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 Gender

# 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))
}
Number Term Gender Percent Correct Standard Error
1 WN2018 Female 0.7021277 0.0302968
2 WN2018 Female 0.6851064 0.0300686
3 WN2018 Female 0.8170213 0.0251248
4 WN2018 Female 0.7234043 0.0292542
5 WN2018 Female 0.6893617 0.0302612
6 WN2018 Female 0.7234043 0.0291326
7 WN2018 Female 0.8468085 0.0235895
8 WN2018 Female 0.8553191 0.0228080
9 WN2018 Female 0.7276596 0.0289995
10 WN2018 Female 0.6000000 0.0316706
11 WN2018 Female 0.4255319 0.0322631
12 WN2018 Female 0.4723404 0.0326208
13 WN2018 Female 0.7805907 0.0266174
14 WN2018 Female 0.7594937 0.0277285
15 WN2018 Female 0.3924051 0.0317967
16 WN2018 Female 0.6666667 0.0305134
17 WN2018 Female 0.6877637 0.0298609
18 WN2018 Female 0.6582278 0.0307194
19 WN2018 Female 0.5907173 0.0318258
20 WN2018 Female 0.3248945 0.0304075
21 WN2018 Female 0.4641350 0.0323115
22 WN2018 Female 0.7581967 0.0274691
23 WN2018 Female 0.8934426 0.0197779
24 WN2018 Female 0.7745902 0.0196302
25 WN2018 Female 0.8606557 0.0221393
26 WN2018 Female 0.9098361 0.0183066
27 WN2018 Female 0.7418033 0.0279454
28 WN2018 Female 0.6639344 0.0304604
29 WN2018 Female 0.4672131 0.0313665
30 WN2018 Female 0.6311475 0.0303187
31 WN2018 Female 0.6188525 0.0310441
32 WN2018 Female 0.7800000 0.0259110
33 WN2018 Female 0.7880000 0.0255754
34 WN2018 Female 0.7760000 0.0260927
35 WN2018 Female 0.5680000 0.0310474
36 WN2018 Female 0.8040000 0.0254270
37 WN2018 Female 0.7240000 0.0284501
38 WN2018 Female 0.7360000 0.0282362
39 WN2018 Female 0.8520000 0.0222707
40 WN2018 Female 0.6280000 0.0305099
41 WN2018 Female 0.7960000 0.0255005
1 WN2018 Male 0.7853403 0.0209742
2 WN2018 Male 0.7094241 0.0230821
3 WN2018 Male 0.8848168 0.0162861
4 WN2018 Male 0.8115183 0.0201900
5 WN2018 Male 0.8455497 0.0187042
6 WN2018 Male 0.7277487 0.0227680
7 WN2018 Male 0.8376963 0.0189520
8 WN2018 Male 0.8952880 0.0156233
9 WN2018 Male 0.8979058 0.0156432
10 WN2018 Male 0.6858639 0.0237546
11 WN2018 Male 0.5759162 0.0253045
12 WN2018 Male 0.5732984 0.0253104
13 WN2018 Male 0.8036649 0.0203145
14 WN2018 Male 0.8507853 0.0182542
15 WN2018 Male 0.6204188 0.0249660
16 WN2018 Male 0.7041885 0.0234207
17 WN2018 Male 0.7748691 0.0213938
18 WN2018 Male 0.7513089 0.0222013
19 WN2018 Male 0.6178010 0.0248206
20 WN2018 Male 0.4083770 0.0250996
21 WN2018 Male 0.5235602 0.0255495
22 WN2018 Male 0.7328244 0.0222370
23 WN2018 Male 0.9134860 0.0140619
24 WN2018 Male 0.8320611 0.0188567
25 WN2018 Male 0.8193384 0.0196628
26 WN2018 Male 0.9083969 0.0144544
27 WN2018 Male 0.7201018 0.0226358
28 WN2018 Male 0.7353690 0.0221429
29 WN2018 Male 0.5776081 0.0246025
30 WN2018 Male 0.6513995 0.0239635
31 WN2018 Male 0.6106870 0.0244895
32 WN2018 Male 0.8019802 0.0198464
33 WN2018 Male 0.8193069 0.0192164
