AT_BANNER([ONEWAY procedure]) AT_SETUP([ONEWAY basic operation]) AT_DATA([oneway.sps], [DATA LIST NOTABLE LIST /QUALITY * BRAND * . BEGIN DATA 7 3 4 3 3 1 2 1 1 1 4 2 2 2 3 2 5 3 1 1 4 1 5 2 2 2 3 3 6 3 END DATA VARIABLE LABELS brand 'Manufacturer'. VARIABLE LABELS quality 'Breaking Strain'. VALUE LABELS /brand 1 'Aspeger' 2 'Bloggs' 3 'Charlies'. ONEWAY quality BY brand /STATISTICS descriptives homogeneity /CONTRAST = -2 1 1 /CONTRAST = 0 -1 1 . ]) AT_CHECK([pspp -O format=csv oneway.sps], [0], [dnl Table: Descriptives ,,,,,,95% Confidence Interval for Mean,,, ,,N,Mean,Std. Deviation,Std. Error,Lower Bound,Upper Bound,Minimum,Maximum Breaking Strain,Aspeger,5,2.20,1.30,.58,.58,3.82,1.00,4.00 ,Bloggs,5,3.20,1.30,.58,1.58,4.82,2.00,5.00 ,Charlies,5,5.00,1.58,.71,3.04,6.96,3.00,7.00 ,Total,15,3.47,1.77,.46,2.49,4.45,1.00,7.00 Table: Test of Homogeneity of Variances ,Levene Statistic,df1,df2,Sig. Breaking Strain,.09,2,12,.913 Table: ANOVA ,,Sum of Squares,df,Mean Square,F,Sig. Breaking Strain,Between Groups,20.13,2,10.07,5.12,.025 ,Within Groups,23.60,12,1.97,, ,Total,43.73,14,,, Table: Contrast Coefficients ,,Manufacturer,, ,,Aspeger,Bloggs,Charlies Contrast,1,-2,1,1 ,2,0,-1,1 Table: Contrast Tests ,,Contrast,Value of Contrast,Std. Error,t,df,Sig. (2-tailed) Breaking Strain,Assume equal variances,1,3.80,1.54,2.47,12,.029 ,,2,1.80,.89,2.03,12,.065 ,Does not assume equal,1,3.80,1.48,2.56,8.74,.031 ,,2,1.80,.92,1.96,7.72,.086 ]) AT_CLEANUP AT_SETUP([ONEWAY with splits]) AT_DATA([oneway-splits.sps], [DATA LIST NOTABLE LIST /QUALITY * BRAND * S *. BEGIN DATA 3 1 1 2 1 1 1 1 1 1 1 1 4 1 1 5 2 1 2 2 1 4 2 2 2 2 2 3 2 2 7 3 2 4 3 2 5 3 2 3 3 2 6 3 2 END DATA VARIABLE LABELS brand 'Manufacturer'. VARIABLE LABELS quality 'Breaking Strain'. VALUE LABELS /brand 1 'Aspeger' 2 'Bloggs' 3 'Charlies'. SPLIT FILE by s. ONEWAY quality BY brand /STATISTICS descriptives homogeneity /CONTRAST = -2 2 /CONTRAST = -1 1 . ]) AT_CHECK([pspp -O format=csv oneway-splits.sps], [0], [Variable,Value,Label S,1.00, Table: Descriptives ,,,,,,95% Confidence Interval for Mean,,, ,,N,Mean,Std. Deviation,Std. Error,Lower Bound,Upper Bound,Minimum,Maximum Breaking Strain,Aspeger,5,2.20,1.30,.58,.58,3.82,1.00,4.00 ,Bloggs,2,3.50,2.12,1.50,-15.56,22.56,2.00,5.00 ,Total,7,2.57,1.51,.57,1.17,3.97,1.00,5.00 Table: Test of Homogeneity of Variances ,Levene Statistic,df1,df2,Sig. Breaking Strain,1.09,1,5,.345 Table: