Data

allData is a list with 20 objects. 4 are the Winter 2018 exam files, 14 are the past exam files. 2 are gender and time cluster information for Winter 2018.

allData_Elements Name
1 WN2018_E4
2 WN2018_E3
3 WN2018_E2
4 WN2018_E1
5 WN2016_E1
6 WN2015_E4
7 WN2015_E3
8 WN2015_E2
9 WN2015_E1
10 WN2014_E4
11 WN2014_E3
12 WN2014_E2
13 WN2014_E1
14 WN2013_E4
15 WN2013_E3
16 WN2013_E2
17 WN2013_E1
18 FA2004_E3
19 WN2018_gender_3cluster
20 WN2018_gender_2cluster

41 Repeated Questions - matched to WN2018 term and original term

Number WN2018_Info OriginalTerm_Info
1 WN2018_E4_Q1 WN2013_E4_Q6
2 WN2018_E4_Q5 WN2013_E4_Q4
3 WN2018_E4_Q7 WN2015_E4_Q3
4 WN2018_E4_Q13 WN2013_E4_Q7
5 WN2018_E4_Q14 WN2013_E4_Q10
6 WN2018_E4_Q16 WN2015_E4_Q17
7 WN2018_E4_Q17 WN2015_E4_Q14
8 WN2018_E4_Q18 WN2015_E4_Q15
9 WN2018_E4_Q19 WN2014_E4_Q12
10 WN2018_E4_Q20 WN2013_E4_Q19
11 WN2018_E4_Q21 WN2013_E4_Q24
12 WN2018_E4_Q22 WN2015_E4_Q19
13 WN2018_E3_Q4 WN2015_E3_Q4
14 WN2018_E3_Q5 WN2013_E3_Q5
15 WN2018_E3_Q6 FA2004_E3_Q5
16 WN2018_E3_Q7 WN2014_E3_Q7
17 WN2018_E3_Q9 WN2015_E3_Q9
18 WN2018_E3_Q12 FA2004_E3_Q12
19 WN2018_E3_Q13 WN2013_E3_Q13
20 WN2018_E3_Q17 FA2004_E3_Q17
21 WN2018_E3_Q18 WN2013_E3_Q18
22 WN2018_E2_Q1 WN2013_E2_Q1
23 WN2018_E2_Q2 WN2015_E2_Q2
24 WN2018_E2_Q4 WN2014_E2_Q4
25 WN2018_E2_Q7 WN2014_E2_Q7
26 WN2018_E2_Q8 WN2013_E2_Q8
27 WN2018_E2_Q11 WN2013_E2_Q11
28 WN2018_E2_Q12 WN2013_E2_Q13
29 WN2018_E2_Q13 WN2014_E2_Q13
30 WN2018_E2_Q14 WN2013_E2_Q14
31 WN2018_E2_Q19 WN2015_E2_Q19
32 WN2018_E1_Q2 WN2016_E1_Q2
33 WN2018_E1_Q3 WN2014_E4_Q4
34 WN2018_E1_Q4 WN2013_E1_Q4
35 WN2018_E1_Q5 WN2014_E1_Q5
36 WN2018_E1_Q6 WN2014_E4_Q6
37 WN2018_E1_Q8 WN2013_E1_Q8
38 WN2018_E1_Q9 WN2016_E1_Q8
39 WN2018_E1_Q12 WN2015_E1_Q12
40 WN2018_E1_Q16 WN2013_E1_Q16
41 WN2018_E1_Q19 WN2014_E1_Q19

Analysis - Percent Correct - by Original Term and WN2018 Sections

Percent correct for 41 repeated questions

percent_Correct_WN2018 <- percent_Correct_OG <- c()
stdError_pCorrect_WN2018 <- stdError_pCorrect_OG <-c ()

pCorrect_WN2018_sec1 <- pCorrect_WN2018_sec2 <- c() 
pCorrect_WN2018_sec3 <- pCorrect_WN2018_sec4 <- c()

stdError_pCorrect_WN2018_sec1 <- stdError_pCorrect_WN2018_sec2 <- c()
stdError_pCorrect_WN2018_sec3 <- stdError_pCorrect_WN2018_sec4 <- c()
# Bootstrap - sample with replacement many times, recalculate percent correct each time. 
# To determine R = 10000 for the boot() function, I kept changing the R value for a specific question until the hundredth place of the standard error did not change. 