34 WN2018 Male 0.8168317 0.0191365
35 WN2018 Male 0.7574257 0.0211885
36 WN2018 Male 0.7722772 0.0206060
37 WN2018 Male 0.8242574 0.0187510
38 WN2018 Male 0.7252475 0.0222904
39 WN2018 Male 0.9306931 0.0127014
40 WN2018 Male 0.5841584 0.0247624
41 WN2018 Male 0.8044554 0.0196529
1 OG Female 0.6681614 0.0316327
2 OG Female 0.6771300 0.0309875
3 OG Female 0.6842105 0.0306285
4 OG Female 0.7713004 0.0280581
5 OG Female 0.7174888 0.0296601
6 OG Female 0.5657895 0.0329115
7 OG Female 0.6973684 0.0304330
8 OG Female 0.7456140 0.0290233
9 OG Female 0.8009950 0.0280870
10 OG Female 0.6457399 0.0320210
11 OG Female 0.6771300 0.0311270
12 OG Female 0.6052632 0.0323296
13 OG Female 0.7412281 0.0287328
14 OG Female 0.7318182 0.0298106
15 OG Female NA NA
16 OG Female 0.6865672 0.0328242
17 OG Female 0.6271930 0.0317047
18 OG Female NA NA
19 OG Female 0.7000000 0.0306765
20 OG Female NA NA
21 OG Female 0.4772727 0.0341304
22 OG Female 0.7008197 0.0294753
23 OG Female 0.7327586 0.0291066
24 OG Female 0.6271930 0.0317720
25 OG Female 0.6622807 0.0315889
26 OG Female 0.8073770 0.0253274
27 OG Female 0.6311475 0.0310733
28 OG Female 0.8811475 0.0205673
29 OG Female 0.5219298 0.0331831
30 OG Female 0.7459016 0.0278455
31 OG Female 0.5043103 0.0330215
32 OG Female 0.8202765 0.0263653
33 OG Female 0.8308458 0.0264963
34 OG Female 0.5510204 0.0316009
35 OG Female 0.6008584 0.0315948
36 OG Female 0.7661692 0.0299176
37 OG Female 0.6612245 0.0300096
38 OG Female 0.6451613 0.0322209
39 OG Female 0.5064935 0.0329156
40 OG Female 0.6244898 0.0312253
41 OG Female 0.7467811 0.0282806
1 OG Male 0.5916824 0.0212956
2 OG Male 0.6446125 0.0207586
3 OG Male 0.7750000 0.0196362
4 OG Male 0.8771267 0.0141304
5 OG Male 0.8015123 0.0172872
6 OG Male 0.6409091 0.0229167
7 OG Male 0.7522727 0.0205534
8 OG Male 0.8136364 0.0187230
9 OG Male 0.8800000 0.0153773
10 OG Male 0.7296786 0.0191199
11 OG Male 0.5500945 0.0214212
12 OG Male 0.6113636 0.0233134
13 OG Male 0.7871854 0.0198038
14 OG Male 0.7424242 0.0191127
15 OG Male NA NA
16 OG Male 0.7510917 0.0201091
17 OG Male 0.5881007 0.0236960
18 OG Male NA NA
19 OG Male 0.6742424 0.0201300
20 OG Male NA NA
21 OG Male 0.4375000 0.0217816
22 OG Male 0.6445242 0.0203019
23 OG Male 0.7732181 0.0198239
24 OG Male 0.7566462 0.0196647
25 OG Male 0.6666667 0.0212854
26 OG Male 0.8025135 0.0167033
27 OG Male 0.6463196 0.0201740
28 OG Male 0.8545781 0.0148968
29 OG Male 0.5971370 0.0221904
30 OG Male 0.7522442 0.0183262
31 OG Male 0.4946004 0.0231028
32 OG Male 0.7600000 0.0196879
33 OG Male 0.8377778 0.0171420
34 OG Male 0.4822695 0.0209597
35 OG Male 0.7686117 0.0187051
36 OG Male 0.7755556 0.0196226
37 OG Male 0.8599291 0.0146037
38 OG Male 0.6757895 0.0214076
39 OG Male 0.6143791 0.0225560
40 OG Male 0.5460993 0.0209255
41 OG Male 0.7283702 0.0201700

