number of students in each open bin
## [1] "Number of submissions in each open bin"##
## long medium short unopened
## 1842 2661 526 738## [1] "Number of submissions in each open bin in each course"##
## BIOL1040 BIOM2011
## long 1725 117
## medium 2402 259
## short 504 22
## unopened 560 178## [1] "Number of submissions (=students) in each open bin for each report in Level 2 course"## report
## open.bin Report 1 Report 2 Sum
## long 88 29 117
## medium 89 170 259
## short 4 18 22
## unopened 104 74 178
## Sum 285 291 576## [1] "Number of submissions (=students) in each open bin for each report in Level 1 course"## report
## open.bin Report 0 Report 1 Report 2 Report 3 Sum
## long 293 681 654 97 1725
## medium 239 695 607 861 2402
## short 22 71 121 290 504
## unopened 67 109 140 244 560
## Sum 621 1556 1522 1492 5191## [1] "Final.Grade.x"
## means sem
## long 64.75581 1.247789
## medium 60.97674 1.647645
## short 45.58333 11.968999
## unopened 57.97573 1.261618
## [1] "Final.Grade.y"
## means sem
## long 73.79360 1.216495
## medium 69.53488 1.407980
## short 52.91667 11.934136
## unopened 69.47816 1.336971## [1] "unopened"
##
## Welch Two Sample t-test
##
## data: sub.df[, final.grade] by sub.df[, report]
## t = -6.2573, df = 203.317, p-value = 2.278e-09
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -15.126910 -7.877944
## sample estimates:
## mean in group Final.Grade.x mean in group Final.Grade.y
## 57.97573 69.47816
##
## [1] "short"
##
## Welch Two Sample t-test
##
## data: sub.df[, final.grade] by sub.df[, report]
## t = -0.4339, df = 4, p-value = 0.6868
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -54.26121 39.59454
## sample estimates:
## mean in group Final.Grade.x mean in group Final.Grade.y
## 45.58333 52.91667
##
## [1] "medium"
##
## Welch Two Sample t-test
##
## data: sub.df[, final.grade] by sub.df[, report]
## t = -3.9488, df = 165.966, p-value = 0.0001159
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -12.83715 -4.27913
## sample estimates:
## mean in group Final.Grade.x mean in group Final.Grade.y
## 60.97674 69.53488
##
## [1] "long"
##
## Welch Two Sample t-test
##
## data: sub.df[, final.grade] by sub.df[, report]
## t = -5.1862, df = 169.89, p-value = 6.05e-07
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -12.477831 -5.597751
## sample estimates:
## mean in group Final.Grade.x mean in group Final.Grade.y
## 64.75581 73.79360## [1] "Final.Grade.x"
## Df Sum Sq Mean Sq F value Pr(>F)
## open.bin.x 3 2863 954.5 5.358 0.00134 **
## Residuals 274 48808 178.1
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = value ~ open.bin.x, data = sub.df)
##
## $open.bin.x
## diff lwr upr p adj
## medium-long -3.779070 -9.040136 1.481996 0.2493230
## short-long -19.172481 -39.434986 1.090024 0.0710802
## unopened-long -6.780086 -11.819395 -1.740776 0.0032777
## short-medium -15.393411 -35.655916 4.869094 0.2043799
## unopened-medium -3.001016 -8.040326 2.038294 0.4154323
## unopened-short 12.392395 -7.813667 32.598457 0.3887006
##
## [1] "Final.Grade.y"
## Df Sum Sq Mean Sq F value Pr(>F)
## open.bin.x 3 2042 680.6 4.149 0.00674 **
## Residuals 274 44943 164.0
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = value ~ open.bin.x, data = sub.df)
##
## $open.bin.x
## diff lwr upr p adj
## medium-long -4.25872093 -9.307194 0.7897524 0.1312454
## short-long -20.87693798 -40.320662 -1.4332142 0.0298774
## unopened-long -4.31544931 -9.151127 0.5202285 0.0989674
## short-medium -16.61821705 -36.061941 2.8255067 0.1232713
## unopened-medium -0.05672838 -4.892406 4.7789494 0.9999900
## unopened-short 16.56148867 -2.828073 35.9510501 0.1236486## [1] "Final.Grade.x"
## means sem
## long 75.90923 0.4626260
## medium 74.15499 0.4984213
## short 71.51515 1.6153752
## unopened 65.33333 1.4797847
## [1] "Final.Grade.y"
## means sem
## long 83.78571 0.3857559
## medium 79.58271 0.4958738
## short 71.06061 1.9022596
## unopened 73.15556 1.6485087
## [1] "Final.Grade"
## means sem
## long 86.96641 0.3902658
## medium 83.66667 0.4412593
## short 75.38710 1.8118606
## unopened 74.18605 1.5408606## [1] "unopened"
## Df Sum Sq Mean Sq F value Pr(>F)
## variable 2 4175 2087.3 9.552 9.9e-05 ***
## Residuals 263 57471 218.5
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 4 observations deleted due to missingness
