Postgraduate Course: Core quantitative data analysis 1 and 2 (SCIL11009)
|School||School of Social and Political Science
||College||College of Arts, Humanities and Social Sciences
|Credit level (Normal year taken)||SCQF Level 11 (Postgraduate)
||Availability||Available to all students
|Summary||The course introduces key statistical ideas and methods for social and political research. It is designed for students who have little or no previous experience or knowledge of statistics, or even a phobia for numbers, or for those who feel they need a refresher course on the subject. The emphasis is on learning and understanding by doing, using 'real' data, rather than memorising formulae or rules of procedure. Each on-line learning module is supplemented by self-tests and activities to give students practice in the exploration and analysis of quantitative data using the Stata software package, copies of which may also be provided free of charge to students for use on their own personal computers. In line with ESRC postgraduate research training guidelines, the aim of the course is to ensure that students are able to understand and use basic quantitative methods.
The course is divided into two parts. Firstly, focusing on exploratory and descriptive data analysis. It considers the theoretical basis for using numbers in social research and examines the production and interpretation of tables as a way of presenting empirical evidence. It introduces fundamental concepts and areas such as cases, variables and values; levels of measurement; the graphical representation of data; measures of central tendency and dispersion; confidence intervals; principles of inference and the logic of obtaining empirical evidence about populations from samples; and hypothesis formulation and testing. The second half of the course explores different statistical tests including difference in means testing, bivariate tests for categorical variables, linear and logistic regression. Students apply these concepts to research questions of their own choosing throughout the course.
This course is intended to introduce students to the use of statistical methods within the social sciences. It is a practical course in which students will learn the basics of conducting their own quantitative research using the Stata statistical software package. The focus of the teaching will be on issues of understanding, critiquing and reporting statistical social science. As such, the course will be of interest, not only to those who wish to conduct quantitative research, but also to those who wish to be able to read, understand, and engage with, quantitative research written by others.
Lectures are provided throughout the course in order to provide students with key information about the different statistical techniques covered by the course. However, the focus of teaching will be on 'learning through doing'. Online teaching materials are provided for each topic, allowing students to study at their own pace and to access detail of the different statistical techniques at a level with which they feel comfortable. These online materials are interactive, providing illustrations of the key research design issues involved in conducting quantitative research. They provide worked examples of the statistical techniques taught on the course, step-by-step examples of how to conduct your own analysis in Stata and guidance as to how to interpret the results provided by the analysis.
In addition to the online materials and lectures, students attend a weekly microlab session in which they complete guided practicals to learn the use of the Stata software package. These small group sessions, led by experienced quantitative researchers, provide a setting in which students can ask for further in depth advice on any techniques, or topics, they feel they do not fully understand.
The material covered on the course will help students develop a critical understanding of why quantitative analysis can be useful within the social sciences, the pitfalls associated with its use, and provide an introduction to the key analytical techniques used in published social science research.
Major topics covered on the course include:-
How to measure things quantitatively: This section will consider whether all types of social phenomenon are appropriate for quantitative analysis, issues around the process of measuring things in quantitative terms and the different forms quantitative data can take.
Summarising data: How can we summarise the board characteristics of data? Identifying 'typical values' and the how spread out cases are. This section will also consider issues around representing data in graphical ways.
Relationships between variables: How can we establish if two different measures are related. We will consider whether the existence of a relationship between two variables is appropriate evidence for establishing a causal link. Consideration will be given as to the different statistical techniques that can be sued to establish if two variables are related with guidance provided on how the choice of technique will vary depending on the type of measurements under consideration. Techniques to be covered will include correlation, simple regression, cross-tabulations, Chi-square, Gamma and Cramer's V.
Statistical analysis with more than two measurements: This section will consider how social phenomenon are typical the result of multiple explanatory factors. It will consider what this means for conducting statistical analysis and look at techniques which allow us to consider multiple possible explanations of an outcome simultaneously. Techniques to be covered will include three-way cross-tabulations, multiple regression and binary regression.
Midway through the course students will submit an assignment that is worth 35% of the course. At the end of the course students will submit an assignment worth 65% of the course. Both assignments will require students to apply the material learnt in the course to a research question of their own choosing. In the final assignment students will also be required to evaluate a peer reviewed article.
Each set of online teaching materials, which students are required to engage with on a weekly basis, concludes with a quiz intended to test students understanding of the topics covered. While not providing part of the formal grading for the course, these quizzes provide students with guidance as to the strength of their understanding and help highlight areas where students might wish to engage in further study.
Written feedback will be provided to all students with regards to their coursework. This feedback will be returned within 15 working days.
Information for Visiting Students
|High Demand Course?
Course Delivery Information
|Academic year 2023/24, Available to all students (SV1)
|Course Start Date
|Learning and Teaching activities (Further Info)
Seminar/Tutorial Hours 20,
Programme Level Learning and Teaching Hours 4,
Directed Learning and Independent Learning Hours
|Additional Information (Learning and Teaching)
|Assessment (Further Info)
|Additional Information (Assessment)
||1) Short Written Assignment (35% of overall mark)
2) Long Written Assignment (65% of overall mark)
||Each set of online teaching materials, which students are required to engage with on a weekly basis, concludes with a quiz intended to test students understanding of the topics covered. While not providing part of the formal grading for the course, these quizzes provide students with guidance as to the strength of their understanding and help highlight areas where students might wish to engage in further study.,
Written feedback will be provided to all students with regards to their final coursework submission. This will be returned within 15 working days.
|No Exam Information
On completion of this course, the student will be able to:
- Understand the links between theory and method, including the potential and limitations of quantitative evidence
- Understand and have a thorough grounding in exploratory and descriptive data analysis
- Understand how to use computer software for statistical analysis of large datasets
- Understand and apply simple and multivariate regression analyses with continuous and discrete data
- collect, clean and analyse data and present the results to professional standards.
|Elliot J. and Marsh C. (2008) Exploring Data (2nd edition), Cambridge: Polity.|
Fielding J. and Gilbert N. (2006) Understanding Social Statistics (2nd edition), London: Sage.
|Graduate Attributes and Skills
|Additional Class Delivery Information
||Lectures in weeks 1-10, plus weekly computer-based workshops. These will be supported by on-line materials to complement each week and drop-in tutorials.
|Keywords||quantitative methods exploration description inference
|Course organiser||Dr Christos Vrakopoulos
|Course secretary||Mr Adam Petras