In the last eight years, we have taught a General Chemistry sophomore course. During all this time, while the chemistry content has not changed significantly, the forms of delivery and assessment have been evolving towards better pedagogies of engagment and towards a clearer assessment of learning objectives.
Let’s just look at how students have performed in the two GC semesters by looking at their final grade in different semesters. (The final grades from Fall12-Spring13 is not correct, not sure why, that being said, you’ll see that the cohort Fall2012 is the only one that consistently scores lower in freshman-GPA and Highschool rank.)
IMPORTANT: We will see statistical significance between years and other demographics when analyzing the final percent grade. However, when we analyze the letter grade, those significances disappear
group1 | n | mean | sd | median | trimmed | mad | min | max | range | |
---|---|---|---|---|---|---|---|---|---|---|
X11 | Fall 2011 | 68 | 78.43 | 10.11 | 79.50 | 78.98 | 9.79 | 42.50 | 95.90 | 53.40 |
X12 | Fall 2012 | 84 | 73.95 | 12.41 | 73.00 | 73.78 | 14.53 | 43.00 | 96.20 | 53.20 |
X13 | Fall 2013 | 69 | 83.81 | 8.17 | 84.70 | 84.49 | 6.82 | 41.60 | 96.50 | 54.90 |
X14 | Fall 2014 | 105 | 80.95 | 9.73 | 81.52 | 81.78 | 8.61 | 40.52 | 98.78 | 58.27 |
X15 | Fall 2015 | 60 | 81.44 | 9.74 | 82.68 | 82.39 | 7.19 | 43.47 | 96.33 | 52.87 |
X16 | Fall 2016 | 35 | 84.17 | 7.24 | 84.95 | 84.60 | 7.82 | 66.98 | 97.14 | 30.16 |
group1 | n | mean | sd | median | trimmed | mad | min | max | range | |
---|---|---|---|---|---|---|---|---|---|---|
X11 | Spring 2011 | 16 | 87.38 | 8.34 | 89.84 | 88.07 | 6.55 | 67.70 | 97.38 | 29.68 |
X12 | Spring 2012 | 45 | 69.01 | 12.95 | 70.70 | 69.90 | 10.53 | 35.60 | 94.00 | 58.40 |
X13 | Spring 2013 | 51 | 81.51 | 9.42 | 82.00 | 82.00 | 9.93 | 53.70 | 97.50 | 43.80 |
X14 | Spring 2014 | 55 | 80.31 | 8.51 | 79.30 | 80.55 | 8.75 | 60.10 | 96.70 | 36.60 |
X15 | Spring 2015 | 61 | 78.51 | 10.00 | 77.78 | 78.55 | 11.92 | 57.26 | 96.14 | 38.88 |
X16 | Spring 2016 | 44 | 77.15 | 9.66 | 78.73 | 77.77 | 9.49 | 54.39 | 94.10 | 39.72 |
X17 | Spring 2017 | 37 | 80.01 | 9.80 | 80.78 | 80.64 | 8.01 | 48.11 | 97.22 | 49.11 |
diff | lwr | upr | p adj | |
---|---|---|---|---|
Fall 2012-Fall 2011 | -4.4779412 | -9.1423644 | 0.186482 | 0.0681540 |
Fall 2013-Fall 2011 | 5.3836530 | 0.4976747 | 10.269631 | 0.0211987 |
Fall 2014-Fall 2011 | 2.5216131 | -1.9292496 | 6.972476 | 0.5843701 |
Fall 2015-Fall 2011 | 3.0089288 | -2.0556712 | 8.073529 | 0.5318824 |
Fall 2016-Fall 2011 | 5.7435885 | -0.2048146 | 11.691992 | 0.0653478 |
Fall 2013-Fall 2012 | 9.8615942 | 5.2158876 | 14.507301 | 0.0000000 |
Fall 2014-Fall 2012 | 6.9995543 | 2.8138662 | 11.185242 | 0.0000345 |
Fall 2015-Fall 2012 | 7.4868700 | 2.6536537 | 12.320086 | 0.0001708 |
