST and LAST Subcomm
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88 ADD VALUE LABELS • The added v
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AGGREGATE AGGREGATE OUTFILE={file}
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92 AGGREGATE Example Example • If
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94 AGGREGATE Example AGGREGATE OUTF
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96 AGGREGATE FIN(varlist,value1,val
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98 AGGREGATE • User-missing value
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ALSCAL ALSCAL VARIABLES=varlist [/F
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102 ALSCAL Operations • ALSCAL ca
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104 ALSCAL ASYMMETRIC Asymmetric da
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106 ALSCAL • INITIAL is the defau
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108 ALSCAL differences model propos
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110 ALSCAL • Algorithmic options
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112 ALSCAL Table 2 Types of configu
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Matrix mode Object by object 114 AL
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Matrix mode Object by object Object
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118 Syntax Reference Basic Specific
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120 Syntax Reference Example DATA L
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122 Syntax Reference PLOT Subcomman
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124 Syntax Reference VARNAME_ Strin
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ANOVA ANOVA [VARIABLES=] varlist BY
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Example Example ANOVA 129 ANOVA VAR
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ANOVA 131 Some restrictions apply t
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Table 2 Combinations of COVARIATES
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Multiple Classification Analysis AN
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APPLY DICTIONARY 137 • The applie
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APPLY DICTIONARY 139 target file as
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APPLY DICTIONARY 141 MRSETS = MERGE
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APPLY DICTIONARY 143
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Options Basic Specification Subcomm
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• Only one method can be specifie
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APPLY Subcommand AREG 149 • Itera
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AREG 151
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Options Basic Specification ARIMA 1
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Limitations Example ARIMA 155 • F
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D Order of differencing. Q Moving-a
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Termination Criteria Subcommands CI
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References ARIMA 161 Akaike, H. 197
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Subcommand Order • VARIABLES must
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Example AUTORECODE 165 DATA LIST /
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BEGIN DATA—END DATA BEGIN DATA da
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BREAK BREAK Overview BREAK controls
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CASEPLOT Overview CASEPLOT [VARIABL
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Subcommand Order • Subcommands ca
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LN and NOLOG Subcommands CASEPLOT 1
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CASEPLOT 177 erence line. Figure 3
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CASEPLOT 179 • If FORMAT=REFERENC
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Options CASESTOVARS 181 Automatic c
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The commands: CASESTOVARS 183 SPLIT
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create a new file with the followin
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SEPARATOR Subcommand CASESTOVARS 18
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CATPCA CATPCA is available in the C
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Optional output. You can request op
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CATPCA 193 • The NORMALIZATION su
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SPORD and SPNOM Keywords The follow
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ACTIVE Keyword The ACTIVE keyword h
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MAXITER Subcommand CATPCA 199 MAXIT
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CATPCA 201 The keyword TO in a vari
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TRIPLOT(varlist(varlist))(n) CATPCA
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CATPCA 205 procedures with the succ
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CATREG Overview CATREG is available
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Operations Limitations CATREG 209
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ANALYSIS Subcommand LEVEL Keyword C
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GROUPING Keyword DISTR Keyword MISS
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PRINT Subcommand CATREG 215 The PRI
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SAVE Subcommand OUTFILE Subcommand
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CCF Overview Options CCF [VARIABLES
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CCF 221 • LN transforms the data
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CCF 223 • If a natural log transf
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CLEAR TRANSFORMATIONS Overview CLEA
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Example Overview Options CLUSTER V1
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CLUSTER 229 • The variable list i
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Measures for Binary Data CLUSTER 23
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DICE[(p[,np])] Dice (or Czekanowski
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Y( x, y) ad - bc = ----------------
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CLUSTER 237 MEDIAN Median clusterin
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CLUSTER 239 • This example displa
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Matrix Input CLUSTER 241 • The ma
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Example Example Example DATA LIST F
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COMMENT {COMMENT} text { * } Overvi
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Cumulative distribution functions (
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Cumulative distribution functions (
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Logical functions: RANGE(varname,ra
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String Transformations COMPUTE 253
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Examples COMPUTE 255 • The string
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COMPUTE 257 • Alternatively, you
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Equivalence COMPUTE 259 STRING DEPT
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CONJOINT Overview CONJOINT is avail
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CONJOINT 263 the experimental profi
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CONJOINT 265 • The ADD FILES comm
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CONJOINT 267 subject is asked to as
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CONJOINT 269 • New value labels a
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PLOT Subcommand CONJOINT 271 The PL
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Basic Specification CORRELATIONS 27
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MISSING Subcommand CORRELATIONS 275
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Example CORRELATIONS 277 • CORREL
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CORRESPONDENCE CORRESPONDENCE is av
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Syntax Rules Operations Limitations
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DIMENSION Subcommand CORRESPONDENCE
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CORRESPONDENCE 285 EUCLID Euclidean
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PLOT Subcommand NONE No output othe
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CORRESPONDENCE 289 DIM1...DIMn Nume
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COUNT COUNT varname=varlist(value l
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COXREG COXREG is available in the A
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Basic Specification COXREG 295 •
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VARIABLES Subcommand COXREG 297 VAR
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COXREG 299 • If the categorical v
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COXREG 301 BSTEP Backward stepwise.
