Statistics & Research Design (PGT) PSYCH5020
- Academic Session: 2020-21
- School: School of Psychology
- Credits: 20
- Level: Level 5 (SCQF level 11)
- Typically Offered: Semester 2
- Available to Visiting Students: No
- Available to Erasmus Students: No
This course is designed to provide a detailed understanding of the use of multilevel regression modeling for data analysis, as well as to provide a basic familiarity with non-parametric approaches and Bayesian modeling. Concepts and techniques are demonstrated using the statistical platform R, which is open source (weblink http://www.r-project.org/) and runs under most operating systems. Learning is reinforced through weekly assignments that involve working with different types of data.
20 hours, 2 hours per week
One open book unseen examination (60%) + coursework based assessment (40%)
Main Assessment In: April/May
■ To introduce students to basic techniques involved in organizing and processing complex datasets.
■ To provide a non-technical introduction to nonparametric and robust techniques to improve on parametric statistics and detect outliers.
■ To provide a basic understanding of the regression framework, including how to express study design through regression.
■ To provide an understanding of multilevel regression models and their use in experimental research.
■ To provide a basic familiarity with Bayesian approaches to modelling data;
■ To train students to use the statistical programming language R.
Intended Learning Outcomes of Course
By the end of this course students will be able to:
■ use R to organize, restructure, and visualise complex datasets;
■ explain the basic ideas behind resampling and robust statistics and their relation to classic parametric techniques;
■ make predictions from a multiple regression equation and explain the interpretation of parameter estimates;
■ express various study designs within a multilevel regression framework;
■ compute basic quantities within a Bayesian framework.
Minimum Requirement for Award of Credits
Students must submit at least 75% by weight of the components (including examinations) of the course's summative assessment.