AT_BANNER([NPAR TESTS]) AT_SETUP([NPAR TESTS BINOMIAL, P < 0.5; N1/N2 < 1]) AT_DATA([npar.sps], [dnl SET FORMAT F8.3. DATA LIST LIST NOTABLE /x * w *. BEGIN DATA. 1 6 2 15 END DATA. WEIGHT BY w. NPAR TESTS /BINOMIAL(0.3) = x . ]) AT_CHECK([pspp -O format=csv npar.sps], [0], [dnl Table: Binomial Test ,,Category,N,Observed Prop.,Test Prop.,Exact Sig. (1-tailed) x,Group1,1.000,6.000,.286,.300,.551 ,Group2,2.000,15.000,.714,, ,Total,,21.000,1.000,, ]) AT_CLEANUP AT_SETUP([NPAR TESTS BINOMIAL, P < 0.5; N1/N2 > 1]) AT_DATA([npar.sps], [dnl SET FORMAT F8.3. DATA LIST LIST NOTABLE /x (F8.0) w (F8.0). BEGIN DATA. 1 7 2 6 END DATA. WEIGHT BY w. NPAR TESTS /BINOMIAL(0.4) = x . ]) AT_CHECK([pspp -O format=csv npar.sps], [0], [dnl Table: Binomial Test ,,Category,N,Observed Prop.,Test Prop.,Exact Sig. (1-tailed) x,Group1,1,7,.538,.400,.229 ,Group2,2,6,.462,, ,Total,,13,1.000,, ]) AT_CLEANUP AT_SETUP([NPAR TESTS BINOMIAL, P < 0.5; N1/N2 = 1]) AT_DATA([npar.sps], [dnl SET FORMAT F8.3. DATA LIST LIST NOTABLE /x (F8.0) w (F8.0). BEGIN DATA. 1 8 2 8 END DATA. WEIGHT BY w. NPAR TESTS /BINOMIAL(0.4) = x . ]) AT_CHECK([pspp -O format=csv npar.sps], [0], [dnl Table: Binomial Test ,,Category,N,Observed Prop.,Test Prop.,Exact Sig. (1-tailed) x,Group1,1,8,.500,.400,.284 ,Group2,2,8,.500,, ,Total,,16,1.000,, ]) AT_CLEANUP AT_SETUP([NPAR TESTS BINOMIAL, P > 0.5; N1/N2 < 1]) AT_DATA([npar.sps], [dnl SET FORMAT F8.3. DATA LIST LIST NOTABLE /x (F8.0) w (F8.0). BEGIN DATA. 1 11 2 12 END DATA. WEIGHT BY w. NPAR TESTS /BINOMIAL(0.6) = x . ]) AT_CHECK([pspp -O format=csv npar.sps], [0], [dnl Table: Binomial Test ,,Category,N,Observed Prop.,Test Prop.,Exact Sig. (1-tailed) x,Group1,1,11,.478,.600,.164 ,Group2,2,12,.522,, ,Total,,23,1.000,, ]) AT_CLEANUP AT_SETUP([NPAR TESTS BINOMIAL, P > 0.5; N1/N2 > 1]) AT_DATA([npar.sps], [dnl SET FORMAT F8.3. DATA LIST LIST NOTABLE /x (F8.0) w (F8.0). BEGIN DATA. 1 11 2 9 END DATA. WEIGHT BY w. NPAR TESTS /BINOMIAL(0.6) = x. ]) AT_CHECK([pspp -O format=csv npar.sps], [0], [dnl Table: Binomial Test ,,Category,N,Observed Prop.,Test Prop.,Exact Sig. (1-tailed) x,Group1,1,11,.550,.600,.404 ,Group2,2,9,.450,, ,Total,,20,1.000,, ]) AT_CLEANUP AT_SETUP([NPAR TESTS BINOMIAL, P > 0.5; N1/N2 = 1]) AT_DATA([npar.sps], [dnl SET FORMAT F8.3. DATA LIST LIST NOTABLE /x (F8.0) w (F8.0). BEGIN DATA. 1 11 2 11 END DATA. WEIGHT BY w. NPAR TESTS /BINOMIAL(0.6) = x. ]) AT_CHECK([pspp -O format=csv npar.sps], [0], [dnl Table: Binomial