Fall 2016-Fall 2012 | 10.2215296 | 4.4688516 | 15.974208 | 0.0000082 |
Fall 2014-Fall 2013 | -2.8620399 | -7.2932841 | 1.569204 | 0.4353187 |
Fall 2015-Fall 2013 | -2.3747242 | -7.4220918 | 2.672643 | 0.7584349 |
Fall 2016-Fall 2013 | 0.3599354 | -5.5738024 | 6.293673 | 0.9999781 |
Fall 2015-Fall 2014 | 0.4873157 | -4.1401366 | 5.114768 | 0.9996654 |
Fall 2016-Fall 2014 | 3.2219753 | -2.3589421 | 8.802893 | 0.5638475 |
Fall 2016-Fall 2015 | 2.7346596 | -3.3470041 | 8.816323 | 0.7918982 |
diff | lwr | upr | p adj | |
---|---|---|---|---|
Spring 2012-Spring 2011 | -18.3719243 | -27.016530 | -9.7273185 | 0.0000000 |
Spring 2013-Spring 2011 | -5.8749308 | -14.385113 | 2.6352511 | 0.3860347 |
Spring 2014-Spring 2011 | -7.0680859 | -15.504043 | 1.3678712 | 0.1676882 |
Spring 2015-Spring 2011 | -8.8701902 | -17.212128 | -0.5282519 | 0.0288746 |
Spring 2016-Spring 2011 | -10.2332149 | -18.903549 | -1.5628812 | 0.0094423 |
Spring 2017-Spring 2011 | -7.3733537 | -16.259708 | 1.5130002 | 0.1767662 |
Spring 2013-Spring 2012 | 12.4969935 | 6.422770 | 18.5712173 | 0.0000001 |
Spring 2014-Spring 2012 | 11.3038384 | 5.334050 | 17.2736265 | 0.0000009 |
Spring 2015-Spring 2012 | 9.5017341 | 3.665559 | 15.3379087 | 0.0000439 |
Spring 2016-Spring 2012 | 8.1387093 | 1.842068 | 14.4353503 | 0.0028667 |
Spring 2017-Spring 2012 | 10.9985706 | 4.407646 | 17.5894949 | 0.0000250 |
Spring 2014-Spring 2013 | -1.1931551 | -6.966573 | 4.5802632 | 0.9963637 |
Spring 2015-Spring 2013 | -2.9952594 | -8.630410 | 2.6398912 | 0.6966880 |
Spring 2016-Spring 2013 | -4.3582841 | -10.469068 | 1.7524994 | 0.3452669 |
Spring 2017-Spring 2013 | -1.4984229 | -7.912023 | 4.9151776 | 0.9928975 |
Spring 2015-Spring 2014 | -1.8021043 | -7.324522 | 3.7203134 | 0.9603189 |
Spring 2016-Spring 2014 | -3.1651291 | -9.172113 | 2.8418544 | 0.7053683 |
Spring 2017-Spring 2014 | -0.3052678 | -6.620048 | 6.0095122 | 0.9999993 |
Spring 2016-Spring 2015 | -1.3630247 | -7.237241 | 4.5111913 | 0.9931556 |
Spring 2017-Spring 2015 | 1.4968365 | -4.691783 | 7.6854560 | 0.9914449 |
Spring 2017-Spring 2016 | 2.8598613 | -3.764772 | 9.4844944 | 0.8600771 |
It does seem like the cohort Fall 2011 - Spring 2012 did significantly different than any other year.
I converted the letter grades into the 4-scale. The plot should only show 4, 3.66, 3.33, 3… but it seems to add more variability… it needs to be fixed… or just run anova on the letter grades (once I learn how to do that)
The final exam is a second opportunity for students to improve their semester exams. Let’s measure how exams score and improvement evolved through the years.
This is plots the increment
There’s something funky about some of these numbers. Fall 2014 doesn’t seem to apply the >40% rule, which I actually implemented.
