The module is aimed at enabling you to understand the principles of qualitative methods and analysis, and equipping you to apply qualitative research methods in practice. The module assumes no previous knowledge of either NVivo or qualitative analysis software training.
This module provides advanced training in the use of qualitative research methods in the Social Sciences, and particularly in the field of Gerontology. It builds on the introductory module Qualitative Methods 1. Indicative topics include ethnographic methods and discourse analysis.
The module progresses through the key phases of qualitative research design and implementation, emphasising the (often iterative) links between these activities: epistemological concerns, research questions, data collection and analysis techniques, and the interpretation and implications of qualitative findings. This module is delivered in interactive sessions taught over 6 weeks. We cover methodological concepts used in applied psychology and provide formative hands-on opportunities for skills development. Sessions are structured to facilitate both conceptual learning and the development of applied research skills. Students are expected to complete pre-class preparatory work and to take an active role in class. The module is aligned with the Research Development Framework (RDF) in the following ways. 1. Knowledge base. •The methods and techniques appropriate for research design •Literacy and numeracy skills and language abilities appropriate for research •How to identify, apply and develop methods and techniques appropriate for research projects. 2. Cognitive abilities. •How to analyse and evaluate findings using appropriate methods •How to recognise and validate problems; formulate and apply solutions to a range of research problems The module assumes basic prior knowledge of qualitative methods equivalent to that provided during an undergraduate degree in Psychology.
This module builds on year 1 research methods teaching. It aims to give students a rigorous critical understanding of a broad range of qualitative data collection and data analysis methods. It covers traditional methods such as interviews and focus groups and documentary research, as well as more cutting-edge tools such as social media analysis and online ethnography. It features an NVivo masterclass for those who are keen to improve their qualitative data analysis skills using the latest software. The module features real world research examples including studies by staff in order to bring these tools and techniques to life. Teaching is partly focused on practical skills but also about learning to critically evaluate the quality of qualitative research The qualitative research methods and tools, students learn about on the module may well be the ones they go on to use in their dissertation, so the module provides an important building block for the final year. As with other research methods modules in the school, the aim is to provide students with robust research skills they can take with them into the labour market.
This module provides an integrated introduction to the main quantitative and qualitative approaches used in political science It equips you with the conceptual understanding and practical skills needed to design, conduct, and evaluate quantitative and qualitative empirical studies. The module covers core topics including research design, sampling, measurement, data collection, and the logic of inference, as well as qualitative methods such as interviewing, focus groups, discourse analysis, and ethnography. You will learn to select appropriate methods for different types of research questions, to critically assess the strengths and limitations of each approach, and to interpret and communicate empirical findings effectively. The module serves as a bridge between philosophical foundations and applied research practice across the MSc Politics pathways.
The purpose of this module is to refresh and build upon statistics knowledge and skills acquired during undergraduate psychology quantitative research methods training. It is divided into 11 sessions that cover a range of quantitative methodological concepts and analysis techniques used in psychology, and provides hands on experience using the statistical software SPSS. The format of sessions include lectures, step-by-step SPSS demonstrations, and formative quizzes and activities to practice what has been learnt.
This module aims to provide you with an opportunity to develop an appreciation of management research in theory and practice. There are two broad objectives: (1) to enhance your knowledge of the research process and enable you to be aware of the problems associated with research, and (2) to prepare you to carry out your own research, in most cases your dissertation.
It is important that we provide bioinformatic cell analysis training to students to significantly improve research possibilities in their future careers in Biomedical Sciences. The quantitative cell biology (QCB) module will focus on the practical use of the methods employed, rather than focussing on just the mathematics and statistical approaches underpinning them. Some of the mathematics and statistics will be discussed, but no prior knowledge will be assumed. The analyses will predominantly be conducted using the R project for statistical computing software (https://www.r-project.org). Students with or without experience of R programming and/or mathematics will be enrolled on this course. Students with no background in this area will not be disadvantaged, as they will be provided with computing support, and training via attendance on a data carpentry course delivered by the Southampton Research Software Group (https://rsgsoton.net), to succeed. There is no opportunity to repeat the year on this programme.
This module familiarises students with the main empirical methodologies used in addressing economic question and in analysing and evaluating economic policy. Econometric methods will presented and applied to actual economic issues, including using appropriate statistical software.
The purpose of this module is to provide you with the necessary skills to undertake quantitative research in finance. In particular, we focus on analysing financial markets and firms’ investment and financing decisions. Lectures will introduce a broad range of topics (e.g. ARCH/GARCH). However, you will discover that by understanding and applying some basic concepts various issues can be analysed in a similar manner. In particular, we will introduce basic theoretical concepts developed in statistics and econometrics. Understanding the main theoretical methods is essential to appreciate the analytical tools and their applications to finance. The module is a compulsory module on the MSc Finance. The module introduces empirical methods used in finance and is a prerequisite for Advanced Time Series Modelling in the 2nd semester. In particular, cross-sectional, panel and time series methods are introduced and applied to financial data. The module will introduce methods developed in econometrics and apply these methods to financial data. The module will stress the relationship between finance, econometrics and statistics. The module will only be offered on the MSc Finance. The module provides an introduction to time series modelling, which will be extended in the optional module Advance Time Series Modelling (MANG6297).
MANG6003 aims to develop statistical reasoning. Via a series of examples and activities, students are introduced to the idea of probability modelling and how it can be applied to aid decision making in uncertain situations, which are frequently encountered in organisations. On successful completion of this module, students should be able to collect relevant data and summarise the main features of an uncertain situation, to identify standard problems and analyse them with the correct statistical tools, to process and analyse data in a statistical computer package, to understand the risks involved in a decision which involves uncertainty, and quantify such risks. Students should also develop problem solving skills, modelling skills, become familiar with a standard statistical computer package (SPSS), and be able to interpret and critically evaluate statistical results.
Assessment in the module takes the form of an online software skills test (worth 10% of the final mark) and a final written exam (worth 90%).
You will be introduced to a number of key statistical concepts and data presentation formats. Beginning with exposure to a variety of data types defining the nature and properties of data you are likely to encounter. Emphasis is placed on distinguishing between population parameters and sample statistics and exploring the nature of distributions. Aided via the introduction of R Studio, a dedicated statistical software, you will become familiar with the concept of central tendency and the measurement of variation, and how these may be presented graphically. Emphasis is placed on information transfer to aid presentations, essays, reports and dissertation. A significant portion of the unit is given to developing your understanding of a variety of common statistical procedures including establishing the presence and strength of a relationships and standard approaches for determining if significant differences exist between groups within a variety of experimental designs. Central to this is the concept of hypotheses testing.
