As seen in the graph and table in the tabs below, the most frequent highest math course taken is MTH 231
in both 2019 and 2020. This course represents 36.8% and 41.0% of the maximum math courses taken in 2019 and 2020, respectively, out of courses that range from MTH 103
to MTH 432
.
MTH | N | % | N | % |
---|---|---|---|---|
103 | 0 | 0.0% | 1 | 0.2% |
107 | 3 | 0.6% | 4 | 0.8% |
111 | 4 | 0.8% | 2 | 0.4% |
114 | 3 | 0.6% | 1 | 0.2% |
121 | 51 | 10.7% | 47 | 9.6% |
131 | 51 | 10.7% | 42 | 8.6% |
132 | 1 | 0.2% | 0 | 0.0% |
141 | 9 | 1.9% | 10 | 2.0% |
231 | 176 | 36.9% | 201 | 41.0% |
241 | 30 | 6.3% | 20 | 4.1% |
242 | 38 | 8.0% | 25 | 5.1% |
305 | 1 | 0.2% | 0 | 0.0% |
331 | 2 | 0.4% | 0 | 0.0% |
341 | 14 | 2.9% | 21 | 4.3% |
405 | 1 | 0.2% | 1 | 0.2% |
425 | 0 | 0.0% | 1 | 0.2% |
426 | 0 | 0.0% | 1 | 0.2% |
432 | 1 | 0.2% | 1 | 0.2% |
No Record | 92 | 19.3% | 112 | 22.9% |
Total | 477 | 100.0% | 490 | 100.0% |
The heatmap displays the most frequent term in which the highest math class was taken, by year. The colors/values correspond to the counts/frequencies that are greater than the median count value (excluding zeros). The median frequency across all year and MTH course combinations for 2019 was 7 and was 6 for 2020 (excluding zeros). Most students took their highest math class within 4 semesters of taking GN 311. For GN 311 2019, the semester where most students took their highest MTH course was the 2018 Spring Term. For GN 311 2020, the 2019 Spring Term contained the highest frequencies of maximum MTH course taken.
NOTE: Students that may have taken their highest math class more than once are counted only once according to the semester in which their grade points were highest (which generally corresponds to the latest enrollment), and then by latest enrollment if grade points were the same across different semesters.
107 | 111 | 114 | 121 | 131 | 132 | 141 | 231 | 241 | 242 | 305 | 331 | 341 | 405 | 432 | Total | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
2020 Spring Term | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
2019 Fall Term | 1 | 0 | 0 | 2 | 4 | 1 | 1 | 14 | 3 | 4 | 0 | 1 | 2 | 0 | 0 | 33 |
2019 Summer Term 2 | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 |
2019 Summer Term 1 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 4 |
2019 Spring Term | 0 | 0 | 0 | 6 | 7 | 0 | 0 | 40 | 2 | 9 | 1 | 0 | 1 | 0 | 1 | 67 |
2018 Fall Term | 0 | 0 | 2 | 10 | 9 | 0 | 3 | 32 | 9 | 10 | 0 | 0 | 3 | 0 | 0 | 78 |
2018 Summer Term 2 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 |
2018 Summer Term 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 2 | 0 | 0 | 1 | 0 | 0 | 4 |
2018 Spring Term | 0 | 1 | 0 | 13 | 10 | 0 | 1 | 47 | 7 | 9 | 0 | 0 | 1 | 1 | 0 | 90 |
2017 Fall Term | 2 | 0 | 1 | 7 | 11 | 0 | 1 | 21 | 4 | 3 | 0 | 1 | 0 | 0 | 0 | 51 |
2017 Summer Term 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
2017 Spring Term | 0 | 1 | 0 | 3 | 1 | 0 | 1 | 10 | 3 | 0 | 0 | 0 | 5 | 0 | 0 | 24 |
2016 Fall Term | 0 | 0 | 0 | 7 | 4 | 0 | 0 | 5 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 17 |
2016 Spring Term | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
2015 Fall Term | 0 | 0 | 0 | 1 | 2 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 5 |
2015 Summer Term 2 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
2014 Fall Term | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
2014 Spring Term | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
2013 Fall Term | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
2013 Spring Term | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
2011 Summer Term 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
