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.