Simulation Methods for Psychologists using R 4H PSYCH4091

  • Academic Session: 2024-25
  • School: School of Psychology and Neuroscience
  • Credits: 10
  • Level: Level 4 (SCQF level 10)
  • Typically Offered: Semester 1
  • Available to Visiting Students: Yes
  • Collaborative Online International Learning: No

Short Description

This course provides a general and practical introduction to simulation methods, with implementations in the R programming language. Examples include applications to popular psychological inference problems, using the percentile bootstrap, permutation, cross-validation and estimation of statistical power and false positives.

Timetable

10 hours of lectures over a 5-week block + 2 hours of group presentations.

Requirements of Entry

Successful completion of level 3H psychology single honours.

Excluded Courses

None

Assessment

100% research report. Students will have 5 days to produce an RMarkdown document in which they analyse real data from the literature.

Are reassessment opportunities available for all summative assessments? Not applicable for Honours courses

Reassessments are normally available for all courses, except those which contribute to the Honours classification. For non-Honours courses, students are offered reassessment in all or any of the components of assessment if the satisfactory (threshold) grade for the overall course is not achieved at the first attempt. This is normally grade D3 for undergraduate students and grade C3 for postgraduate students. Exceptionally it may not be possible to offer reassessment of some coursework items, in which case the mark achieved at the first attempt will be counted towards the final course grade. Any such exceptions for this course are described below. 

Course Aims

The aim of this course is to introduce students to simulation methods using R.

The course teaches practical R skills including how to run simulations, for instance to estimate statistical power, and how to implement the percentile bootstrap and cross-validation and how they can be used to make statistical inferences. Other practical skills include the illustration of results in ggplot2 and the creation of reproducible reports using RMarkdown. Practical applications include inferences about group comparisons and regression analyses.

Intended Learning Outcomes of Course

By the end of this course students will be able to:

■ Critically evaluate the goal and implementation of the percentile bootstrap and cross-validation and simulation methods at an abstract level;

■ Interact with and write R code implementing the percentile bootstrap, cross validation and simulations;

■ Apply the percentile bootstrap and cross-validation to inferences on measures of central tendency and regressions;

■ Illustrate raw data, bootstrap, cross-validation and simulation results using ggplot2;

■ Use the percentile bootstrap, cross-validation and other simulation methods to make statistical inferences and interpret the results, including the description of p values, confidence intervals and performance measures;

■ Write reproducible reports using RMarkdown.

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.