HS2000Fall2017ClassSurveyTable1.HS2000studentde.docx

HS 2000 Fall 2017 Class Survey
Table 1. HS2000 student demographic
characteristics
n %percent
Sample size 350
Sex
Females
Males
244
106
69.7%
30.3%
Majors
Biomedical, Diagnostic, and Therapeutic
Sciences
3 0.9%
Wellness and Health Promotion 11 3.1 %
Health Sciences Majors
• Exercise Science
• Integrative & Holistic Medicine
• Nutrition concentration
• Pre-health Professional
• Pre-PT
• Undecided

26
18
29
81
84
27
7.4%
5.1%
8.3%
23.1%
24.0%
7.7%
Other/Undecided (not Health Sciences) 71 20.3%
Living situation
• Off-campus (not with parents/guardians)
• Off-campus with parents/guardians.
• On-campus (dorms/residence halls,
campus apartments, fraternity/sorority
house).
49
198
103
14.0%
56.6%
29.4%
Table 2. HS2000 student characteristics of age,

credits, and GPA
n Mean ± st.dev Range
Age (years) 350 20.2± 4.3 17-64
Credits taken Fall 2017 350 15.0± 2.8 4-28
GPA 350 3.4 ± 0.5 1.0-4.8
Table 3a. HS2000 student sleepbehaviors and
caffeine intake
Sleep Measures
n Mean ± st.dev Range
Hours of Sleep1 350 6.8 ± 1.32 3-14
Sleep Quality Score2 324 6.1 ± 2.6 0-17
Caffeine Intake
Caffeine Survey
Score3
350 5.94± 4.5 0-26
Table 3b. Frequency and type of caffeine intake
among HS2000 students
Caffeinate Intake and Sources4 (n=350)
Never n(%) Lowintake n(%) Moderate Intake
n(%)
High Intake n(%)
Coffee 131 (37.4%) 126 (36%) 74 (21%) 19
(5.4%)
Espresso/

Cappuccino
210 (60%) 127 (36.3%) 11 (3.1%) 2 (0.6%)
Tea (black or
green)
180 (51.4%) 147 (42.0%) 21 (6.0%) 2 (0.6%)
Carbonated
Beverages
(Soda/pop)
138 (39.4%) 170 (48.6%) 33 (9.4%) 9 (2.6%)
Energy Drinks 267 (76.3%) 78 (22.3%) 4 (1.1%)
1 (0.3%)
Supplements or
Medications
279 (79.7%) 59 (16.9%) 11 (3.1%) 1 (0.3%)
1 Based on students’ self-reported hours of
sleep
2 PittsburghSleep Quality Index: 6
subcomponent scores were calculated from the
questions.
Ranges of the subcomponents were 0-3. All
subcomponents were added to create a final
Sleep
Quality Score. Scores could range from 0-18.
Higher scores indicate lower quality of
sleep
3 Caffeine Survey Score: All answers to

questions in Lynch’s Caffeine Survey were given
a numerical
value 0-8 (0=Never, 8=5 or more servings per day).
Scores for each question were added for a
total
Score. Ranges could be from 0 to 48. Higher
scores indicate higher caffeine intake.
4 Answers for the Caffeine Survey were combined to
create 4 categories as follows
• Never: Reported “Never” consuming those
beverages
• Low Intake: Reported consuming a
beverage/supplement less than once per week, 2-
3 per
week, or 4-6 per week.
• Moderate Intake: Reported consuming a
beverage/supplement once or twice daily.
• High Intake: Reported consuming a
beverage/supplement 3-5 or more times per
day.
Table 4. Correlations between hours of sleep,
sleepquality scores, and caffeine survey scores
Hours of Sleep Sleep Quality Scores
Caffeine Score
r-value p-value r-value p-value r-value p-
value
Hours of Sleep 1 -0.549 0.01 -0.210
0.01
Sleep Quality Scores -0.549 0.01 1 0.301
.01

