- Module description
Applied Quantitative Data Analysis (POLM809)
The purpose of the course is to improve your quantitative skills and to stimulate interest in quantitative methods across humanities and social sciences. A basic understanding of data collection, analysis and interpretation is essential for contemporary research in many disciplines, both to enable researchers to make direct use of these techniques in their own research and for meaningfully engaging with work that uses these approaches. The course prepares you to conduct research on topics that involve quantitative evidence; however, we note that the line between quantitative and qualitative data is often blurred (e.g. nominal categories). This module aims to build upon the closely linked modules on research methods training [HPSM044 and HPSM Qual Methods XXX] to deliver detailed methodological and technical knowledge of a wide range of quantitative analytical methods used in social science research. You will acquire skills to analyse data in various forms and using a variety of quantitative tools and techniques. You will learn the strengths and weaknesses of various techniques and be taught how to deal with issues such as missing data and data bias. By the end of a programme of practical demonstrations, associated lectures, and practical assignments this module aims to have enhanced your skills in the analysis and presentation of quantitative data appropriate to a wide range of research problems. There will be an emphasis placed upon applying the techniques learned and the practical experience of analysing quantitative data sets. You will learn how to construct data sets from individual and aggregate level data. You will then learn how to analyse the data using the appropriate statistical tools. You will learn how to apply advanced parametric and non-parametric statistics, multivariate statistics, multiple regression to original and canned data sets. Yous will also learn various techniques for the visual display of data. You will focus on the analysis of questionnaires, historical data, content analysis and other data sources. Examples will be drawn from the humanities and social sciences.