So let’s check that I obtain the same result if I plot grade exams from BoSCO data
TODO: Check ~/Research/R/myCourses theres a lot of work there
Also, you need to merge the demographics that you are using and include letter grade and gender as well as exams and homework… it may be problematic to add F17/S18 because I’m missing so much DEM_
There are different variables that we want to look at. Performance factors such as ACT scores or GPA or High School rank , as well as demographic factors such as ethnicity, first-year generation
group1 | n | mean | sd | median | trimmed | mad | min | max | range | |
---|---|---|---|---|---|---|---|---|---|---|
X11 | Fall 2011 | 64 | 24.75 | 3.50 | 24.5 | 24.67 | 3.71 | 18 | 33 | 15 |
X12 | Fall 2012 | 70 | 24.41 | 4.01 | 24.0 | 24.29 | 4.45 | 17 | 34 | 17 |
X13 | Fall 2013 | 66 | 25.14 | 2.82 | 25.0 | 25.26 | 2.97 | 18 | 31 | 13 |
X14 | Fall 2014 | 101 | 25.47 | 3.21 | 26.0 | 25.40 | 2.97 | 17 | 34 | 17 |
X15 | Fall 2015 | 57 | 24.72 | 3.19 | 24.0 | 24.70 | 2.97 | 18 | 32 | 14 |
X16 | Fall 2016 | 32 | 24.94 | 2.64 | 25.0 | 24.96 | 2.97 | 19 | 30 | 11 |
group1 | n | mean | sd | median | trimmed | mad | min | max | range | |
---|---|---|---|---|---|---|---|---|---|---|
X11 | Spring 2011 | 16 | 25.88 | 4.18 | 25.5 | 25.79 | 3.71 | 19 | 34 | 15 |
X12 | Spring 2012 | 42 | 25.57 | 3.47 | 25.0 | 25.56 | 2.97 | 18 | 33 | 15 |
X13 | Spring 2013 | 44 | 26.23 | 4.15 | 26.0 | 26.17 | 4.45 | 18 | 34 | 16 |
X14 | Spring 2014 | 52 | 24.90 | 2.76 | 25.0 | 25.17 | 2.97 | 18 | 29 | 11 |
X15 | Spring 2015 | 58 | 26.10 | 3.36 | 26.0 | 26.04 | 2.97 | 19 | 34 | 15 |
X16 | Spring 2016 | 42 | 25.10 | 3.27 | 25.0 | 25.00 | 2.97 | 18 | 32 | 14 |
X17 | Spring 2017 | 33 | 25.27 | 2.47 | 26.0 | 25.30 | 2.97 | 21 | 30 | 9 |
We see that the second semester is a subselection of the first semester with a higher ACT math score. Therefore, we can just use GenChem1 for the analysis.