Caffeine Survey Scores -0.210 0.01 0.301 .001 1
Table 5. Comparisons of sleepand caffeine intake
between male and female HS2000 students
Males Females
n Mean ± st.dev n Mean ± st.dev
p-value
Hours of Sleep 106 7.0 ± 1.5 244 6.8 ±1.2
0.201
Sleep Quality
Scores
99 5.7 ± 2.5 225 6.2 ±2.6 0.089
Caffeine Survey
Scores
106 5.9 ± 4.9 244 5.9 ± 4.2 0.999
Table 6. Comparisons of sleepand caffeine intake
among students who live on-campus and
off-campus
On-campus Off-campus
n Mean ± st.dev n Mean ± st.dev
p-value
Hours of Sleep 103 7.1 ± 1.2 247 6.7 ±1.3
0.016
Sleep Quality
Scores
96 6.0 ± 2.6 228 6.1 ±2.6 0.663

Caffeine Survey
Scores
103 5.3 ± 4.9 247 6.1 ± 4.7 0.135
Figure 1. Relationship between hours of sleep
and caffeine intake
Figure 2. Relationship between sleepquality and
caffeine intake
r-value= -0.210
p-value= <0.01
r-value= 0.301
p-value= <0.01
HS2000 Class Survey Questions
THE FOLLOWING QUESTIONS ASK FOR INFORMATION
ABOUT YOURSELF.
What is your sex? Male or Female

How many credits are you enrolled in this
semester?
What is your approximate GPA? If you are
new to OU, please put your graduating high
school GPA.
How old are you? (years)
What is your major?
• Health Sciences (pre-health professional)
• Health Sciences, Pre-PT
• Health Sciences, Exercise Science concentration
• Health Sciences, Nutrition concentration
• Health Sciences, Integrative and holistic medicine
• Wellness and health promotion
• Biomedical, Diagnostic, and Therapeutic Sciences
• Other (Please state)
• Undecided
Why are you taking HS201? (select one)
• It is required for my major.
• To complete the Gen Ed for Science and Technology
• Other
Where do you currently live?
•On-Campus (residencehall; frat/sorority house; campus
apts, etc.)
•Off-Campus in parent/guardian home

•Off-Campus NOT with parent/guardian
THE FOLLOWING QUESTIONS RELATE TO YOUR
USUAL SLEEP HABITS DURING THE PAST
MONTH ONLY. YOUR ANSWERS SHOULD
INDICATE THE MOST ACCURATE REPLY
FOR THE
MAJORITY OF DAYS ANDNIGHTS IN THE PAST
MONTH.
*These questions are from the PittsburghSleep Quality
Index, a questionnaire developed
and tested by Buysse and colleagues (1989).
1. During the past month, when have you usually
gone to bed at night? PLEASE INDICATE
AM or PM! For example, 2:00AM or 11:00PM
2. During the past month, how long (in minutes)
has it usually taken you to fall asleep at
night? _______________minutes
3. During the past month, when have you usually
gotten up in the morning? Please write
AM. For example: 6:45AM or 10:00AM.
4. During the past month, how many hours of
actual sleepdid you get at night? (This
may
be different than the number of hours you
spend in bed.)Please writeyour answer in
HOURS.

FOR EACH OF THE REMAINING QUESTIONS,
SELECT THE ONE BEST RESPONSE.
5. During the past month, how oftenhave you had
trouble sleeping because you cannot get
to sleepwithin 30 minutes?
Not during the past month
Less than once a week
Onceor twice a week
Three or moretimes a week
trouble sleeping because you wakeup
in the middle of the nightor earlymorning?
Onceor twice a week
Three or more times a week
trouble sleeping because you have to
get up to use the bathroom?
Onceor twice a week
trouble sleeping because you cannot
breathe comfortably?
Onceor twice a week

trouble sleeping because you cough or
snore loudly?
Onceor twice a week
trouble sleeping because you feel too
cold?
Onceor twice a week
trouble sleeping because you feel too
hot?
Onceor twice a week
trouble sleeping because you had bad
dreams?
Onceor twice a week

trouble sleeping because you have
pain?
Onceor twice a week
trouble sleeping for otherreasons not
covered in the previous questions?
Onceor twice a week
15. During the past month, how oftenhave you taken
medicine (prescribed or “over the
counter”) to help you sleep?
Onceor twice a week
trouble staying awake while driving,
eating meals, or engaging in social activity?
Onceor twice a week
17. During the past month, how much of a
problem has it been for you to keep up enough
enthusiasm to get things done?