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = value ~ variable, data = sub.df)
##
## $variable
## diff lwr upr p adj
## Final.Grade.y-Final.Grade.x 7.822222 2.628121 13.016324 0.0013234
## Final.Grade-Final.Grade.x 8.852713 3.598562 14.106864 0.0002714
## Final.Grade-Final.Grade.y 1.030491 -4.223660 6.284642 0.8889464
##
## [1] "short"
## Df Sum Sq Mean Sq F value Pr(>F)
## variable 2 716 357.8 1.711 0.183
## Residuals 191 39935 209.1
## 4 observations deleted due to missingness
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = value ~ variable, data = sub.df)
##
## $variable
## diff lwr upr p adj
## Final.Grade.y-Final.Grade.x -0.4545455 -6.400297 5.491206 0.9821848
## Final.Grade-Final.Grade.x 3.8719453 -2.168944 9.912834 0.2866050
## Final.Grade-Final.Grade.y 4.3264907 -1.714398 10.367380 0.2109694
##
## [1] "medium"
## Df Sum Sq Mean Sq F value Pr(>F)
## variable 2 30140 15070 97.64 <2e-16 ***
## Residuals 1990 307152 154
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 20 observations deleted due to missingness
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = value ~ variable, data = sub.df)
##
## $variable
## diff lwr upr p adj
## Final.Grade.y-Final.Grade.x 5.427720 3.836861 7.018578 0
## Final.Grade-Final.Grade.x 9.511674 7.908644 11.114704 0
## Final.Grade-Final.Grade.y 4.083954 2.480924 5.686985 0
##
## [1] "long"
## Df Sum Sq Mean Sq F value Pr(>F)
## variable 2 43163 21582 186.9 <2e-16 ***
## Residuals 1996 230542 116
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 17 observations deleted due to missingness
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = value ~ variable, data = sub.df)
##
## $variable
## diff lwr upr p adj
## Final.Grade.y-Final.Grade.x 7.876488 6.501332 9.251644 0e+00
## Final.Grade-Final.Grade.x 11.057186 9.673136 12.441236 0e+00
## Final.Grade-Final.Grade.y 3.180698 1.796648 4.564748 2e-07## [1] "Categorised based on open.bin.x ie Open Bin for Report 1"
## [1] "Final.Grade.x"
## Df Sum Sq Mean Sq F value Pr(>F)
## open.bin.x 3 9502 3167 19.99 1.06e-12 ***
## Residuals 1495 236924 158
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = value ~ open.bin.x, data = sub.df)
##
## $open.bin.x
## diff lwr upr p adj
## medium-long -1.754234 -3.521223 0.01275549 0.0525067
## short-long -4.394075 -8.570583 -0.21756642 0.0347317
## unopened-long -10.575893 -14.210131 -6.94165488 0.0000000
## short-medium -2.639841 -6.816628 1.53694554 0.3644726
## unopened-medium -8.821659 -12.456217 -5.18710140 0.0000000
## unopened-short -6.181818 -11.428816 -0.93482083 0.0132583
##
## [1] "Categorised based on open.bin.x ie Open Bin for Report 1"
## [1] "Final.Grade.y"
## Df Sum Sq Mean Sq F value Pr(>F)
## open.bin.x 3 18491 6164 42.87 <2e-16 ***
## Residuals 1495 214936 144
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = value ~ open.bin.x, data = sub.df)
##
## $open.bin.x
## diff lwr upr p adj
## medium-long -4.203002 -5.886001 -2.520002 0.0000000
## short-long -12.725108 -16.703096 -8.747121 0.0000000
## unopened-long -10.630159 -14.091652 -7.168666 0.0000000
## short-medium -8.522106 -12.500359 -4.543854 0.0000003
## unopened-medium -6.427157 -9.888954 -2.965359 0.0000117
## unopened-short 2.094949 -2.902644 7.092543 0.7029703
##
## [1] "Categorised based on open.bin.x ie Open Bin for Report 1"
## [1] "Final.Grade"
## Df Sum Sq Mean Sq F value Pr(>F)
## open.bin.x 3 18635 6212 49.15 <2e-16 ***
## Residuals 1450 183240 126
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 45 observations deleted due to missingness
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = value ~ open.bin.x, data = sub.df)
##
## $open.bin.x
## diff lwr upr p adj
## medium-long -3.299746 -4.899891 -1.699601 0.0000008
## short-long -11.579315 -15.421179 -7.737452 0.0000000
## unopened-long -12.780366 -16.096546 -9.464185 0.0000000
## short-medium -8.279570 -12.122454 -4.436685 0.0000002
## unopened-medium -9.480620 -12.797983 -6.163258 0.0000000
## unopened-short -1.201050 -6.018139 3.616039 0.9185882## [1] "Categorised based on open.bin.y ie Open Bin for Report 2"
## [1] "Final.Grade.x"
## Df Sum Sq Mean Sq F value Pr(>F)
## open.bin.y 3 9949 3316 20.97 2.63e-13 ***
## Residuals 1495 236477 158
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = value ~ open.bin.y, data = sub.df)