As we can see below. There is no significant difference in ACT throughout the years
diff | lwr | upr | p adj | |
---|---|---|---|---|
Fall 2012-Fall 2011 | -0.3357143 | -1.9769059 | 1.3054774 | 0.9919345 |
Fall 2013-Fall 2011 | 0.3863636 | -1.2784117 | 2.0511390 | 0.9856326 |
Fall 2014-Fall 2011 | 0.7153465 | -0.8007861 | 2.2314792 | 0.7559117 |
Fall 2015-Fall 2011 | -0.0307018 | -1.7589701 | 1.6975666 | 1.0000000 |
Fall 2016-Fall 2011 | 0.1875000 | -1.8670492 | 2.2420492 | 0.9998340 |
Fall 2013-Fall 2012 | 0.7220779 | -0.9060719 | 2.3502278 | 0.8010990 |
Fall 2014-Fall 2012 | 1.0510608 | -0.4247621 | 2.5268838 | 0.3217601 |
Fall 2015-Fall 2012 | 0.3050125 | -1.3880045 | 1.9980295 | 0.9955408 |
Fall 2016-Fall 2012 | 0.5232143 | -1.5017715 | 2.5482001 | 0.9767972 |
Fall 2014-Fall 2013 | 0.3289829 | -1.1730225 | 1.8309883 | 0.9889559 |
Fall 2015-Fall 2013 | -0.4170654 | -2.1329539 | 1.2988231 | 0.9823133 |
Fall 2016-Fall 2013 | -0.1988636 | -2.2430100 | 1.8452827 | 0.9997726 |
Fall 2015-Fall 2014 | -0.7460483 | -2.3181344 | 0.8260379 | 0.7513444 |
Fall 2016-Fall 2014 | -0.5278465 | -2.4528701 | 1.3971770 | 0.9699093 |
Fall 2016-Fall 2015 | 0.2182018 | -1.8779779 | 2.3143814 | 0.9996831 |
so, pretty crappy r-squared for both 0.2042773 and 0.1569021, respectively. We need to find a better predictor. Let’s see cumulative GPA before enrolling
While ACT.Math historically seems to correlate well, since we’re teaching sophomores, previous GPA is even a better predictor
group1 | n | mean | sd | median | trimmed | mad | min | max | range | |
---|---|---|---|---|---|---|---|---|---|---|
X11 | Fall 2011 | 65 | 3.11 | 0.46 | 3.14 | 3.13 | 0.52 | 1.88 | 3.93 | 2.05 |
X12 | Fall 2012 | 79 | 2.94 | 0.53 | 2.94 | 2.95 | 0.59 | 1.44 | 3.98 | 2.54 |
X13 | Fall 2013 | 69 | 3.18 | 0.44 | 3.21 | 3.19 | 0.53 | 1.84 | 3.98 | 2.14 |
X14 | Fall 2014 | 105 | 3.00 | 0.48 | 2.98 | 3.00 | 0.47 | 1.33 | 4.00 | 2.67 |
X15 | Fall 2015 | 60 | 3.00 | 0.39 | 3.00 | 2.98 | 0.33 | 2.18 | 3.97 | 1.79 |
X16 | Fall 2016 | 35 | 3.12 | 0.48 | 3.26 | 3.14 | 0.42 | 2.06 | 4.00 | 1.94 |
group1 | n | mean | sd | median | trimmed | mad | min | max | range | |
---|---|---|---|---|---|---|---|---|---|---|
X11 | Spring 2011 | 16 | 3.46 | 0.43 | 3.54 | 3.47 | 0.46 | 2.73 | 4.00 | 1.27 |
X12 | Spring 2012 | 43 | 3.18 | 0.39 | 3.19 | 3.19 | 0.40 | 2.33 | 3.95 | 1.62 |
X13 | Spring 2013 | 50 | 3.20 | 0.48 | 3.25 | 3.24 | 0.39 | 2.05 | 3.97 | 1.92 |
X14 | Spring 2014 | 53 | 3.25 | 0.44 | 3.27 | 3.26 | 0.50 | 2.18 | 3.98 | 1.80 |
X15 | Spring 2015 | 61 | 3.18 | 0.46 | 3.19 | 3.18 | 0.49 | 2.19 | 4.00 | 1.81 |
X16 | Spring 2016 | 44 | 3.07 | 0.41 | 3.05 | 3.06 | 0.36 | 2.13 | 3.96 | 1.83 |
X17 | Spring 2017 | 36 | 3.23 | 0.40 | 3.25 | 3.25 | 0.34 | 2.13 | 4.00 | 1.87 |
diff | lwr | upr | p adj | |
---|---|---|---|---|
Fall 2012-Fall 2011 | -0.1679124 | -0.3930252 | 0.0572004 | 0.2709961 |
Fall 2013-Fall 2011 | 0.0728361 | -0.1595233 | 0.3051956 | 0.9469705 |
Fall 2014-Fall 2011 | -0.1069817 | -0.3191411 | 0.1051777 | 0.7000993 |