bootfunc <- function(d, i) {
  d1 <- d[i]
  return(mean(d1))
}
# Do these commands for all 41 questions...
percent_Correct_WN2018[1] = mean(allData[[1]][9][[1]])
stdError_pCorrect_WN2018[1] = sd(boot(allData[[1]][9][[1]], bootfunc, 10000)$t)

percent_Correct_OG[1] = mean(allData[[14]][14][[1]])
stdError_pCorrect_OG[1] = sd(boot(allData[[14]][14][[1]], bootfunc, 10000)$t)

pCorrect_WN2018_sec1[1] = mean(filter(WN2018Score_sec[[4]], 
                                      section == 100)[9][[1]])
pCorrect_WN2018_sec2[1] = mean(filter(WN2018Score_sec[[4]], 
                                      section == 200)[9][[1]])
pCorrect_WN2018_sec3[1] = mean(filter(WN2018Score_sec[[4]], 
                                      section == 300)[9][[1]])
pCorrect_WN2018_sec4[1] = mean(filter(WN2018Score_sec[[4]], 
                                      section == 400)[9][[1]])
stdError_pCorrect_WN2018_sec1[1] = sd(boot(filter(WN2018Score_sec[[4]],
                                                  section == 100)[9][[1]],
                                           bootfunc, 10000)$t)
stdError_pCorrect_WN2018_sec2[1] = sd(boot(filter(WN2018Score_sec[[4]],
                                                  section == 200)[9][[1]],
                                           bootfunc, 10000)$t)
stdError_pCorrect_WN2018_sec3[1] = sd(boot(filter(WN2018Score_sec[[4]],
                                                  section == 300)[9][[1]],
                                           bootfunc, 10000)$t)
stdError_pCorrect_WN2018_sec4[1] = sd(boot(filter(WN2018Score_sec[[4]],
                                                  section == 400)[9][[1]],
                                           bootfunc, 10000)$t)
Number Section Percent Correct Standard Error n
1 OG All 0.6293605 0.0186849 NA
2 OG All 0.6540698 0.0182538 NA
3 OG All 0.7593496 0.0170604 NA
4 OG All 0.8430233 0.0137919 NA
5 OG All 0.7863372 0.0156290 NA
6 OG All 0.6325203 0.0192975 NA
7 OG All 0.7593496 0.0172227 NA
8 OG All 0.8016260 0.0161760 NA
9 OG All 0.8629032 0.0136775 NA
10 OG All 0.7281977 0.0168663 NA
11 OG All 0.6191860 0.0185391 NA
12 OG All 0.6243902 0.0195574 NA
13 OG All 0.7777778 0.0169211 NA
14 OG All 0.7503650 0.0165920 NA
15 OG All 0.4850575 0.0237356 NA
16 OG All 0.7335474 0.0178727 NA
17 OG All 0.6094771 0.0199748 NA
18 OG All 0.6091954 0.0234054 NA
19 OG All 0.7007299 0.0175405 NA
20 OG All 0.2896552 0.0215955 NA
21 OG All 0.4656934 0.0189381 NA
22 OG All 0.6847978 0.0172692 NA
23 OG All 0.7622821 0.0170847 NA
24 OG All 0.7291982 0.0172163 NA
25 OG All 0.6717095 0.0180423 NA
26 OG All 0.8284519 0.0140710 NA
27 OG All 0.6652720 0.0175749 NA
28 OG All 0.8730823 0.0122900 NA
29 OG All 0.5975794 0.0189631 NA
30 OG All 0.7726639 0.0155704 NA
31 OG All 0.5198098 0.0196866 NA
32 OG All 0.7756410 0.0167580 NA
33 OG All 0.8451613 0.0146125 NA
34 OG All 0.5076709 0.0188483 NA
35 OG All 0.7319277 0.0172207 NA
36 OG All 0.7790323 0.0166205 NA
37 OG All 0.8019526 0.0147973 NA
38 OG All 0.6971154 0.0184392 NA
39 OG All 0.6069731 0.0193384 NA
40 OG All 0.6080893 0.0182326 NA
41 OG All 0.7515060 0.0167526 NA
1 WN2018 All 0.7536232 0.0172350 621
2 WN2018 All 0.6988728 0.0186974 621
3 WN2018 All 0.8599034 0.0139333 621
4 WN2018 All 0.7777778 0.0165720 621
5 WN2018 All 0.7842190 0.0164317 621
6 WN2018 All 0.7262480 0.0178870 621
7 WN2018 All 0.8421900 0.0145882 621
