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DRPS : Course Catalogue : School of Philosophy, Psychology and Language Sciences : Psychology

Undergraduate Course: Big Data and Psychological Science (PSYL10178)

Course Outline
SchoolSchool of Philosophy, Psychology and Language Sciences CollegeCollege of Arts, Humanities and Social Sciences
Credit level (Normal year taken)SCQF Level 10 (Year 3 Undergraduate) AvailabilityAvailable to all students
SCQF Credits20 ECTS Credits10
SummaryPsychologists are now using "big data" with the aim of testing hypotheses about the mind in less artificial contexts at scale. Psycholinguists have tested their computational models using natural language corpora, behavioural economists have used online product reviews to understand decision-making, and social psychologists have used social media data to study the effects of networks on beliefs and behaviour. How do we collect this sort of data and what is its value for understanding the mind and behaviour? This course covers how to use and evaluate psychological research that uses big data.
Course description In the first weeks of the course, we will cover introductory material to topics such as the motivation to use big data to study psychological questions and introductory material on programming in R to scrape and analyse social media data. The course will then cover influential studies which relied on big data to examine psychological questions (e.g., the examination of personality from blog posts or the spread or reduction of misinformation on Twitter), critical analysis of the merits and limitations of these studies, along with the ethical implications of using open datasets.
Entry Requirements (not applicable to Visiting Students)
Pre-requisites Students MUST have passed: Psychology 2A (PSYL08011) AND Psychology 2B (PSYL08012)
Prohibited Combinations Other requirements None
Information for Visiting Students
Pre-requisitesVisiting students should be studying Psychology as their degree major, and have completed at least 3 Psychology courses at grade B or above. We will only consider University/College level courses. Applicants should note that, as with other popular courses, meeting the minimum does NOT guarantee admission.

**Please note that upper level Psychology courses are high-demand, meaning that they have a very high number of students wishing to enrol in a very limited number of spaces.** These enrolments are managed strictly by the Visiting Student Office, in line with the quotas allocated by the department, and all enquiries to enrol in these courses must be made through the CAHSS Visiting Student Office. It is not appropriate for students to contact the department directly to request additional spaces.
High Demand Course? Yes
Course Delivery Information
Academic year 2023/24, Available to all students (SV1) Quota:  0
Course Start Semester 1
Timetable Timetable
Learning and Teaching activities (Further Info) Total Hours: 200 ( Lecture Hours 10, Seminar/Tutorial Hours 10, Programme Level Learning and Teaching Hours 4, Directed Learning and Independent Learning Hours 176 )
Assessment (Further Info) Written Exam 0 %, Coursework 100 %, Practical Exam 0 %
Additional Information (Assessment) Coursework - Project Proposal (1000 words) 30%
Coursework - Project (2000 words) 70%
Feedback Not entered
No Exam Information
Learning Outcomes
On completion of this course, the student will be able to:
  1. Critically examine research that uses social media data to study psychological questions.
  2. Write R scripts to scrape social media data to gain direct experience of how this data is acquired to evaluate it critically.
  3. Generate testable hypotheses that might be answered using big data
  4. Evaluate statistical issues of using correlational datasets to study psychological mechanisms.
  5. Consider the ethical implications of research based on mining social media data and articulate how the project is compatible with the British Psychological Science Code of Ethics.
Reading List
Jones, M. N. (Ed.). (2017). Big data in cognitive science. New York, NY, USA:: Routledge.

Pennycook, G., Epstein, Z., Mosleh, M., Arechar, A. A., Eckles, D., & Rand, D. G. (2021). Shifting attention to accuracy can reduce misinformation online. Nature, 592(7855), 590-595.
Additional Information
Graduate Attributes and Skills Not entered
KeywordsNot entered
Course organiserDr Zachary Horne
Course secretaryMiss Georgiana Gherasim
Tel: (0131 6)50 3440
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