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Late chronotypes and early classes
Researchers: Andrew SID Lang, Philip P Nelson, Moses Satralkar, Ailin Li, Claire E Ferguson, Laura A Kaneta

Data Collection and Curation
We used a standard MEQ (Morningness-Eveningness Questionnaire) instrument [1] programmed into a Google Form to collect responses from several first semester freshmen level courses (**what were they?**) during the first few weeks of class. This resulted in a dataset of 650 responses.

We curated our data by removing all non-freshmen, all entries not aged 17-19, and several duplicates (student who were in more than one course that we surveyed).

When we wrote the questionnaire, we left the answers free response. This resulted in some non-standard responses for questions 11, 12, and 19. We conjecture that a few students who felt a little torn between two adjoining categories entered a value between the two standard responses. We ended up curating several zeros to ones for question 19 but decided to leave the rest of the data as originally entered: question 11 has 5 ones, 13 threes, and 15 fives; question 12 had 24 ones and 13 fours; and question 19 had 47 threes and 9 fives.

Once the semester had ended we collected the grades of these students and worked out their overall semester GPA and their GPAs for hourly bins corresponding to class start times: 7:00-7:59 AM, 8:00-8:59 AM, 9:00-9:59 AM, 10:00-10:59 AM, 11:00-11:59 AM, 12:00-12:59 PM, 1:00-1:59 PM, 2:00-2:59 PM, 3:00-3:59 PM, 4:00-4:59 PM, 5:00-5:59 PM, and 6:00-6:59 PM. We did not include grades from classes from which the student withdrew and for classes that had different start times on different days we took the time from the day with the longest class period.

This left a final data file with 402 unique records that is ready for modeling.

Data Analysis
Our dataset consists of MEQ Scores and first-semester GPAs by class starting time of 402 first-time college freshmen aged 17-19. Scores can range from 16-86; however our scores range from 17-68 with the following distribution between types [1]:


 * Type || Range || N || % || Female || Male ||
 * definite evening || 16-30 || 12 || 3% || 7 || 5 ||
 * moderate evening || 31-41 || 95 || 24% || 65 || 30 ||
 * intermediate || 42-58 || 258 || 64% || 171 || 87 ||
 * moderate morning || 59-69 || 37 || 9% || 22 || 15 ||
 * definite morning || 70-86 || 0 || 0% || 0 || 0 ||


 * GPA vs Chronotype**

The trend line shows the evening types obtain lower grades compared to morning types.


 * GPA vs Chronotype by Gender**

The trend lines show that the effect is more significant for males than females. code library(ggplot2) #graphics library setwd("C://...") mydata = read.csv(file="20180327 17-19 yrs Ready for Analysis.csv",header=TRUE,row.names="id") summary(mydata)
 * 1) R Code

ggplot(mydata, aes(x=Total, y=GPA)) + geom_point(color='#2980B9', size = 4) + geom_smooth(method=lm, color='#2C3E50') #plotting the data

GPAlmAll <- lm(GPA ~ Total + Sex + US.Resident + College, data=mydata) summary (GPAlmAll) [output] Call: lm(formula = GPA ~ Total + Sex + US.Resident + College, data = mydata)

Residuals: Min     1Q  Median      3Q     Max -2.8381 -0.3453 0.1963  0.5524  1.1150

Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept)                    2.784120   0.254341  10.946  < 2e-16 *** Total                          0.012216   0.004294   2.845  0.00468 ** Sex                           -0.129858   0.083112  -1.562  0.11899 US.Resident                   -0.105936   0.143670  -0.737  0.46134 CollegeBusiness               -0.007007   0.124435  -0.056  0.95513 CollegeEducation               0.103880   0.153129   0.678  0.49793 CollegeNursing                 0.110656   0.132337   0.836  0.40357 CollegeScience and Engineering -0.139757  0.103054  -1.356  0.17583 CollegeTheology and Ministry   0.123386   0.146178   0.844  0.39914 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.7471 on 393 degrees of freedom Multiple R-squared: 0.04753,   Adjusted R-squared:  0.02814 F-statistic: 2.451 on 8 and 393 DF, p-value: 0.01342 [output]

GPAlmGender <- lm(GPA ~ Total + Sex, data=mydata) summary(GPAlmGender) [output] Call: lm(formula = GPA ~ Total + Sex, data = mydata)