8 WN2018 All 0.8792271 0.0131169 621
9 WN2018 All 0.8325282 0.0149436 621
10 WN2018 All 0.6537842 0.0192754 621
11 WN2018 All 0.5169082 0.0201116 621
12 WN2018 All 0.5362319 0.0197930 621
13 WN2018 All 0.7929374 0.0163213 623
14 WN2018 All 0.8170144 0.0154955 623
15 WN2018 All 0.5345104 0.0200639 623
16 WN2018 All 0.6869984 0.0188511 623
17 WN2018 All 0.7415730 0.0175745 623
18 WN2018 All 0.7174960 0.0180381 623
19 WN2018 All 0.6099518 0.0197686 623
20 WN2018 All 0.3772071 0.0192761 623
21 WN2018 All 0.4991974 0.0200005 623
22 WN2018 All 0.7445483 0.0172968 642
23 WN2018 All 0.9065421 0.0116840 642
24 WN2018 All 0.8068536 0.0155703 642
25 WN2018 All 0.8333333 0.0146708 642
26 WN2018 All 0.9065421 0.0115984 642
27 WN2018 All 0.7274143 0.0173384 642
28 WN2018 All 0.7056075 0.0179484 642
29 WN2018 All 0.5342679 0.0196309 642
30 WN2018 All 0.6417445 0.0185978 642
31 WN2018 All 0.6137072 0.0193236 642
32 WN2018 All 0.7939394 0.0156680 660
33 WN2018 All 0.8060606 0.0153625 660
34 WN2018 All 0.8000000 0.0156072 660
35 WN2018 All 0.6863636 0.0180785 660
36 WN2018 All 0.7833333 0.0162391 660
37 WN2018 All 0.7863636 0.0160493 660
38 WN2018 All 0.7287879 0.0174217 660
39 WN2018 All 0.9015152 0.0116074 660
40 WN2018 All 0.6000000 0.0191306 660
41 WN2018 All 0.8000000 0.0157346 660
1 100 0.6570048 0.0331544 207
2 100 0.7149758 0.0313865 207
3 100 0.8888889 0.0218998 207
4 100 0.7826087 0.0282426 207
5 100 0.7971014 0.0281296 207
6 100 0.7004831 0.0316926 207
7 100 0.8260870 0.0261368 207
8 100 0.8599034 0.0241065 207
9 100 0.8309179 0.0259795 207
10 100 0.5942029 0.0339932 207
11 100 0.4927536 0.0349024 207
12 100 0.5458937 0.0348215 207
13 100 0.7714286 0.0288050 210
14 100 0.8047619 0.0274546 210
15 100 0.4857143 0.0343417 210
16 100 0.6857143 0.0317330 210
17 100 0.7476190 0.0299435 210
18 100 0.7285714 0.0307490 210
19 100 0.5523810 0.0343224 210
20 100 0.4714286 0.0344802 210
21 100 0.4809524 0.0344610 210
22 100 0.7761905 0.0286758 210
23 100 0.9142857 0.0194899 210
24 100 0.8523810 0.0247635 210
25 100 0.8619048 0.0238155 210
26 100 0.9095238 0.0195644 210
27 100 0.7428571 0.0300012 210
28 100 0.7285714 0.0304445 210
29 100 0.5190476 0.0345562 210
30 100 0.6476190 0.0329031 210
31 100 0.6333333 0.0331645 210
32 100 0.7899543 0.0275199 219
33 100 0.7579909 0.0286676 219
34 100 0.8036530 0.0269656 219
35 100 0.7031963 0.0311181 219
36 100 0.8127854 0.0261121 219
37 100 0.8264840 0.0254677 219
38 100 0.6940639 0.0309506 219
39 100 0.9178082 0.0185267 219
40 100 0.5205479 0.0341461 219
41 100 0.8127854 0.0261150 219
1 200 0.8201754 0.0253398 228
2 200 0.6622807 0.0318491 228
3 200 0.8201754 0.0257776 228
4 200 0.8026316 0.0263451 228
5 200 0.7763158 0.0273645 228
6 200 0.7324561 0.0300375 228
7 200 0.8157895 0.0254758 228
8 200 0.8859649 0.0211574 228
9 200 0.8289474 0.0246963 228
10 200 0.6622807 0.0310901 228
11 200 0.5219298 0.0328752 228
12 200 0.4956140 0.0330940 228
13 200 0.8122271 0.0257102 229
14 200 0.7991266 0.0262606 229
15 200 0.5283843 0.0328931 229
16 200 0.7248908 0.0295432 229
17 200 0.7423581 0.0287846 229
18 200 0.7161572 0.0298782 229
19 200 0.6331878 0.0318186 229
20 200 0.2794760 0.0295100 229
21 200 0.4847162 0.0333618 229
22 200 0.7167382 0.0295297 233
23 200 0.9227468 0.0176087 233
24 200 0.7982833 0.0267591 233
25 200 0.8154506 0.0252528 233
26 200 0.9184549 0.0180106 233
