Table of Matched 41 Repeated Questions

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 Gain Scores

Percent correct for 41 repeated questions

gainscore <- (percent_Correct_WN2018 - percent_Correct_OG) / (1 - percent_Correct_OG)
numeratorError <- sqrt(stdError_pCorrect_WN2018^2 + stdError_pCorrect_OG^2)
denominatorError <- stdError_pCorrect_OG
stdError_gainscore <- gainscore * sqrt((numeratorError/(percent_Correct_WN2018 - percent_Correct_OG))^2 + (denominatorError/(1- percent_Correct_OG))^2)
Number Percent Correct WN2018 Term Standard Error WN2018 Term Percent Correct Original Term Standard Error Original Term Gain Score Standard Error Gain Score
1 0.7536232 0.0173840 0.6293605 0.0185487 0.3352657 0.0706109
2 0.6988728 0.0183605 0.6540698 0.0178806 0.1295146 0.0743879
3 0.8599034 0.0139503 0.7593496 0.0170489 0.4178418 0.0962068
4 0.7777778 0.0167120 0.8430233 0.0136843 -0.4156379 -0.1422891
5 0.7842190 0.0165504 0.7863372 0.0158022 -0.0099138 -0.1071004
6 0.7262480 0.0178672 0.6325203 0.0197036 0.2550554 0.0736609
7 0.8421900 0.0146815 0.7593496 0.0172756 0.3442355 0.0973958
8 0.8792271 0.0129858 0.8016260 0.0158012 0.3911856 0.1077070
9 0.8325282 0.0148626 0.8629032 0.0136076 -0.2215592 -0.1486201
10 0.6537842 0.0191538 0.7281977 0.0170181 -0.2737778 -0.0958127
11 0.5169082 0.0199846 0.6191860 0.0184753 -0.2685769 -0.0726466
12 0.5362319 0.0198969 0.6243902 0.0193546 -0.2347073 -0.0748833
13 0.7929374 0.0163498 0.7777778 0.0167529 0.0682183 0.1054655
14 0.8170144 0.0154059 0.7503650 0.0164895 0.2669877 0.0921021
15 0.5345104 0.0200143 0.4850575 0.0235897 0.0960359 0.0602377
16 0.6869984 0.0184026 0.7335474 0.0176692 -0.1746988 -0.0964449
17 0.7415730 0.0175803 0.6094771 0.0194638 0.3382540 0.0692446
18 0.7174960 0.0179636 0.6091954 0.0233588 0.2771221 0.0771998
19 0.6099518 0.0193156 0.7007299 0.0174362 -0.3033316 -0.0887274
20 0.3772071 0.0194575 0.2896552 0.0218252 0.1232527 0.0413359
21 0.4991974 0.0198657 0.4656934 0.0188519 0.0627056 0.0513046
22 0.7445483 0.0170680 0.6847978 0.0172037 0.1895625 0.0775770
23 0.9065421 0.0115402 0.7622821 0.0168798 0.6068536 0.0962063
24 0.8068536 0.0156074 0.7291982 0.0172659 0.2867610 0.0878699
25 0.8333333 0.0146430 0.6717095 0.0182739 0.4923195 0.0764130
26 0.9065421 0.0114313 0.8284519 0.0139710 0.4552086 0.1115675
27 0.7274143 0.0174120 0.6652720 0.0177245 0.1856503 0.0748763
28 0.7056075 0.0180413 0.8730823 0.0124486 -1.3195543 -0.2158200
29 0.5342679 0.0198044 0.5975794 0.0190430 -0.1573267 -0.0686779
30 0.6417445 0.0188064 0.7726639 0.0154090 -0.5758844 -0.1138475
31 0.6137072 0.0193149 0.5198098 0.0198215 0.1955420 0.0581979
32 0.7939394 0.0157079 0.7756410 0.0166752 0.0815584 0.1022860
33 0.8060606 0.0156056 0.8451613 0.0145019 -0.2525253 -0.1396035
34 0.8000000 0.0155755 0.5076709 0.0185322 0.5937677 0.0540122
35 0.6863636 0.0180278 0.7319277 0.0172872 -0.1699694 -0.0938149
36 0.7833333 0.0162359 0.7790323 0.0167413 0.0194647 0.1055513
37 0.7863636 0.0160144 0.8019526 0.0149393 -0.0787132 -0.1107428
38 0.7287879 0.0173731 0.6971154 0.0184757 0.1045695 0.0839739
39 0.9015152 0.0114678 0.6069731 0.0197105 0.7494196 0.0691301
40 0.6000000 0.0191936 0.6080893 0.0183997 -0.0206406 -0.0678498
41 0.8000000 0.0155551 0.7515060 0.0169561 0.1951515 0.0935513 
ggplot(percentCorrect_WN2018_OG) + 
  geom_point(aes(Number, `Gain Score`)) + 
  geom_errorbar(aes(Number, ymin = `Gain Score` - `Standard Error Gain Score`, 
                    ymax = `Gain Score` + `Standard Error Gain Score`)) +
  geom_hline(yintercept = 0, color = c("#56B4E9"))

ggplot(percentCorrect_WN2018_OG) + 
  geom_histogram(aes(`Gain Score`), 
                 fill = c("#009E73"), alpha = .7, binwidth = .1) +
  geom_vline(xintercept = 0, linetype = "longdash", alpha = .6) + geom_vline(xintercept = mean(percentCorrect_WN2018_OG$`Gain Score`), linetype = "dashed", color = c("#CC79A7"))

Reordered plot by order of questions as they appear in Winter 2018 exams.

All exams were combined, so if there are two exams with the same question number, the earlier exam would be placed first. Example: Exams 1, 3, and 4 all had a repeated question for their 5th question. So they are plotted in the order E1_Q5, E3_Q5, E4_Q5.