NUTR551 Analysis of Nutrition Data
General Information
Course #
Term
Year
Number of credits
Course pre-requisite(s)
551
Fall
2021
3
NUTR 337
Course co-requisite(s)
NUTR 450
Course schedule (day/time)
M 2:35pm – 4:00pm (Lectures, in class), W 10:05am – 11:25am (Labs, online)
Location for lectures (M):
RAYMND 3-048
Zoom link for labs (W):
https://mcgill.zoom.us/j/89643160068
Instructor Information
Name and Title
E-mail
Office hours
Location
Communication plan
TA Information
Name
E-mail
Virtual office hours
Communication plan
Professor Daiva Nielsen
daiva.nielsen@mcgill.ca
Wednesdays 11:30am-1:30pm
MS2-035 or via Zoom
For meeting requests outside of scheduled office hours, please e-mail the
instructor 2 business days in advance of the requested meeting day/time
Hannah Han
yang.han3@mail.mcgill.ca
By request, please e-mail the TA 2 business days in advance of the requested
meeting day/time
The TA will monitor the Discussion Board on business days and respond to
questions
Course Overview
The course covers theoretical and applied aspects of analysis of nutrition data. Data analyses will be
conducted using statistical software and publicly available nutrition databases (NHANES and CCHS).
Lectures and laboratory sessions will familiarize students with the appropriate use of analytical
techniques for dietary and anthropometric data, and how to report study results clearly and concisely.
The process of drawing inferences from study results will be discussed throughout the course.
1
Instructor Message Regarding Remote Delivery for laboratory classes
The remote learning context presents challenges for all involved. While our labs will be conducted
exclusively through Zoom, I am committed to fostering a dynamic and stimulating learning environment
for this course. Participation and fluid communication during class time will be an important aspect to
achieve the most satisfying learning experience. In order to foster this level of engagement, the class
activities and methods of assessment have been crafted in such a way to maximize students’ individual
and team-based engagement with the subject matter.
The myCourses discussion board will also be an important resource for continuing the exchange of ideas
outside of class time. Please use the discussion board as much as possible. The TA and I will actively
monitor and respond to posts on the discussion board in a timely manner. Students should regularly
check their e-mail and myCourses for course updates (minimum twice per week).
A mid-course evaluation will occur halfway through the semester where students will have an
opportunity to provide detailed feedback and suggestions for improvement regarding the course
structure, which will assist in addressing any weaknesses for the remaining duration of the course. A
number of resources are available to support students in their academic success in the context of a
remote learning environment. I encourage you review this material: Student-specific Guidelines for
Remote Teaching and Learning AND Remote Learning Resources.
Learning Outcomes
By the end of this course, students will be able to:
1.
Understand the progression from database development and management, data analyses, and
reporting and interpretation of study results.
2.
Become familiar with and be able to use SPSS statistical computing software program.
3.
Be able to select and use appropriate statistical tests.
4.
Be familiar with optimal research design to address various research questions.
5.
Be able to interpret the results of testing research questions and report results in a clear and concise
manner.
Course Content
We will discuss theoretical concepts in statistics, in addition to practical aspects of data collection, database
organization, and data analysis. Analyses will be conducted using NHANES and CCHS data. The course builds
from basic concepts in data analysis to commonly used statistical tests in nutrition research. Specific
considerations for dietary data are presented throughout. Readings of primary research articles are used as
examples of these statistical tests. Thorough discussion of these articles develops students’ competencies with
understanding what kinds of statistical tests can be used according to the research questions and study design.
Applied practice in course labs builds students’ skills in conducting statistical analyses.
2
Instructional Method
Lectures will be delivered in class and labs will be hosted on Zoom. The applicable Zoom link to be used for labs
will be posted on myCourses. For information about using Zoom, please review McGill’s Remote Learning
Resources. I will present theoretical material and examples of applied data analysis during lectures. A required
reading of a primary research article is assigned in most weeks. Please read the article in advance of the
scheduled lecture as a discussion of this article will comprise a proportion of the lecture time. We will focus on
assessing the data and statistical approaches, as well as interpreting the article’s tables and figures. The labs will
involve hands-on practice with data analyses that flow from what was presented in the corresponding lecture.
The methods of assessments will evaluate students’ knowledge and comprehension of the subject matter, as
well as ability to apply the material by conducting the data analyses that were practiced in class.
Expectations for Student Participation
Students are expected to come prepared to class by reading the assigned article in advance of class. During labs,
students will follow along with the instructor in a step-by-step manner to practice conducting data analyses with
SPSS software. At any point in class, students may raise their hand to ask their question. The instructor will also
reserve 5 minutes at the end of class to respond to questions. For lab sessions on Zoom, students should use the
raise hand function to ask questions. Students must remain on mute unless they are asking a question during
the lab time to ensure the best audio quality.
