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