ANOVA ,,Sum of Squares,df,Mean Square,F,Sig. Breaking Strain,Between Groups,2.41,1,2.41,1.07,.349 ,Within Groups,11.30,5,2.26,, ,Total,13.71,6,,, Table: Contrast Coefficients ,,Manufacturer, ,,Aspeger,Bloggs Contrast,1,-2,2 ,2,-1,1 Table: Contrast Tests ,,Contrast,Value of Contrast,Std. Error,t,df,Sig. (2-tailed) Breaking Strain,Assume equal variances,1,2.60,2.52,1.03,5,.349 ,,2,1.30,1.26,1.03,5,.349 ,Does not assume equal,1,2.60,3.22,.81,1.32,.539 ,,2,1.30,1.61,.81,1.32,.539 Variable,Value,Label S,2.00, Table: Descriptives ,,,,,,95% Confidence Interval for Mean,,, ,,N,Mean,Std. Deviation,Std. Error,Lower Bound,Upper Bound,Minimum,Maximum Breaking Strain,Bloggs,3,3.00,1.00,.58,.52,5.48,2.00,4.00 ,Charlies,5,5.00,1.58,.71,3.04,6.96,3.00,7.00 ,Total,8,4.25,1.67,.59,2.85,5.65,2.00,7.00 Table: Test of Homogeneity of Variances ,Levene Statistic,df1,df2,Sig. Breaking Strain,.92,1,6,.374 Table: ANOVA ,,Sum of Squares,df,Mean Square,F,Sig. Breaking Strain,Between Groups,7.50,1,7.50,3.75,.101 ,Within Groups,12.00,6,2.00,, ,Total,19.50,7,,, Table: Contrast Coefficients ,,Manufacturer, ,,Bloggs,Charlies Contrast,1,-2,2 ,2,-1,1 Table: Contrast Tests ,,Contrast,Value of Contrast,Std. Error,t,df,Sig. (2-tailed) Breaking Strain,Assume equal variances,1,4.00,2.07,1.94,6,.101 ,,2,2.00,1.03,1.94,6,.101 ,Does not assume equal,1,4.00,1.83,2.19,5.88,.072 ,,2,2.00,.91,2.19,5.88,.072 ]) AT_CLEANUP AT_SETUP([ONEWAY with missing values]) dnl Check that missing are treated properly AT_DATA([oneway-missing1.sps], [DATA LIST NOTABLE LIST /v1 * v2 * dep * vn *. BEGIN DATA . . 1 4 3 3 1 2 2 2 1 2 1 1 1 2 1 1 1 2 4 4 1 2 5 5 2 2 2 2 2 2 4 4 2 2 2 2 2 2 3 3 2 2 7 7 3 2 4 4 3 2 5 5 3 2 3 3 3 2 6 6 3 2 END DATA ONEWAY v1 v2 BY dep /STATISTICS descriptives homogeneity /MISSING ANALYSIS . ]) AT_DATA([oneway-missing2.sps], [DATA LIST NOTABLE LIST /v1 * v2 * dep * vn * . BEGIN DATA 4 . 1 2 3 3 1 2 2 2 1 2 1 1 1 2 1 1 1 2 4 4 1 2 5 5 2 2 2 2 2 2 4 4 2 2 2 2 2 2 3 3 2 2 7 7 3 2 4 4 3 2 5 5 3 2 3 3 3 2 6 6 3 2 END DATA ONEWAY v1 v2 BY dep /STATISTICS descriptives homogeneity /MISSING LISTWISE . ]) AT_CHECK([pspp -O format=csv oneway-missing1.sps > first.out], [0]) AT_CHECK([pspp -O format=csv oneway-missing2.sps > second.out], [0]) AT_CHECK([diff first.out second.out], [0], []) dnl Now a test with missing values in the independent variable AT_DATA([oneway-missing3.sps], [DATA LIST NOTABLE LIST /v1 * v2 * dep * vn * . BEGIN DATA 4 2 . 