Recordings of sessions
In-person lectures will not be recorded. Online labs will be recorded and made available on myCourses.
Your image, voice and name may be disclosed to classmates. Note that by remaining in sessions that are
being recorded, you are agreeing to the recording. Please read the Guidelines on Remote Teaching and
Learning: https://www.mcgill.ca/tls/instructors/class-disruption/strategies/guidelines-remote
Required Course Materials
SPSS software available through McGill
https://mcgill.service-now.com/itportal?id=kb_article&sysparm_article=KB0010741
3
Required Readings Before Class (PDF files will be posted on myCourses):
1. Everest and Emery (2018) Data Visualization Checklist:
https://datavizchecklist.stephanieevergreen.com/assets/DataVizChecklist_Feb2018.pdf
2. Harding et al (2010) Maintaining Adequate Nutrition, Not Probiotic Administration, Prevents Growth
Stunting and Maintains Skeletal Muscle Protein Synthesis Rates in a Piglet Model of Colitis. Pediatric
Research. 67(3): 268-273.
3. O’Neil et al (2015) Consumption of apples is associated with a better diet quality and reduced risk of
obesity in children: National Health and Nutrition Examination Survey (NHANES) 2003–2010. Nutr J.
14:48.
4. Baddour et al (2013) Validity of the Willett food frequency questionnaire in assessing the iron intake of
French-Canadian pregnant women. Nutrition 29(5):752-6.
5. Josse et al (2011) Increased consumption of dairy foods and protein during diet- and exercise-induced
weight loss promotes fat mass loss and lean mass gain in overweight and obese premenopausal women.
J Nutr. 141(9):1626-34
6. Tsilas et al (2017) Relation of total sugars, fructose and sucrose with incident type 2 diabetes: a
systematic review and meta-analysis of prospective cohort studies. CMAJ 189(20):E711-E720
7. Berry et al (2020) Human postprandial responses to food and potential for precision nutrition Nature
Med. 26: 964–973.
Optional Course Materials
Introductory statistical material that complements course content is available at:
http://www.bmj.com/about-bmj/resources-readers/publications/statistics-square-one
Saracino G, Jennings LW, Hasse JM. Basic Statistical Concepts in Nutrition Research, Nutrition in Clinical
Practice, 10.1177/0884533613478636, 28, 2, (182-193), (2013) (Available through McGill Library)
Annotated output for SPSS is available at:
https://stats.idre.ucla.edu/other/annotatedoutput/
Resources for data visualization considerations:
https://nnlm.gov/data/data-visualization
https://www.popdata.bc.ca/events/etu/webinar/Tableau_Jun17_2020
Evaluation
The course will consist of a midterm, open-book SPSS assessment, group project, and final exam.
Individual methods of assessments are intended to evaluate the student’s knowledge, comprehension,
and application of course content. The group project will involve synthesizing and applying data-related
content presented over the full course and will demonstrate students’ ability to work as a team. Students
will be matched in groups by the instructor. Students will follow the same grading scheme, however,
higher level data analysis activities will be assigned to graduate students. Work submitted for evaluation
as part of this course may be checked with text matching software within myCourses
4
Assignment instructions and grading rubrics will be available on myCourses. Students are encouraged to post
questions about the evaluations on the course Discussion Board. The lab assessment and group project will be
released and submitted via myCourses: FAQs for students using myCourses: Assignments. Late submissions will
be subject to a deduction penalty of 10% per day up to 3 days past the deadline. After 3 days any late submission
will be given a grade of 0.