2011 Spring Term | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
2010 Fall Term | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
Total | 3 | 4 | 3 | 51 | 51 | 1 | 9 | 176 | 30 | 38 | 1 | 2 | 14 | 1 | 1 | 385 |
103 | 107 | 111 | 114 | 121 | 131 | 141 | 231 | 241 | 242 | 341 | 405 | 425 | 426 | 432 | Total | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
2020 Spring Term | 1 | 1 | 1 | 0 | 2 | 7 | 0 | 21 | 1 | 4 | 3 | 0 | 0 | 1 | 0 | 42 |
2019 Fall Term | 0 | 0 | 0 | 0 | 3 | 3 | 1 | 43 | 2 | 7 | 1 | 1 | 1 | 0 | 0 | 62 |
2019 Summer Term 2 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
2019 Summer Term 1 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 4 |
2019 Spring Term | 0 | 0 | 1 | 0 | 7 | 9 | 1 | 45 | 5 | 3 | 6 | 0 | 0 | 0 | 1 | 78 |
2018 Fall Term | 0 | 1 | 0 | 0 | 12 | 5 | 3 | 35 | 2 | 6 | 1 | 0 | 0 | 0 | 0 | 65 |
2018 Summer Term 2 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 |
2018 Summer Term 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 2 |
2018 Spring Term | 0 | 1 | 0 | 0 | 9 | 5 | 0 | 28 | 3 | 0 | 4 | 0 | 0 | 0 | 0 | 50 |
2017 Fall Term | 0 | 0 | 0 | 0 | 10 | 8 | 4 | 12 | 2 | 3 | 3 | 0 | 0 | 0 | 0 | 42 |
2017 Summer Term 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
2017 Spring Term | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 7 | 2 | 0 | 1 | 0 | 0 | 0 | 0 | 12 |
2016 Fall Term | 0 | 0 | 0 | 1 | 1 | 2 | 0 | 5 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 10 |
2016 Spring Term | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 |
2015 Fall Term | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 |
2015 Summer Term 2 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
2014 Fall Term | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
2014 Spring Term | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
2013 Fall Term | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
2013 Spring Term | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
2011 Summer Term 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
2011 Spring Term | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
2010 Fall Term | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
Total | 1 | 4 | 2 | 1 | 47 | 42 | 10 | 201 | 20 | 25 | 21 | 1 | 1 | 1 | 1 | 378 |
193 students took ST 311 prior to taking GN 311 in the fall of 2019. 213 students took ST 311 prior to taking GN 311 in the spring of 2020. This table excludes students that took ST 311 during their enrollment in GN 311.
NOTE: Students that may have taken ST 311 more than once are counted only once according to the semester in which their grade points were highest (which generally corresponds to the latest enrollment), and then by latest enrollment if grade points were the same across different semesters.
Term | N | % | N | % |
---|---|---|---|---|
2019 Fall Term | 0 | 0.0% | 67 | 31.5% |
2019 Summer Term 2 | 2 | 1.0% | 0 | 0.0% |
2019 Summer Term 1 | 3 | 1.6% | 3 | 1.4% |
2019 Spring Term | 50 | 25.9% | 51 | 23.9% |
2018 Fall Term | 62 | 32.1% | 45 | 21.1% |
2018 Summer Term 1 | 3 | 1.6% | 2 | 0.9% |
2018 Spring Term | 33 | 17.1% | 23 | 10.8% |
2017 Fall Term | 20 | 10.4% | 10 | 4.7% |
2017 Summer Term 2 | 1 | 0.5% | 0 | 0.0% |
2017 Spring Term | 12 | 6.2% | 9 | 4.2% |
2016 Fall Term | 4 | 2.1% | 2 | 0.9% |
2016 Spring Term | 0 | 0.0% | 1 | 0.5% |
2015 Fall Term | 1 | 0.5% | 0 | 0.0% |
2014 Fall Term | 1 | 0.5% | 0 | 0.0% |
2011 Fall Term | 1 | 0.5% | 0 | 0.0% |
Total | 193 | 100.0% | 213 | 100.0% |
36 students were enrolled in ST 311 while taking GN 311 during the fall of 2019. 28 students are currently enrolled in ST 311 and GN 311 for the spring, 2020.
