FUNDAMENTALS OF QUANTITATIVE DATA ANALYSIS - 2026/7

Module Overview

This course is a lab-based, practical introduction designed for students with little or no prior experience in statistics. It focuses on developing foundational skills in data analysis using the freely available jamovi software, alongside an understanding of the underlying concepts. Students with more advanced statistical knowledge should consider PSYM187 - Advanced Quantitative Data Analysis instead.

Module provider

Psychology

Module Leader

FEHER DA SILVA Carolina (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

None.

Module content

Indicative content:

1. Basic statistics concepts
2. Introduction to Jamovi
3. How to choose statistical analyses
4. Enhancing data quality, e.g., power analysis, data screening, and reliability
5. Making predictions, e.g., linear regression
6. Significance of group differences, e.g., t-tests, ANOVA, and variants

Assessment pattern

Alternative Assessment

Reasonable adjustments to assignments are made on a case-by-case basis - please contact the module convenor to discuss as soon as possible and before you start the assignment.

Assessment Strategy

The assessment strategy is designed to provide students with the opportunity to demonstrate knowledge of common statistical procedures used in Psychology and the ability to use jamovi to conduct statistical analysis. Thus, the summative assessment for this module consists of:
* Online data analysis short-answers test
* Online data analysis exam
In both assessments, students will be provided with a dataset they are required to analyse, and will submit both their answers and evidence of the process they used to obtain those answers.

Module aims

• This module aims to provide students with a foundational understanding of core statistical principles and practical data analysis skills using Jamovi.

Learning outcomes

Methods of Teaching / Learning

The learning and teaching strategy is designed to give students the opportunity to gain hands-on experience of using jamovi to solve real, research-based statistical questions in psychology. There will be a combination of lectures and computer-based exercises with in-class formative feedback.

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: PSYM188

Other information

The School of Psychology is committed to developing graduates with strengths in Employability, Digital Capabilities, Global and Cultural Capabilities, Sustainability, and Resourcefulness and Resilience. This module contributes to the development of the following capabilities:

Employability

This module will equip students with the ability to evaluate and conduct research with quantitative methods, which will prepare them for jobs in different sectors requiring a research and development element.

Digital Capabilities

This module will develop students' digital capabilities in quantitative data analysis, statistical reasoning, and the communication of research findings. Students will gain practical experience using digital learning environments and specialist statistical software to support data analysis and interpretation. SurreyLearn will be used to access teaching materials, submit work, and engage with digital learning activities.

Students will use statistical software, including jamovi, to conduct and interpret statistical analyses as well as present their results, and G*Power to understand and perform statistical power calculations relevant to research design.

The module also introduces students to the responsible and effective use of AI tools available through the My AI Surrey platform. Students will learn how AI can support conceptual understanding and revision while maintaining academic integrity by conducting all statistical analyses independently using the designated software.

Global and Cultural Capabilities

The module will use examples of statistical problem solving from all over the world to demonstrate the universality of the need for quantitative analyses in different settings. Further, many of the class exercises are group-based, which offers a chance for students from different cultural backgrounds to learn from each other, thus building such competencies as cross-cultural understanding and tolerance.

Sustainability

Many of the examples used in the class involve social, economic, and environmental problems, and students will get to appreciate the power of quantitative analysis tools in promoting sustainability.

Resourcefulness and Resilience

Students will use a range of sources to help understand statistical concepts, make decisions in analyses, and present the results of their analyses. Working in groups during class exercises will help build teamwork skills. Working to deadlines will make students more resourceful in terms of more effective allocation of time and effort. Finding solutions for real-life statistical problems will require students to excel in resourcefulness and resilience.

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.