Fall 2015-Fall 2011 | -0.1160769 | -0.3567413 | 0.1245875 | 0.7384580 |
Fall 2016-Fall 2011 | 0.0137802 | -0.2680571 | 0.2956175 | 0.9999925 |
Fall 2013-Fall 2012 | 0.2407485 | 0.0192443 | 0.4622527 | 0.0242003 |
Fall 2014-Fall 2012 | 0.0609307 | -0.1392812 | 0.2611426 | 0.9531219 |
Fall 2015-Fall 2012 | 0.0518354 | -0.1783657 | 0.2820366 | 0.9874925 |
Fall 2016-Fall 2012 | 0.1816926 | -0.0912643 | 0.4546495 | 0.3999188 |
Fall 2014-Fall 2013 | -0.1798178 | -0.3881444 | 0.0285087 | 0.1350707 |
Fall 2015-Fall 2013 | -0.1889130 | -0.4262055 | 0.0483794 | 0.2048113 |
Fall 2016-Fall 2013 | -0.0590559 | -0.3380193 | 0.2199075 | 0.9905679 |
Fall 2015-Fall 2014 | -0.0090952 | -0.2266461 | 0.2084557 | 0.9999966 |
Fall 2016-Fall 2014 | 0.1207619 | -0.1416144 | 0.3831382 | 0.7750540 |
Fall 2016-Fall 2015 | 0.1298571 | -0.1560608 | 0.4157750 | 0.7847500 |
So when we plot previous GPA (typicall first year GPA) against final grade
so, better r-squared for both 0.656591 and 0.5840838, respectively
For large schools, Highschool ranking can be used as a better measurement than HS GPA… also, HS-GPA is currently unavailable :). The units are given in percentile, so the higher the better
group1 | n | mean | sd | median | trimmed | mad | min | max | range | |
---|---|---|---|---|---|---|---|---|---|---|
X11 | Fall 2011 | 60 | 79.18 | 14.08 | 80.5 | 80.48 | 15.57 | 46 | 97 | 51 |
X12 | Fall 2012 | 65 | 73.57 | 17.01 | 76.0 | 74.98 | 16.31 | 20 | 99 | 79 |
X13 | Fall 2013 | 57 | 81.21 | 12.33 | 81.0 | 82.04 | 13.34 | 47 | 99 | 52 |
X14 | Fall 2014 | 86 | 79.43 | 16.62 | 84.0 | 81.56 | 13.34 | 26 | 99 | 73 |
X15 | Fall 2015 | 51 | 81.27 | 13.83 | 86.0 | 82.93 | 8.90 | 37 | 99 | 62 |
X16 | Fall 2016 | 25 | 82.28 | 11.75 | 85.0 | 82.95 | 10.38 | 60 | 98 | 38 |
diff | lwr | upr | p adj | |
---|---|---|---|---|
Fall 2012-Fall 2011 | -5.6141026 | -13.2615244 | 2.033319 | 0.2876124 |
Fall 2013-Fall 2011 | 2.0271930 | -5.8736280 | 9.928014 | 0.9774141 |
Fall 2014-Fall 2011 | 0.2468992 | -6.9383845 | 7.432183 | 0.9999987 |
Fall 2015-Fall 2011 | 2.0911765 | -6.0444897 | 10.226843 | 0.9772356 |
Fall 2016-Fall 2011 | 3.0966667 | -7.0718166 | 13.265150 | 0.9527517 |
Fall 2013-Fall 2012 | 7.6412955 | -0.1100688 | 15.392660 | 0.0558988 |
Fall 2014-Fall 2012 | 5.8610018 | -1.1596093 | 12.881613 | 0.1616772 |
Fall 2015-Fall 2012 | 7.7052790 | -0.2853243 | 15.695882 | 0.0659401 |
Fall 2016-Fall 2012 | 8.7107692 | -1.3420278 | 18.763566 | 0.1318975 |
Fall 2014-Fall 2013 | -1.7802938 | -9.0761070 | 5.515519 | 0.9819290 |
Fall 2015-Fall 2013 | 0.0639835 | -8.1694637 | 8.297431 | 1.0000000 |
Fall 2016-Fall 2013 | 1.0694737 | -9.1774107 | 11.316358 | 0.9996775 |
Fall 2015-Fall 2014 | 1.8442772 | -5.7052249 | 9.393779 | 0.9818377 |
Fall 2016-Fall 2014 | 2.8497674 | -6.8561055 | 12.555640 | 0.9594958 |
Fall 2016-Fall 2015 | 1.0054902 | -9.4235429 | 11.434523 | 0.9997815 |
Fall2012 seems to stand up again as students who were ill prepared and therefore they performed significantly worse than any other year.