NOTE: Few students may have also taken ST 311 prior to current ST 311 course enrollment with respect to their GN 311 cohort.
N | % | N | % | |
---|---|---|---|---|
2019 | 36 | 8.2% | 441 | 92.5% |
2020 | 28 | 5.7% | 462 | 94.3% |
The average GPA for students who took ST 311 and GN 311 during Fall 2019 (indicated by the ‘Current (2019)’ row) is 3.0. The average GPA for students who did not take (or are not taking) ST 311 concurrently with GN 311, in 2019 and 2020 (middle row), is 3.3. The overall average GPA including all students (see notes below) is 3.1.
Min | Q1 | Median | Mean | Q3 | Max | N | |
---|---|---|---|---|---|---|---|
Current (2019) | 1.0 | 2.7 | 3.0 | 3.0 | 3.3 | 4.0 | 36 |
Prior | 1.0 | 3.0 | 3.3 | 3.3 | 4.0 | 4.3 | 404 |
Overall | 0.0 | 2.7 | 3.3 | 3.1 | 4.0 | 4.3 | 468 |
The table below displays 2020 in blue. The counts and percentages of correct, incorrect, and ‘No Record’ responses are indicated in the columns below.
N | % | N | % | N | % | N | % | N | % | N | % | |
---|---|---|---|---|---|---|---|---|---|---|---|---|
Q9 | 343 | 71.9% | 114 | 23.9% | 20 | 4.2% | 378 | 77.1% | 95 | 19.4% | 17 | 3.5% |
Q10 | 321 | 67.3% | 139 | 29.1% | 17 | 3.6% | 320 | 65.3% | 154 | 31.4% | 16 | 3.3% |
Both | 246 | 51.6% | 214 | 44.9% | 17 | 3.6% | 268 | 54.7% | 206 | 42.0% | 16 | 3.3% |
Below is a frequency table for questions 9, 10 and both that tabulates correct, incorrect and no record responses by whether or not ST 311 was taken. The first two rows are for 2019 and the second two rows are for 2020. These questions are prior to intervention in the spring 2020 semester so we would roughly expect to see the same trends across 2019 and 2020 assuming similar populations of students.
Correct | Incorrect | No Record | Correct | Incorrect | No Record | Correct | Incorrect | No Record | ||
---|---|---|---|---|---|---|---|---|---|---|
2019 | Not Taken | 175 | 60 | 13 | 169 | 70 | 9 | 125 | 114 | 9 |
Took 311 | 168 | 54 | 7 | 152 | 69 | 8 | 121 | 100 | 8 | |
2020 | Not Taken | 202 | 39 | 10 | 168 | 73 | 10 | 150 | 91 | 10 |
Took 311 | 176 | 56 | 7 | 152 | 81 | 6 | 118 | 115 | 6 |
Using a binomial glm model logodds(BothCorrect) ~ HighestMTH + tookST311 + ST311current
did not yield any significant factors.
Marginal binomial models for the log odds of answering each separate question correctly, using whether or not a student has taken ST 311 as the factor of interest indicated no significant association of taking ST 311 and grades for 2019 on correct answers for each question and both combined. The same results were found for 2020 except for question 10 where the model indicates a significant association between taking ST 311 and correct answers at the .032 significane level using a Wald Test. However, the chi-square test does not indicate significance and accounting for the multiple tests, the association is not likely significant.
‘No Record’ responses were excluded from these models.
Below is the summary output for the binomial model coefficients for question 9 in 2019 along with the chi-square association test. The last two rows are the same models for question 9 in 2020. All other tests of interest are similar in that there is no evidence of statistically significant association between taking ST 311 on answer status.
