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Saturday, April 14

  1. page Sleep edited This Page Has Moved: Late Chronotypes and Early Classes Late chronotypes and early classes Re…

    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
    Introduction
    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.
    (view changes)
    11:20 am

Friday, April 6

  1. page Sleep edited ... < 0.0001 Chronotype and Time Period ... 15), and evening afternoon (16, 17, library…
    ...
    < 0.0001
    Chronotype and Time Period
    ...
    15), and eveningafternoon (16, 17,
    library(Publish)
    setwd("...")
    mydata = read.csv(file="20180405GPAByTimeOfDayWithChronotypeWithTimeType.csv",header=TRUE,row.names="id")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")
    (view changes)
    10:30 am
  2. page Sleep edited ... < 0.0001 Chronotype and Time Period The data was data {20180405GPAByTimeOfDayWithChron…
    ...
    < 0.0001
    Chronotype and Time Period
    The data was data {20180405GPAByTimeOfDayWithChronotypeWithTimeType.csv} was subsetted in
    library(Publish)
    setwd("...")
    ...
    [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]
    (view changes)
    10:28 am
  3. page Sleep edited ... 11.2779 < 0.0001 Chronotype and Time Period The data was subsetted in morning (7,8, and…
    ...
    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 evening (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]
    {ChronotypeAndTimePeriod.png}
    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
    (view changes)
    10:27 am
  4. page Sleep edited ... 0.798187 {ResultsControllingForGender.png} ... slope values. {ResultsControllingForGe…
    ...
    0.798187
    {ResultsControllingForGender.png}
    ...
    slope values.
    {ResultsControllingForGenderTrendLine.png}
    The results trend line:
    ...
    Individual trend lines:
    Trend Line Coefficients:
    Row Column p-value DF Term Value StdErr t-value p-value
    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
    0.0512913
    9
    Time
    0.0486018
    0.0216339
    2.24655
    0.0512913

    intercept 2.611 0.284101 9.19038 <
    2.611
    0.284101
    9.19038
    <
    0.0001
    GPA Middle 60% 0.001727 9 Time 0.0420615 0.0095652 4.39734 0.001727
    0.001727
    9
    Time
    0.0420615
    0.0095652
    4.39734
    0.001727

    intercept 2.89689 0.125612 23.0621 <
    2.89689
    0.125612
    23.0621
    <
    0.0001
    GPA Top 20% 0.476167 9 Time 0.0159039 0.021392 0.743452 0.476167
    0.476167
    9
    Time
    0.0159039
    0.021392
    0.743452
    0.476167

    intercept 3.16824 0.280923 11.2779 <
    3.16824
    0.280923
    11.2779
    <
    0.0001
    References
    1. Terman M, Terman JS. Light therapy for seasonal and nonseasonal depression: efficacy, protocol, safety, and side
    (view changes)
    10:20 am

Thursday, April 5

  1. page Sleep edited ... 0 0 GPA vs Chronotype {20180327AllDataGPA.png} The trend line shows the evening types ob…
    ...
    0
    0
    GPA vs Chronotype
    {20180327AllDataGPA.png}
    The trend line shows the evening types obtain lower grades compared to morning types.
    GPA vs Chronotype by Gender
    {20180327AllDataGPAByGender.png}
    The trend lines show that the effect is more significant for males than females.

    #R Code
    library(ggplot2) #graphics library
    ...
    0.798187
    {ResultsControllingForGender.png}
    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.
    {ResultsControllingForGenderTrendLine.png}
    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.
    {TopMiddleBottom.png}
    The model results are as follows (the red color indicates less than 50 data values):
    Individual trend lines:
    PanesTrend Line CoefficientsCoefficients:
    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
    ...
    GPA Top 20% 0.476167 9 Time 0.0159039 0.021392 0.743452 0.476167
    intercept 3.16824 0.280923 11.2779 < 0.0001
    GPA vs Chronotype
    {20180327AllDataGPA.png}
    The trend line shows the evening types obtain lower grades compared to morning types.
    GPA vs Chronotype by Gender
    {20180327AllDataGPAByGender.png}
    The trend lines show that the effect is more significant for males than females.

    References
    1. Terman M, Terman JS. Light therapy for seasonal and nonseasonal depression: efficacy, protocol, safety, and side
    (view changes)
    7:42 am
  2. page Sleep edited ... 0.798187 {ResultsControllingForGender.png} {ResultsControllingForGenderTrendLine.png} T…
    ...
    0.798187
    {ResultsControllingForGender.png}
    {ResultsControllingForGenderTrendLine.png}
    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.
    {TopMiddleBottom.png}
    The model results are as follows (the red color indicates less than 50 data values):
    Individual trend lines:
    Panes Line CoefficientsLine Coefficients
    Row Column p-value DF Term Value StdErr t-value p-valueColumn p-value DF Term Value StdErr t-value p-value
    GPA BottomBottom 20% 0.0512913 9 Time 0.0486018 0.0216339 2.24655 0.05129130.0512913 9 Time 0.0486018 0.0216339 2.24655 0.0512913
    intercept 2.611 0.284101 9.19038 <2.611 0.284101 9.19038 < 0.0001
    GPA MiddleMiddle 60% 0.001727 9 Time 0.0420615 0.0095652 4.39734 0.0017270.001727 9 Time 0.0420615 0.0095652 4.39734 0.001727
    intercept 2.89689 0.125612 23.0621 <2.89689 0.125612 23.0621 < 0.0001
    GPA TopTop 20% 0.476167 9 Time 0.0159039 0.021392 0.743452 0.4761670.476167 9 Time 0.0159039 0.021392 0.743452 0.476167
    intercept 3.16824 0.280923 11.2779 <3.16824 0.280923 11.2779 < 0.0001
    GPA vs Chronotype
    {20180327AllDataGPA.png}
    (view changes)
    7:36 am

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