Test ,,Category,N,Observed Prop.,Test Prop.,Exact Sig. (1-tailed) x,Group1,1,11,.500,.600,.228 ,Group2,2,11,.500,, ,Total,,22,1.000,, ]) AT_CLEANUP AT_SETUP([NPAR TESTS BINOMIAL, P = 0.5; N1/N2 < 1]) AT_DATA([npar.sps], [dnl SET FORMAT F8.3. DATA LIST LIST NOTABLE /x (F8.0) w (F8.0). BEGIN DATA. 1 8 2 15 END DATA. WEIGHT BY w. NPAR TESTS /BINOMIAL = x . ]) AT_CHECK([pspp -O format=csv npar.sps], [0], [dnl Table: Binomial Test ,,Category,N,Observed Prop.,Test Prop.,Exact Sig. (2-tailed) x,Group1,1,8,.348,.500,.210 ,Group2,2,15,.652,, ,Total,,23,1.000,, ]) AT_CLEANUP AT_SETUP([NPAR TESTS BINOMIAL, P = 0.5; N1/N2 > 1]) AT_DATA([npar.sps], [dnl SET FORMAT F8.3. DATA LIST LIST NOTABLE /x (F8.0) w (F8.0). BEGIN DATA. 1 12 2 6 END DATA. WEIGHT BY w. NPAR TESTS /BINOMIAL(0.5) = x. ]) AT_CHECK([pspp -O format=csv npar.sps], [0], [dnl Table: Binomial Test ,,Category,N,Observed Prop.,Test Prop.,Exact Sig. (2-tailed) x,Group1,1,12,.667,.500,.238 ,Group2,2,6,.333,, ,Total,,18,1.000,, ]) AT_CLEANUP AT_SETUP([NPAR TESTS BINOMIAL, P = 0.5; N1/N2 = 1]) AT_DATA([npar.sps], [dnl SET FORMAT F8.3. DATA LIST LIST NOTABLE /x (F8.0) w (F8.0). BEGIN DATA. 1 10 2 10 END DATA. WEIGHT BY w. NPAR TESTS /BINOMIAL(0.5) = x . ]) AT_CHECK([pspp -O format=csv npar.sps], [0], [dnl Table: Binomial Test ,,Category,N,Observed Prop.,Test Prop.,Exact Sig. (2-tailed) x,Group1,1,10,.500,.500,1.000 ,Group2,2,10,.500,, ,Total,,20,1.000,, ]) AT_CLEANUP AT_SETUP([NPAR TESTS BINOMIAL, P = 0.5; N1/N2 = 1 Cutpoint]) AT_DATA([npar.sps], [dnl SET FORMAT F8.3. DATA LIST LIST NOTABLE /x * w *. BEGIN DATA. 9 3 10 7 11 16 END DATA. WEIGHT BY w. NPAR TESTS /BINOMIAL(0.5) = x (10) . ]) AT_CHECK([pspp -O format=csv npar.sps], [0], [dnl Table: Binomial Test ,,Category,N,Observed Prop.,Test Prop.,Exact Sig. (2-tailed) x,Group1,<= 10,10.000,.385,.500,.327 ,Group2,,16.000,.615,, ,Total,,26.000,1.000,, ]) AT_CLEANUP AT_SETUP([NPAR TESTS BINOMIAL, P = 0.5; N1/N2 = 1 Named values]) AT_DATA([npar.sps], [dnl SET FORMAT F8.3. DATA LIST LIST NOTABLE /x * w *. BEGIN DATA. 10 10 15 45 20 13 END DATA. WEIGHT BY w. NPAR TESTS /BINOMIAL(0.5) = x (10, 20) . ]) AT_CHECK([pspp -O format=csv npar.sps], [0], [dnl Table: Binomial Test ,,Category,N,Observed Prop.,Test Prop.,Exact Sig. (2-tailed) x,Group1,10.000,10.000,.435,.500,.678 ,Group2,20.000,13.000,.565,, ,Total,,23.000,1.000,, ]) AT_CLEANUP AT_SETUP([NPAR TESTS CHISQUARE]) AT_DATA([npar.sps], [dnl DATA LIST NOTABLE LIST /x * y * w *. BEGIN