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Quantitative Methods for the Behavioural & Social Sciences


This course aims to provide behavioural and social science graduates with high-quality training in quantitative research methods and statistics, which advances on their undergraduate-level training and is tailored to their future doctoral research training needs. It also aims to provide academic staff and other professionals with a platform to undertake certified training in a specialised skill-set, which is necessary to facilitate the testing of specific research questions via the collection, sourcing and analysis of quantitative data, for the production of high-quality, high-impact research outputs.


This new blended-learning PgCert in Quantitative Methods for the Behavioural and Social Sciences has been designed by experienced quantitative research staff in the School of Psychology, based within the Faculty of Life and Health Sciences, at Ulster University. Targeted primarily at prospective PhD applicants who seek focussed and condensed quantitative analysis and methodology skills training; academics who seek flexible and accessible training to become both research literate and active; and researchers who seek expert guidance and training to enhance their quantitative analytic and methodological knowledge and skill, the course is a timely addition to the postgraduate training portfolio of UU. The course offers certified training in a specialised skill-set necessary (i) to design and conduct research studies using quantitative methodologies, (ii) to manipulate and analyse primary and secondary sources of quantitative data appropriately, and (iii) to present resulting research findings in the form of high-quality research outputs. The blended-learning mode of delivery (Coleraine) will offer students the freedom and flexibility to study for a postgraduate award in their own time and at their own pace, whilst also offering expert face-to-face support and guidance to master aspects of methods and statistics training that are traditionally recognised to be challenging for the novice researcher/analyst.

Teaching and learning assessment

Teaching will involve a combination of on-campus teaching blocks (comprising lectures and practical computing and statistics workshops), as well as online lectures, seminars/interactive sessions and practicalexercises. Students will be expected to engage with the material presented, contribute to discussion boards, and will be encouraged to consolidate their skills as independent learners.

Entry requirements

Entry Requirements

Applicants must hold a degree in the behavioural or social sciences (e.g. psychology,
geography, political science, health economics, sociology, or equivalent) or demonstrate
their ability to undertake the course through the accreditation of prior experiential learning.

English Language Requirements

English language requirements for international applicants
The minimum requirement for this course is Academic IELTS 6.0 with no band score less than 5.5. Trinity ISE: Pass at level III also meets this requirement.

Ulster recognises a number of other English language tests and comparable IELTS equivalent scores.

Exemptions and transferability

Students may apply for Accreditation of Prior Learning (APL) for Module PSY707 (Foundations in data analysis using SPSS -10 credits) if evidence can be provided to indicate that the learning outcomes for this module have been already been obtained. Applicants can enquire about APL at the time of applying.



Students will be required to attend the Coleraine campus for a face-to-face teaching block in Semester 3 (late August/early September). The duration of time spent on campus during the teaching block will vary depending on the optional modules taken, but students can expect to be on campus for between 2-5 days (9am-5pm each day). The remainder of the course is delivered online via Ulster's virtual learning environment (Blackboard). Students are expected to go into the online environment on a regular basis and engage with the learning material. Students are expected to contribute to online weekly activities within the modules and complete these within the weekly deadlines set. Attendance is monitored.

Part-time students must take modules PSY707 and PSY708/PSY709/PSY710 in Semester 3&1, but take modules PSY706 and PSY711 in Semester 2.

Careers or further progression

Career options
The demands for quantitative skills in the workplace has risen sharply in the past two decades. Upon successful completion of this course, graduates will be ideally positioned to apply for early-career posts (e.g. research assistant; project manager posts) in a wide variety of academic and non-academic settings. It is expected that many graduates will use this course as a stepping-stone to doctoral-level research study. Professionals undertaking this course as continuing professional development may be able to diversify into other roles within their current/future employment (e.g. assume more research-related tasks in teaching, supervision, and enhance their own personal programme of research).

Further enquiries

Course Director: Dr Orla McBride
T: +44 (0)28 70123987

Admissions Services
T: +44 (0)28 70123210

International Admissions Office

Subjects taught

Year one
This module introduces students to the cornerstones of survey methodology. It provides students with the skills necessary to conduct a comprehensive and robust survey for the collection of primary quantitative data. It also teaches students how to identify and source rich, high-quality secondary data resources from reputable data repositories for research purposes. Students will also be knowledgeable of important professional, legal, and ethical issues in relation to data management and storage.

Foundations in data analysis using SPSS
This module establishes a foundation of basic research skills by introducing key concepts of: the scientific method; research designs used in the behavioural and social sciences; a range of graphical and descriptive statistical techniques; statistical inference; hypothesis testing; and, the application of SPSS in data analysis.

Principles of research design
Researchers in the social sciences must have a good understanding of and grounding in both the practice and philosophies of social science research. This module facilitates students to become informed consumers and producers of research. Students will explore different approaches to knowledge construction and examine various research paradigms, their approaches to enquiry and their underlying assumptions. Students will evaluate a range of research designs and methodological processes and will have opportunities to consider the principles which underpin and guide research.

Introduction to the general linear model
This module presents methods relating to statistical data analysis of data collected from both survey and experimental research. Issues relating to data quality, experimental and non-experimental design, and multivariate statistical analysis will be addressed during lectures and additional experience in the use of multivariate statistical techniques is gained through practical computer based sessions.

Introduction to latent variable modelling
This module introduces students to the principles of latent variable modelling (LVM) and how such statistical models can be specified and tested. LVM is now the preferred method of statistical analyses in all social sciences due to its power and flexibility. This module will cover the theoretical and statistical basis of LVM, and provide the students with the skills to specify, estimate and interpret such models. The module will be delivered by means of lectures and practical session. There will be extensive use of secondary data resources.

Analysing longitudinal data
This module is optional
The module seeks to develop students' knowledge and understanding of methods for analysing longitudinal data within a latent variable framework. Latent growth models, mixture models and models involving mediation and moderation will be described, together with combinations thereof. Students will be introduced to the concepts, terms and approaches underlying these models and will gain experience implementing and estimating the models using appropriate statistical software.

Application date

Application is through the University's online application system.

Enrolment and start dates

Start date: September 2019

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