so, crappy r-squared for both 0.1667476 and 0.0552476, respectively
Given the good correlation given above between previous GPA and final grade, let’s then analyze how students of different demographics perform in chemistry when compared to their incoming GPA. In other words, instead of comparing how first-generation vs non-first-generation do, it is more interesting to see how considering their college readiness (as desribed by GPA) how they did in GenChem
Look at how previous GPA and GenChem grades is among gender identities
group1 | n | mean | sd | median | trimmed | mad | min | max | range | |
---|---|---|---|---|---|---|---|---|---|---|
X11 | F | 286 | 3.08 | 0.45 | 3.06 | 3.08 | 0.48 | 1.88 | 4 | 2.12 |
X12 | M | 117 | 3.00 | 0.53 | 3.04 | 3.01 | 0.49 | 1.33 | 4 | 2.67 |
group1 | n | mean | sd | median | trimmed | mad | min | max | range | |
---|---|---|---|---|---|---|---|---|---|---|
X11 | F | 270 | 24.61 | 3.09 | 25 | 24.61 | 2.97 | 17 | 34 | 17 |
X12 | M | 111 | 25.82 | 3.71 | 26 | 25.83 | 2.97 | 17 | 34 | 17 |
group1 | n | mean | sd | median | trimmed | mad | min | max | range | |
---|---|---|---|---|---|---|---|---|---|---|
X11 | F | 239 | 81.02 | 14.21 | 84.0 | 82.64 | 11.86 | 25 | 99 | 74 |
X12 | M | 96 | 74.40 | 16.42 | 76.5 | 75.46 | 17.05 | 20 | 99 | 79 |
From the above, we can see that females come to GenChem with very slightly higher GPA and remarkably better HS ranking, but with a lower ACT-math score. Also, males have a broader range of values and higher standard deviation, this tell us that male performance may not be treated as a single group, and it may require a further finer classification. In any case, How will these three factors affect their performance in GenChem?
group1 | n | mean | sd | median | trimmed | mad | min | max | range | |
---|---|---|---|---|---|---|---|---|---|---|
X11 | F | 289 | 79.74 | 9.84 | 81.0 | 80.49 | 9.49 | 42.50 | 98.78 | 56.28 |
X12 | M | 120 | 80.88 | 12.12 | 82.1 | 82.36 | 11.15 | 40.52 | 97.14 | 56.62 |
group1 | n | mean | sd | median | trimmed | mad | min | max | range | |
---|---|---|---|---|---|---|---|---|---|---|
X11 | F | 196 | 78.18 | 10.51 | 79.28 | 78.88 | 9.62 | 38.20 | 97.50 | 59.30 |
X12 | M | 102 | 80.31 | 10.19 | 80.57 | 80.76 | 13.00 | 56.39 | 97.22 | 40.83 |
While it may look like males do better than females, even though females came with better GPA and HS ranking, there is actually no significant difference when compared the two groups in general.
However, when the two groups are compared each semester we notice that Fall 2011 is the only semester with a significant difference between sexes.
Before we jump into conclusions, however, we may need to look into how the females in Fall 2011 performed compared to other semester’s females.
We saw that females had performed significantly lower in Fall2011, and almost significantly higher in Fall2013 than males. However, we see that these differences may also be explained by the differences with the incoming GPAs, but not by HS ranking. Also, many students lack HS Ranking so the statistics may be lacking.