Percent Correct of Females and Males in Winter 2018 and OG terms

Note that missing values correspond to the 3 Fall 2004 questions.

## Warning: Removed 2 rows containing missing values (geom_point).
## Warning: Removed 2 rows containing missing values (geom_errorbar).

## Warning: Removed 4 rows containing missing values (geom_point).
## Warning: Removed 4 rows containing missing values (geom_errorbar).

Percent Correct Differences Between Females and Males for Winter 2018 and OG terms

Note that missing values correspond to the 3 Fall 2004 questions.

Number Term Percent Correct Difference (F-M) Standard Error
1 WN2018 -0.0832127 0.0368485
2 WN2018 -0.0243177 0.0379065
3 WN2018 -0.0677955 0.0299415
4 WN2018 -0.0881141 0.0355450
5 WN2018 -0.1561880 0.0355751
6 WN2018 -0.0043444 0.0369742
7 WN2018 0.0091122 0.0302596
8 WN2018 -0.0399688 0.0276458
9 WN2018 -0.1702462 0.0329497
10 WN2018 -0.0858639 0.0395892
11 WN2018 -0.1503843 0.0410027
12 WN2018 -0.1009580 0.0412884
13 WN2018 -0.0230742 0.0334838
14 WN2018 -0.0912917 0.0331977
15 WN2018 -0.2280138 0.0404269
16 WN2018 -0.0375218 0.0384655
17 WN2018 -0.0871054 0.0367338
18 WN2018 -0.0930811 0.0379022
19 WN2018 -0.0270837 0.0403602
20 WN2018 -0.0834824 0.0394285
21 WN2018 -0.0594252 0.0411924
22 WN2018 0.0253723 0.0353418
23 WN2018 -0.0200434 0.0242673
24 WN2018 -0.0574709 0.0272198
25 WN2018 0.0413173 0.0296104
26 WN2018 0.0014391 0.0233251
27 WN2018 0.0217015 0.0359629
28 WN2018 -0.0714345 0.0376583
29 WN2018 -0.1103950 0.0398640
30 WN2018 -0.0202520 0.0386455
31 WN2018 0.0081654 0.0395408
32 WN2018 -0.0219802 0.0326383
33 WN2018 -0.0313069 0.0319902
34 WN2018 -0.0408317 0.0323580
35 WN2018 -0.1894257 0.0375885
36 WN2018 0.0317228 0.0327282
37 WN2018 -0.1002574 0.0340736
38 WN2018 0.0107525 0.0359742
39 WN2018 -0.0786931 0.0256381
40 WN2018 0.0438416 0.0392941
41 WN2018 -0.0084554 0.0321949
1 OG 0.0764790 0.0381330
2 OG 0.0325176 0.0372980
3 OG -0.0907895 0.0363825
4 OG -0.1058262 0.0314153
5 OG -0.0840235 0.0343303
6 OG -0.0751196 0.0401042
7 OG -0.0549043 0.0367234
8 OG -0.0680223 0.0345384
9 OG -0.0790050 0.0320210
10 OG -0.0839387 0.0372949
11 OG 0.1270355 0.0377857
12 OG -0.0061005 0.0398588
13 OG -0.0459573 0.0348965
14 OG -0.0106061 0.0354114
15 OG NA NA
16 OG -0.0645245 0.0384942
17 OG 0.0390923 0.0395814
18 OG NA NA
19 OG 0.0257576 0.0366914
20 OG NA NA
21 OG 0.0397727 0.0404885
22 OG 0.0562954 0.0357905
23 OG -0.0404595 0.0352162
24 OG -0.1294532 0.0373652
25 OG -0.0043860 0.0380910
26 OG 0.0048636 0.0303393
27 OG -0.0151720 0.0370478
28 OG 0.0265694 0.0253954
29 OG -0.0752072 0.0399191
30 OG -0.0063425 0.0333350
31 OG 0.0097099 0.0403009
32 OG 0.0602765 0.0329050
33 OG -0.0069320 0.0315580
34 OG 0.0687509 0.0379200
35 OG -0.1677533 0.0367166
36 OG -0.0093864 0.0357786
37 OG -0.1987046 0.0333743
38 OG -0.0306282 0.0386843
39 OG -0.1078856 0.0399025
40 OG 0.0783905 0.0375885
41 OG 0.0184109 0.0347364 