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CRITERIA Subcommand COXREG 303 CRIT
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COXREG 305 TABLE Write the survival
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CREATE CREATE new series={CSUM (ser
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Example CREATE NEWVAR1 = DIFF(OLDVA
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LAG Function CREATE 311 LAG creates
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Example CREATE TICKMA = MA(TICKETS,
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T4253H Function CREATE 315 T4253H p
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Overview Options CROSSTABS 317 CROS
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Example CROSSTABS 319 • The varia
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CROSSTABS 321 • VARIABLES names t
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CROSSTABS 323 CORR Display Pearson
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FORMAT Subcommand CROSSTABS 325 By
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CROSSTABS 327 • The split-file gr
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CROSSTABS 329
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Operations CSDESCRIPTIVES 331 • C
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CSDESCRIPTIVES 333 • Plan file an
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CSDESCRIPTIVES 335 • The set of s
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CSDESCRIPTIVES 337
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CSSELECT 339 inclusion probabilitie
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PLAN Subcommand CSSELECT 341 The PL
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Example CSPLAN /PLAN OUTFILE=’c:\
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CSSELECT 345
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Design Block: Stages 2 and 3 CSPLAN
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Example Overview Options CSPLAN SAM
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CSPLAN 351 • A PLAN subcommand is
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Stratified sample design CSPLAN SAM
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CSPLAN 355 • The variable samplew
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CSPLAN 357 Typically the previous w
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CSPL AN 359 • CLUSTER is required
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SIZE Subcommand CSPLAN 361 The SIZE
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MINSIZE Keyword CSPLAN 363 MINSIZE
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Keyword Default Root Name Descripti
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CSPLAN 367 strata only the last one
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CSPLAN 369
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CSTABULATE 371 • This specificati
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CSTABULATE 373 on the left defines
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CSTABULATE 375 • For example, /SU
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ADJUST= {BONFERRONI} ORIGIN=COLUMN
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Example Think of self as liberal or
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Example CTABLES /TABLE HAPPY. Gener
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Scale Variables CTABLES 383 Scale v
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CTABLES 385 • Each summary functi
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Table 2 Summary functions: scale va
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Table 3 Summary functions: multiple
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Missing Values in Summaries CTABLES
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Example CTABLES 393 CTABLES /TABLE
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CTABLES 395 • The SUBTOTAL keywor
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Totals Example CTABLES /TABLE AGE [
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CTABLES 399 CORNER Corner text. Cor
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Pairwise Comparisons of Proportions
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CTABLES 403 EMPTY Fill characters u
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CURVEFIT CURVEFIT [VARIABLES=] varn
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Operations CURVEFIT 407 • When CU
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CURVEFIT 409 • This command fits
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APPLY Subcommand CURVEFIT 411 APPLY
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Example Overview Options Week and y
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Fixed-Format Data DATA LIST 415 •
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DATA LIST 417 LIST Freefield data w
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DATA LIST 419 • RECORDS can be us
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Example DATA LIST 421 INPUT PROGRAM
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Variable Names DATA LIST 423 • Va
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Example * Mixing column-style and F
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DATA LIST 427 • For freefield dat
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DATA LIST 429 • The width (w) por
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DATE DATE keyword [starting value [
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DATE 433 • The starting value is
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DATE 435 • For DAY_, the starting
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Example 5 DATE Y 1950 Q 2 M. DATE 4
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The following is a partial listing
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Example DEFINE sesvars () age sex e
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Example Example * Macro without arg
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DEFINE—!ENDDEFINE 445 • The mac
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DEFINE—!ENDDEFINE 447 • This ex
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Example * Keyword !CHAREND. DEFINE
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DEFINE—!ENDDEFINE 451 !DEFAULT De
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DEFINE—!ENDDEFINE 453 !HEAD (str)
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Conditional Processing DEFINE—!EN
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DEFINE—!ENDDEFINE 457 • The fir
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DEFINE—!ENDDEFINE 459
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DESCRIPTIVES Overview Options DESCR
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Z Scores DESCRIPTIVES 463 The z-sco
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MAX Maximum observed value. SUM Sum
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DISCRIMINANT DISCRIMINANT GROUPS=va
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Basic Specification DISCRIMINANT 46
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DISCRIMINANT 471 • Variables shou
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Example DISCRIMINANT 473 DISCRIMINA
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DISCRIMINANT 475 • You can set FI
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SAVE Subcommand DISCRIMINANT 477 SA
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STATISTICS Subcommand DISCRIMINANT
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• The default keywords for CLASSI
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Matrix Input DISCRIMINANT 483 recor
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Example Example Example * Use matri
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DISPLAY DISPLAY [SORTED] [{NAMES**
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DISPLAY 489 • If the keyword SORT
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Example Example DOCUMENT 491 • If
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Overview DO IF 493 The DO IF—END
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Flow of Control DO IF 495 • If th
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Example DO IF 497 DO IF (YRHIRED GT
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Nested DO IF Structures DO IF 499 T
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DO REPEAT—END REPEAT DO REPEAT st
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Example DO REPEAT—END REPEAT 503
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Figure 2 Output from the PRINT subc
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ECHO ECHO "text". Example Overview
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Operations Example END CASE 509 •
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END CASE 511 • Data are extracted
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END CASE 513 BEGIN DATA 1 AC 91 2 C
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END CASE 515 • The frequencies ta
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Example Example *Select cases. INPU
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EXAMINE EXAMINE VARIABLES=varlist [
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Example EXAMINE VARIABLES=MIPERGAL
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• The method keywords follow the
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STATISTICS Subcommand EXAMINE 525 S
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EXAMINE 527 PAIRWISE Delete cases w
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EXPORT EXPORT OUTFILE=file [/TYPE={
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EXPORT 531 • Tape density—200,
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DROP and KEEP Subcommands EXPORT 53
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EXSMOOTH EXSMOOTH is available in t
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Subcommand Order Syntax Rules Opera
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Table 1 Models for different types
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Example EXSMOOTH 541 EXSMOOTH VAR2
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INITIAL Subcommand EXSMOOTH 543 •
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References EXSMOOTH 545 • The fir
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Example Overview Options FACTOR VAR
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Operations Example FACTOR 549 • V
