Module code: PSY2017

Module Overview

This module builds on knowledge gained in year 1 modules PSY1020 and PSY1032. In particular, it reflects on what you previously learnt on ANOVAs, regression, and correlation, before moving on to more complex versions of these statistical analysis. For example, you will learn how to conduct mixed ANOVAs rather than simple one-way ANOVAs and build on your understanding of a simple regression to conduct advanced multiple and logistic regressions.  This will extend your portfolio of statistical analyses, enabling greater flexibility in the application of your skills to real world situations, thus enhancing your employability skillset.

Classes each week are structured around a statistical analysis, exploring its theoretical and mathematical basis, via research examples. How to conduct and correctly report each analysis will also be examined. In class activities and weekly workshops, offer a practical component. Predominantly this uses the digital software Jamovi, in a supportive environment with lecturers and teaching assistants. 

Module provider


Module Leader

PAYNE Sarah (Psychology)

Number of Credits: 15

ECTS Credits: 7.5

Framework: FHEQ Level 5

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

Overall student workload

Workshop Hours: 20

Independent Learning Hours: 102

Lecture Hours: 22

Guided Learning: 2

Captured Content: 4

Module Availability

Semester 1

Prerequisites / Co-requisites

PSY1035 (Introduction to Statistics and Data Analysis) must be completed prior to taking this module.

Module content

Indicative topic areas are:

  • Revision and introduction of statistical concepts, methods, and key topics

  • Advanced ANOVA-related techniques

  • Simple and multiple regression techniques

  • Logistic regression

  • Scale Development

  • Mediation and Moderation

Assessment pattern

Assessment type Unit of assessment Weighting
Coursework Research Designs, Statistical Report and Reflections (6 pages) 100

Alternative Assessment


Assessment Strategy

The assessment strategy is designed to provide students with the opportunity to demonstrate all the learning outcomes.

Thus, the summative assessment for this module consists of one assignment that covers all the learning objectives (1 to 5).

The assessment tests students’ knowledge gained from the module. Specifically, it assesses their ability to identify which statistical analysis would be suitable to apply to research scenarios (LO2) and if it is appropriate given the conditions and test assumptions (LO1). It will also assess students’ ability to work with data sets and run analyses in Jamovi (LO3) including running tests for assumptions (LO4). Students will present their findings in a report to showcase their understanding of data sets and interpretation of statistical analysis results (LO5). Additionally, questions may include i) requesting justifications for the analysis chosen and conducted, ii) reflections on their appropriateness given the conditions and iii) alternative approaches and/or research designs that are suitable for similar analysis procedures.

Formative assessment and feedback

To support assignment preparation, class and workshop activities will incorporate opportunities to demonstrate these learning outcomes during class activities (LO 1 & 2), and workshops (LO 1, 3, 4, & 5). Verbal feedback is provided in lectures and in workshops, while written answer sheets available for the workshops. Furthermore, peer feedback is provided when working together on the class and workshop activities, thus supporting the development of student resourcefulness and resilience

Module aims

  • Develop students' ability to understand the theoretical and mathematical basis of advanced statistical procedures.
  • Strengthen students' decision-making for choosing the appropriate advanced statistical procedure for analysing experimental and observational data, with knowledge of the assumptions and limitations of each procedure.
  • Advance students' skillset in conducting univariate and multivariate statistical analysis.
  • Progress students' ability to conduct advanced statistical procedures using digital tools such as Jamovi and interpret results.
  • Progress students' skills in utilising the correct formats for presenting data and results.
  • Develop skills in collaboratively solving research statistical questions.

Learning outcomes

Attributes Developed
001 Recognise when advanced statistical procedures are appropriate given the conditions and test assumptions. KCPT
002 Know which statistical analysis should be conducted for a given research problem and its data set. KCPT
003 Run univariate and multivariate analyses in Jamovi and understand how to handle large data sets and its output. KPT
004 Evaluate the assumptions, robustness, power, strengths, and limitations for each statistical procedure covered. KC
005 Interpret and report the results of advanced statistical analyses appropriately. KCPT

Attributes Developed

C - Cognitive/analytical

K - Subject knowledge

T - Transferable skills

P - Professional/Practical skills

Methods of Teaching / Learning

The learning and teaching strategy is designed to ensure the module aims are met and students perform optimally in respect to the learning outcomes.