Onceor twice a week
THE NEXT QUESTIONS WILL ASK YOUABOUT
caffeinated BEVERAGE INTAKE*
*These questions were developed by Dr. Lynch, a
Health Sciences professor, specifically for
this class survey.
1. Please indicate your average total use, during
the past TWO WEEKS of Regular coffee (1
serving=8oz)
Never
Less than once per week
2-3 per week
4-6 per week
One per day
2 servings per day
3 servings per day
4 servings per day
5 or more servings per day
the past TWO WEEKS of Espresso or
cappuccino (noteif you have a DOUBLE espresso or
cappuccino please count it as TWO
servings)
Never
2-3 per week
4-6 per week

One per day
2 servings per day
3 servings per day
4 servings per day
the past TWO WEEKS of Regular or
Caffeinated tea, black or green tea (1
serving=8oz)
Never
2-3 per week
4-6 per week
One per day
2 servings per day
3 servings per day
4 servings per day
the past TWO WEEKS of Caffeinated
regular or diet pop/soda (e.g. Pepsi, Coke, Faygo,
Mountain Dew, Dr. Pepper) (1
serving=12oz)
Never
2-3 per week
4-6 per week
One per day
2 servings per day
3 servings per day

4 servings per day
the past TWO WEEKS of Energy drinks
(e.g Monster, Red Bull)(1 serving=1 can)
Never
2-3 per week
4-6 per week
One per day
2-3 per week
4-6 per week
2 servings per day
3 servings per day
4 servings per day
6. Finally, in the past two weeks, how oftendid
you take supplements or medications
containing caffeine?
Never
2-3 per week
4-6 per week
One per day
2-3 per week
4-6 per week
2 servings per day
3 servings per day
4 servings per day

What is a P-value?
I have found that many students are unsure about the
interpretation of P-values and other
concepts related to tests of significance. These ideas are used
repeatedly in various
applications so it is important that they be understood. I will
explain the concepts in
general terms first, then their application in the problem of
assessing normality.
We wish to test a null hypothesis against an alternative
hypothesis using a dataset. The two
hypotheses specify two statistical models for the process that
produced the data. The
alternative hypothesis is what we expect to be true if the null
hypothesis is false. We
cannot prove that the alternative hypothesis is true but we may
be able to demonstrate that
the alternative is much more plausible than the null hypothesis
given the data. This
demonstration is usually expressed in terms of a probability (a
P-value) quantifying the
strength of the evidence against the null hypothesis in favor of
the alternative.
We ask whether the data appear to be consistent with the null
hypothesis or whether it is
unlikely that we would obtain data of this kind if the null
hypothesis were true, assuming
that at least one of the two hypotheses is true. We address this
question by calculating the
value of a test statistic, i.e., a particular real-valued function of

the data. To decide whether
the value of the test statistic is consistent with the null
hypothesis, we need to know what
sampling variability to expect in our test statistic if the null
hypothesis is true. In other
words, we need to know the null distribution, the distribution of
the test statistic when the
null hypothesis is true. In many applications, the test statistic is
defined so that its null
distribution is a “named” distribution for which tables are
widely accessible; e.g., the
standard normal distribution, the Binomial distribution with n =
100 and p = 1/2, the t
distribution with 4 degrees of freedom, the chi-square
distribution with 23 degrees of
freedom, the F distribution with 2 and 20 degrees of freedom.
Now, given the value of the test statistic (a number), and the
null distribution of the test
statistic (a theoretical distribution usually represented by a
probability density), we want to
see whether the test statistic is in the middle of the distribution
(consistent with the null
hypothesis) or out in a tail of the distribution (making the
alternative hypothesis seem more
plausible). Sometimes we will want to consider the right-hand
tail, sometimes the left-hand
tail, and sometimes both tails, depending on how the test
statistic and alternative hypothesis
are defined. Suppose that large positive values of the test
statistic seem more plausible
under the alternative hypothesis than under the null hypothesis.
Then we want a measure
of how far out our test statistic is in the right-hand tail of the
null distribution. The P-value
provides a measure of this distance. The P-value (in this