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FACTOR 551 • By default, FACTOR u
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FACTOR 553 DET Determinant of the c
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• The factor pattern matrix is ro
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ROTATION Subcommand FACTOR 557 ROTA
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FACTOR 559 • The ROTATION subcomm
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Example Example Example Example FAC
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FACTOR 563 Jöreskog, K. G. 1977. F
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MULTIPUNCH Column binary file. IMAG
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FILE TYPE—END FILE TYPE For mixed
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Syntax Rules FILE TYPE—END FILE T
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Example * A grouped file of student
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FILE TYPE—END FILE TYPE 573 GROUP
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FILE TYPE—END FILE TYPE 575 • T
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FILE TYPE—END FILE TYPE 577 • T
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FILE TYPE—END FILE TYPE 579 WARN
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FILE TYPE—END FILE TYPE 581 BEGIN
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Example FILTER 583 • Both system-
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Example * A command file. FINISH 58
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Syntax Rules FIT 587 • If OBS is
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FLIP FLIP [[VARIABLES=] {ALL }] {va
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FLIP 591 Example Using the untransp
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FORMATS FORMATS varlist(format) [va
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Table 1 Output data formats (Contin
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FREQUENCIES FREQUENCIES [VARIABLES=
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Example FREQUENCIES 599 • FREQUEN
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FREQUENCIES 601 • MISSING and NOM
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Example FREQUENCIES 603 FREQUENCIES
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Example FREQUENCIES VAR=AGE /STATS=
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Options GENLOG 607 mates for unsatu
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GENLOG 609 • To separate the inde
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GENLOG 611 • For each effect, GEN
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GENLOG 613 DESIGN The design matrix
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GENLOG 615 • The saved variables
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GET GET FILE=file [/KEEP={ALL** }]
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GET 619 • Variables can be specif
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GET CAPTURE GET CAPTURE {ODBC }* [/
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GET CAPTURE 623 • You can display
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Overview GET DATA 625 GET DATA read
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READNAMES Subcommand GET DATA 627 O
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QUALIFIER Subcommand GET DATA 629 T
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GET SAS Overview Options GET SAS DA
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GET SAS 633 • Value labels read f
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GET TRANSLATE GET TRANSLATE FILE=fi
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GET TRANSLATE 637 • The first row
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GET TRANSLATE 639 • For string va
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GET TRANSLATE 641 • RANGE cannot
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GLM: Overview GLM is available in t
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GLM: Overview 645 • Display expec
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Model 2: Fixed-effects ANOVA and MA
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GLM: Overview 649 For univariate an
Page 651 and 652: GLM: Univariate GLM is available in
Page 653 and 654: Basic Specification GLM: Univariate
Page 655 and 656: GLM: Univariate 655 • If you spec
Page 657 and 658: INTERCEPT Subcommand GLM: Univariat
Page 659 and 660: GLM: Univariate 659 LOF Perform a l
Page 661 and 662: GLM: Univariate 661 • A coefficie
Page 663 and 664: Example GLM: Univariate 663 GLM DEP
Page 665 and 666: Example GLM: Univariate 665 GLM DEP
Page 667 and 668: GLM: Univariate 667 • Multiple PO
Page 669 and 670: COMPARE(factor) ADJ(method) GLM: Un
Page 671 and 672: DESIGN Subcommand GLM: Univariate 6
Page 673 and 674: Overview Options GLM: Multivariate
Page 675 and 676: GLM: Multivariate 675 Residual cova
Page 677 and 678: GLM: Repeated Measures GLM is avail
Page 679 and 680: Subcommand Order • The list of de
Page 681 and 682: GLM: Repeated Measures 681 • Leve
Page 683 and 684: GLM: Repeated Measures 683 • WSFA
Page 685 and 686: GLM: Repeated Measures 685 • Thes
Page 687 and 688: GRAPH 687 The following table shows
Page 689 and 690: GRAPH 689 NGT(x) Number of cases fo
Page 691 and 692: • The default span (2) and sigma
Page 693 and 694: GRAPH 693 Figure 3 /BAR=MEAN(SALBEG
Page 695 and 696: GRAPH 695 Figure 9 /BAR(STACKED)=CO
Page 697 and 698: LINE Subcommand GRAPH 697 LINE crea
Page 699 and 700: Figure 19 /LINE=MEAN(THEFT AUTO BUR
Page 701: Figure 25 /LINE(AREA)=COUNT BY TIME
Page 705 and 706: GRAPH 705 the variable must be dich
Page 707 and 708: ERRORBAR Subcommand GRAPH 707 ERROR
Page 709 and 710: GRAPH 709 MATRIX Scatterplot matrix
Page 711 and 712: Figure 49 /SCATTERPLOT(XYZ)=JOBCAT
Page 713 and 714: Figure 52 PARETO(CUM SIMPLE)=SUM(DE
Page 715 and 716: Elements and Attributes Dependent o
Page 717 and 718: HILOGLINEAR Overview HILOGLINEAR is
Page 719 and 720: Operations HILOGLINEAR 719 • HILO
Page 721 and 722: Example HILOGLINEAR 721 HILOGLINEAR
Page 723 and 724: Example * An Incomplete Rectangular
Page 725 and 726: HILOGLINEAR 725 NONE No plots. Spec
Page 727 and 728: HOMALS Overview Options HOMALS is a
Page 729 and 730: Example HOMALS 729 Values outside o
Page 731 and 732: HOMALS 731 The total number of vali
Page 733 and 734: Example HOMALS 733 HOMALS VARIABLES
Page 735 and 736: HOMALS 735 LEVEL String variable LE
Page 737 and 738: Basic Specification IF 737 The basi
Page 739 and 740: Example Example Example IF (AGE > 2
Page 741 and 742: Example Example IF 741 tions on eve
Page 743 and 744: {VERTICAL } {THREE } [/EFFECT={NONE
Page 745 and 746: Overview Options IGRAPH 745 The int
Page 747 and 748: CATORDER Subcommand IGRAPH 747 The
Page 749 and 750: Example IGRAPH /VIEWNAME=’Scatter
Page 751 and 752: CHARTLOOK Subcommand IGRAPH 751 CHA
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FORMAT Subcommand IGRAPH 753 For ch
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IGRAPH 755 The INTERPOLATE keyword
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IGRAPH 757 START num Indicates the
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Example IGRAPH /X1=VAR(region) TYPE
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ERRORBAR Subcommand IGRAPH 761 Erro
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IGRAPH 763 REGRESSION Fits a straig
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IGRAPH 765 Number of Cases Less Tha
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IMPORT IMPORT FILE=file [/TYPE={COM
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IMPORT 769 • DROP excludes a vari
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INCLUDE INCLUDE FILE=file Example O
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INFO This command is not available
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Types of Information The following
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INPUT PROGRAM—END INPUT PROGRAM I
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Example Example * Select cases with
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KEYED DATA LIST KEYED DATA LIST KEY
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Syntax Rules KEYED DATA LIST 783
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Example * Reading a keyed file: rea
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KM Overview KM is available in the
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KM 789 • Only one status variable
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STRATA Subcommand KM 791 STRATA ide
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TEST Subcommand KM 793 TEST specifi
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SAVE Subcommand KM 795 SAVE saves t
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Example LEAVE 797 • Numeric varia
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Operations Example Example LIST 799
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LIST 801 • If SPLIT FILE is in ef
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Temporary variables created by LOGI
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Limitations Example LOGISTIC REGRES
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LOGISTIC REGRESSION 807 DEVIATION(r
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LOGISTIC REGRESSION 809 Wald statis
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ID Subcommand LOGISTIC REGRESSION 8
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LOGISTIC REGRESSION 813 • POUT re
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LOGISTIC REGRESSION 815 • Assigne
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LOGLINEAR 817 multiway contingency
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Example Example Example Example LOG
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LOGLINEAR 821 • To enter cell cov
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CONTRAST Subcommand LOGLINEAR 823 C
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Example * Contrasts for a logistic
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Example LOGLINEAR RESPONSE(1,2) BY
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Example * Equiprobability model LOG
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Options LOOP—END LOOP 831 Missing
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LOOP—END LOOP 833 • The program
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LOOP—END LOOP 835 time, resulting
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LOOP—END LOOP 837 • The loop is