Residuals: Min     1Q  Median      3Q     Max -2.9894 -0.3603 0.2018  0.5513  1.0066

Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 2.680268   0.205459  13.045  < 2e-16 *** Total       0.012391   0.004282   2.894  0.00402 ** Sex        -0.170118   0.078754  -2.160  0.03136 * --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.748 on 399 degrees of freedom Multiple R-squared: 0.03072,   Adjusted R-squared:  0.02586 F-statistic: 6.323 on 2 and 399 DF, p-value: 0.001979 [output]

confint(GPAlmGender, level=0.95) # CIs for model parameters [output] 2.5 %     97.5 % (Intercept)  2.276350120  3.08418664 Total       0.003972952  0.02080941 Sex        -0.324942073 -0.01529337 [output]

GPAlm <- lm(GPA ~ Total, data=mydata) summary(GPAlm) [output] Call: lm(formula = GPA ~ Total, data = mydata)

Residuals: Min     1Q  Median      3Q     Max -2.9293 -0.3643 0.1778  0.5857  1.0130

Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 2.636441  0.205390  12.836  < 2e-16 *** Total      0.012090   0.004299   2.812  0.00517 ** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.7514 on 400 degrees of freedom Multiple R-squared: 0.01939,   Adjusted R-squared:  0.01693 F-statistic: 7.908 on 1 and 400 DF, p-value: 0.005165 [output]

lm <- lm(X7am ~ Total + Sex, data=mydata) summary(lm) confint(lm, level=0.95) # CIs for model parameters [output] Call: lm(formula = X7am ~ Total + Sex, data = mydata)
 * 1) Now do just GPA vs. Total Score for all times.

Residuals: Min     1Q  Median      3Q     Max -3.1128 -0.3263 0.5305  0.7239  1.0268

Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 2.54713    0.52732   4.830 4.36e-06 *** Total       0.01551    0.01077   1.440    0.153 Sex        -0.07029    0.19281  -0.365    0.716 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.005 on 112 degrees of freedom (287 observations deleted due to missingness) Multiple R-squared: 0.01849,   Adjusted R-squared:  0.0009604 F-statistic: 1.055 on 2 and 112 DF, p-value: 0.3517

2.5 %    97.5 % (Intercept)  1.502308773 3.59195336 Total      -0.005830429 0.03685108 Sex        -0.452315382 0.31174105 [output] code

GPA when controlling for gender
 * Time || N || Slope || 95% CI || p-value ||
 * 7am || 113 || 0.01551 || -0.005830429 - 0.03685108 || 0.153 ||
 * 8am || 224 || 0.017807 || 0.001105433 - 0.03450847 || 0.0368 ||
 * 9am || 325 || 0.011562 || -0.001864789 - 0.02498809 || 0.0912 ||
 * 10am || 235 || 0.013259 || 0.0008523036 - 0.02566505 || 0.0363 ||
 * 11am || 18 || 0.01928 || -0.01463043 - 0.05318793 || 0.2468 ||
 * 12pm || 239 || 0.008412 || -0.005886458 - 0.02271084 || 0.248 ||
 * 1pm || 272 || -0.002481 || -0.01588811 - 0.0109260 || 0.716 ||
 * 2pm || 326 || 0.011813 || 0.001633317 - 0.02199244 || 0.0231 ||
 * 3pm || 238 || 0.017493 || 0.003886605 - 0.03110015 || 0.012 ||
 * 4pm || 46 || 0.006865 || -0.02266914 - 0.03639984 || 0.642 ||
 * 5pm || 31 || -0.003902 || -0.03647586 - 0.02867283 || 0.808 ||
 * 6pm || 39 || 0.004018 || -0.0275687 - 0.03560398 || 0.798187 ||

The model slopes (size of effect of MEQ score on GPA controlled by Gender) by class start time were analysed. The color is size of confidence interval and the label is the number of data points used to create the slope values.

