This course provides participants with a firm working knowledge of a wide range of statistical models.
This course 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 course can be taken individually or combined over a period of time towards a Postgraduate Certificate of Professional Development https://www.ulster.ac.uk/courses/201819/postgraduate-certificate-of-professional-development-16046
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About this course
In this section
This course aims to provide a deeper understanding of the concepts underpinning the General Linear Model (GLM) and its application to commonly used statistical techniques such as analysis of variance, bivariate and multiple regression, logistic and multinomial regression, as well as introducing exploratory and confirmatory factor analysis.
The course begins by exploring the general linear model and its application in ANOVA, ANCOVA, MANOVA and MANCOVA with repeated measures models. The course will describe simple bivariate regression and correlation and build gradually to the multivariate case, which incorporates a number of predictor variables. In addition to examining regression models with a continuous outcome variable, time will be devoted to data situations in which the outcome variable is either dichotomous or polytomous, i.e. binary and multinomial logistic regression models. Moreover, exploratory factor analysis (EFA) will be covered in some depth, with the focus on its usefulness as a data reduction method: the EFA model primarily involve reducing a large number of observed variables to a lesser number of latent factors, the purpose of which is to explain the structural relationship between the observed variables parsimoniously. The course will conclude with an introduction to the Confirmatory Factor Analysis models and its applications using advanced statistical software. The assumptions underpinning the use of all techniques will be considered throughout the course, together with identifying some strategies for assessing potential violations.
Linked programmesPgCertPD Postgraduate Certificate of Professional Development, PgCert Quantitative Methods for the Behavioural and Social Sciences
100% coursework - (a) written practical report which demonstrates proficiency in using and interpreting t-test/ANOVA/ANCOVA/MANCOVA using a Randomised Control Trial (RCT) dataset (50%) and (b) written assignment employing different analytic techniques to test a set of hypotheses relating to an applied health research study (50%).
This course requires attendance for three full days (9am - 5pm) during the period from 20 August to 31 August 2018 (dates to be confirmed).
Degree in the behavourial or social sciences (e.g. psychology, geography, political science, health econmics, sociology or equivalent).
English Language Requirements
Applicants whose first language is not English must meet the minimum English entrance requirements of the University and will need to provide recent evidence of this (certified within the last two years).
Most of our courses require a minimum English level of IELTS 6.0 or equivalent, with no band score under 5.5. Trinity ISE: Pass at level III also meets this requirement.
Please see details of the English language qualifications and certificates we can accept - https://www.ulster.ac.uk/__data/assets/pdf_file/0005/177404/Other-english-language-tests-and-qualifications-2017.pdf
International applicants will also require a short-term study visa. Further information is available at https://www.ulster.ac.uk/international/visa-immigration
- 20 August 2018
Fees and funding
In this section
- Northern Ireland & EU:
- England, Scotland & Wales:
Information about how to pay for a course including different payment options is available at
Scholarships, awards and prizes
Fee waivers may be available to those who meet the eligibility criteria. More information is available from FlexEd@ulster.ac.uk