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MANOVA: Overview MANOVA is availabl
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Overview Example 2 * Analysis of Co
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MANOVA: Univariate MANOVA is availa
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MANOVA: Univariate 845 in the model
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MANOVA: Univariate 847 WITHIN Terms
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MANOVA: Univariate 849 The specifie
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MANOVA: Univariate 851 it generally
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PARAMETERS Keyword MANOVA: Univaria
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ERROR Keyword MANOVA: Univariate 85
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RESIDUALS Subcommand MANOVA: Univar
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MANOVA: Univariate 859 CELLPLOTS Ce
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Split Files and Variable Order MANO
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DESIGN Subcommand MANOVA: Univariat
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MANOVA: Univariate 865 • The numb
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MANOVA: Univariate 867 with similar
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MANOVA: Multivariate MANOVA is avai
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Limitations MANOVA: Multivariate 87
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MANOVA: Multivariate 873 DEVIATION
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MANOVA: Multivariate 875 • You mu
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MANOVA: Multivariate 877 SINGLEDF S
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MANOVA: Multivariate 879 ROTATE Rot
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CONDITIONAL and UNCONDITIONAL Keywo
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MANOVA: Repeated Measures 883 descr
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MANOVA: Repeated Measures 885 • I
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Example MANOVA: Repeated Measures 8
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MANOVA: Repeated Measures 889 • A
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1 MAPS MAPS {/GVAR = VAR(varname)[V
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Operations 893 MAPS • Each MAPS c
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895 MAPS • The total (sum) of sal
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Example 897 MAPS standard deviation
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Example 899 MAPS MAPS /GVAR = VAR(f
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Example 901 MAPS MAPS /GVAR = VAR(c
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Number of Cases Less Than (N of Cas
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906 MATCH FILES Variable Names. You
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908 MATCH FILES • If the files ha
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910 MATCH FILES RENAME Subcommand R
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912 MATCH FILES • FIRST creates a
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914 MATRIX—END MATRIX INV Inverse
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916 MATRIX—END MATRIX • A symme
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918 MATRIX—END MATRIX Example {11
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920 MATRIX—END MATRIX • All of
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922 MATRIX—END MATRIX ** &* &/ &*
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924 MATRIX—END MATRIX • The res
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926 MATRIX—END MATRIX COMPUTE Sta
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928 MATRIX—END MATRIX A = 111 111
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930 MATRIX—END MATRIX DET(M) Dete
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932 MATRIX—END MATRIX MOD(M,S) Re
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934 MATRIX—END MATRIX and the num
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936 MATRIX—END MATRIX • Constan
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938 MATRIX—END MATRIX • The fir
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940 MATRIX—END MATRIX • A logic
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942 MATRIX—END MATRIX • The mat
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944 MATRIX—END MATRIX FORMAT Spec
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946 MATRIX—END MATRIX HOLD Specif
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948 MATRIX—END MATRIX NAMES Speci
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950 MATRIX—END MATRIX • If more
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952 MATRIX—END MATRIX Valid keywo
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954 MATRIX—END MATRIX • The mat
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956 MATRIX—END MATRIX FNAMES Spec
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MATRIX DATA MATRIX DATA VARIABLES=v
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960 MATRIX DATA Table 1 summarizes
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962 MATRIX DATA Example • No othe
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964 MATRIX DATA Example * Matrix da
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966 MATRIX DATA • ROWTYPE_ is spe
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968 MATRIX DATA FULL do not enter t
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970 MATRIX DATA • The split varia
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972 MATRIX DATA Example MATRIX DATA
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974 MATRIX DATA Within-Cells Record
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976 MATRIX DATA • Because ROWTYPE
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978 MCONVERT • If there are multi
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MEANS Overview Options MEANS [TABLE
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982 MEANS • The first table list
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984 MEANS LINEARITY Test of lineari
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986 MISSING VALUES • For date for
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MIXED MIXED is available in the Adv
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990 MIXED Models The following are
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992 MIXED Basic Specification • T
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994 MIXED ARMA1 Autoregressive movi
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996 MIXED Example matrix: σ2 1 σ2
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998 MIXED Variable List The variabl
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1000 MIXED WITH (option) Covariate
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1002 MIXED • The following option
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1004 MIXED Otherwise, it will be ig
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1006 MIXED REGWGT Subcommand The RE
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1008 MIXED Example MIXED SCORE BY S
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1010 MIXED Interpretation of Random
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1012 MIXED
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1014 Syntax Reference Examples MODE
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1016 MRSETS These variables are cod
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1018 MRSETS DISPLAY Subcommand /DIS
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1020 MULT RESPONSE Options did not
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1022 MULT RESPONSE • The group na
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1024 MULT RESPONSE TABLES Subcomman
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1026 MULT RESPONSE ROW Row percenta
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1028 MULT RESPONSE The following ke
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1030 MVA Overview Options [/REGRESS
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1032 MVA Symbols The symbols displa
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1034 MVA Examples MVA VARIABLES=pop
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1036 MVA PERCENT=n Omit rows for va
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1038 MVA TPATTERN Subcommand The TP
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1040 MVA CONVERGENCE=value Converge
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1042 MVA ADDTYPE Type of distributi
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NLR NLR and CNLR are available in t
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1046 NLR Basic Specification The ba
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1048 NLR • The two COMPUTE statem
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1050 NLR Example MODEL PROGRAM A=1,
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1052 NLR • Some new parameters ma
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1054 NLR Example MODEL PROGRAM A=.5
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1056 NLR sponding to a more accurat
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1058 NLR Nonlinear Constraints Nonl
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1060 NLR • CNLR generates the boo
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1062 N OF CASES Example GET FILE=CI
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1064 NOMREG Overview Options NOMREG
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1066 NOMREG mates convergence crite
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1068 NOMREG B are factors. The expr
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1070 NOMREG • The initial model c
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1072 NOMREG • Only one OUTFILE su
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1074 NOMREG SCALE Subcommand The SC
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NONPAR CORR Overview Options NONPAR
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1078 NONPAR CORR Example NONPAR COR
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1080 NONPAR CORR Format of the Matr
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NPAR TESTS NPAR TESTS [CHISQUARE=va
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1084 NPAR TESTS Options Statistical
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1086 NPAR TESTS • If more than 0.