2 3 3 1 2 2 2 1 2 1 1 1 2 1 1 1 2 4 4 1 2 5 5 2 2 2 2 2 2 4 4 2 2 2 2 2 2 3 3 2 2 7 7 3 2 4 4 3 2 5 5 3 4 3 3 3 2 6 6 3 2 END DATA ONEWAY v1 v2 BY dep /STATISTICS descriptives homogeneity /MISSING ANALYSIS . ]) AT_CHECK([pspp -O format=csv oneway-missing3.sps > third.out], [0]) AT_CHECK([diff first.out third.out], [0], []) AT_CLEANUP AT_SETUP([ONEWAY descriptives subcommand]) AT_DATA([oneway-descriptives.sps], [DATA LIST NOTABLE LIST /QUALITY * BRAND * . BEGIN DATA 13 11 12 11 11 11 11 11 14 11 15 25 12 25 14 25 12 25 13 25 17 301 14 301 15 301 13 301 16 301 END DATA ONEWAY quality BY brand /STATISTICS descriptives . ]) AT_CHECK([pspp -O format=csv oneway-descriptives.sps], [0], [Table: Descriptives ,,,,,,95% Confidence Interval for Mean,,, ,,N,Mean,Std. Deviation,Std. Error,Lower Bound,Upper Bound,Minimum,Maximum QUALITY,11.00,5,12.20,1.30,.58,10.58,13.82,11.00,14.00 ,25.00,5,13.20,1.30,.58,11.58,14.82,12.00,15.00 ,301.00,5,15.00,1.58,.71,13.04,16.96,13.00,17.00 ,Total,15,13.47,1.77,.46,12.49,14.45,11.00,17.00 Table: ANOVA ,,Sum of Squares,df,Mean Square,F,Sig. QUALITY,Between Groups,20.13,2,10.07,5.12,.025 ,Within Groups,23.60,12,1.97,, ,Total,43.73,14,,, ]) AT_CLEANUP AT_SETUP([ONEWAY homogeneity subcommand]) AT_DATA([oneway-homogeneity.sps], [DATA LIST NOTABLE LIST /QUALITY * BRAND * . BEGIN DATA 13 11 12 11 11 11 11 11 14 11 15 25 12 25 14 25 12 25 13 25 17 301 14 301 15 301 13 301 16 301 END DATA ONEWAY quality BY brand /STATISTICS homogeneity . ]) AT_CHECK([pspp -O format=csv oneway-homogeneity.sps], [0], [Table: Test of Homogeneity of Variances ,Levene Statistic,df1,df2,Sig. QUALITY,.09,2,12,.913 Table: ANOVA ,,Sum of Squares,df,Mean Square,F,Sig. QUALITY,Between Groups,20.13,2,10.07,5.12,.025 ,Within Groups,23.60,12,1.97,, ,Total,43.73,14,,, ]) AT_CLEANUP AT_SETUP([ONEWAY multiple variables]) dnl check that everything works ok when several different dependent variables are specified. dnl This of course does not mean that we're doing a multivariate analysis. It's just like dnl running several tests at once. AT_DATA([multivar.sps], [DATA LIST notable LIST /x * y * z * g *. begin data. 1 1 0 10 1 1 9 10 9 1 2 10 1 1 3 20 1 1 8 20 1 1 1 20 1 1 2 20 0 1 3 20 1 1 4 30 0 1 5 30 1 1 6 30 0 1 7 30 1 2 8 30 2 2 9 30 1 2 1 30 1 2 0 30 1 2 2 40 8 2 3 40 1 2 4 40 1 2 9 40 9 2 8 40 7 3 7 40 2 3 6 40 3 3 5 40 end data. ONEWAY x y z by g /STATISTICS = DESCRIPTIVES HOMOGENEITY /CONTRAST 3 2 0 -5 /CONTRAST 2 -9 7 0 . ]) AT_CHECK([pspp -o pspp.csv multivar.sps]) dnl Some machines return 3.88 instead of 3.87 below (see bug #31611). AT_CHECK([sed 's/^,Within Groups,3.88/,Within Groups,3.87/' pspp.csv], [0], [dnl Table: Descriptives ,,,,,,95% Confidence Interval for Mean,,, ,,N,Mean,Std. Deviation,Std. Error,Lower Bound,Upper Bound,Minimum,Maximum x,10.00,3,3.67,4.62,2.67,-7.81,15.14,1.00,9.00 ,20.00,5,.80,.45,.20,.24,1.36,.00,1.00 ,30.00,8,.88,.64,.23,.34,1.41,.00,2.00 ,40.00,8,4.00,3.42,1.21,1.14,6.86,1.00,9.00 ,Total,24,2.25,2.83,.58,1.05,3.45,.00,9.00 y,10.00,3,1.00,.00,.00,1.00,1.00,1.00,1.00 ,20.00,5,1.00,.00,.00,1.00,1.00,1.00,1.00 ,30.00,8,1.50,.53,.19,1.05,1.95,1.00,2.00 ,40.00,8,2.38,.52,.18,1.94,2.81,2.00,3.00 ,Total,24,1.63,.71,.15,1.32,1.93,1.00,3.00 z,10.00,3,3.67,4.73,2.73,-8.07,15.41,.00,9.00 ,20.00,5,3.40,2.70,1.21,.05,6.75,1.00,8.00 ,30.00,8,5.00,3.21,1.13,2.32,7.68,.00,9.00 ,40.00,8,5.50,2.45,.87,3.45,7.55,2.00,9.00 ,Total,24,4.67,2.99,.61,3.40,5.93,.00,9.00 Table: Test of Homogeneity of Variances ,Levene Statistic,df1,df2,Sig. x,18.76,3,20,.000 y,71.41,3,20,.000 z,.89,3,20,.463 Table: ANOVA ,,Sum of Squares,df,Mean Square,F,Sig. x,Between Groups,56.16,3,18.72,2.92,.059 ,Within Groups,128.34,20,6.42,, ,Total,184.50,23,,, y,Between Groups,7.75,3,2.58,13.33,.000 ,Within Groups,3.87,20,.19,, ,Total,11.63,23,,, z,Between Groups,17.47,3,5.82,.62,.610 ,Within Groups,187.87,20,9.39,, ,Total,205.33,23,,, Table: Contrast Coefficients ,,g,,, ,,10.00,20.00,30.00,40.00 Contrast,1,3,2,0,-5 ,2,2,-9,7,0 Table: Contrast Tests ,,Contrast,Value of Contrast,Std. Error,t,df,Sig. (2-tailed) x,Assume equal variances,1,-7.40,6.67,1.11,20,.280 ,,2,6.26,12.32,.51,20,.617 ,Does not assume equal,1,-7.40,10.04,-.74,4.53,.497 ,,2,6.26,5.85,1.07,2.87,.366 y,Assume equal variances,1,-6.88,1.16,5.94,20,.000 ,,2,3.50,2.14,1.63,20,.118 ,Does not assume equal,1,-6.88,.91,-7.51,7.00,.000 ,,2,3.50,1.32,2.65,7.00,.033 z,Assume equal variances,1,-9.70,8.07,1.20,20,.243 ,,2,11.73,14.91,.79,20,.440 ,Does not assume equal,1,-9.70,9.57,-1.01,3.64,.373 ,,2,11.73,14.53,.81,9.88,.438 ]) AT_CLEANUP dnl Tests that everything treats weights properly AT_SETUP([ONEWAY vs. weights]) AT_DATA([oneway-unweighted.sps], [DATA LIST NOTABLE LIST /QUALITY * BRAND * W *. BEGIN DATA 3 1 1 2 1 1 1 1 1 1 1 1 4 1 1 5 2 1 2 2 1 4 2 1 4 2 1 4 2 1 2 2 1 2 2 1 3 2 1 7 3 1 4 3 