The results trend line: This shows that MEQ scores are more significant for early course than for later ones.
 * Panes |||| Line |||||||||| Coefficients ||
 * Row || Column || p-value || DF || Term || Value || StdErr || t-value || p-value ||
 * Slope || Time || 0.0379781 || 10 || Time || -0.0012825 || 0.0005367 || -2.3897 || 0.0379781 ||
 * || intercept || 0.026001 || 0.0069596 || 3.73598 || 0.0038721 ||

The data was split by MEQ score into the top and bottom 20%, leaving 60% in the middle. Then average GPA by class starting time was analysed for each group.
 * More Analysis**



The model results are as follows (the red color indicates less than 50 data values): Individual trend lines:

Trend Line Coefficients:
 * Row Column || p-value || DF || Term || Value || StdErr || t-value || p-value ||
 * GPA Bottom 20% || 0.0512913 || 9 || Time || 0.0486018 || 0.0216339 || 2.24655 || 0.0512913 ||
 * intercept ||  ||   ||   || 2.611 || 0.284101 || 9.19038 || < 0.0001 ||
 * GPA Middle 60% || 0.001727 || 9 || Time || 0.0420615 || 0.0095652 || 4.39734 || 0.001727 ||
 * intercept ||  ||   ||   || 2.89689 || 0.125612 || 23.0621 || < 0.0001 ||
 * GPA Top 20% || 0.476167 || 9 || Time || 0.0159039 || 0.021392 || 0.743452 || 0.476167 ||
 * intercept ||  ||   ||   || 3.16824 || 0.280923 || 11.2779 || < 0.0001 ||

The was subsetted in morning (7,8, and 9), middle-of-the-day (11, 12, 13, 14, and 15), and afternoon (16, 17, and18) classes. Then we used R to find the relationship between GPA and Chronotype for each subset.
 * Chronotype and Time Period**

code library(Publish)

setwd("...")

mydata = read.csv(file="20180405GPAByTimeOfDayWithChronotypeWithTimeType.csv", header=TRUE,row.names="id")

A1 <- subset(mydata,TimePeriod=="Morning" & Chronotype=="definite evening") A2 <- subset(mydata,TimePeriod=="Morning" & Chronotype=="intermediate") A3 <- subset(mydata,TimePeriod=="Morning" & Chronotype=="moderate evening") A4 <- subset(mydata,TimePeriod=="Morning" & Chronotype=="moderate morning") B1 <- subset(mydata,TimePeriod=="Middle of the Day" & Chronotype=="definite evening") B2 <- subset(mydata,TimePeriod=="Middle of the Day" & Chronotype=="intermediate") B3 <- subset(mydata,TimePeriod=="Middle of the Day" & Chronotype=="moderate evening") B4 <- subset(mydata,TimePeriod=="Middle of the Day" & Chronotype=="moderate morning") C1 <- subset(mydata,TimePeriod=="Afternoon" & Chronotype=="definite evening") C2 <- subset(mydata,TimePeriod=="Afternoon" & Chronotype=="intermediate") C3 <- subset(mydata,TimePeriod=="Afternoon" & Chronotype=="moderate evening") C4 <- subset(mydata,TimePeriod=="Afternoon" & Chronotype=="moderate morning")

ci.mean(A1$GPA) ci.mean(A2$GPA) ci.mean(A3$GPA) ci.mean(A4$GPA) ci.mean(B1$GPA) ci.mean(B2$GPA) ci.mean(B3$GPA) ci.mean(B4$GPA) ci.mean(C1$GPA) ci.mean(C2$GPA) ci.mean(C3$GPA) ci.mean(C4$GPA)

[output] > ci.mean(A1$GPA) mean CI-95% 2.56 [1.74;3.39] > ci.mean(A2$GPA) mean CI-95% 3.19 [3.10;3.29] > ci.mean(A3$GPA) mean CI-95% 2.94 [2.76;3.12] > ci.mean(A4$GPA) mean CI-95% 3.43 [3.25;3.60] > ci.mean(B1$GPA) mean CI-95% 3.19 [2.86;3.51] > ci.mean(B2$GPA) mean CI-95% 3.38 [3.31;3.44] > ci.mean(B3$GPA) mean CI-95% 3.20 [3.07;3.32] > ci.mean(B4$GPA) mean CI-95% 3.49 [3.35;3.63] > ci.mean(C1$GPA) mean CI-95% 3.15 [2.44;3.86] > ci.mean(C2$GPA) mean CI-95% 3.56 [3.45;3.66] > ci.mean(C3$GPA) mean CI-95% 3.36 [3.12;3.60] > ci.mean(C4$GPA) mean CI-95% 3.63 [3.40;3.87] [output] code



The results show a typical increase of GPA for all chronotypes as the day goes on but the rate of increase is, as expected, dependent on chronotype.