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1088 NPAR TESTS Operations Example
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1090 NPAR TESTS Example • A test
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1094 NPAR TESTS MEDIAN Subcommand S
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1096 NPAR TESTS MOSES Subcommand Sy
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1100 NPAR TESTS Example • Under t
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NUMERIC NUMERIC varlist[(format)] [
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OLAP CUBES OLAP CUBES {varlist} BY
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1106 OLAP CUBES • A Layered Repor
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1108 OLAP CUBES The percentage diff
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OMS Note: Square brackets used in t
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1112 OMS bles from the FREQUENCIES
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1114 OMS Example OMS /SELECT TABLES
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1116 OMS be followed by an equals s
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1118 OMS SVWSOXML XML used by Smart
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1120 OMS • The first OMS command
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1122 OMS keyword must be followed b
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1124 OMS Figure 4 Single two-dimens
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1126 OMS Figure 6 Multiple Tables w
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1128 OMS Figure 8 Tables with diffe
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1130 OMS tics in the columns. To in
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1132 OMS Figure 11 Variable names i
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1134 OMS Figure 13 OXML for simple
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1136 OMS Command and Subtype Identi
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1138 OMS Table 2 Command Command Id
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OMSINFO OMSINFO. Example OMSINFO. O
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13 1156 OMSEND OMSEND Note: Square
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OMSLOG OMSLOG FILE = ’filespec’
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ONEWAY Overview Options ONEWAY varl
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1162 ONEWAY Analysis List The analy
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1164 ONEWAY groups. In addition, ho
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1166 ONEWAY • Up to 10 RANGE subc
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1168 ONEWAY • The dependent varia
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ORTHOPLAN Overview Options ORTHOPLA
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1172 ORTHOPLAN Example • The FACT
Page 1174 and 1175:
1174 ORTHOPLAN • MINIMUM is follo
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1176 Syntax Reference Basic Specifi
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1178 Syntax Reference SETS Subcomma
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1180 Syntax Reference CONVERGENCE S
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1182 Syntax Reference SAVE Subcomma
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PACF Overview Options PACF [VARIABL
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PACF 1187 • DIFF differences the
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PACF 1189 • If SEASONAL is specif
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PARTIAL CORR Overview Options PARTI
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VARIABLES Subcommand PARTIAL CORR 1
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FORMAT Subcommand FORMAT determines
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Format of the Matrix Data File Spli
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PARTIAL CORR 1199 • REGRESSION co
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PERMISSIONS PERMISSIONS FILE=’fil
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PLANCARDS Overview Options PLANCARD
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Example PLANCARDS 1205 DATA LIST FR
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Figure 2 Single-profile format Prof
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Profile # 1 Circle the number in th
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1212 PLUM Basic Specification The b
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1214 PLUM value. The criterion is n
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1216 PLUM CELLINFO Cell Information
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1218 PLUM • Covariates can be con
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POINT POINT KEY=varname [FILE=file]
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1222 POINT Example • DATA LIST re
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PPLOT PPLOT [VARIABLES=] varlist [/
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1226 PPLOT Operations • Subcomman
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1228 PPLOT FRACTION Subcommand FRAC
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1230 PPLOT • In low resolution, a
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1232 PPLOT • If a natural log tra
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PREDICT PREDICT [{start date }] [TH
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1236 PREDICT • If there is a gap
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PRESERVE PRESERVE Overview PRESERVE
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1240 Syntax Reference Basic Specifi
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1242 Syntax Reference Example DATA
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1244 Syntax Reference CONVERGENCE S
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1248 Syntax Reference VARNAME_ Stri
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1250 PRINT Syntax Rules • A slash
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1252 PRINT Strings You can specify
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1254 PRINT Example PRINT OUTFILE=PR
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1256 PRINT EJECT Basic Specificatio
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PRINT FORMATS PRINT FORMATS varlist
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1260 PRINT FORMATS Example COMPUTE
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1262 PRINT SPACE Example PRINT / NA
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PROBIT Overview Options PROBIT is a
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1266 PROBIT • If individual cases
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1268 PROBIT • Keywords BY and WIT
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1270 PROBIT mately six significant
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1272 PROBIT RMP Relative median pot
