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PSY40760

Academic Year 2026/2027

Advanced Data Analysis and Statistics in R. (PSY40760)

Subject:
Psychology
College:
Social Sciences & Law
School:
Psychology
Level:
4 (Masters)
Credits:
5
Module Coordinator:
Dr Ondrej Zika
Trimester:
Autumn
Mode of Delivery:
On Campus
Internship Module:
No
How will I be graded?
Letter grades

Curricular information is subject to change.

This module is for postgraduate students in Psychology and Social Sciences. It is designed for
students in the MSc Behavioural Neuroscience programme, and aims to support them in
developing their quantitative data analysis skills to an advanced level. The module will focus on
acquiring the data analytic and statistical skills necessary to complete a research project of
publishable quality.
This module provides an in-depth exploration of advanced statistical techniques essential for
psychological research, using R and JASP as the primary tools for analysis. The curriculum
emphasises essential data science skills that have been overlooked in traditional teaching
approaches, including programming, data visualisation, data wrangling, and reproducible
reports. Students will gain a deeper understanding of probability and inference through data
simulation and hands-on work with real psychological datasets.
Through a combination of lectures, interactive coding sessions, and applied exercises, students
will develop the skills necessary to conduct, interpret, and communicate advanced statistical
analyses in psychology using modern, reproducible workflows.

About this Module

Learning Outcomes:

By the end of this module, students will be able to:

● Apply advanced statistical techniques to psychological data using R and JASP
● Perform data wrangling, visualisation, and programming for efficient and
reproducible analyses.
● Choose, apply and interpret the appropriate statistical model for your dataset.
● Communicate findings effectively through clear and transparent reporting.

Indicative Module Content:

Key Topics Covered:
● Estimation, Probability and Inference
● Transforming raw data into a more usable format. This involves cleaning, structuring,
and enriching data so that it’s ready for analysis (i.e Data wrangling)
● Data visualisation and the grammar of graphics
● Effect sizes and Confidence intervals
● Advanced Regression Techniques: Multiple regression, logistic regression, and
hierarchical regression models
● Linear Mixed-Effects Models: Handling nested and repeated measures data in
psychology
● Introduction to Bayesian inference and applications in psychological research
● Responsible and sustainable use of generative AI to augment data analysis workflows.

The United Nations identified seventeen Sustainable Development Goals (SDGs) as core to the 2030 Agenda for Sustainable Development, and UCD contributes in general to SDG 4 Quality Education. Further SDGs explored within this module if relevant are listed below. A scale of 1 - 5 indicates the extent to which the SDG is covered.


 

Student Effort Hours:
Student Effort Type Hours
Seminar (or Webinar)

18
Practical

9
Computer Aided Lab

6
Specified Learning Activities

21
Autonomous Student Learning

70
Total

124

Approaches to Teaching and Learning:
Teaching and Learning Approaches:
● Statistics Lectures
● Hands-on coding workshops in R
● Applied exercises with real psychological datasets
● Case studies and critical discussions on statistical applications
● Students will be expected to complete coding tasks, engage with learning resources, and
read between sessions.

Requirements, Exclusions and Recommendations
Learning Recommendations:



A foundational/undergraduate level understanding of Null Hypothesis Significance Testing, including applying and interpreting ANOVA, t-tests, and correlations.
Previous experience in the statistical analysis and interpretation of data sets


Module Requisites and Incompatibles
Incompatibles:
PSY40440 - Adv. Research Methods & Stats


 

Assessment Strategy
Description Timing Component Scale Must Pass Component % of Final Grade In Module Component Repeat Offered
Report(s): Students will be provided with code and required to comment on the code to describe what the code is doing.
Students will be provided with a dataset and will prepare the dataset for analysis.		 Week 7 Standard conversion grade scale 40% No
25
 No
Exam (Take-Home): Students must analyse a dataset, justify the choice of statistical model, report the statistical analysis in a results section in APA format, and provide the R code and output. Week 14 Standard conversion grade scale 40% No
60
 No
Participation in Learning Activities: Completion and submission of coding tasks in the R learning interface. Week 3, Week 5, Week 7, Week 9, Week 11 Pass/Fail Grade Scale No
15
 No

Carry forward of passed components
Yes
 

Resit In Terminal Exam
Spring No
Please see Student Jargon Buster for more information about remediation types and timing. 

Feedback Strategy/Strategies

• Feedback individually to students, post-assessment
• Online automated feedback

How will my Feedback be Delivered?

Automated feedback on key skills in R. Students receive individual written feedback on the report that prepares them for the online open-book end-of-term exam.

Name Role
Professor Nuala Brady Lecturer / Co-Lecturer
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