DATA. 1 2 1 2 1 3 3.1 1 4 3.2 2 1 4 2 2 5 3 1 1 4 2 END DATA. WEIGHT BY w. NPAR TESTS CHISQUARE=x y . NPAR TESTS CHISQUARE=y /EXPECTED=3 4 5 4 . NPAR TESTS CHISQUARE=x y(2, 4) /EXPECTED = 6 10 3 . ]) AT_CHECK([pspp -O format=csv npar.sps], [0], [dnl Table: x ,Observed N,Expected N,Residual 1.00,3.00,2.33,.67 2.00,3.00,2.33,.67 3.10,4.00,2.33,1.67 3.20,1.00,2.33,-1.33 4.00,2.00,2.33,-.33 5.00,1.00,2.33,-1.33 Total,14.00,, Table: y ,Observed N,Expected N,Residual 1.00,7.00,3.50,3.50 2.00,4.00,3.50,.50 3.00,1.00,3.50,-2.50 4.00,2.00,3.50,-1.50 Total,14.00,, Table: Test Statistics ,x,y Chi-Square,3.14,6.00 df,5,3 Asymp. Sig.,.68,.11 Table: y ,Observed N,Expected N,Residual 1.00,7.00,2.63,4.38 2.00,4.00,3.50,.50 3.00,1.00,4.38,-3.38 4.00,2.00,3.50,-1.50 Total,14.00,, Table: Test Statistics ,y Chi-Square,10.61 df,3 Asymp. Sig.,.01 Table: Frequencies ,x,,,,y,,, ,Category,Observed N,Expected N,Residual,Category,Observed N,Expected N,Residual 1,2.00,3.00,3.16,-.16,2.00,4.00,2.21,1.79 2,3.00,5.00,5.26,-.26,3.00,1.00,3.68,-2.68 3,4.00,2.00,1.58,.42,4.00,2.00,1.11,.89 Total,,10.00,,,,7.00,, Table: Test Statistics ,x,y Chi-Square,.13,4.13 df,2,2 Asymp. Sig.,.94,.13 ]) AT_CLEANUP AT_SETUP([NPAR TESTS CHISQUARE expected values missing]) AT_DATA([npar.sps], [dnl DATA LIST NOTABLE LIST /x * y * w *. BEGIN DATA. 1 2 1 2 1 3 3.1 1 4 3.2 2 1 4 2 2 5 3 1 1 4 2 END DATA. WEIGHT BY w. NPAR TESTS CHISQUARE=y /EXPECTED = 3 4 5 4 3 1 . ]) AT_CHECK([pspp -O format=csv npar.sps], [1], [dnl "error: CHISQUARE test specified 6 expected values, but 4 distinct values were encountered in variable y." Table: Test Statistics ,y Chi-Square,.00 df,0 Asymp. Sig.,1.00 ]) AT_CLEANUP AT_SETUP([NPAR TESTS CHISQUARE with DESCRIPTIVES]) AT_DATA([npar.sps], [dnl DATA LIST NOTABLE LIST /x * y * w * . BEGIN DATA. 1 2 1 2 1 3 3.1 1 4 3.2 2 1 4 2 2 5 3 1 1 4 2 . 5 1 END DATA. WEIGHT BY w. MISSING VALUES x (4). NPAR TESTS CHISQUARE=x y(-2,5) /MISSING=ANALYSIS /STATISTICS=DESCRIPTIVES . ]) AT_CHECK([pspp -O format=csv npar.sps], [0], [dnl Table: Frequencies ,x,,,,y,,, ,Category,Observed N,Expected N,Residual,Category,Observed N,Expected N,Residual 1,-2.00,.00,1.50,-1.50,-2.00,.00,1.88,-1.88 2,-1.00,.00,1.50,-1.50,-1.00,.00,1.88,-1.88 3,.00,.00,1.50,-1.50,.00,.00,1.88,-1.88 4,1.00,3.00,1.50,1.50,1.00,7.00,1.88,5.13 5,2.00,3.00,1.50,1.50,2.00,4.00,1.88,2.13 6,3.00,5.00,1.50,3.50,3.00,1.00,1.88,-.88 7,4.00,.00,1.50,-1.50,4.00,2.00,1.88,.13 8,5.00,1.00,1.50,-.50,5.00,1.00,1.88,-.88 