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.07044141 0.1496025 -7.1552365 8.352878e-13
## tookST311Took 311 -0.06453852 0.2164527 -0.2981645 7.655776e-01
## Number of cases in table: 477
## Number of factors: 2
## Test for independence of all factors:
## Chisq = 1.5042, df = 2, p-value = 0.4714
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.6447061 0.1749043 -9.403462 5.279685e-21
## tookST311Took 311 0.4995737 0.2326595 2.147231 3.177488e-02
## Number of cases in table: 490
## Number of factors: 2
## Test for independence of all factors:
## Chisq = 5.069, df = 2, p-value = 0.0793
Intervention occurred between homework 2 and homework 6. We want to look at any possible impact the intervention may have had. There are a number of questions from homework 6 to consider here. There are binary indicators of correct/incorrect for each and one summary average variable that aggregates across the questions.
For all of the questions, ignoring missing values (students that didn’t complete the question and students that didn’t have a cohort) the first quartile of binary scores is 1. This indicates that 75% of people got an individual question correct. The sample proportion of correct responses for each question is given below as well as the summary stats on the aggregate variable.
means <- full_data %>% group_by(Cohort) %>% select(Cohort, ends_with("bin")) %>%
summarize_if(.predicate = function(x) is.numeric(x), .funs = funs(mean="mean"), na.rm = TRUE)
kable(means[1:2,], col.names = c("Cohort", paste("Q", c(3:10, 15:19)), "Aggregate"), digits = 3) %>%
kable_styling(bootstrap_options = "striped",full_width = T)
Cohort | Q 3 | Q 4 | Q 5 | Q 6 | Q 7 | Q 8 | Q 9 | Q 10 | Q 15 | Q 16 | Q 17 | Q 18 | Q 19 | Aggregate |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Fall2019 | 0.978 | 0.978 | 0.876 | 0.868 | 0.868 | 0.863 | 0.857 | 0.854 | 0.881 | 0.951 | 0.911 | 0.867 | 0.880 | 0.900 |
Spring2020 | 0.969 | 0.966 | 0.921 | 0.906 | 0.901 | 0.864 | 0.841 | 0.846 | 0.890 | 0.959 | 0.931 | 0.927 | 0.921 | 0.921 |
Looking closely at the questions themselves, I’m going to remove question 8, 9, and 10 (Keeping all decimal digits during your calculations and rounding your answer to 3 decimal digits, what is the calculated chi-square value? What is the number for the degrees of freedom? What is the critical value? Round to two decimal places.) as these weren’t things focused on in the intervention.
full_data <- select(full_data, -HW6_Q_8_score_bin, -HW6_Q_9_score_bin, - HW6_Q_10_score_bin)
Conducting a quick comparison of these sample proportions by question using a logistic regression model to get a p-value (assumptions likely not met but still somewhat useful).
full_data <- full_data[!is.na(full_data$Cohort),]
#create indicator of math above 100 level or not
full_data$MTHLevel <- ifelse(full_data$MTHcourse > 200, "200's", "100's")
sum_glm <- function(data, response){
fit <- glm(data[[response]] ~ Cohort, data = data)
means <- aggregate(data[[response]] ~ Cohort, data = data, FUN = mean)
resp <- gregexpr(response, pattern = "(Q_\\d+)")
ret <- c(substring(response, resp[[1]], resp[[1]]+attr(resp[[1]],'match.length')-1), round(c(fit$coefficients, means[,2], means[2,2]-means[1,2], summary(fit)$coefficients[2,4]), 4))
names(ret) <- c("Response", "Intercept", "Beta1", "2019 mean", "2020 mean", "Difference", "p-value")
ret
}
bin_names <- c("HW6_Q_3_score_bin", "HW6_Q_4_score_bin", "HW6_Q_5_score_bin",
"HW6_Q_6_score_bin", "HW6_Q_7_score_bin", "HW6_Q_15_score_bin",
