ADVANCED QUANTITATIVE DATA ANALYSIS - 2026/7

Module Overview

This module is offered to students on specialised MSc programmes in the School of Psychology, who are required to take either this advanced module or PSYM188 Fundamentals of Quantitative Data Analysis depending on the extent of their prior statistics training. 

This module is designed to extend students' skills by equipping them with a range of advanced quantitative methods and associated statistical techniques that are used in research in psychology and closely related social science disciplines. This will help students to develop a broader and more advanced analytical skillset which enables a greater flexibility in how they can approach research datasets. These skills will be very good preparation for using quantitative techniques in doctoral research, professional psychology training, and/or careers that use data analysis (e.g., research, social statistics), enhancing students' employability in these sectors. 

This module is a suitable option for students who already have a good grounding in statistics from their undergraduate/prior studies, as this module assumes familiarity with basic statistical concepts and mainstream tests such as ANOVA and regression. Students who do NOT have a strong grounding in statistics from their prior studies should instead choose PSYM188 Fundamentals of Quantitative Data Analysis.

Module provider

Psychology

Module Leader

HEPPER Erica (Psychology)

Number of Credits: 15

ECTS Credits: 7.5

Framework: FHEQ Level 7

Module cap (Maximum number of students): N/A

Module Availability

Semester 2

Prerequisites / Co-requisites

BSc in Psychology or a closely related numerate discipline with good performance on statistics modules. Students on relevant MSc programmes must take either this module or PSYM188 Fundamentals of Quantitative Data Analysis.

Module content

The specific module content will reflect the expertise of the contributing staff, enabling students to learn from relevant experts and gain an appreciation of how the quantitative methods are used in advanced scholarship and practice.

Indicative key topics may include some of the following:
¿ Refresher of the general linear model
¿ Mediation and moderation
¿ Structural equation modelling
¿ Multilevel (mixed-effects) modelling
¿ Meta-analysis
¿ Big data
¿ Bayesian analysis

Assessment pattern

Alternative Assessment

Reasonable adjustments to assessments are made on a case-by-case basis. Please talk to the module leader as soon as possible and before you start the assessment.

Assessment Strategy

The assessment strategy is designed to provide students with the opportunity to demonstrate each of the learning outcomes and show their new skills in a range of the taught methods.

Thus, the summative assessment for this module consists of:

Mid-term Data Analysis Test

Students will be given dataset(s) to conduct and report analysis using techniques covered by that point in the module.

Coursework

Students will be given a number of datasets to analyse and write up the results using a range of techniques covered in the module. Students will be given adequate time to work on the assessment after being provided with the datasets.

Students will also submit evidence of the process of producing their answers.

Formative assessment and feedback

Workshops will include worked examples and students will receive formative feedback on their performance and understanding in these exercises.

Students can also ask questions to receive verbal feedback (from staff and each other) on their topic understanding and progress in each workshop. The module convenor will engage with the SurreyLearn discussion board and respond to queries or issues that arise there.

Module aims

• Introduce a range of advanced quantitative methods used in research in psychology and closely related disciplines
• Give students hands-on experience of conducting, interpreting and reporting output using a range of advanced statistical techniques
• Strengthen students¿ analytical decision-making when handling real quantitative datasets
• Develop students¿ skills to prepare them for a career that involves quantitative research or data analysis

Learning outcomes

Methods of Teaching / Learning

The learning and teaching strategy is designed to give students a chance to understand the fundamentals of each method and engage with them in a practical way, seeing the journey of a dataset through cleaning, analysis and writing up.

The learning and teaching methods will include a combination of didactic lecture content where appropriate alongside practical workshops with support from staff. The specific hourly breakdown between lecture and workshop activities may vary depending on the topic and student needs.

Indicated Lecture Hours (which may also include seminars, tutorials, workshops and other contact time) are approximate and may include in-class tests where one or more of these are an assessment on the module. In-class tests are scheduled/organised separately to taught content and will be published on to student personal timetables, where they apply to taken modules, as soon as they are finalised by central administration. This will usually be after the initial publication of the teaching timetable for the relevant semester.

Reading list

https://readinglists.surrey.ac.uk
Upon accessing the reading list, please search for the module using the module code: PSYM187

Other information

Surrey's Curriculum Framework is committed to developing graduates with strengths in Employability, Digital Capabilities, Global and Cultural Capabilities, Sustainability and Resourcefulness and Resilience. This module is designed to allow students to develop knowledge, skills and capabilities in the following areas: Resourcefulness and resilience: Students will be challenged to develop an understanding of new and potentially unfamiliar techniques, stretching their expertise and preparing them for learning new techniques quickly in future employment or training. Digital Capabilities: Students will learn to use cutting edge statistics software and analysis techniques. They will also have access to the suite of MyAI Surrey tools to enhance their learning. They will learn to critically consider the value and risks of using AI to assist analysis such as coding. Employability: Students will learn new skills that are desirable in doctoral studies, professional training and jobs that use statistics.

Please note that the information detailed within this record is accurate at the time of publishing and may be subject to change. This record contains information for the most up to date version of the programme / module for the 2026/7 academic year.