1 5 3 1 5 3 1 3 3 1 6 3 1 END DATA. WEIGHT BY W. ONEWAY quality BY brand /STATISTICS descriptives homogeneity . ]) AT_CHECK([pspp -o pspp-unweighted.csv oneway-unweighted.sps], [0], [ignore], [ignore]) AT_DATA([oneway-weighted.sps], [DATA LIST NOTABLE LIST /QUALITY * BRAND * W *. BEGIN DATA 3 1 1 2 1 1 1 1 2 4 1 1 5 2 1 2 2 1 4 2 3 2 2 2 3 2 1 7 3 1 4 3 1 5 3 2 3 3 1 6 3 1 END DATA. WEIGHT BY W. ONEWAY quality BY brand /STATISTICS descriptives homogeneity . ]) AT_CHECK([pspp -o pspp-weighted.csv oneway-weighted.sps], [0], [ignore], [ignore]) AT_CHECK([diff pspp-weighted.csv pspp-unweighted.csv], [0]) AT_CLEANUP AT_SETUP([ONEWAY posthoc LSD and BONFERRONI]) AT_DATA([oneway-pig.sps],[dnl SET FORMAT F12.3. data list notable list /pigmentation * family *. begin data. 36 1 39 1 43 1 38 1 37 1 46 2 47 2 47 2 47 2 43 2 40 3 50 3 44 3 48 3 50 3 45 4 53 4 56 4 52 4 56 4 end data. oneway pigmentation by family /statistics = descriptives /posthoc = lsd bonferroni alpha (0.05) . ]) AT_CHECK([pspp -O format=csv oneway-pig.sps], [0], [Table: Descriptives ,,,,,,95% Confidence Interval for Mean,,, ,,N,Mean,Std. Deviation,Std. Error,Lower Bound,Upper Bound,Minimum,Maximum pigmentation,1.000,5,38.600,2.702,1.208,35.245,41.955,36.000,43.000 ,2.000,5,46.000,1.732,.775,43.849,48.151,43.000,47.000 ,3.000,5,46.400,4.336,1.939,41.016,51.784,40.000,50.000 ,4.000,5,52.400,4.506,2.015,46.806,57.994,45.000,56.000 ,Total,20,45.850,5.967,1.334,43.057,48.643,36.000,56.000 Table: ANOVA ,,Sum of Squares,df,Mean Square,F,Sig. pigmentation,Between Groups,478.950,3,159.650,12.927,.000 ,Within Groups,197.600,16,12.350,, ,Total,676.550,19,,, Table: Multiple Comparisons (pigmentation) ,,,Mean Difference,,,95% Confidence Interval, ,(I) family,(J) family,(I - J),Std. Error,Sig.,Lower Bound,Upper Bound LSD,1.000,2.000,-7.400,2.223,.004,-12.112,-2.688 ,,3.000,-7.800,2.223,.003,-12.512,-3.088 ,,4.000,-13.800,2.223,.000,-18.512,-9.088 ,2.000,1.000,7.400,2.223,.004,2.688,12.112 ,,3.000,-.400,2.223,.859,-5.112,4.312 ,,4.000,-6.400,2.223,.011,-11.112,-1.688 ,3.000,1.000,7.800,2.223,.003,3.088,12.512 ,,2.000,.400,2.223,.859,-4.312,5.112 ,,4.000,-6.000,2.223,.016,-10.712,-1.288 ,4.000,1.000,13.800,2.223,.000,9.088,18.512 ,,2.000,6.400,2.223,.011,1.688,11.112 ,,3.000,6.000,2.223,.016,1.288,10.712 Bonferroni,1.000,2.000,-7.400,2.223,.025,-14.086,-.714 ,,3.000,-7.800,2.223,.017,-14.486,-1.114 ,,4.000,-13.800,2.223,.000,-20.486,-7.114 ,2.000,1.000,7.400,2.223,.025,.714,14.086 ,,3.000,-.400,2.223,1.000,-7.086,6.286 ,,4.000,-6.400,2.223,.065,-13.086,.286 ,3.000,1.000,7.800,2.223,.017,1.114,14.486 ,,2.000,.400,2.223,1.000,-6.286,7.086 ,,4.000,-6.000,2.223,.095,-12.686,.686 ,4.000,1.000,13.800,2.223,.000,7.114,20.486 ,,2.000,6.400,2.223,.065,-.286,13.086 ,,3.000,6.000,2.223,.095,-.686,12.686 ]) AT_CLEANUP AT_SETUP([ONEWAY posthoc Tukey HSD and Games-Howell]) AT_DATA([oneway-tukey.sps],[dnl set format = f11.3. data list notable list /libido * dose *. begin data. 3 0 2 0 1 0 1 0 4 0 5 1 2 1 4 1 2 1 3 1 7 2 4 2 5 2 3 2 6 2 end data. variable label dose 'Dose of Viagra'. add value labels dose 0 'Placebo' 1 '1 Dose' 2 '2 Doses'. oneway libido by dose /posthoc tukey gh. ]) AT_CHECK([pspp -O format=csv oneway-tukey.sps], [0], [Table: ANOVA ,,Sum of Squares,df,Mean Square,F,Sig. libido,Between Groups,20.133,2,10.067,5.119,.025 ,Within Groups,23.600,12,1.967,, ,Total,43.733,14,,, Table: Multiple Comparisons (libido) ,,,Mean Difference,,,95% Confidence Interval, ,(I) Dose of Viagra,(J) Dose of Viagra,(I - J),Std. Error,Sig.,Lower Bound,Upper Bound Tukey HSD,Placebo,1 Dose,-1.000,.887,.516,-3.366,1.366 ,,2 Doses,-2.800,.887,.021,-5.166,-.434 ,1 Dose,Placebo,1.000,.887,.516,-1.366,3.366 ,,2 Doses,-1.800,.887,.147,-4.166,.566 ,2 Doses,Placebo,2.800,.887,.021,.434,5.166 ,,1 Dose,1.800,.887,.147,-.566,4.166 Games-Howell,Placebo,1 Dose,-1.000,.887,.479,-3.356,1.356 ,,2 Doses,-2.800,.887,.039,-5.439,-.161 ,1 Dose,Placebo,1.000,.887,.479,-1.356,3.356 ,,2 Doses,-1.800,.887,.185,-4.439,.839 ,2 Doses,Placebo,2.800,.887,.039,.161,5.439 ,,1 Dose,1.800,.887,.185,-.839,4.439 ]) AT_CLEANUP AT_SETUP([ONEWAY posthoc Sidak]) AT_DATA([oneway-sidak.sps],[dnl SET FORMAT F20.4. DATA LIST notable LIST /program score. BEGIN DATA. 1 9 1 12 1 14 1 11 1 13 2 10 2 6 2 9 2 9 2 10 3 12 3 14 3 11 3 13 3 11 4 9 4 8 4 11 4 7 4 8 END DATA. ONEWAY score BY program /MISSING ANALYSIS /POSTHOC = SIDAK. ]) AT_CHECK([pspp -O format=csv oneway-sidak.sps], [0], [Table: ANOVA ,,Sum of Squares,df,Mean Square,F,Sig. score,Between Groups,54.9500,3,18.3167,7.0449,.003 ,Within Groups,41.6000,16,2.6000,, ,Total,96.5500,19,,, Table: Multiple Comparisons (score) ,,,Mean Difference,,,95% Confidence Interval, ,(I) program,(J) program,(I - J),Std. Error,Sig.,Lower Bound,Upper Bound Šidák,1.0000,2.0000,3.0000,1.0198,.056,-.0575,6.0575 ,,3.0000,-.4000,1.0198,.999,-3.4575,2.6575 ,,4.0000,3.2000,1.0198,.038,.1425,6.2575 ,2.0000,1.0000,-3.0000,1.0198,.056,-6.0575,.0575 ,,3.0000,-3.4000,1.0198,.025,-6.4575,-.3425 ,,4.0000,.2000,1.0198,1.000,-2.8575,3.2575 ,3.0000,1.0000,.4000,1.0198,.999,-2.6575,3.4575 ,,2.0000,3.4000,1.0198,.025,.3425,6.4575 ,,4.0000,3.6000,1.0198,.017,.5425,6.6575 ,4.0000,1.0000,-3.2000,1.0198,.038,-6.2575,-.1425 ,,2.0000,-.2000,1.0198,1.000,-3.2575,2.8575 ,,3.0000,-3.6000,1.0198,.017,-6.6575,-.5425 ]) AT_CLEANUP AT_SETUP([ONEWAY posthoc Scheffe]) AT_DATA([oneway-scheffe.sps],[dnl set format = f11.3. data list notable list /usage * group *. begin data. 21.00 1 19.00 1 18.00 1 25.00 1 14.00 1 13.00 1 24.00 1 19.00 1 20.00 1 21.00 1 15.00 2 10.00 2 13.00 2 16.00 2 14.00 2 24.00 2 16.00 2 14.00 2 18.00 2 16.00 2 10.00 3 7.00 3 13.00 3 20.00 3 .00 3 8.00 3 6.00 3 1.00 3 12.00 3 14.00 3 18.00 4 15.00 4 3.00 4 27.00 4 6.00 4 14.00 4 13.00 4 11.00 4 9.00 4 18.00 4 end data. variable label usage 'Days of Use'. add value labels group 0 'none' 1 'one' 2 'two' 3 'three' 4 'four'. oneway usage by group /posthoc scheffe. ]) AT_CHECK([pspp -O format=csv oneway-scheffe.sps], [0], [Table: ANOVA ,,Sum of Squares,df,Mean Square,F,Sig. Days of Use,Between Groups,555.275,3,185.092,6.663,.001 ,Within Groups,1000.100,36,27.781,, ,Total,1555.375,39,,, Table: Multiple Comparisons (Days of Use) ,,,Mean Difference,,,95% Confidence Interval, ,(I) group,(J) group,(I - J),Std. Error,Sig.,Lower Bound,Upper Bound Scheffé,one,two,3.800,2.357,.467,-3.112,10.712 ,,three,10.300,2.357,.001,3.388,17.212 ,,four,6.000,2.357,.110,-.912,12.912 ,two,one,-3.800,2.357,.467,-10.712,3.112 ,,three,6.500,2.357,.072,-.412,13.412 ,,four,2.200,2.357,.832,-4.712,9.112 ,three,one,-10.300,2.357,.001,-17.212,-3.388 ,,two,-6.500,2.357,.072,-13.412,.412 ,,four,-4.300,2.357,.358,-11.212,2.612 ,four,one,-6.000,2.357,.110,-12.912,.912 ,,two,-2.200,2.357,.832,-9.112,4.712 ,,three,4.300,2.357,.358,-2.612,11.212 ]) AT_CLEANUP AT_SETUP([ONEWAY bad contrast count]) AT_DATA([oneway-bad-contrast.sps],[dnl DATA LIST NOTABLE LIST /height * weight * temperature * sex *. BEGIN DATA. 1884 88.6 39.97 0 1801 90.9 39.03 0 1801 91.7 38.98 0 1607 56.3 36.26 1 1608 46.3 46.26 1 1607 55.9 37.84 1 1604 56.6 36.81 1 1606 56.1 34.56 1 END DATA. ONEWAY /VARIABLES= height weight temperature BY sex /CONTRAST = -1 1 /CONTRAST = -3 3 /CONTRAST = 2 -2 1 /CONTRAST = -9 9 . ]) AT_CHECK([pspp -O