Let’s compare the GPA before enrolling in GenChem for students of color vs the rest.
group1 | n | mean | sd | median | trimmed | mad | min | max | range | |
---|---|---|---|---|---|---|---|---|---|---|
X11 | N | 340 | 3.09 | 0.47 | 3.11 | 3.10 | 0.47 | 1.33 | 4.00 | 2.67 |
X12 | Y | 73 | 2.84 | 0.46 | 2.78 | 2.82 | 0.43 | 1.90 | 3.97 | 2.07 |
group1 | n | mean | sd | median | trimmed | mad | min | max | range | |
---|---|---|---|---|---|---|---|---|---|---|
X11 | N | 348 | 81.06 | 10.20 | 82.34 | 82.13 | 8.65 | 40.52 | 98.78 | 58.27 |
X12 | Y | 73 | 74.67 | 10.48 | 75.30 | 74.58 | 12.04 | 43.47 | 96.02 | 52.56 |
group1 | n | mean | sd | median | trimmed | mad | min | max | range | |
---|---|---|---|---|---|---|---|---|---|---|
X11 | N | 260 | 79.13 | 10.33 | 79.74 | 79.75 | 10.53 | 43.7 | 97.5 | 53.8 |
X12 | Y | 49 | 74.44 | 12.76 | 72.95 | 75.31 | 9.87 | 35.6 | 96.7 | 61.1 |
Graphically we can see below what the means above already tell us, while people of color do worse in GenChem1 than whites, they were already doing worse before enrolling.
From above we can see that even though students of color perform below the average in GenChem1, they already come with a lower “college readiness”. In other words, the cause for lower performance of students of color must be found elsewhere. For example, one could look at Highschool data such as ACT scores or Highschool rank.
Let’s see what we mean by students of color, and how they did during their first year
group1 | n | mean | sd | median | trimmed | mad | min | max | range | |
---|---|---|---|---|---|---|---|---|---|---|
X11 | 0 | NaN | NA | NA | NaN | NA | Inf | -Inf | -Inf | |
X12 | Am. Indian | 5 | 2.74 | 0.36 | 2.62 | 2.74 | 0.43 | 2.33 | 3.23 | 0.90 |
X13 | Asian | 38 | 2.88 | 0.47 | 2.81 | 2.86 | 0.36 | 1.90 | 3.97 | 2.07 |
X14 | Black | 29 | 2.96 | 0.50 | 2.89 | 2.95 | 0.56 | 2.12 | 3.95 | 1.83 |
X15 | Hawaiian | 1 | 3.11 | NA | 3.11 | 3.11 | 0.00 | 3.11 | 3.11 | 0.00 |
X16 | Hispanic | 13 | 2.78 | 0.38 | 2.70 | 2.76 | 0.36 | 2.27 | 3.47 | 1.20 |
X17 | NS | 3 | 2.72 | 0.66 | 2.37 | 2.72 | 0.07 | 2.32 | 3.48 | 1.16 |
X18 | White | 324 | 3.10 | 0.47 | 3.11 | 3.11 | 0.47 | 1.33 | 4.00 | 2.67 |
We can also run an anova among different ethnicities, but in any case it’s hard to do statistics on such small numbers maybe only black and asian are large enough to be compared with whites.
