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DEGREE REGULATIONS & PROGRAMMES OF STUDY 2022/2023

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

Undergraduate Course: Data Analysis for Psychology in R 2 (PSYL08015)

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 2 Undergraduate) AvailabilityAvailable to all students
SCQF Credits20 ECTS Credits10
SummaryThis course provides a thorough introduction to linear models applied to both correlational and experimental study designs in answering psychological questions. We will cover the theoretical background to linear models, as well as practical implementation using R.
Course description This course will provide a thorough introduction to the linear model, and describe how a variety of methods discussed in the psychological literature (correlation, t-tests, ANOVA, regression) are all specific examples of linear models. We will teach students how to specify, run and interpret linear models to answer a variety of psychological questions, analysing data from both correlational and experimental study designs.

Specifically, the course will cover data cleaning, management, and visualization, linear models with continuous and categorical predictors and outcomes, model assumption checking and diagnostics, and model interpretation.

Lectures will primarily provide the theoretical background alongside applied examples. Labs will provide the practical data skills as well as practice producing reproducible documents using RMarkdown.
Entry Requirements (not applicable to Visiting Students)
Pre-requisites It is RECOMMENDED that students have passed Data Analysis for Psychology in R1 (PSYL08013)
Students MUST have passed: Psychology 1A (PSYL08009) AND Psychology 1B (PSYL08010) OR Informatics 1 - Cognitive Science (INFR08020)
Co-requisites
Prohibited Combinations Other requirements None
Information for Visiting Students
Pre-requisitesNone
High Demand Course? Yes
Course Delivery Information
Academic year 2022/23, 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 20, Summative Assessment Hours 4, Programme Level Learning and Teaching Hours 4, Directed Learning and Independent Learning Hours 112 )
Assessment (Further Info) Written Exam 60 %, Coursework 40 %, Practical Exam 0 %
Additional Information (Assessment) Weekly Quiz (10%): best 14/18 quiz scores.
Report (30%)
Exam (60%)
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)2:00
Learning Outcomes
On completion of this course, the student will be able to:
  1. Understand and interpret linear models for continuous outcomes and predictors.
  2. Understand and interpret linear models for experimental designs with categorical predictors.
  3. Understand and interpret generalized linear models for binary outcomes.
  4. Understand and discuss statistical inference, model building and model evaluation for linear models.
  5. Implement the above referenced models using R and present and interpret the results.
Reading List
Reading will be predominantly drawn from a series of free open source texts.

Cetinkaya-Rundel, M. & Hardin, J. OpenIntro::Introduction to Modern Statistics. (https://openintro-ims.netlify.app/ )

Wickham, H. (2016). ggplot2: elegant graphics for data analysis. Springer.

Wickham, H. (2014). Advanced R. Chapman and Hall/CRC.

In addition, draft materials from in the prep textbook being written by Alex Doumas, Aja Murray and Tom Booth (SAGE) will be provided to students. Further reading may be added during course development.
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 to the specification and estimation of linear models for a wide variety of research questions, and the accurate presentation of these results in text, tables and visualizations.
KeywordsResearch Methods,Statistics
Contacts
Course organiserMs Emma Waterston
Tel:
Email: Emma.Waterston@ed.ac.uk
Course secretaryMiss Susan Scobie
Tel: (0131 6)51 5505
Email: sscobie@ed.ac.uk
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