format=csv oneway-bad-contrast.sps], [0], [dnl "oneway-bad-contrast.sps:18: warning: ONEWAY: In contrast list 3, the number of coefficients (3) does not equal the number of groups (2). This contrast list will be ignored." Table: ANOVA ,,Sum of Squares,df,Mean Square,F,Sig. height,Between Groups,92629.63,1,92629.63,120.77,.000 ,Within Groups,4601.87,6,766.98,, ,Total,97231.50,7,,, weight,Between Groups,2451.65,1,2451.65,174.59,.000 ,Within Groups,84.25,6,14.04,, ,Total,2535.90,7,,, temperature,Between Groups,1.80,1,1.80,.13,.733 ,Within Groups,84.55,6,14.09,, ,Total,86.36,7,,, Table: Contrast Coefficients ,,sex, ,,.00,1.00 Contrast,1,-1,1 ,2,-3,3 ,3,-9,9 Table: Contrast Tests ,,Contrast,Value of Contrast,Std. Error,t,df,Sig. (2-tailed) height,Assume equal variances,1,-222.27,20.23,10.99,6,.000 ,,2,-666.80,60.68,10.99,6,.000 ,,3,-2000.40,182.03,10.99,6,.000 ,Does not assume equal,1,-222.27,27.67,-8.03,2.00,.015 ,,2,-666.80,83.02,-8.03,2.00,.015 ,,3,-2000.40,249.07,-8.03,2.00,.015 weight,Assume equal variances,1,-36.16,2.74,13.21,6,.000 ,,2,-108.48,8.21,13.21,6,.000 ,,3,-325.44,24.63,13.21,6,.000 ,Does not assume equal,1,-36.16,2.19,-16.48,5.42,.000 ,,2,-108.48,6.58,-16.48,5.42,.000 ,,3,-325.44,19.75,-16.48,5.42,.000 temperature,Assume equal variances,1,-.98,2.74,.36,6,.733 ,,2,-2.94,8.22,.36,6,.733 ,,3,-8.83,24.67,.36,6,.733 ,Does not assume equal,1,-.98,2.07,-.47,4.19,.660 ,,2,-2.94,6.22,-.47,4.19,.660 ,,3,-8.83,18.66,-.47,4.19,.660 ]) AT_CLEANUP AT_SETUP([ONEWAY crash on single category independent variable]) AT_DATA([crash.sps],[ input program. loop #i = 1 to 10. compute test = #i. end case. end loop. end file. end input program. compute x = 1. oneway test by x. ]) AT_CHECK([pspp -O format=csv crash.sps], [0], [ignore]) AT_CLEANUP AT_SETUP([ONEWAY crash on missing dependent variable]) AT_DATA([crash2.sps],[dnl data list notable list /dv1 * dv2 * y * . begin data. 2 . 2 1 . 2 1 . 1 2 . 4 3 . 4 4 . 4 5 . 4 end data. ONEWAY /VARIABLES= dv1 dv2 BY y /STATISTICS = DESCRIPTIVES /POSTHOC = BONFERRONI LSD SCHEFFE SIDAK TUKEY /MISSING = ANALYSIS . ]) AT_CHECK([pspp -O format=csv crash2.sps], [0], [ignore]) AT_CLEANUP AT_SETUP([ONEWAY Games-Howell test with few cases]) AT_DATA([crash3.sps],[dnl data list notable list /dv * y * . begin data. 2 2 1 2 1 1 2 4 3 4 end data. ONEWAY /VARIABLES= dv BY y /POSTHOC = GH . ]) AT_CHECK([pspp -O format=csv crash3.sps], [0], [ignore]) AT_CLEANUP