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1274 PROCEDURE OUTPUT Example PROCE
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1276 PROXIMITIES Example Overview O
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1278 PROXIMITIES RANGE Standardize
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1280 PROXIMITIES CORRELATION( xy ,
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1282 PROXIMITIES 0, 1, 1, 0, 0 for
Page 1284 and 1285:
1284 PROXIMITIES K1[(p[,np])] Kulcz
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1286 PROXIMITIES SS5[(p[,np])] Soka
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1288 PROXIMITIES • When used with
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1290 PROXIMITIES analysis. If a var
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1292 PROXIMITIES Example Example GE
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1294 PROXIMITIES
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1296 Syntax Reference Overview Opti
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1302 Syntax Reference TORGERSON Tor
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1304 Syntax Reference SPLINE Monoto
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1306 Syntax Reference number of cas
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1308 Syntax Reference DIFFSTRESS Co
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1310 Syntax Reference PLOT Subcomma
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1312 Syntax Reference cases in the
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1314 QUICK CLUSTER distances betwee
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1316 QUICK CLUSTER is specified, QU
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1318 QUICK CLUSTER NONE No addition
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RANK RANK [VARIABLES=] varlist [({A
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1322 RANK • String variables cann
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1324 RANK TIES Subcommand TIES dete
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RATIO STATISTICS RATIO STATISTICS n
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1328 RATIO STATISTICS • Keywords
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1330 RATIO STATISTICS MEAN Mean. Th
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1332 Syntax Reference Syntax Rules
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RECODE For numeric variables: RECOD
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1336 RECODE • SYSMIS specifies th
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1338 RECODE • Values for V1 throu
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RECORD TYPE For mixed file types: R
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1342 RECORD TYPE Example * Reading
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1344 RECORD TYPE Example * A mixed
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1346 RECORD TYPE Example * Specifyi
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1348 RECORD TYPE • SPREAD can be
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1350 REFORMAT Example * Convert an
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1352 REGRESSION Overview Options RE
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1354 REGRESSION • The DEPENDENT s
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1356 REGRESSION • When more than
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1358 REGRESSION Equation Statistics
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1360 REGRESSION PIN[(value)] Probab
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1362 REGRESSION • COMPUTE creates
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1364 REGRESSION • If SELECT is sp
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1366 REGRESSION MISSING Subcommand
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1368 REGRESSION: Residuals (RESIDUA
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1370 REGRESSION: Residuals • REGR
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1372 REGRESSION: Residuals are RESI
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1374 REGRESSION: Residuals Example
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RELIABILITY Overview RELIABILITY VA
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1378 RELIABILITY • This example a
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1380 RELIABILITY ICC Subcommand ICC
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1382 RELIABILITY Matrix Output Matr
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1384 RELIABILITY Example Example Ex
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RENAME VARIABLES RENAME VARIABLES {
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REPEATING DATA REPEATING DATA [FILE
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1390 REPEATING DATA • The DATA LI
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1392 REPEATING DATA Example Figure
Page 1394 and 1395:
1394 REPEATING DATA STARTS Subcomma
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1396 REPEATING DATA • In the data
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1398 REPEATING DATA CONTINUED Subco
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1400 REPEATING DATA in positions 1-
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REPORT REPORT [/FORMAT=[{MANUAL }]
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1404 REPORT Column Widths. Default
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1406 REPORT statistics. The maximum
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1408 REPORT Example • The maximum
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1410 REPORT headings with the first
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1412 REPORT Figure 1 Page layout fo
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1414 REPORT Example REPORT FORMAT=A
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1416 REPORT Column Format The follo
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1418 REPORT BREAK Subcommand BREAK
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1420 REPORT (OFFSET) Position of th
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1422 REPORT • To use the summary
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1424 REPORT means of all three vari
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1426 REPORT • The summary title m
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1428 REPORT Table 3 Default print f
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1430 REPORT • The right-justified
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1432 REREAD Subcommand Order Subcom
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1434 REREAD FILE Subcommand FILE sp
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1436 REREAD Example * Reading both
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RMV RMV new variables={LINT (varlis
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1440 RMV • NEWVAR1 will have the