"HW6_Q_16_score_bin", "HW6_Q_17_score_bin", "HW6_Q_18_score_bin",
"HW6_Q_19_score_bin")
lapply(X = bin_names, FUN = sum_glm, data = full_data)
## [[1]]
## Response Intercept Beta1 2019 mean 2020 mean Difference p-value
## "Q_3" "0.9785" "-0.0097" "0.9785" "0.9688" "-0.0097" "0.4166"
##
## [[2]]
## Response Intercept Beta1 2019 mean 2020 mean Difference p-value
## "Q_4" "0.9784" "-0.0125" "0.9784" "0.9659" "-0.0125" "0.3053"
##
## [[3]]
## Response Intercept Beta1 2019 mean 2020 mean Difference p-value
## "Q_5" "0.876" "0.0453" "0.876" "0.9213" "0.0453" "0.0461"
##
## [[4]]
## Response Intercept Beta1 2019 mean 2020 mean Difference p-value
## "Q_6" "0.8679" "0.0385" "0.8679" "0.9064" "0.0385" "0.1058"
##
## [[5]]
## Response Intercept Beta1 2019 mean 2020 mean Difference p-value
## "Q_7" "0.8679" "0.033" "0.8679" "0.9009" "0.033" "0.1704"
##
## [[6]]
## Response Intercept Beta1 2019 mean 2020 mean Difference p-value
## "Q_15" "0.8808" "0.0091" "0.8808" "0.8899" "0.0091" "0.7037"
##
## [[7]]
## Response Intercept Beta1 2019 mean 2020 mean Difference p-value
## "Q_16" "0.9514" "0.0081" "0.9514" "0.9594" "0.0081" "0.6027"
##
## [[8]]
## Response Intercept Beta1 2019 mean 2020 mean Difference p-value
## "Q_17" "0.9111" "0.0198" "0.9111" "0.9308" "0.0198" "0.3278"
##
## [[9]]
## Response Intercept Beta1 2019 mean 2020 mean Difference p-value
## "Q_18" "0.8672" "0.0599" "0.8672" "0.9271" "0.0599" "0.0088"
##
## [[10]]
## Response Intercept Beta1 2019 mean 2020 mean Difference p-value
## "Q_19" "0.8804" "0.0408" "0.8804" "0.9213" "0.0408" "0.0696"
Three of these are significant or marginally signficant and in the direction we’d hope for. That’s a good sign.
We’ll also consider just the students that missed at least one question from the earlier homework assignment. Perhaps they are the ones that will show improvement.
#filter by missing an earlier question or having no record
missed_data <- filter(full_data, bothQscorrect != 1)
lapply(X = bin_names, FUN = sum_glm, data = missed_data)
## [[1]]
## Response Intercept Beta1 2019 mean 2020 mean Difference p-value
## "Q_3" "0.9665" "-0.0242" "0.9665" "0.9423" "-0.0242" "0.2873"
##
## [[2]]
## Response Intercept Beta1 2019 mean 2020 mean Difference p-value
## "Q_4" "0.9719" "-0.036" "0.9719" "0.9359" "-0.036" "0.1135"
##
## [[3]]
## Response Intercept Beta1 2019 mean 2020 mean Difference p-value
## "Q_5" "0.8212" "0.0359" "0.8212" "0.8571" "0.0359" "0.3834"
##
## [[4]]
## Response Intercept Beta1 2019 mean 2020 mean Difference p-value
## "Q_6" "0.8212" "7e-04" "0.8212" "0.8219" "7e-04" "0.9872"
##
## [[5]]
## Response Intercept Beta1 2019 mean 2020 mean Difference p-value
## "Q_7" "0.8156" "7e-04" "0.8156" "0.8163" "7e-04" "0.9874"
##
## [[6]]
## Response Intercept Beta1 2019 mean 2020 mean Difference p-value
## "Q_15" "0.8475" "0.0192" "0.8475" "0.8667" "0.0192" "0.623"
##
## [[7]]
## Response Intercept Beta1 2019 mean 2020 mean Difference p-value
## "Q_16" "0.9438" "-0.0034" "0.9438" "0.9404" "-0.0034" "0.8949"
##
## [[8]]
## Response Intercept Beta1 2019 mean 2020 mean Difference p-value
## "Q_17" "0.8939" "-0.005" "0.8939" "0.8889" "-0.005" "0.8851"
##
## [[9]]
## Response Intercept Beta1 2019 mean 2020 mean Difference p-value
## "Q_18" "0.8079" "0.0847" "0.8079" "0.8926" "0.0847" "0.0346"
##
## [[10]]
## Response Intercept Beta1 2019 mean 2020 mean Difference p-value
## "Q_19" "0.8239" "0.0755" "0.8239" "0.8993" "0.0755" "0.052"
Looking like just the last two questions showed improvement (none went the wrong way) when averaged across things.