##
## $open.bin.y
## diff lwr upr p adj
## medium-long 0.177397 -1.656949 2.0117431 0.9945972
## short-long -4.669653 -7.895298 -1.4440085 0.0011674
## unopened-long -8.160936 -11.230226 -5.0916466 0.0000000
## short-medium -4.847050 -8.094389 -1.5997114 0.0007422
## unopened-medium -8.338333 -11.430415 -5.2462523 0.0000000
## unopened-short -3.491283 -7.565706 0.5831402 0.1226177
##
## [1] "Categorised based on open.bin.y ie Open Bin for Report 2"
## [1] "Final.Grade.y"
## Df Sum Sq Mean Sq F value Pr(>F)
## open.bin.y 3 22309 7436 52.66 <2e-16 ***
## Residuals 1495 211118 141
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = value ~ open.bin.y, data = sub.df)
##
## $open.bin.y
## diff lwr upr p adj
## medium-long -2.215959 -3.949163 -0.48275506 0.0056843
## short-long -8.674146 -11.721936 -5.62635691 0.0000000
## unopened-long -12.621657 -15.521712 -9.72160118 0.0000000
## short-medium -6.458187 -9.526475 -3.38989980 0.0000004
## unopened-medium -10.405698 -13.327288 -7.48410738 0.0000000
## unopened-short -3.947510 -7.797278 -0.09774228 0.0419686
##
## [1] "Categorised based on open.bin.y ie Open Bin for Report 2"
## [1] "Final.Grade"
## Df Sum Sq Mean Sq F value Pr(>F)
## open.bin.y 3 26497 8832 73.03 <2e-16 ***
## Residuals 1450 175377 121
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 45 observations deleted due to missingness
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = value ~ open.bin.y, data = sub.df)
##
## $open.bin.y
## diff lwr upr p adj
## medium-long -3.517135 -5.136233 -1.8980381 0.0000002
## short-long -10.335039 -13.209562 -7.4605154 0.0000000
## unopened-long -14.218735 -17.072132 -11.3653379 0.0000000
## short-medium -6.817903 -9.715432 -3.9203748 0.0000000
## unopened-medium -10.701600 -13.578171 -7.8250284 0.0000000
## unopened-short -3.883696 -7.614121 -0.1532715 0.0375741## [1] "Total number of students in each open bin (rows = Report 1 open bin; columns = Report 2 open bin)"## open.bin.y
## open.bin.x long medium short unopened Sum
## long 429 194 24 22 669
## medium 187 350 74 52 663
## short 8 30 13 15 66
## unopened 20 19 7 42 88
## Sum 644 593 118 131 1486## [1] "Percentage of students in each Report 1 open bin (rows) who end up in each Report 2 open bin (columns)"## open.bin.y
## open.bin.x long medium short unopened Sum
## long 0.64125561 0.28998505 0.03587444 0.03288490 1.00000000
## medium 0.28205128 0.52790347 0.11161388 0.07843137 1.00000000
## short 0.12121212 0.45454545 0.19696970 0.22727273 1.00000000
## unopened 0.22727273 0.21590909 0.07954545 0.47727273 1.00000000## [1] "Percentage of students in each Report 1 open bin (rows) who end up in each Report 3 open bin (columns)"## open.bin
## open.bin.x long medium short unopened Sum
## long 0.10122699 0.66717791 0.14570552 0.08588957 1.00000000
## medium 0.04043546 0.56920684 0.22706065 0.16329705 1.00000000
## short 0.01612903 0.27419355 0.41935484 0.29032258 1.00000000
## unopened 0.01190476 0.26190476 0.17857143 0.54761905 1.00000000## [1] "Percentage of students in each Report 2 open bin (rows) who end up in each Report 3 open bin (columns)"## open.bin
## open.bin.y long medium short unopened Sum
## long 0.11285266 0.65360502 0.15203762 0.08150470 1.00000000
## medium 0.03119584 0.60658579 0.22530329 0.13691508 1.00000000
## short 0.03539823 0.41592920 0.38938053 0.15929204 1.00000000
## unopened 0.00000000 0.23008850 0.09734513 0.67256637 1.00000000## [1] "Total number of students in each open bin (rows = Report 1 open bin; columns = Report 2 open bin)"## open.bin.y
## open.bin.x long medium short unopened Sum
## long 10 63 4 9 86
## medium 10 48 8 19 85
## short 0 0 1 2 3
## unopened 9 52 3 38 102
## Sum 29 163 16 68 276## [1] "Percentage of students in each Report 1 open bin (rows) who end up in each Report 2 open bin (columns)"## open.bin.y
## open.bin.x long medium short unopened Sum
## long 0.11627907 0.73255814 0.04651163 0.10465116 1.00000000
## medium 0.11764706 0.56470588 0.09411765 0.22352941 1.00000000
## short 0.00000000 0.00000000 0.33333333 0.66666667 1.00000000
## unopened 0.08823529 0.50980392 0.02941176 0.37254902 1.00000000