situation) is the probability to the
right of our test statistic calculated using the null distribution.
The further out the test
statistic is in the tail, the smaller the P-value, and the stronger
the evidence against the null
hypothesis in favor of the alternative.
The P-value can be interpreted in terms of a hypothetical
repetition of the study. Suppose
the null hypothesis is true and a new dataset is obtained
independently of the first dataset
but using the same sampling procedure. If the new dataset is
used to calculate a new value
of the test statistic (same formula but new data), what is the
probability that the new value
will be further out in the tail (assuming a one-tailed test) than
the original value? This
probability is the P-value.
The P-value is often incorrectly interpreted as the probability
that the null hypothesis is
true. Try not to make this mistake. In a frequentist
interpretation of probability, there is
nothing random about whether the hypothesis is true, the
randomness is in the process
generating the data. One can interpret “the probability that the
null hypothesis is true” using
subjective probability, a measure of one’s belief that the null
hypothesis is true. One can
then calculate this subjective probability by specifying a prior
probability (subjective belief
before looking at the data) that the null hypothesis is true, and
then use the data and the

model to update one’s subjective probability. This is called the
Bayesian approach because
Bayes’ Theorem is used to update subjective probabilities to
reflect new information.
When reporting a P-value to persons unfamiliar with statistics,
it is often necessary to use
descriptive language to indicate the strength of the evidence. I
tend to use the following
sort of language. Obviously the cut-offs are somewhat arbitrary
and another person might
use different language.
P > 0.10 No evidence against the null hypothesis. The data
appear to be
consistent with the null hypothesis.
0.05 < P < 0.10 Weak evidence against the null hypothesis in
favor of the alternative.
0.01 < P < 0.05 Moderate evidence against the null hypothesis
in favor of the
alternative.
0.001 < P < 0.01 Strong evidence against the null hypothesis in
favor of the
alternative.
P < 0.001 Very strong evidence against the null hypothesis in
favor of the
alternative.
In using this kind of language, one should keep in mind the
difference between statistical
significance and practical significance. In a large study one
may obtain a small P-value

even though the magnitude of the effect being tested is too
small to be of importance (see
the discussion of power below). It is a good idea to support a
P-value with a confidence
interval for the parameter being tested.
A P-value can also be reported more formally in terms of a
fixed level α test. Here α is a
number selected independently of the data, usually 0.05 or 0.01,
more rarely 0.10. We
reject the null hypothesis at level α if the P-value is smaller
than α, otherwise we fail to
reject the null hypothesis at level α. I am not fond of this kind
of language because it
suggests a more definite, clear-cut answer than is often
available. There is essentially no
difference between a P-value of 0.051 and 0.049. In some
situations it may be necessary
to proceed with some course of action based on our belief in
whether the null or alternative
hypothesis is true. More often, it seems better to report the P-
value as a measure of
evidence.
A fixed level α test can be calculated without first calculating a
P-value. This is done by
comparing the test statistic with a critical value of the null
distribution corresponding to the
level α. This is usually the easiest approach when doing hand
calculations and using
statistical tables, which provide percentiles for a relatively
small set of probabilities. Most
statistical software produces P-values which can be compared
directly with α. There is no
need to repeat the calculation by hand.

Fixed level α tests are needed for discussing the power of a test,
a useful concept when
planning a study. Suppose we are comparing a new medical
treatment with a standard
treatment, the control. The null hypothesis is that of no
treatment effect (no difference
between treatment and control). The alternative hypothesis is
that the treatment effect
(mean difference of treatment minus control using some
outcome variable) is positive. We
want to have good chance of reporting a small P-value assuming
the alternative hypothesis
is true and the magnitude of the effect is large enough to be of
practical importance. The
power of a level α test is defined to be the probability that the
null hypothesis will be
rejected at level α (i.e., the P-value will be less than α)
assuming the alternative hypothesis
is true. The power generally depends on the variability of the
data (lower variance, higher
power), the sample size (higher n, higher power), and the
magnitude of the effect (larger
effect, higher power).
Assessing normality using the Ryan-Joiner test.
Null hypothesis: the data {x1, ..., xn} are a random sample of
size n from a normal
distribution.
Alternative hypothesis: the data are a random sample from
some other distribution.

