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 17-19 yrs Freshmen Only.

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.

#R 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)
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]
#Now do just GPA vs. Total Score for all times.
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)
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]
#...

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:

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

This shows that MEQ scores are more significant for early course than for later ones.

More Analysis
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.

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

Chronotype and Time Period
The data 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.

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]

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.

References

1. Terman M, Terman JS. Light therapy for seasonal and nonseasonal depression: efficacy, protocol, safety, and side
effects. CNS Spectrums, 2005;10:647-663. (Downloadable at www.cet.org)

## This Page Has Moved: Late Chronotypes and Early Classes

## 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 17-19 yrs Freshmen Only.

## 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]:GPA vs ChronotypeThe trend line shows the evening types obtain lower grades compared to morning types.

GPA vs Chronotype by GenderThe trend lines show that the effect is more significant for males than females.

GPA when controlling for gender

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:

More AnalysisThe 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.

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

Individual trend lines:

Trend Line Coefficients:

Chronotype and Time PeriodThe data 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.

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.

## References

1. Terman M, Terman JS. Light therapy for seasonal and nonseasonal depression: efficacy, protocol, safety, and sideeffects. CNS Spectrums, 2005;10:647-663. (Downloadable at www.cet.org)