2020/21 Full-time Postgraduate course
Faculty of Life and Health Sciences
School of Psychology
Short-term, flexible and focused training in quantitative research methods, data analysis, and statistics, for aspiring and developing researchers.
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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.
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This 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.
Structure and Content
The course is a 60-credit PgCert in Quantitative Methods for the Behavioural and Social
Sciences. There are three compulsory modules and students elect to do one optional module from a choice of three:
PSY707: Foundations in data analysis using SPSS (10 credits)
PSY706: Survey methodology (15 credits) - solely online
PSY711: Principles of research design (15 credits) - solely online
Options (Students elect to complete one; all 20-credits)
PSY708: Introduction to the general linear model
PSY709: Introduction to latent variable modelling
PSY710: Analysing longitudinal data
Four computing and statistics modules (PSY707, 708, 709, and 710) are delivered
on-campus in condensed face-to-face teaching blocks in Semester 3 (late-August/early September). The face-to-face computing and statistics sessions provide students with an
opportunity to learn the practical elements of the module in an interactive
environment. Students choose one optional module that most closely aligns to their individual research interests and training needs. PSY708 covers a wide range of commonly used statistical techniques such as analysis of variance (ANOVA), bivariate and multiple regression, logistic and multinomial regression, as well as introducing exploratory and confirmatory factor analysis. PSY708 would be most applicable for students who have had limited experience in the analysis of quantitative data in their primary degree, or for those experienced academics who may have covered this material to some extent in their
primary degree years previously. PSY709 introduces students to the latent variable modelling framework, and would be most suitable for students who wish to test more complex research hypotheses using a robust statistical framework. PSY710 focuses on developing skills that are necessary for data that is longitudinal in nature (e.g. data collected on individuals or a group over time). This module would be most suitable for students who have a good general knowledge of latent variable modelling, and wish to apply this type of analysis to longitudinal data. Students will be guided to select the optional module that is most suitable and relevant to their training needs.
The two fully online modules, PSY706 and PSY711, are more theoretical/conceptual in nature, covering all aspects of survey methodology and the principles of research design. Both modules will teach students the main strategies and stages for designing research studies to collect high quality, robust quantitative data, while also teaching students the necessary skills to be able to critically evaluate findings from published research studies in their discipline. These two modules have been designed to help students understand the design and methodological processes that must be undertaken to collect quantitative data that is of a high quality and amendable to empirical investigation using appropriate
This course has been designed by experienced quantitative research staff in the School of Psychology. The School of Psychology at Ulster has a long and strong tradition of employing academic and research staff with expertise in quantitative research methods. Staff interests and expertise have ensured that a strong emphasis is always placed on helping students to acquire the necessary skills to conduct high-quality research using a range of methodologies. Staff also have substantive research interests in applied psychology, with a specific focus on mental health and wellbeing (as evidenced by extensive research funding and multiple peer-reviewed research publications in high-quality international peer-reviewed journals). Much of this internationally recognised research is conducted using national, epidemiological, cross-sectional and longitudinal data resources from around the world under the theme of ‘Mental Health’, within the Psychology Research Institute. Since 2005, senior staff have also successfully secured substantial grant income from the Economic and Social Research Council (ESRC) to provide training in advanced statistical modelling to research staff and students at institutions in Northern Ireland and across the UK. They are also one of the leading recipients of ESRC Secondary Data Analysis Initiative (SDAI) funding for administrative data analysis in the UK. The majority of the teaching team are Fellows of the Higher Education Academy, some of whom have received recognition for their teaching.
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.
Full-time students take all modules in Semester 3&1 (spanning late-August to mid-January).
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.
The content for each course is summarised on the relevant course page, along with an overview of the modules that make up the course.
Each course is approved by the University and meets the expectations of:
As part of your course induction, you will be provided with details of the organisation and management of the course, including attendance and assessment requirements - usually in the form of a timetable. For full-time courses, the precise timetable for each semester is not confirmed until close to the start date and may be subject to some change in the early weeks as all courses settle into their planned patterns. For part-time courses which require attendance on particular days and times, an expectation of the days and periods of attendance will be included in the letter of offer. A course handbook is also made available.
Courses comprise modules for which the notional effort involved is indicated by its credit rating. Each credit point represents 10 hours of student effort. Undergraduate courses typically contain 10- or 20-credit modules (more usually 20) and postgraduate course typically 15- or 30-credit modules.
The normal study load expectation for an undergraduate full-time course of study in the standard academic year is 120 credit points. This amounts to around 36-42 hours of expected teaching and learning per week, inclusive of attendance requirements for lectures, seminars, tutorials, practical work, fieldwork or other scheduled classes, private study, and assessment. Part-time study load is the same as full-time pro-rata, with each credit point representing 10 hours of student effort.