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ROC Overview Options ROC varlist BY
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1444 ROC MISSING Subcommand Cases w
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SAMPLE SAMPLE {decimal value} {n FR
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SAVE SAVE OUTFILE=’filespec’ [/
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1450 SAVE Example GET FILE=HUBEMPL.
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1452 SAVE Example GET FILE=PRSNL. C
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1454 SAVE PERMISSIONS Subcommand Th
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1456 Syntax Reference SAVE MODEL Ov
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1458 Syntax Reference TYPE Subcomma
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1460 SAVE TRANSLATE ** Required for
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1462 SAVE TRANSLATE • User-missin
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1464 SAVE TRANSLATE The following t
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1466 SAVE TRANSLATE WK1 1-2-3 Relea
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1468 SAVE TRANSLATE CELLS Subcomman
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1470 SAVE TRANSLATE Example SAVE TR
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1472 SCRIPT
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1474 Syntax Reference Subcommand Or
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1476 Syntax Reference PERIOD Subcom
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SELECT IF SELECT IF [(]logical expr
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1480 SELECT IF Missing Values • I
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1482 SELECT IF Example Example Exam
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1484 SET [HEADER={NO** }] {YES } {B
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1486 SET Operations Example • Set
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1488 SET LABELS Displays variable l
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1490 SET JOURNAL Subcommand Alias t
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1492 SET SPSSB in the server versio
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1494 SET • You can specify any le
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1496 SET Example SET SMALL = 0. SET
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SHOW SHOW [ALL] [BLANKS] [BLKSIZE]
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1500 SHOW EPOCH Range of years for
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1502 SHOW WEIGHT Variable used to w
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1504 SORT CASES addition, special c
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1506 SPCHART Overview Options SPCHA
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1508 SPCHART • The default span (
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1510 SPCHART Figure 3 S chart Data
Page 1512 and 1513:
1512 SPCHART Figure 4 Individuals c
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1514 SPCHART Figure 7 NP chart Data
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1516 SPCHART based on the Poisson d
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1518 SPCHART • A duplicated subco
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1520 SPCHART CAPSIGMA Subcommand Th
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1522 SPCHART fixed control limits a
Page 1524 and 1525:
1524 Syntax Reference SPECTRA Overv
Page 1526 and 1527:
1526 Syntax Reference Syntax Rules
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1528 Syntax Reference TUKEY Tukey-H
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1532 Syntax Reference References
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1534 SPLIT FILE • AGGREGATE ignor
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STRING STRING varlist (An) [/varlis
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SUBTITLE SUBTITLE [’]text[’] Ex
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SUMMARIZE SUMMARIZE [TABLES=]{varli
Page 1542 and 1543:
1542 SUMMARIZE Example the FORMAT s
Page 1544 and 1545:
1544 SUMMARIZE GEOMETRIC Geometric
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SURVIVAL Overview SURVIVAL is avail
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1548 SURVIVAL Operations • SURVIV
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1550 SURVIVAL • INTERVALS produce
Page 1552 and 1553:
1552 SURVIVAL HAZARD Plot the hazar
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1554 SURVIVAL large samples. It is
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1556 SURVIVAL WRITE Subcommand Form
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1558 SURVIVAL • Record type 31 is
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1560 SYSFILE INFO
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1562 Syntax Reference Operations Ex
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1564 TEMPORARY Example • The XSAV
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TITLE TITLE [’]text[’] Example
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TSET TSET [PRINT={DEFAULT**}] [/NEW
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1570 TSET MXPREDICT Subcommand MXPR
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TSPLOT TSPLOT [VARIABLES=] variable
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1574 TSPLOT Subcommand Order • Su
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1576 TSPLOT Example TSPLOT TICKETS
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1578 TSPLOT Figure 3 FORMAT=REFEREN
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1580 TSPLOT UNIFORM Scale uniformly
Page 1582 and 1583:
1582 T-TEST degrees of freedom, and
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1584 T-TEST PAIRS Subcommand PAIRS
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2SLS Overview Options 2SLS is avail
Page 1588 and 1589:
1588 2SLS • In this example, Y1 i
Page 1590 and 1591:
1590 2SLS • In this example, the
Page 1592 and 1593:
TWOSTEP CLUSTER Overview TWOSTEP CL
Page 1594 and 1595:
1594 TWOSTEP CLUSTER Example TWOSTE
Page 1596 and 1597:
1596 TWOSTEP CLUSTER Example TWOSTE
Page 1598 and 1599:
1598 TWOSTEP CLUSTER STATE Save the
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1600 TWOSTEP CLUSTER SUMMARY Descri
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1602 UNIANOVA Overview Options ** D
Page 1604 and 1605:
1604 UNIANOVA Example UNIANOVA YIEL
Page 1606 and 1607:
1606 UNIANOVA appropriate effect is
Page 1608 and 1609:
1608 UNIANOVA PRINT Subcommand PRIN
Page 1610 and 1611:
1610 UNIANOVA Example UNIANOVA DEP
Page 1612 and 1613:
1612 UNIANOVA • B1 versus B2 at A
Page 1614 and 1615:
1614 UNIANOVA HELMERT Helmert contr
Page 1616 and 1617:
1616 UNIANOVA Post Hoc Tests Statis
Page 1618 and 1619:
1618 UNIANOVA QREGW Ryan-Einot-Gabr
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1620 UNIANOVA SRESID Studentized re
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UPDATE UPDATE FILE={master file} {*