Test statistic: r = the correlation between the data and the
normal scores.
The normal scores are defined by the following graph.
Normal score
corresponding to xi
Standard
normal density
Shaded area equals
{rank(xi ) - 3/8}/{n + 1/4}
Rationale: If the data are a sample from a normal distribution
then the normal probability
plot (plot of normal scores against the data) will be close to a
straight line, and the
correlation r will be close to 1. If the data are sampled from a
non-normal distribution then
the plot may show a marked deviation from a straight line,
resulting in a smaller correlation
r. Smaller values of r are therefore regarded as stronger
evidence against the null
hypothesis.
Null distribution of r: I do not know whether this distribution
has a name. We might call it
the Ryan-Joiner distribution, corresponding to the name of the
test. The density will be
skewed to the left, with most of the probability close to 1, as in
the picture below.
P-value: The probability to the left of the observed correlation

r calculated using the null
distribution; i.e., the area under the density to the left of r. You
do not need to know how
to calculate this. Minitab does the calculation for you.
Interpretation: If you want to use simple descriptive language,
you can use the table above.
The strength of evidence is described directly in terms of the P-
value.
r
density for the null
distribution of r
1
P-value =
shaded area
Total area
under curve is
one.
“Health Behaviors in HS2000 Students”
Winter 2018
Assignment formatting guidelines:
· Your report should contain the following sections numbered
and titled as shown below. Do NOT submit your assignment in
essay format, type your answers directly into the sections
below.
· In addition to the points below, you will be graded on the
quality of writing, which includes use of proper grammar,
spelling, and clarity of thought in writing.

· Reports must be typed using Times New Roman 12 point font
or 11 point Arial, Calibri, or Cambria with 1” margins.
· Reports must be uploaded to moodle by the due date as a Word
document (.doc, .docx) or as a rich text file (.rtf). Any other
formats will not be graded and late penalties will apply.
· APA format must be used for all in-text citations and for the
formatting of the references section.
Learning Objectives
In completing this assignment students will demonstrate that
they can:
1. Create a testable hypothesis and interpret statistical data to
identify hypothesis support.
2. Explain and critique study design and variable measurement.
3. Describe sample characteristics and identify similarities with
and differences between the sample and the study population.
4. Apply recommendations to the interpretation of sample
characteristics.
5. Interpret p-values and r-values to explain statistical
relationships between variables.
6. Develop conclusions about a research study grounded in the
data collected and analyzed.
7. Identify health risks and health benefits of sleep behaviors
and caffeine consumption and draw conclusions for
recommendations.
TOPIC OF RESEARCH REPORT #1
This Research Report will examine the relationship between
sleep characteristics and caffeine intake in HS2000 students.
Data collected from a survey of HS2000 students, was analyzed

and the results posted on moodle for you to analyze in exploring
this concept.
Winter 2018
1
STEP 1: HYPOTHESIS FORMATION (6 points total)
A hypothesis is a statement that predicts a relationship between
variables. It is specific and testable. A hypothesis is written in
one sentence and includes both independent and dependent
variables. In this report, you will write a hypothesis that
predicts how caffeine intake affects sleep (or, how sleep affects
caffeine intake).
In one sentence, write your hypothesis about the relationship
between sleep behaviors and caffeine consumption. Be specific
about what variables your hypothesis includes and how you
expect the independent variable to influence/change the
dependent variable. At the end of the assignment, you will
assess whether or not your hypothesis was supported by the
data, so please make sure your hypothesis fits with the content
of this assignment and the variables that were measured.
1. Write your hypothesis. (4 points)
2. What is the independent variable? (1 point)
3. What is the dependent variable? (1 point)
STEP 2: METHODS (20 points total)
The methods of a research study are how data are

measured/collected. In our class survey study, data was
collected from students taking HS2000 in the Winter of 2018. In
this section you will describe and explain how the data were
collected and identify strengths and weaknesses of the methods.
4. Is this class survey study cross-sectional, longitudinal
(prospective), or experimental? Describe the strengths and
weaknesses of this type of study? (6 points)
5. The class survey included two questionnaires: The Pittsburgh
Sleep Quality Questionnaire1 and Dr. Lynch’s Caffeine
Consumption survey, which was made up by a Health Sciences
professor for this survey. For each survey, describe the survey,
WHAT variables were measured, and HOW they were measured.
Do not repeat the questions and answer choices, give an overall
description of the surveys. (6 points)
5.a. Pittsburgh Sleep Quality Questionnaire
5.b. Caffeine Consumption Survey
6. What is one strength and one weakness of the survey methods
used to explore the relationship between sleep and caffeine? (4
points)
7. Identify one way that our study could be made stronger.
Consider either better measurements for the variables or a
different study type. Explain why this is stronger. (4 points)
STEP3: RESULTS (44 points total)
The results section is where researchers describe their sample,
their findings, and explain statistical relationships. In this
section you will also evaluate generalizability of the findings,