Postgraduate Master’s courses typically comprise 180 credits, taken in three semesters when studied full-time. A Postgraduate Certificate (PGCert) comprises 60 credits and can usually be completed on a part-time basis in one year. A 120-credit Postgraduate Diploma (PGDip) can usually be completed on a part-time basis in two years.
Class contact times vary by course and type of module. Typically, for a module predominantly delivered through lectures you can expect at least 3 contact hours per week (lectures/seminars/tutorials). Laboratory classes often require a greater intensity of attendance in blocks. Some modules may combine lecture and laboratory. The precise model will depend on the course you apply for and may be subject to change from year to year for quality or enhancement reasons. Prospective students will be consulted about any significant changes.
Assessment methods vary and are defined explicitly in each module. Assessment can be a combination of examination and coursework but may also be only one of these methods. Assessment is designed to assess your achievement of the module’s stated learning outcomes. You can expect to receive timely feedback on all coursework assessment. The precise assessment will depend on the module and may be subject to change from year to year for quality or enhancement reasons. You will be consulted about any significant changes.
Coursework can take many forms, for example: essay, report, seminar paper, test, presentation, dissertation, design, artefacts, portfolio, journal, group work. The precise form and combination of assessment will depend on the course you apply for and the module. Details will be made available in advance through induction, the course handbook, the module specification and the assessment timetable. The details are subject to change from year to year for quality or enhancement reasons. You will be consulted about any significant changes.
Normally, a module will have 4 learning outcomes, and no more than 2 items of assessment. An item of assessment can comprise more than one task. The notional workload and the equivalence across types of assessment is standardised.
Calculation of the Final Award
The class of Honours awarded in Bachelor’s degrees is usually determined by calculation of an aggregate mark based on performance across the modules at Levels 5 and 6, (which correspond to the second and third year of full-time attendance).
Level 6 modules contribute 70% of the aggregate mark and Level 5 contributes 30% to the calculation of the class of the award. Classification of integrated Master’s degrees with Honours include a Level 7 component. The calculation in this case is: 50% Level 7, 30% Level 6, 20% Level 5. At least half the Level 5 modules must be studied at the University for Level 5 to be included in the calculation of the class.
All other qualifications have an overall grade determined by results in modules from the final level of study. In Master’s degrees of more than 200 credit points the final 120 points usually determine the overall grading.
The University employs over 1,000 suitably qualified and experienced academic staff - 59% have PhDs in their subject field and many have professional body recognition.
Courses are taught by staff who are Professors (25%), Readers, Senior Lecturers (18%) or Lecturers (57%).
We require most academic staff to be qualified to teach in higher education: 82% hold either Postgraduate Certificates in Higher Education Practice or higher. Most academic staff (81%) are accredited fellows of the Higher Education Academy (HEA) - the university sector professional body for teaching and learning. Many academic and technical staff hold other professional body designations related to their subject or scholarly practice.
The profiles of many academic staff can be found on the University’s departmental websites and give a detailed insight into the range of staffing and expertise. The precise staffing for a course will depend on the department(s) involved and the availability and management of staff. This is subject to change annually and is confirmed in the timetable issued at the start of the course.
Occasionally, teaching may be supplemented by suitably qualified part-time staff (usually qualified researchers) and specialist guest lecturers. In these cases, all staff are inducted, mostly through our staff development programme ‘First Steps to Teaching’. In some cases, usually for provision in one of our out-centres, Recognised University Teachers are involved, supported by the University in suitable professional development for teaching.
Figures correct for academic year 2019-2020.
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Here is a guide to the subjects studied on this course.
Courses are continually reviewed to take advantage of new teaching approaches and developments in research, industry and the professions. Please be aware that modules may change for your year of entry. The exact modules available and their order may vary depending on course updates, staff availability, timetabling and student demand. Please contact the course team for the most up to date module list.
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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.
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.
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.
This module is optional
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.
This module is optional
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.
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.
We recognise a range of qualifications for admission to our courses. In addition to the specific entry conditions for this course you must also meet the University’s General Entrance Requirements.
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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 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.
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.
Typically we require applicant for taught programmes to hold the equivalent of a UK first degree (usually in a relevant subject area). Please refer to the specific entry requirements for your chosen course of study as outlined in the online prospectus. We consider students who have good grades in the following:
|Level 12 English Lang in HSD|
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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).
Course Director: Dr Orla McBride
Admissions Office - Anne Henderson / Karen Gibson
International Admissions Office