## Warning: Removed 1 rows containing missing values (geom_point).
## Warning: Removed 1 rows containing missing values (geom_errorbar).

## Warning: Removed 2 rows containing missing values (geom_point).
## Warning: Removed 2 rows containing missing values (geom_errorbar).

Difference in Percent Correct Differences Between Females and Males for the different terms.

If percent correct difference for a question is positive, this means that females performed better on this question than males.

When comparing between terms, a positive difference means that the gender gap favors women more in the Winter 2018 term than the original term. This does not mean that female performance was better than male. It only means that the difference in performance is better (for women) in the Winter 2018 term than the original term.

Only 38 questions plotted here because I am not taking into account the questions taken from Fall 2004 (Question numbers 15, 18, and 20).

Example: Question 7

Females OG = 70% +- 3%

Males OG = 75% +- 2%

Females OG - Males OG = -5% +- 4%

Females WN2018 = 85% +- 2%

Males WN2018 = 84% +- 2%

Females WN2018 - Males WN2018 = 1% +- 3%

Difference in Percent Correct Difference (WN2018 - OG) = 6% +- 5%

So originally, women performed 5% worse than males. In Winter 2018 they performed 1% better. So the change from OG to Winter 2018 is 6%. The difference in performance between females and males is 6% better (for females) than in the OG term.

The orange point represents the average difference in percent correct difference from OG to WN2018. The average difference is -2%, meaning the gender gap worsened after students were given extended time on exams.

Number Difference (WN2018-OG) Standard Error
1 1 -0.1596917 0.0530277
2 2 -0.0568353 0.0531794
3 3 0.0229940 0.0471187
4 4 0.0177121 0.0474380
5 5 -0.0721645 0.0494384
6 6 0.0707752 0.0545476
7 7 0.0640165 0.0475842
8 8 0.0280535 0.0442402
9 9 -0.0912412 0.0459459
10 10 -0.0019251 0.0543895
11 11 -0.2774198 0.0557583
12 12 -0.0948575 0.0573886
13 13 0.0228831 0.0483624
14 14 -0.0806856 0.0485392
16 16 0.0270027 0.0544187
17 17 -0.1261977 0.0540006
19 19 -0.0528413 0.0545455
21 21 -0.0991979 0.0577592
22 22 -0.0309231 0.0502991
23 23 0.0204161 0.0427678
24 24 0.0719823 0.0462285
25 25 0.0457033 0.0482462
26 26 -0.0034245 0.0382693
27 27 0.0368735 0.0516321
28 28 -0.0980040 0.0454211
29 29 -0.0351878 0.0564152
30 30 -0.0139094 0.0510362
31 31 -0.0015445 0.0564591
32 32 -0.0822567 0.0463465
33 33 -0.0243749 0.0449364
34 34 -0.1095826 0.0498495
35 35 -0.0216724 0.0525453
36 36 0.0411092 0.0484896
37 37 0.0984472 0.0476955
38 38 0.0413807 0.0528263
39 39 0.0291925 0.0474291
40 40 -0.0345489 0.0543776
41 41 -0.0268663 0.0473617