compare reported student behaviors with recommendations, and
identify health risks or benefits of the behaviors.
8. Compare and contrast the study sample (HS2000 class)
characteristics (Found in Table 1 & 2) to the OU student
population characteristics (found in the supplemental document
on Moodle) on the following variables. Be specific in your
answers (use numbers or percent in your comparisons of both
groups) and describe how the groups are similar or different. (6
points, 2 points each)
a. Age
b. Male/female ratio
c. Residence
9. Do you think the findings of this survey are generalizable to
the OU student population as a whole? Why or why not?
Consider both the sample characteristics examined in Question
8 and other factors that impact generalizability. (3 points)
The following questions ask you to examine data presented in
Table 3.
10. a. Compared to recommendations for hours of sleep, how
are HS2000 students doing on sleep? (2 points)
10.b. Based on this assessment and existing research, what are
some long-term OR short-term health risks (or benefits) that the
average HS2000 student might experience? You must use and
cite at least 1 credible source. You MAY NOT use or cite your
textbook. Be sure to add your source to the references in
Question 17. (4 points)
11. a. Although average caffeine consumption could not be

calculated, you can see ranges in students’ consumption of
caffeine. Summarize the caffeine consumption habits and
sources of caffeine consumption of students in HS2000 (2
points)
11.b. Based on existing research, identify ONE health benefit
and ONE health risk of caffeine consumption. You must use and
cite at least 1 credible source. You MAY NOT use or cite your
textbook. Be sure to add your source to the references in
Question 17. (4 points)
Now that you have described the study sample characteristics,
you will examine the relationships between caffeine intake and
sleep. These analyses can be found in Table 4 and in the
Figures.
12. Identify and interpret the statistical relationship between the
following sleep variables and caffeine consumption. Include the
statistics (p-values and r-values), an interpretation of the r-
value, and a statement about significance.
a. Hours of sleep and caffeine consumption (3.5 points)
a.1. r-value:
a.2. p-value:
a.3. Is this statistically significant?
a.4. What is your interpretation of the relationship between
these variables based on these statistics?
b. Sleep quality index score and caffeine consumption (3.5
points)
a.1. r-value:
a.2. p-value:
a.3. Is this statistically significant?

a.4. What is your interpretation of the relationship between
these variables based on these statistics?
13. Based on these results, what do you conclude is the
relationship between caffeine intake and sleep? (2 points)
14. Was your hypothesis supported? Explain WHY you think it
was/was not supported. That is, thinking critically, why do you
think this result occurred? If your hypothesis was supported,
what is the link between variables. If it was not, what are some
alternative explanations? You are graded on your assessment of
support and for your explanations, NOT that your hypothesis
was correct. (4 points)
Different groups of students may have different sleep habits or
caffeine use. Our statistical analysis also examines differences
in sleep quality and caffeine use between men and women and
students’ living situations. Look at Table 5 and 6, which shows
the group means along with a p-value for group differences.
15. Identify which variables are significantly different between
men and women and explain the differences (e.g. if there is a
significant difference in caffeine use, explain whether men or
women are higher and by how much). (2 points)
16. Identify which variables are significantly different between
students who live on-campus and those who live off-campus.
Again explain the differences. (2 points)
17. References (6 points) Using APA formatting, include full
reference information for sources cited in questions 10 and 11.
You will be scored based on the quality of your references
(peer-reviewed, credible sources), as well as utilizing correct
APA formatting.

References used in these instructions
1. Buysse,D.J., Rynolds, C.F., Monk, T.H., Berman, S.R.,
Kupfer, D.J. (1989). The Pittsburgh Sleep Quality Index: A new
instruction for psychiatric practice and research. Psychiatry
Res, 28(2), 193- 213.

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