Bootstrap: Robust Statistical Inferences 4H PSYCH4091
- Academic Session: 2022-23
- School: School of Psychology and Neuroscience
- Credits: 10
- Level: Level 4 (SCQF level 10)
- Typically Offered: Semester 1
- Available to Visiting Students: Yes
- Available to Erasmus Students: Yes
The Bootstrap is a statistical method that uses data-driven simulations to make inferences. This course provides a general and practical introduction to the bootstrap, with very little mathematics. Examples include applications to popular psychological inference problems.
5 sessions of 2 hours over 5 weeks.
Requirements of Entry
Completion of Level 3 Honours Psychology
100% research report. Students will produce an RMarkdown document containing answers to problems and re-analyses of published datasets (2,500 words).
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.
The aim of this course is to introduce students to the bootstrap and how it can be used to make statistical inferences.
The course teaches practical R skills including how to run simulations, implement the bootstrap, illustrate results in ggplot2 and make reproducible reports using Rmarkdown. Practical applications include inferences about group comparisons and correlation 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 at an abstract level
■ Interact with and write R code implementing the percentile bootstrap
■ Apply the bootstrap to inferences on measures of central tendency and correlations
■ Illustrate raw data and bootstrap results using ggplot2
■ Use the percentile bootstrap to make statistical inferences and interpret the results, including the description of p values and confidence intervals
■ 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.