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

Undergraduate Course: Data Analysis for Psychology in R1 (PSYL08013)

Course Outline
SchoolSchool of Philosophy, Psychology and Language Sciences CollegeCollege of Arts, Humanities and Social Sciences
Credit level (Normal year taken)SCQF Level 8 (Year 1 Undergraduate) AvailabilityAvailable to all students
SCQF Credits20 ECTS Credits10
SummaryThis course provides foundations in working with data, probability, hypothesis testing and the use of R statistical programming environment.
Course description The course is taught based on a mixture of theoretical and practical lectures, labs, and independent learning tasks. In semester 1, lectures cover fundamental principles of describing data and of probability theory. In semester 2, lectures build up to discussion of how we make inferences about our hypotheses in psychology, dealing with probability distributions, sampling, and hypothesis testing. The course then introduces simple statistical tests for two variables by way of example. The course starts from scratch, assuming no knowledge of programming.

In the practical lectures and labs, the course introduces the fundamental principles of R programming, with a focus on understanding in a general way how R works, such that these principles can be applied to the use of R for applied data analysis. Students will apply this learning to topics such as basic calculation, data management, plotting and use of simple statistical tests.

Collectively the course will teach basic programming and data analysis skills, including the principles of applying quantitative analysis to answering research questions, and the fundamentals of writing up and reporting results in an accurate way.
Entry Requirements (not applicable to Visiting Students)
Pre-requisites Co-requisites
Prohibited Combinations Other requirements Priority will be given to Year 1 students, in particular those who need to take this course as a requirement of their degree programme.
Information for Visiting Students
Pre-requisitesVisiting students welcome.
High Demand Course? Yes
Course Delivery Information
Academic year 2023/24, Available to all students (SV1) Quota:  0
Course Start Full Year
Timetable Timetable
Learning and Teaching activities (Further Info) Total Hours: 200 ( Lecture Hours 40, Supervised Practical/Workshop/Studio Hours 20, Formative Assessment Hours 16, Programme Level Learning and Teaching Hours 4, Directed Learning and Independent Learning Hours 120 )
Assessment (Further Info) Written Exam 60 %, Coursework 40 %, Practical Exam 0 %
Additional Information (Assessment) Weekly Quizzes (10% best 10 of 14)
Report (30%)
Exam (60%)

The DAPR1 report, released in semester 2 and worth 30% of the final grade, is a group-based assessment which builds upon pair programming principles and allows peer to peer learning of a new programming language while students are also being taught statistics.
The pedagogical rationale for the group-based work is to introduce first-year students to coding gradually, while they get to grips with novel statistical concepts, and to have peers acting as 'navigators' (responsible for identifying potential problems with the code or strategy) while another student acts as the 'driver' (the person responsible for actually writing the code). The roles are switched so that everyone in the group has experience of each role.
This structure mirrors the labs, where students will work in small groups and be divided in 'drivers' and 'navigators' to produce three formative reports for which they will receive feedback and will prepare them for the assessed report.
Feedback Formative feedback is available via the weekly quizzes, office hours, lab sessions and discussion boards.
Exam Information
Exam Diet Paper Name Hours & Minutes
Main Exam Diet S2 (April/May)DAPR1 Paper 12:00
Learning Outcomes
On completion of this course, the student will be able to:
  1. Understand how to describe different types of data graphically and statistically.
  2. Understand the fundamentals of probability and how it relates to hypothesis testing.
  3. Understand the structure of a hypothesis test and how this is implemented in psychology.
  4. Complete appropriate data manipulations, plots and analyses using R programming language.
  5. Understand the purpose of, and to be able to compute and interpret, simple statistical tests using R.
Reading List
Additional Information
Graduate Attributes and Skills The course will develop students' skills in working with and using data to answer a research question of interest. Particular attention will be given on how to draw inferences beyond the observed data and the generalizability of such conclusions.
Keywordsresearch methods; statistics; psychology
Course organiserDr Umberto Noe
Tel: (0131 6)51 1990
Course secretaryMiss Georgiana Gherasim
Tel: (0131 6)50 3440
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