Total,,12.00,,,,15.00,, Table: Test Statistics ,x,y Chi-Square,17.33,22.87 df,7,7 Asymp. Sig.,.02,.00 Table: Descriptive Statistics ,N,Mean,Std. Deviation,Minimum,Maximum ,,,,, x,12.00,2.47,1.19,1.00,5.00 y,15.00,2.07,1.33,1.00,5.00 ]) AT_CLEANUP AT_SETUP([NPAR TESTS CHISQUARE, listwise missing]) AT_DATA([npar.sps], [dnl DATA LIST NOTABLE LIST /x * y * w * . BEGIN DATA. 1 2 1 2 1 3 3.1 1 4 3.2 2 1 4 2 2 5 3 1 1 4 2 . 5 1 END DATA. WEIGHT BY w. * MISSING VALUES x (4). NPAR TESTS CHISQUARE=x y(-2,5) /MISSING=LISTWISE /STATISTICS=DESCRIPTIVES . ]) AT_CHECK([pspp -O format=csv npar.sps], [0], [dnl Table: Frequencies ,x,,,,y,,, ,Category,Observed N,Expected N,Residual,Category,Observed N,Expected N,Residual 1,-2.00,.00,1.75,-1.75,-2.00,.00,1.75,-1.75 2,-1.00,.00,1.75,-1.75,-1.00,.00,1.75,-1.75 3,.00,.00,1.75,-1.75,.00,.00,1.75,-1.75 4,1.00,3.00,1.75,1.25,1.00,7.00,1.75,5.25 5,2.00,3.00,1.75,1.25,2.00,4.00,1.75,2.25 6,3.00,5.00,1.75,3.25,3.00,1.00,1.75,-.75 7,4.00,2.00,1.75,.25,4.00,2.00,1.75,.25 8,5.00,1.00,1.75,-.75,5.00,.00,1.75,-1.75 Total,,14.00,,,,14.00,, Table: Test Statistics ,x,y Chi-Square,13.43,26.00 df,7,7 Asymp. Sig.,.06,.00 Table: Descriptive Statistics ,N,Mean,Std. Deviation,Minimum,Maximum ,,,,, x,14.00,2.69,1.23,1.00,5.00 y,14.00,1.86,1.10,1.00,4.00 ]) AT_CLEANUP AT_SETUP([NPAR TESTS WILCOXON]) AT_DATA([npar.sps], [dnl data list notable list /foo * bar * w (f8.0). begin data. 1.00 1.00 1 1.00 2.00 1 2.00 1.00 1 1.00 4.00 1 2.00 5.00 1 1.00 19.00 1 2.00 7.00 1 4.00 5.00 1 1.00 12.00 1 2.00 13.00 1 2.00 2.00 1 12.00 .00 2 12.00 1.00 1 13.00 1.00 1 end data variable labels foo "first" bar "second". weight by w. npar test /wilcoxon=foo with bar (paired) /missing analysis /method=exact. ]) AT_CHECK([pspp -o pspp.csv npar.sps]) AT_CHECK([cat pspp.csv], [0], [dnl Table: Ranks ,,N,Mean Rank,Sum of Ranks first - second,Negative Ranks,8,6.00,48.00 ,Positive Ranks,5,8.60,43.00 ,Ties,2,, ,Total,15,, Table: Test Statistics ,first - second Z,-.18 Asymp. Sig. (2-tailed),.86 Exact Sig. (2-tailed),.89 Exact Sig. (1-tailed),.45 ]) AT_CLEANUP AT_SETUP([NPAR TESTS WILCOXON with missing values]) AT_DATA([npar.sps], [dnl data list notable list /foo * bar * dummy *. begin data. 1.00 1.00 1 1.00 2.00 1 2.00 1.00 1 1.00 4.00 . 2.00 5.00 . 1.00 19.00 . 2.00 7.00 1 4.00 5.00 1 1.00 12.00 1 2.00 13.00 1 2.00 2.00 1 12.00 .00 1 12.00 .00 1 34.2 . 