Fall 2021 - GERO 803 G100

Analytical Techniques for Gerontological Research (4)

Class Number: 5192

Delivery Method: In Person

Overview

  • Course Times + Location:

    Sep 8 – Dec 7, 2021: Wed, 9:30 a.m.–12:20 p.m.
    Vancouver

Description

CALENDAR DESCRIPTION:

This course has been specifically designed to provide training in quantitative data analysis using SPSSx Programming Language with a focus on behavioral research problems in gerontology.

COURSE DETAILS:

The purpose of this course is the provide students with a firsthand perspective of what it is like to conduct quantitative research. We will use data to learn something about the world, with a specific focus on data that can be used to learn something about aging.

Students will be introduced to what it means to think statistically and probabilistically, and how statistical models can serve as (imperfect) tools to translate quantitative data into understanding. We will discuss basic principles of data science as relates to data management, data visualization, and reproducible research. Further, we will use foundational statistical techniques, with a focus on linear regression modeling using ordinary least squares techniques. If time permits, we will expand our toolbox to include generalized linear models.

Each class will be a mixture of lecture and hands-on data science and analysis using the R programming language via Rstudio. By the end of the semester, students will have completed a project, which will entail using real data related to gerontology (e.g., from the 2008 Canadian Community Health Survey Healthy Aging Module) to evaluate a statistical model (i.e., a hypothesis about the world) of the students’ choosing. This will require students to formulate a hypothesis, identify data to test the hypothesis, fit a model to the data that represents the hypothesis, and then draw conclusions from the fitted model. Students will create a reproducible Rmarkdown notebook that communicates the results of this project.

COURSE-LEVEL EDUCATIONAL GOALS:

The goal is for students to have a deeper appreciation for statistical thinking, how we make inferences about the world, and the inherent uncertainty in those inferences. More concretely, by the end of the course, students will be able to use R to do some basic data wrangling and visualization, model associations among variables using regression techniques, and to communicate those findings using Rmarkdown. Students will demonstrate their gained knowledge through completion of regular assignments and completion of the course project.

Grading

  • Quasi-weekly assignments (8) - 10% each 80%
  • Course project 20%

REQUIREMENTS:

I assume that students have had some exposure to research methodology and statistics during their undergraduate studies, but that many of the details have been forgotten. Therefore, students are expected to attend the Introduction to R workshop to be held on January 6th from 1-4pm at Harbour Centre. 

Materials

MATERIALS + SUPPLIES:

1. Laptop computer running MacOS or Windows – need to bring to class
2. R programming language — freely available at r-project.org
3. Rstudio — freely available at rstudio.com

 

REQUIRED READING:

1. Poldrack, R. (2019). Statistical Thinking for the 21st Century. (Freely available at http://statsthinking21.org/index.html#an-open-source-book)
2. Legler, J. & Roback, P. (2019). Broadening Your Statistical Horizons. (Freely available at https://bookdown.org/roback/bookdown-bysh/)
3. Grolemund, G. & Wickham, H. (2017). R for Data Science. (Freely available at https://r4ds.had.co.nz)

RECOMMENDED READING:

Helpful links and resources:

1. RStudio Cheat Sheets (https://rstudio.com/resources/cheatsheets/) - cheat sheets for doing data science in R
2. Seeing-theory (https://seeing-theory.brown.edu/index.html) - a visual and interactive introduction to statistical concepts
3. UCLA Stats resources (https://stats.idre.ucla.edu/) - help on just about any statistical technique
4. stackoverflow.com and stats.stackexchange.com - valuable question and answer websites. Nearly every stats related question I’ve thought of has been posed and answered here.

Optional reading:
Spiegelhalter, D. (2019). The Art of Statistics: Learning from Data.

Graduate Studies Notes:

Important dates and deadlines for graduate students are found here: http://www.sfu.ca/dean-gradstudies/current/important_dates/guidelines.html. The deadline to drop a course with a 100% refund is the end of week 2. The deadline to drop with no notation on your transcript is the end of week 3.

Registrar Notes:

ACADEMIC INTEGRITY: YOUR WORK, YOUR SUCCESS

SFU’s Academic Integrity web site http://www.sfu.ca/students/academicintegrity.html is filled with information on what is meant by academic dishonesty, where you can find resources to help with your studies and the consequences of cheating.  Check out the site for more information and videos that help explain the issues in plain English.

Each student is responsible for his or her conduct as it affects the University community.  Academic dishonesty, in whatever form, is ultimately destructive of the values of the University. Furthermore, it is unfair and discouraging to the majority of students who pursue their studies honestly. Scholarly integrity is required of all members of the University. http://www.sfu.ca/policies/gazette/student/s10-01.html

TEACHING AT SFU IN FALL 2021

Teaching at SFU in fall 2021 will involve primarily in-person instruction, with approximately 70 to 80 per cent of classes in person/on campus, with safety plans in place.  Whether your course will be in-person or through remote methods will be clearly identified in the schedule of classes.  You will also know at enrollment whether remote course components will be “live” (synchronous) or at your own pace (asynchronous).

Enrolling in a course acknowledges that you are able to attend in whatever format is required.  You should not enroll in a course that is in-person if you are not able to return to campus, and should be aware that remote study may entail different modes of learning, interaction with your instructor, and ways of getting feedback on your work than may be the case for in-person classes.

Students with hidden or visible disabilities who may need class or exam accommodations, including in the context of remote learning, are advised to register with the SFU Centre for Accessible Learning (caladmin@sfu.ca or 778-782-3112) as early as possible in order to prepare for the fall 2021 term.