Simon Fraser University
Engaging the World

Course Outlines

Spring 2026 - PSYC 391 D300

Selected Topics in Psychology (3)

Foundations of Coding

Class Number: 4714

Delivery Method: In Person

Overview

  • Course Times + Location:

    Jan 5 – Apr 10, 2026: Fri, 2:30–5:20 p.m.
    Burnaby

  • Prerequisites:

    PSYC 201. Other prerequisites vary by topic offering.

Description

CALENDAR DESCRIPTION:

Course can be repeated for credit. Students may not take this course for further credit if similar topics are covered. See Psychology department website for course description.

COURSE DETAILS:

Prerequisite: PSYC 201W: Introduction to Research Methods in Psychology

Course Description: This course introduces the concepts, methods, and applications of big data in psychology. Students will learn how large-scale datasets, digital traces, and emerging technologies are transforming psychological research across various domains, while considering ethical and reproducibility challenges. The focus of this course is on conceptual understanding and critical evaluation of big data approaches in psychological science. No prior programming experience is required.

 

COURSE-LEVEL EDUCATIONAL GOALS:

By the end of this course, students will be able to
• Understand the role of big data in psychological research.
• Identify the strengths, limitations, and ethical considerations of using large datasets.
• Become familiar with key approaches in artificial intelligence, natural language processing, and social network analysis, as applied in psychology.
• Describe applications of big data across different areas of psychology, such as health, social, and education.
• Reflect on their own learning journey and consider how big data methods can inform their research.
• Critically evaluate emerging opportunities and challenges in the future of data science in psychology.

Grading

  • Class Participation and Engagement: 9%
  • Weekly Quizzes: 11%
  • Paper 1: 15%
  • Paper 2: 15%
  • Mid-Term Exam: 25%
  • Final Exam: 25%
  • This is a tentative outline. The grading provided above will be reviewed in the first lecture and may be adjusted slightly.

NOTES:

Topics:
Introduction to big data in psychology; comparison of big data and traditional research methods; large-scale and online research designs; digital and sensor-based data sources; artificial intelligence in psychology; natural language processing; social network analysis; and ethics, reproducibility, and responsible use of big data and future directions.

REQUIREMENTS:

Lectures:
This course is delivered in person through weekly three-hour lectures, with a break at the midpoint. Lectures include in-class discussions and activities where students work in assigned small groups, and groups submit their responses on Canvas.

Materials

REQUIRED READING:

Bainbridge, W. A. (2025). Big Data in the Psychological Sciences. Cambridge University Press

REQUIRED READING NOTES:

Your personalized Course Material list, including digital and physical textbooks, are available through the SFU Bookstore website by simply entering your Computing ID at: shop.sfu.ca/course-materials/my-personalized-course-materials.

Registrar Notes:

ACADEMIC INTEGRITY: YOUR WORK, YOUR SUCCESS

At SFU, you are expected to act honestly and responsibly in all your academic work. Cheating, plagiarism, or any other form of academic dishonesty harms your own learning, undermines the efforts of your classmates who pursue their studies honestly, and goes against the core values of the university.

To learn more about the academic disciplinary process and relevant academic supports, visit: 


RELIGIOUS ACCOMMODATION

Students with a faith background who may need accommodations during the term are encouraged to assess their needs as soon as possible and review the Multifaith religious accommodations website. The page outlines ways they begin working toward an accommodation and ensure solutions can be reached in a timely fashion.