Students build on their prior knowledge and experience, interpreting descriptive statistics (PSY 1020) and performing advanced versions of previously learnt statistical procedures (PSY1032). Similarly, each week’s class builds upon the prior class, with progressively more difficult analysis being conducted (e.g. simple regression then multiple regression then logistic regression). Knowledge is therefore progressively attained and allows opportunities to reflect on what has already been learnt.

Classes consist of providing core information on the theoretical and mathematical basis of the advanced statistical procedures. Research problems from across the globe and from psychology colleagues are used to show the applicability of performing these statistical procedures and the real-world issues they help support.  

Demonstrations of how to conduct analysis in Jamovi, the output it provides, and how to interpret and report the results, is presented. This open-source software extends students digital capabilities and makes it a future resource that can be used outside of academia, thus enhancing employability.

Dispersed within the theoretical and practical information, are class activities for students to work on, individually or in pairs. This provides an active student-centered component to the class, developing students' resourcefulness and resilience. Weekly computer workshops consist of individual or paired activities involving the investigation and analysis of data sets utilising Jamovi software, that draws attention to specific statistical considerations or practices. All activities test students’ application and understanding of the topic in a manner similar to the assignment. Answers are provided verbally by the lecturer or workshop tutors during the sessions and are also later available online for those who work at a different pace. Discussion forums and an end of module review class provides further opportunities for students to seek clarification or support.

These opportunities for active participation provide students with the opportunity for deeper learning as they can i) review their knowledge on a topic, ii) apply that knowledge to a new problem, and iii) gain feedback on their understanding. Furthermore, students can collaboratively work on these activities to build up their ability to communicate on the research design problem and appropriate statistical analysis given the conditions. This is a transferable skill for when students need to communicate their decisions to others. For example, in the short term this will be important for communicating with dissertation supervisors, and in the long term is necessary for team roles in future employment.

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
Upon accessing the reading list, please search for the module using the module code: PSY2017

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 is designed to allow students to develop knowledge, skills, and capabilities in the following areas:


Vital transferrable decision-making skills necessary in employment are developed throughout this module. In particular, students learn to interpret and understand a research problem (e.g. determine what is the independent and dependent variables), assess its conditions (e.g. does it meet assumptions of certain statistical tests) and derive a decision (e.g. choose an advanced statistical analysis to perform) through a systematic appraisal of the provided information. Furthermore, being able to comprehensively report these results (e.g. document the statistical output) is just as vital for coherent communication.

Digital Capabilities:

Students will continue to develop their skills in open-source software Jamovi, used for statistical analysis. Further understanding of how to use it and interpret the output will create a student who is digitally capable of performing statistical analyses outside of education and in further employment. It also extends their ability to perform advanced statistical analysis in subsequent module assignments and in their dissertation if they so choose.  

Global and Cultural Capabilities:

Presentation of research examples from across the globe, where statistical analyses are conducted on a range of global and sustainable development issues, the module will highlight the value the statistical analysis has in multiple situations. The class and workshop activities also enable collaboratively working with peers of diverse cultural and heritage experiences, thus helping develop competencies to engage respectfully and effectively with different people.

Resourcefulness and Resilience:

Through multiple opportunities to conduct activities with peers, students can build supportive relationships to explore their understanding of the problem and apply, with reasons, their knowledge to collectively decide the appropriate statistical analysis to perform. The opportunities to verbally discuss and communicate their thoughts will help provide a deeper learning and understanding of the statistical questions.

Programmes this module appears in

Programme Semester Classification Qualifying conditions
Psychology BSc (Hons)(CORE) 1 Core Each unit of assessment must be passed at 40% to pass the module

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 2024/5 academic year.