Name of Assessment
Date
% of final
grade
In-Class Midterm
October 18, 2021
30%
In-Class Open-book SPSS assessment: SPSS data activity
November 3,
2021
15%
Group Assignment: poster presentation 8%, written report 6%, peer
feedback 1%
November 29/
December 3,
2021
15%
Final Exam: Cumulative, covering material from the entire term
TBD
40%
5
Weekly Schedule
Week
Date
Lecture Description
Lab Description
1
Sept 1*
N/A
Course Overview:
Introduction to data
and overview of SPSS
software
N/A
Measures of central
tendencies and
variability: Computing
summary statistics,
making tables and
figures
Starting Points in Data Analysis:
Assessing normality, outliers,
overview of hypothesis testing
Tests of normality and
identification/handling
of outliers
Sept 20,
22
Statistical Tests for Continuous
and Categorical Variables
Analyzing continuous
and categorical data
MW
Chi-squared test, t-test,
correlation
Sept 27,
29
Analysis of Variance
W
2
Sept 8*
W
3
Sept 13,
15
MW
4
5
6
7
MW
One-way, two-way/factorial,
MANOVA, repeated measures
Oct 4, 6
Linear and Logistic Regression
MW
Model creation, estimates, odds
ratios, confidence intervals
Oct 13*
N/A
Assignments and/or
Readings Due
Article (required):
Evergreen and Emery,
2018
Article (required):
Harding et al. 2010
ANOVA analyses
Regression analyses,
Analysis of Covariance
(ANCOVA)
Fall Reading Break
W
6
Article (required): O’Neil
et al. 2015
8
Oct 18,
20
In Class Midterm
Validation of Dietary
Intake Assessment
MW
Article (required):
Baddour et al. 2013
Nutrition validation
studies, biomarkers,
energy misreporting
(online mid-course
evaluation)
9
10
Oct 25,
27
Experimental Studies:
Randomized Controlled Trials
Power calculations,
methods of handling
missing data
Article (required): Josse
et al. 2011
MW
Randomization, statistical power
Nov 1, 3
Review session prior to lab
assessment
In-Class Open Book
SPSS Assessment
(Online)
LAB ASSESSMENT
Nov 8,
10
Hierarchies of Scientific
Evidence
Guest lecture: Metaanalysis
Article (required): Tsilas
et al. 2017
MW
Observational studies,
Experimental Studies, MetaAnalyses
Nov 15,
17
Data Reproducibility and
Transparency, Genomic
Databases
MW
11
12
MW
Tutorial on Git and
sample genomic
databases
Git software, navigating genomic
data
13
14
Nov 22,
24
Advances in Data Science and
Relevance for Nutrition
MW
Modern measurement tools and
modern analytical approaches
Nov 29,
Dec 1
Oral Poster Presentations (8
groups)
MW
7
Guest lecture: Data
considerations for
personalized nutrition
Article (required): Berry
et al. 2020
Oral Poster
Presentations (8
groups)
SUBMIT: Final poster
and written report
(electronic files)
15
Dec 6
M
Choosing an Appropriate
Statistical Test
N/A
Review of course material and
take-home messages
*Indicates week begins on a Wednesday
Note: Lectures occur on Mondays. Labs occur on Wednesdays.
McGill Policy Statements
Language of Submission: In accord with McGill University’s Charter of Students’ Rights, students in this course
have the right to submit in English or in French any written work that is to be graded. This does not apply to
courses in which acquiring proficiency in a language is one of the objectives.
Conformément à la Charte des droits de l’étudiant de l’Université McGill, chaque étudiant a le droit de soumettre
en français ou en anglais tout travail écrit devant être noté (sauf dans le cas des cours dont l’un des objets est la
maîtrise d’une langue).
Academic Integrity: McGill University values academic integrity. Therefore, all students must understand the
meaning and consequences of cheating, plagiarism and other academic offences under the Code of Student
Conduct and Disciplinary Procedures” (see McGill’s guide to academic honesty for more information).
The University Student Assessment Policy exists to ensure fair and equitable academic assessment for all
students and to protect students from excessive workloads. All students and instructors are encouraged to
review this Policy, which addresses multiple aspects and methods of student assessment, e.g. the timing of
evaluation due dates and weighting of final examinations.
© Instructor-generated course materials (e.g., handouts, notes, summaries, exam questions) are protected by
law and may not be copied or distributed in any form or in any medium without explicit permission of the
instructor. Note that infringements of copyright can be subject to follow up by the University under the Code of
Student Conduct and Disciplinary Procedures.
As the instructor of this course I endeavor to provide an inclusive learning environment. However, if
you experience barriers to learning in this course, do not hesitate to discuss them with me and the
Office for Students with Disabilities, 514-398-6009.
McGill University is on land which has long served as a site of meeting and exchange amongst Indigenous
peoples, including the Haudenosaunee and Anishinabeg nations. We acknowledge and thank the diverse
Indigenous people whose footsteps have marked this territory on which peoples of the world now gather.
L’Université McGill est sur un emplacement qui a longtemps servi de lieu de rencontre et d'échange entre les
peuples autochtones, y compris les nations Haudenosaunee et Anishinabeg. Nous reconnaissons et remercions
les divers peuples autochtones dont les pas ont marqué ce territoire sur lequel les peuples du monde entier se
réunissent maintenant.
8
End-of-course evaluations are one of the ways that McGill works towards maintaining and improving the quality
of courses and the student’s learning experience. You will be notified by e-mail when the evaluations are
available. Please note that a minimum number of responses must be received for results to be available to
students.
In the event of extraordinary circumstances beyond the University’s control, the content and/or evaluation
scheme in this course is subject to change.
9