To further investigate the effect of the intervention we’ll look at a break down comparing cohorts for correct/incorrect question status by things like:
MTHLevel
)tookST311
)NC_LVL_BOT_DESCR
)STDNT_RACE_IPEDS
)STUDENT_GENDER_IPEDS
)ORIG_ENROLL_STATUS
)RuralStatus
)FirstGenStatus
)First just the means across some combinations.
out2 <- lapply(X = bin_names, FUN = q_means, data = full_data, preds = preds[c(1,2,7,9)])
out2e <- lapply(FUN = nice_it, X= out2)
for(x in 1:length(out2e)){
print(kable(out2e[[x]], digits = 3, col.names = c("MathLevel", "Transfer", "First Gen", "Avg2019", "Avg2020", "n2019", "n2020", "AvgDiff"), caption = paste0("Question ", qs[x], " Summary")) %>%
kable_styling(bootstrap_options = "striped",full_width = T) )
}
MathLevel | Transfer | First Gen | Avg2019 | Avg2020 | n2019 | n2020 | AvgDiff |
---|---|---|---|---|---|---|---|
100’s | New Student | First Gen | 0.909 | 1.000 | 11 | 10 | 0.091 |
200’s | New Student | First Gen | 1.000 | 0.976 | 33 | 42 | -0.024 |
100’s | New Transfer Student | First Gen | 1.000 | 1.000 | 9 | 5 | 0.000 |
200’s | New Transfer Student | First Gen | 1.000 | 0.818 | 8 | 11 | -0.182 |
100’s | New Student | Not First | 0.975 | 0.984 | 80 | 63 | 0.009 |
200’s | New Student | Not First | 0.979 | 0.978 | 190 | 180 | -0.001 |
100’s | New Transfer Student | Not First | 1.000 | 1.000 | 11 | 11 | 0.000 |
200’s | New Transfer Student | Not First | 0.957 | 1.000 | 23 | 17 | 0.043 |
100’s | New Student | Unknown | 1.000 | 0.667 | 3 | 3 | -0.333 |
200’s | New Student | Unknown | 1.000 | 1.000 | 2 | 3 | 0.000 |
100’s | New Transfer Student | Unknown | NA | 1.000 | NA | 3 | NA |
200’s | New Transfer Student | Unknown | 1.000 | 0.500 | 2 | 4 | -0.500 |
MathLevel | Transfer | First Gen | Avg2019 | Avg2020 | n2019 | n2020 | AvgDiff |
---|---|---|---|---|---|---|---|
100’s | New Student | First Gen | 0.909 | 1.000 | 11 | 10 | 0.091 |
200’s | New Student | First Gen | 1.000 | 1.000 | 33 | 42 | 0.000 |
100’s | New Transfer Student | First Gen | 1.000 | 0.600 | 9 | 5 | -0.400 |
200’s | New Transfer Student | First Gen | 1.000 | 0.818 | 8 | 11 | -0.182 |
100’s | New Student | Not First | 0.962 | 0.984 | 79 | 63 | 0.022 |
200’s | New Student | Not First | 0.984 | 0.983 | 190 | 179 | -0.001 |
100’s | New Transfer Student | Not First | 1.000 | 1.000 | 11 | 11 | 0.000 |
200’s | New Transfer Student | Not First | 0.957 | 0.941 | 23 | 17 | -0.015 |
100’s | New Student | Unknown | 1.000 | 0.667 | 3 | 3 | -0.333 |
200’s | New Student | Unknown | 1.000 | 1.000 | 2 | 3 | 0.000 |
100’s | New Transfer Student | Unknown | NA | 1.000 | NA | 3 | NA |
200’s | New Transfer Student | Unknown | 1.000 | 0.500 | 2 | 4 | -0.500 |
MathLevel | Transfer | First Gen | Avg2019 | Avg2020 | n2019 | n2020 | AvgDiff |