27 200 0.7081545 0.0296456 233
28 200 0.6781116 0.0306953 233
29 200 0.5193133 0.0329520 233
30 200 0.6137339 0.0315099 233
31 200 0.5879828 0.0322446 233
32 200 0.7982833 0.0260931 233
33 200 0.8454936 0.0238030 233
34 200 0.8497854 0.0231153 233
35 200 0.7038627 0.0300123 233
36 200 0.7896996 0.0268366 233
37 200 0.7510730 0.0279496 233
38 200 0.7725322 0.0274050 233
39 200 0.9098712 0.0186795 233
40 200 0.6266094 0.0316937 233
41 200 0.7982833 0.0264097 233
1 300 0.7792208 0.0335713 154
2 300 0.6948052 0.0367692 154
3 300 0.8961039 0.0247005 154
4 300 0.8116883 0.0314149 154
5 300 0.8311688 0.0305324 154
6 300 0.7727273 0.0336127 154
7 300 0.8961039 0.0247896 154
8 300 0.9025974 0.0237054 154
9 300 0.8506494 0.0287374 154
10 300 0.7207792 0.0360011 154
11 300 0.5389610 0.0405681 154
12 300 0.6168831 0.0390268 154
13 300 0.8039216 0.0324074 153
14 300 0.8692810 0.0270840 153
15 300 0.6274510 0.0388870 153
16 300 0.6862745 0.0370400 153
17 300 0.7647059 0.0342877 153
18 300 0.7581699 0.0346978 153
19 300 0.6666667 0.0382592 153
20 300 0.4248366 0.0400938 153
21 300 0.5163399 0.0403266 153
22 300 0.7515152 0.0332127 165
23 300 0.9030303 0.0230893 165
24 300 0.8000000 0.0313300 165
25 300 0.8303030 0.0289923 165
26 300 0.9151515 0.0214337 165
27 300 0.7393939 0.0340140 165
28 300 0.7333333 0.0346152 165
29 300 0.5636364 0.0383845 165
30 300 0.6727273 0.0365283 165
31 300 0.6303030 0.0378202 165
32 300 0.7790698 0.0317100 172
33 300 0.7965116 0.0305897 172
34 300 0.7325581 0.0340885 172
35 300 0.6627907 0.0358372 172
36 300 0.7441860 0.0336642 172
37 300 0.8081395 0.0298452 172
38 300 0.7151163 0.0343136 172
39 300 0.8779070 0.0246360 172
40 300 0.5988372 0.0378870 172
41 300 0.7848837 0.0311216 172
1 400 0.7812500 0.0726224 32
2 400 0.8750000 0.0579086 32
3 400 0.7812500 0.0732291 32
4 400 0.4062500 0.0875292 32
5 400 0.5312500 0.0879502 32
6 400 0.6250000 0.0862821 32
7 400 0.8750000 0.0585302 32
8 400 0.8437500 0.0641500 32
9 400 0.7812500 0.0734022 32
10 400 0.6562500 0.0846850 32
11 400 0.5312500 0.0864236 32
12 400 0.3750000 0.0859247 32
13 400 0.7419355 0.0783643 31
14 400 0.7741935 0.0744586 31
15 400 0.4516129 0.0886908 31
16 400 0.4193548 0.0883812 31
17 400 0.5806452 0.0893970 31
18 400 0.4516129 0.0901393 31
19 400 0.5483871 0.0893454 31
20 400 0.2258065 0.0749255 31
21 400 0.6451613 0.0845375 31
22 400 0.7058824 0.0781173 34
23 400 0.7647059 0.0735598 34
24 400 0.6176471 0.0834099 34
25 400 0.7941176 0.0695505 34
26 400 0.7647059 0.0730556 34
27 400 0.7058824 0.0778615 34
28 400 0.6176471 0.0835097 34
29 400 0.5882353 0.0842227 34
30 400 0.6470588 0.0816309 34
31 400 0.5882353 0.0842483 34
32 400 0.8611111 0.0579035 36
33 400 0.8888889 0.0528892 36
34 400 0.7777778 0.0691756 36
35 400 0.5833333 0.0825406 36
36 400 0.7500000 0.0720095 36
37 400 0.6666667 0.0799192 36
38 400 0.7222222 0.0743427 36
39 400 0.8611111 0.0579041 36
40 400 0.9166667 0.0463602 36
41 400 0.8055556 0.0662782 36

Winter 2018 by Section and Overall vs. Original Terms

Not including section 400 performance for individual sections because size of error bars makes it difficult to compare to the other sections. However, the WN2018 All data points do include section 400. So in Question 4, for example, the WN2018 All percent correct point is lower than the other 3 sections. But section 400 is much lower (~0.4), which accounts for this.