1 12.00 1.00 1 13.00 1.00 1 end data variable labels foo "first" bar "second". npar test /wilcoxon=foo with bar (paired) /missing analysis /method=exact. ]) AT_CHECK([pspp -o pspp.csv npar.sps]) dnl This is the same output as the previous test. AT_CHECK([cat pspp.csv], [0], [dnl Table: Ranks ,,N,Mean Rank,Sum of Ranks first - second,Negative Ranks,8,6.00,48.00 ,Positive Ranks,5,8.60,43.00 ,Ties,2,, ,Total,15,, Table: Test Statistics ,first - second Z,-.18 Asymp. Sig. (2-tailed),.86 Exact Sig. (2-tailed),.89 Exact Sig. (1-tailed),.45 ]) AT_CLEANUP AT_SETUP([NPAR TESTS SIGN]) AT_DATA([npar.sps], [dnl set format = F9.3. data list notable list /age * height rank *. begin data. 10 12 11 12 13 13 13 14 12 12 12 10 9 9 10 10.3 10.2 12 end data. npar tests /sign=age height WITH height rank (PAIRED) /MISSING ANALYSIS /METHOD=EXACT . ]) AT_CHECK([pspp -o pspp.csv npar.sps]) dnl Some machines return .313 instead of .312 for the Point Probability dnl (see bug #31611). AT_CHECK([sed 's/\.313$/.312/' pspp.csv], [0], [dnl Table: Frequencies ,,N age - height,Negative Differences,3 ,Positive Differences,1 ,Ties,2 ,Total,6 height - rank,Negative Differences,2 ,Positive Differences,3 ,Ties,1 ,Total,6 Table: Test Statistics ,age - height,height - rank Exact Sig. (2-tailed),.625,1.000 Exact Sig. (1-tailed),.312,.500 Point Probability,.250,.312 ]) AT_CLEANUP AT_SETUP([NPAR Kruskal-Wallis test]) dnl Simple case AT_DATA([kw-simple.sps], [dnl set format = F9.3. data list notable list /gv * xscore *. begin data 1 96 1 128 1 83 2 132 2 135 2 109 3 115 1 61 1 101 2 82 2 124 3 149 3 166 3 147 end data. value label /gv 1 "timed out" 2 "hit wicket" 3 "handled the ball". npar tests /kruskal-wallis xscore by gv (1, 3) . ]) AT_CHECK([pspp -o pspp.csv kw-simple.sps]) AT_CHECK([cat pspp.csv], [0], [dnl Table: Ranks ,gv,N,Mean Rank xscore,timed out,5,4.400 ,handled the ball,4,11.500 ,hit wicket,5,7.400 ,Total,14, Table: Test Statistics ,,xscore Chi-Square,,6.406 df,,2 Asymp. Sig.,,.041 ]) dnl Now try a missing value in the group variable AT_DATA([kw-missing-group.sps], [dnl set format = F9.3. data list notable list /gv * xscore *. begin data 1 96 1 128 1 83 1 61 1 101 2 82 2 124 2 132 2 135 2 109 3 115 3 149 3 166 3 147 2.5 344 end data. missing values gv (2.5). value label /gv 1 "timed out" 2 "hit wicket" 3 "handled the ball". npar tests /kruskal-wallis xscore by gv (1, 3) /missing=exclude . ]) AT_CHECK([pspp -o pspp2.csv kw-missing-group.sps]) dnl The result should be the same as before AT_CHECK([diff pspp.csv pspp2.csv], [0]) AT_CLEANUP AT_SETUP([NPAR Kruskal-Wallis multiple-variables]) AT_DATA([kw-multi.sps], [dnl set format = F9.3. data list notable list /gv * xscore * yscore. begin data 1 96 . 