Applied Quantitative Project
This unit requires students to complete an independent project which involves working with quantitative datasets and/ or working in an organisation.
Central to any quantitative analysis is data, and as such, data requires careful management. This unit will provide you with all the necessary knowledge and skills to competently manage data in a range of settings.
Foundations of Quantitative Analysis
This unit provides an introduction to the key foundations of quantitative analysis. It examines key quantitative concepts (such as, sampling, validity, generalisability) and their relationship to research design and analysis. The presentation of data (including data visualisation) will be explored. The unit will review key stages of analysis, including univariate, bivariate analyses; statistical tests for significance; simple linear regression; recoding; hypothesis and construction.
Quantitative Methodology and Research Design
This unit will provide an introduction to social science research methodology and design. It will explore the underpinning historical philosophies that have influenced social science inquiry generally and quantitative approaches specifically. Relatedly it will explore how such philosophical approaches have shaped research design and core methods concepts.
Correlation and regression are regarded as the meat and potatoes of contemporary social science. This unit focuses on regression which is by far the most widely used and versatile technique in quantitative research. It will introduce you to the theory and practice of regression. The unit comprises taught and practical components in equal proportions. Emphasis will be given on illustrating multiple regression through social science examples. SPSS will be used for the practical sessions in lab. However, those with advanced skills on statistical software will be encouraged to use STATA.
Questionnaire Design and Testing Psychometric Properties
Questionnaire is one of the most commonly used tools for collecting quantitative data. Good quality data in quantitative surveys depends on the successful designing of a questionnaire. This unit has two parts which are linked with each other. The first part will provide you with the tools required to design questionnaires in an efficient and effective manner. The second part is about how to develop multi-item scales in questionnaire and test their reliability and validity. In both parts, a practical approach will be used allowing you to discuss your own research interests and design for your own questionnaire. Teaching and learning will take place through lectures, workshops and lab activities.
Statistics in Practice: Engagement, Co-production and Impact
The overarching aim of this unit is to enable you to develop a critical awareness of the real world context of quantitative research. It will focus on the identification and development of your career-ready skills, specifically in relation to research impact, knowledge mobilisation,dissemination strategies and the presentation of research.
Likely Optional Units
Evaluation using Quantitative Methods
This module introduces you to evaluation with a focus on the use of quantitative methods. A range of evaluation frameworks (experimental and quasi-experimental) that tend to favour quantitative methods will be explored. You will assess the strengths and weaknesses of different evaluation frameworks and methods and also place these in the context of underlying epistemological debates. The potential for evaluation to influence policy and practice will be an important consideration during the module. We will look at the use of Systematic Review and meta-analysis, and discuss the potential for evidence-based policy. Much quantitative evaluation culminates in economic evaluation and we will look at widely used approaches to economic evaluation, including Cost Benefit Analysis.
Mixed Methods in Social Research
This unit explores the foundations of mixed method research, the applied considerations in planning and designing a mixed method research study. It will also give practical opportunity to conduct a mixed method study, develop an acute awareness of mixed method analytic techniques and gain valuable experience in disseminating mixed method findings through a poster presentation and report writing.
Social and behavioural scientists aim to explain variability in human behaviour and attitudes. However, many of these data have a hierarchical or clustered structure because of shared membership of social contexts - the family, the school, the workplace and so on. One way in which social scientists further their understanding of social behaviour is by using statistical models to analyse quantitative data. A weakness of the way in which these models are often applied to social data is that they focus too much on the individual, and too little on the social and institutional contexts in which individuals are located. Multi-level modelling aims to redress the balance by emphasising both individuals and their social contexts.
This unit is designed to give you an introduction to the theory and application of multi-level models. The main aim of the unit is to provide you with the necessary conceptual understanding and practical skills to be able to undertake report and interpret a multi-level linear model analysis through hands on practical sessions in labs, using SPSS/Stata/MLwiN statistical software package.