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = allGC1$TG_Total.Grade.... ~ allGC1$DEM_Ethnicity)
##
## $`allGC1$DEM_Ethnicity`
## diff lwr upr p adj
## Am. Indian- -1.2520311 -24.3127791 21.808717 0.9999998
## Asian- 1.8944333 -17.0079964 20.796863 0.9999879
## Black- 0.2271630 -18.9237458 19.378072 1.0000000
## Hawaiian- 6.1165333 -30.3457108 42.578777 0.9996050
## Hispanic- -0.4325654 -20.6581794 19.793049 1.0000000
## NS- -4.9680000 -30.7507001 20.814700 0.9990190
## White- 5.7148568 -12.5997033 24.029417 0.9807262
## Asian-Am. Indian 3.1464644 -11.8319308 18.124860 0.9982867
## Black-Am. Indian 1.4791941 -13.8115804 16.769969 0.9999905
## Hawaiian-Am. Indian 7.3685644 -27.2225576 41.959686 0.9981266
## Hispanic-Am. Indian 0.8194657 -15.7975718 17.436503 0.9999999
## NS-Am. Indian -3.7159689 -26.7767169 19.344779 0.9996975
## White-Am. Indian 6.9668879 -7.2624335 21.196209 0.8117453
## Black-Asian -1.6672703 -9.3686684 6.034128 0.9979230
## Hawaiian-Asian 4.2221000 -27.7474084 36.191609 0.9999204
## Hispanic-Asian -2.3269987 -12.4081536 7.754156 0.9968822
## NS-Asian -6.8624333 -25.7648630 12.039996 0.9553407
## White-Asian 3.8204235 -1.4689372 9.109784 0.3535646
## Hawaiian-Black 5.8893703 -26.2276802 38.006421 0.9992900
## Hispanic-Black -0.6597284 -11.1994223 9.879965 0.9999995
## NS-Black -5.1951630 -24.3460719 13.955746 0.9915567
## White-Black 5.4876938 -0.6305412 11.605929 0.1158453
## Hispanic-Hawaiian -6.5490987 -39.3183386 26.220141 0.9987569
## NS-Hawaiian -11.0845333 -47.5467775 25.377711 0.9834226
## White-Hawaiian -0.4016765 -32.0271526 31.223800 1.0000000
## NS-Hispanic -4.5354346 -24.7610486 15.690179 0.9974027
## White-Hispanic 6.1474222 -2.7829165 15.077761 0.4184543
## White-NS 10.6828568 -7.6317033 28.997417 0.6360504
Let’s compare the GPA before enrolling in GenChem for 1st generation vs the rest. Notice for how many people we have information (a total of 421 students in Genchem1 and 309 in GenChem2)
group1 | n | mean | sd | median | trimmed | mad | min | max | range | |
---|---|---|---|---|---|---|---|---|---|---|
X11 | 20 | 2.85 | 0.58 | 2.70 | 2.79 | 0.45 | 1.88 | 4.00 | 2.12 | |
X12 | N | 248 | 3.05 | 0.48 | 3.09 | 3.07 | 0.50 | 1.33 | 3.98 | 2.65 |
X13 | Y | 145 | 3.07 | 0.44 | 3.02 | 3.06 | 0.43 | 1.90 | 4.00 | 2.10 |
group1 | n | mean | sd | median | trimmed | mad | min | max | range | |
---|---|---|---|---|---|---|---|---|---|---|
X11 | 24 | 72.63 | 14.01 | 73.62 | 72.63 | 15.20 | 42.50 | 97.14 | 54.64 | |
X12 | N | 252 | 79.81 | 10.51 | 81.07 | 80.73 | 10.29 | 40.52 | 97.07 | 56.55 |
X13 | Y | 145 | 81.41 | 9.36 | 82.18 | 82.20 | 8.03 | 53.00 | 98.78 | 45.78 |
group1 | n | mean | sd | median | trimmed | mad | min | max | range | |
---|---|---|---|---|---|---|---|---|---|---|
X11 | 16 | 78.97 | 11.79 | 79.30 | 79.09 | 14.52 | 60.1 | 96.14 | 36.04 | |
X12 | N | 187 | 78.26 | 10.96 | 79.10 | 78.94 | 11.12 | 38.2 | 97.50 | 59.30 |
X13 | Y | 106 | 78.52 | 10.64 | 79.42 | 79.24 | 10.49 | 35.6 | 96.70 | 61.10 |
First generation seem to do slightly better at UMR than or the same than non-first gens. Are they coming in with equal preparation?
It seems that the first generation students we’ve been accepting are already better prepared than the non-first generation.