1 128 . 1 83 . 2 132 132 2 135 135 2 109 109 3 115 115 1 61 . 1 101 . 2 82 82 2 124 124 3 149 149 3 166 166 3 147 147 4 . 96 4 . 128 4 . 83 4 . 61 4 . 101 end data. value label /gv 1 "timed out" 2 "hit wicket" 3 "handled the ball" 4 "bowled" 5 "lbw" . npar tests /k-w xscore yscore by gv (1, 5) . ]) AT_CHECK([pspp -o pspp.csv kw-multi.sps]) AT_CHECK([cat pspp.csv], [0], [dnl Table: Ranks ,gv,N,Mean Rank xscore,timed out,5,4.400 ,handled the ball,4,11.500 ,hit wicket,5,7.400 ,Total,14, yscore,handled the ball,4,11.500 ,bowled,5,4.400 ,hit wicket,5,7.400 ,Total,14, Table: Test Statistics ,,xscore,yscore, Chi-Square,,6.406,6.406, df,,2,2, Asymp. Sig.,,.041,.041, ]) AT_CLEANUP AT_SETUP([NPAR TESTS Runs]) AT_DATA([npar-runs.sps], [dnl set format F11.4. data list notable list /score * w *. begin data 4 6 . 4 4 3 3 20 2 29 1 42 6 18 5 7 6 78 5 10 6 46 5 5 6 17 5 1 6 11 4 2 3 7 2 6 1 10 4 13 3 22 3 11 2 24 1 18 4 4 3 12 2 10 1 25 4 4 3 7 2 3 1 4 4 2 3 3 2 2 1 4 end data. weight by w. npar tests /runs (MEDIAN) = score /runs (MEAN) = score /runs (MODE) = score . ]) AT_CHECK([pspp -o pspp.csv npar-runs.sps]) AT_CHECK([cat pspp.csv], [0], [dnl Table: Runs Test ,score Test Value (median),3.0000 Cases < Test Value,177.0000 Cases ≥ Test Value,309.0000 Total Cases,486.0000 Number of Runs,12 Z,-20.9931 Asymp. Sig. (2-tailed),.0000 Table: Runs Test ,score Test Value (mean),3.6379 Cases < Test Value,259.0000 Cases ≥ Test Value,227.0000 Total Cases,486.0000 Number of Runs,12 Z,-21.0650 Asymp. Sig. (2-tailed),.0000 Table: Runs Test ,score Test Value (mode),6.0000 Cases < Test Value,316.0000 Cases ≥ Test Value,170.0000 Total Cases,486.0000 Number of Runs,11 Z,-21.0742 Asymp. Sig. (2-tailed),.0000 ]) AT_CLEANUP AT_SETUP([NPAR TESTS Friedman]) AT_DATA([npar-friedman.sps], [dnl set format F15.4. data list notable list /x * y * z. begin data 9.5 6.5 8.1 8.0 6.0 6.0 7.0 6.5 4.2 9.5 5.0 7.3 9.0 7.0 6.2 8.5 6.9 6.5 7.5 8.0 6.5 6.0 8.0 3.1 5.0 6.0 4.9 7.5 7.5 6.2 end data. npar tests /friedman = x y z. ]) AT_CHECK([pspp -o pspp.csv npar-friedman.sps]) AT_CHECK([cat pspp.csv], [0], [dnl Table: Ranks ,Mean Rank x,2.6500 y,2.1000 z,1.2500 Table: Test Statistics N,10 Chi-Square,10.4737 df,2 Asymp. Sig.,.0053 ]) AT_CLEANUP AT_SETUP([NPAR TESTS Mann-Whitney]) AT_DATA([npar-mann-whitney.sps], [dnl