---|---|---|---|---|---|---|---|
100’s | New Student | First Gen | 0.636 | 0.667 | 11 | 9 | 0.030 |
200’s | New Student | First Gen | 0.970 | 0.927 | 33 | 41 | -0.043 |
100’s | New Transfer Student | First Gen | 0.778 | 1.000 | 9 | 5 | 0.222 |
200’s | New Transfer Student | First Gen | 0.750 | 0.818 | 8 | 11 | 0.068 |
100’s | New Student | Not First | 0.838 | 0.935 | 80 | 62 | 0.098 |
200’s | New Student | Not First | 0.905 | 0.926 | 190 | 176 | 0.021 |
100’s | New Transfer Student | Not First | 0.909 | 1.000 | 11 | 10 | 0.091 |
200’s | New Transfer Student | Not First | 0.818 | 1.000 | 22 | 17 | 0.182 |
100’s | New Student | Unknown | 0.667 | 1.000 | 3 | 2 | 0.333 |
200’s | New Student | Unknown | 1.000 | 1.000 | 2 | 3 | 0.000 |
100’s | New Transfer Student | Unknown | NA | 0.667 | NA | 3 | NA |
200’s | New Transfer Student | Unknown | 1.000 | 0.667 | 2 | 3 | -0.333 |
MathLevel | Transfer | First Gen | Avg2019 | Avg2020 | n2019 | n2020 | AvgDiff |
---|---|---|---|---|---|---|---|
100’s | New Student | First Gen | 0.636 | 0.750 | 11 | 8 | 0.114 |
200’s | New Student | First Gen | 0.970 | 0.878 | 33 | 41 | -0.092 |
100’s | New Transfer Student | First Gen | 0.778 | 0.800 | 9 | 5 | 0.022 |
200’s | New Transfer Student | First Gen | 0.750 | 0.909 | 8 | 11 | 0.159 |
100’s | New Student | Not First | 0.838 | 0.903 | 80 | 62 | 0.066 |
200’s | New Student | Not First | 0.889 | 0.920 | 190 | 176 | 0.031 |
100’s | New Transfer Student | Not First | 0.909 | 1.000 | 11 | 10 | 0.091 |
200’s | New Transfer Student | Not First | 0.818 | 0.941 | 22 | 17 | 0.123 |
100’s | New Student | Unknown | 0.667 | 1.000 | 3 | 2 | 0.333 |
200’s | New Student | Unknown | 1.000 | 1.000 | 2 | 3 | 0.000 |
100’s | New Transfer Student | Unknown | NA | 0.667 | NA | 3 | NA |
200’s | New Transfer Student | Unknown | 1.000 | 0.667 | 2 | 3 | -0.333 |
MathLevel | Transfer | First Gen | Avg2019 | Avg2020 | n2019 | n2020 | AvgDiff |
---|---|---|---|---|---|---|---|
100’s | New Student | First Gen | 0.636 | 0.556 | 11 | 9 | -0.081 |
200’s | New Student | First Gen | 0.970 | 0.878 | 33 | 41 | -0.092 |
100’s | New Transfer Student | First Gen | 0.778 | 0.800 | 9 | 5 | 0.022 |
200’s | New Transfer Student | First Gen | 0.750 | 0.818 | 8 | 11 | 0.068 |
100’s | New Student | Not First | 0.838 | 0.935 | 80 | 62 | 0.098 |
200’s | New Student | Not First | 0.895 | 0.915 | 190 | 176 | 0.020 |
100’s | New Transfer Student | Not First | 0.909 | 1.000 | 11 | 10 | 0.091 |
200’s | New Transfer Student | Not First | 0.818 | 0.941 | 22 | 17 | 0.123 |
100’s | New Student | Unknown | 0.667 | 1.000 | 3 | 2 | 0.333 |
200’s | New Student | Unknown | 1.000 | 1.000 | 2 | 3 | 0.000 |
100’s | New Transfer Student | Unknown | NA | 0.667 |