Statistics & Research Design (PGT) PSYCH5020

  • Academic Session: 2019-20
  • 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

Short Description

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.

Timetable

20 hours, 2 hours per week

Requirements of Entry

Typically a 2:1 honours degree in psychology or related discipline.

For the MSc Brain Sciences, at least a second class (2:2) honours degree in neuroscience, physiology, psychology or acceptable equivalent(s).

Assessment

One open book unseen examination (60%) + coursework based assessment (40%)

Main Assessment In: April/May

Course Aims

Aims

■ 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.