SET FORMAT = F11.4 data list notable list /height * sex (f1.0). begin data. 201 1 84 1 83 1 94 1 88 0 99 0 55 0 69 0 86 1 79 1 91 0 201 0 88 1 85 1 82 1 88 0 75 0 99 0 81 0 72 1 89 1 92 1 80 0 82 0 76 0 65 0 85 0 76 1 145 1 24 1 end data. NPAR TESTS /M-W = height BY sex (0,1). ]) AT_CHECK([pspp -o pspp.csv npar-mann-whitney.sps]) AT_CHECK([cat pspp.csv], [0], [dnl Table: Ranks ,N,,,Mean Rank,,Sum of Ranks, ,0,1,Total,0,1,0,1 height,15.0000,15.0000,30.0000,14.5333,16.4667,218.0000,247.0000 Table: Test Statistics ,Mann-Whitney U,Wilcoxon W,Z,Asymp. Sig. (2-tailed) height,98.0000,218.0000,-.6020,.5472 ]) AT_CLEANUP AT_SETUP([NPAR TESTS Cochran]) AT_DATA([npar-cochran.sps], [dnl set format f11.3. data list notable list /v1 * v2 * v3 * v4 * v5 * v6 * v7 *. begin data. 2 1 1 2 1 1 2 2 2 2 2 1 1 1 1 1 2 2 1 1 2 2 2 2 2 1 1 2 2 1 2 1 1 2 1 1 2 2 1 1 1 1 1 2 2 2 2 2 2 2 2 1 2 1 1 1 1 2 1 2 1 1 2 end data. npar tests /cochran = v1 to v7 . ]) AT_CHECK([pspp -o pspp.csv npar-cochran.sps]) AT_CHECK([cat pspp.csv], [0], [dnl Table: Frequencies ,Value, ,Success (2),Failure (1) v1,5,4 v2,6,3 v3,6,3 v4,7,2 v5,1,8 v6,2,7 v7,5,4 Table: Test Statistics N,9 Cochran's Q,12.735 df,6 Asymp. Sig.,.047 ]) AT_CLEANUP AT_SETUP([NPAR TESTS Kendall]) AT_DATA([npar-kendall.sps], [dnl SET FORMAT F14.3. data list notable list /v1 * v2 * v3 begin data. 7 7 2 5 6 5 8 6 4 5 7 4 5 4 4 8 6 5 6 3 5 7 6 5 8 5 5 . 2 2 5 4 5 3 4 4 5 1 2 5 2 1 7 6 5 6 3 4 6 6 6 5 4 5 4 3 4 9 1 1 6 2 1 3 7 8 6 3 4 4 4 4 5 4 3 6 5 2 4 4 8 4 6 4 6 5 5 7 8 6 5 3 5 end data. npar tests /kendall = all . ]) AT_CHECK([pspp -o pspp.csv npar-kendall.sps]) AT_CHECK([cat pspp.csv], [0], [dnl Table: Ranks ,Mean Rank v1,2.500 v2,1.817 v3,1.683 Table: Test Statistics N,30 Kendall's W,.233 Chi-Square,13.960 df,2 Asymp. Sig.,.001 ]) AT_CLEANUP AT_SETUP([NPAR TESTS McNemar]) AT_DATA([mcnemar.sps], [dnl set format = F12.3. data list notable list /v1 * v2 * junk *. begin data. 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 1 0 0 1 0 0 1 1 0 1 1 0 1 1 0 1 1 0 1 1 1 0 1 1 0 1 1 1 1 1 1 1 1 1 0 1 1 0 1 1 1 end data. npar tests /mcnemar = v1 WITH v2 junk. ]) AT_CHECK([pspp -O format=csv mcnemar.sps], [0], [dnl Table: v1 & v2 v1,v2, ,.000,1.000 .000,4,9 1.000,2,5 Table: v1 & junk v1,junk, ,.000,1.000 .000,8,5 1.000,2,5 Table: Test Statistics ,N,Exact Sig. (2-tailed),Exact Sig. (1-tailed),Point Probability v1 & v2,20,.065,.033,.027 v1 & junk,20,.453,.227,.164 ]) AT_CLEANUP