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1624 UPDATE • UPDATE copies all v
Page 1626 and 1627:
1626 UPDATE BY Subcommand BY specif
Page 1628 and 1629:
1628 UPDATE • IN has only one spe
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1630 USE • Values on keyword YEAR
Page 1632 and 1633:
1632 USE USE THRU 5. • This comma
Page 1634 and 1635:
1634 VALUE LABELS Operations Exampl
Page 1636 and 1637:
VARCOMP Overview Options VARCOMP is
Page 1638 and 1639:
1638 VARCOMP • You can specify mu
Page 1640 and 1641:
1640 VARCOMP The default is 1.0E-8.
Page 1642 and 1643:
1642 VARCOMP Nesting within an inte
Page 1644 and 1645:
VARIABLE LABELS VARIABLE LABELS var
Page 1646 and 1647:
VARIABLE LEVEL VARIABLE LEVEL varli
Page 1648 and 1649:
VARSTOCASES VARSTOCASES /MAKE new v
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1650 VARSTOCASES The command: VARST
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1652 VARSTOCASES Example VARSTOCASE
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1654 VARSTOCASES COUNT Subcommand W
Page 1656 and 1657:
1656 VECTOR Syntax Rules • Multip
Page 1658 and 1659:
1658 VECTOR Example VECTOR X,Y(A5,6
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1660 VECTOR • Aggregating by vari
Page 1662 and 1663:
1662 VERIFY Operations • VERIFY r
Page 1664 and 1665:
1664 WEIGHT Example • Each WEIGHT
Page 1666 and 1667:
1666 WLS without erasing values sav
Page 1668 and 1669:
1668 WLS DELTA Subcommand DELTA, al
Page 1670 and 1671:
1670 WLS Example WLS X1 WITH Y1 /SO
Page 1672 and 1673:
1672 WRITE Basic Specification The
Page 1674 and 1675:
1674 WRITE Strings • MOHIRED, YRH
Page 1676 and 1677:
WRITE FORMATS WRITE FORMATS varlist
Page 1678 and 1679:
1678 WRITE FORMATS Example COMPUTE
Page 1680 and 1681:
1680 XSAVE Basic Specification The
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1682 XSAVE • Keyword ALL on KEEP
Page 1684:
1684 XSAVE • The only specificati
Page 1687 and 1688:
Appendix A Commands and Program Sta
Page 1689 and 1690:
Determining Command Order Commands
Page 1691 and 1692:
Table A.1 Commands and program stat
Page 1693 and 1694:
Input Program Commands Commands and
Page 1695 and 1696:
Appendix B IMPORT/EXPORT Character
Page 1697 and 1698:
Position Graphic Macintosh Microsof
Page 1699 and 1700:
Position Graphic Macintosh Microsof
Page 1701:
Position Graphic Macintosh 222 â 1
Page 1704 and 1705:
1704 Appendix C Figure C.1 File REC
Page 1706 and 1707:
1706 Appendix C Figure C.4 NESTED.D
Page 1708 and 1709:
1708 Appendix C • The output from
Page 1710 and 1711:
1710 Appendix C FILE TYPE GROUPED R
Page 1712 and 1713:
1712 Appendix C Figure C.8 LIST out
Page 1714 and 1715:
1714 Appendix C • REPEATING DATA
Page 1716 and 1717:
1716 Appendix C The loop terminates
Page 1718 and 1719:
1718 Appendix D Example 1: Automati
Page 1720 and 1721:
1720 Appendix D • Commands betwee
Page 1722 and 1723:
1722 Appendix D Figure D.7 Listing
Page 1724 and 1725:
1724 Appendix D You can modify the
Page 1726 and 1727:
1726 Appendix D • !TOKENS(1) spec
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1728 Appendix D Figure D.15 Output
Page 1730 and 1731:
1730 Appendix D Figure D.19 !DATAGE
Page 1733 and 1734:
Appendix A Trends Options: Special
Page 1735 and 1736:
Trends Options: Special Considerati
Page 1737 and 1738:
Trends Options: Special Considerati
Page 1739 and 1740:
Subject Index active data file cach
Page 1741 and 1742:
inary shape difference in Distances
Page 1743 and 1744:
in Logistic Regression procedure, 8
Page 1745 and 1746:
in Multidimensional Scaling procedu
Page 1747 and 1748:
scratch files, 1492 data dictionary
Page 1749 and 1750:
matrices, 479 matrix input, 482 mat
Page 1751 and 1752:
in Hierarchical Loglinear Analysis
Page 1753 and 1754:
cell weights, 610, 821 contrasts, 8
Page 1755 and 1756:
hierarchical loglinear analysis, 71
Page 1757 and 1758:
in Autoregression procedure, 148- 1
Page 1759 and 1760:
See also Linear Regression procedur
Page 1761 and 1762:
linear transformations, 871 naming
Page 1763 and 1764:
date format variables, 986 defining
Page 1765 and 1766:
function precision, 1056 infinite s
Page 1767 and 1768:
in Cox Regression procedure, 302 in
Page 1769 and 1770:
output, 1489, 1494 output page size
Page 1771 and 1772:
elative risk ratio in Crosstabs pro
Page 1773 and 1774:
scientific notation controlling dis
Page 1775 and 1776:
in ROC Curve procedure, 1445 standa
Page 1777 and 1778:
fixed format, 414, 415, 416, 423 fr
Page 1779 and 1780:
date format variables, 1633 in Homo
Page 1781 and 1782:
Syntax Index !BLANKS (function) DEF
Page 1783 and 1784:
AGGREGATE (command), 90 BREAK subco
Page 1785 and 1786:
AR (subcommand) ARIMA command, 158
Page 1787 and 1788:
REPORT command, 1417 BLANKS (subcom
Page 1789 and 1790:
CASENUM$ (system variable) in PRINT
Page 1791 and 1792:
REGRESSION command, 1360 CIN (subco
Page 1793 and 1794:
SHOW command, 1499 COMPUTE (command
Page 1795 and 1796:
TWOSTEP CLUSTER command, 1600 COUNT
Page 1797 and 1798:
WRITE subcommand, 325 CROSSVALID (k
Page 1799 and 1800:
Symbols D (keyword) CLUSTER command
Page 1801 and 1802:
NPAR TESTS command, 1100 ONEWAY com
Page 1803 and 1804:
DISPER (keyword) CLUSTER command, 2
Page 1805 and 1806:
MIXED command, 999 UNIANOVA command
Page 1807 and 1808:
EXPERIMENTAL (keyword) ANOVA comman
Page 1809 and 1810:
MEANS command, 983 PROXSCAL command
Page 1811 and 1812:
GAC (keyword) OLAP CUBES command, 1
Page 1813 and 1814:
TEST(SSCP) keyword, 674 TUKEY keywo
Page 1815 and 1816:
HISTORY (keyword) CATPCA command, 2
Page 1817 and 1818:
INCLUDE (command), 771 FILE subcomm
Page 1819 and 1820:
JITTER (keyword) IGRAPH command, 75
Page 1821 and 1822:
LEAVE (command), 796 LEFT (keyword)
Page 1823 and 1824:
CWEIGHT subcommand, 821 DESIGN subc
Page 1825 and 1826:
ROVMAP subcommand, 896 SHOWLABEL su
Page 1827 and 1828:
RMV command, 1440 MEAN (keyword) AN
Page 1829 and 1830:
MANOVA command, 858 MEANS command,
Page 1831 and 1832:
CATREG command, 212 MULTIPUNCH (key
Page 1833 and 1834:
NMISS (function) AGGREGATE command,
Page 1835 and 1836:
NOREFERENCE (keyword) TSPLOT comman
Page 1837 and 1838:
CATREG command, 215 ODBC (keyword)
Page 1839 and 1840:
RATIO STATISTICS command, 1328 REGR
Page 1841 and 1842:
PER (keyword) SPECTRA command, 1530
Page 1843 and 1844:
SUMMARY keyword, 1216 TEST subcomma
Page 1845 and 1846:
CATREG command, 215 CLUSTER command
Page 1847 and 1848:
QUANT (keyword) CATPCA command, 200
Page 1849 and 1850:
with FORMATS command, 1349 REG (key
Page 1851 and 1852:
CSTABULATE command, 374 CURVEFIT co
Page 1853 and 1854:
SAMPLEFILE (subcommand) CSSELECT co
Page 1855 and 1856:
SEED (keyword) CSSELECT command, 34
Page 1857 and 1858:
MXLOOPS subcommand, 1500 MXMEMORY s
Page 1859 and 1860:
SP (subcommand) ARIMA command, 156-
Page 1861 and 1862:
DISCRIMINANT command, 479 FREQUENCI
Page 1863 and 1864:
Symbols T (function) MATRIX command
Page 1865 and 1866:
TOLERANCE (subcommand) DISCRIMINANT
Page 1867 and 1868:
UNCLASSIFIED (keyword) DISCRIMINANT
Page 1869 and 1870:
REPORT command, 1414, 1418 value la
Page 1871 and 1872:
VIN (subcommand) DISCRIMINANT comma
Page 1873 and 1874:
X1MULTIPLIER (keyword) IGRAPH comma
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