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DRPS : Course Catalogue : School of Social and Political Science : Postgrad (School of Social and Political Studies)

Postgraduate Course: Core quantitative data analysis for social research: part 1 (PGSP11078)

Course Outline
SchoolSchool of Social and Political Science CollegeCollege of Arts, Humanities and Social Sciences
Credit level (Normal year taken)SCQF Level 11 (Postgraduate) AvailabilityAvailable to all students
SCQF Credits10 ECTS Credits5
SummaryThe 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 online learning module is supplemented by self-tests and activities to give students practice in the exploration and analysis of quantitative data using the SPSS 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 focuses 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; and patterns of causality in three or more variables.
Course description This course is intended to introduce students to key concepts relevant to the use of statistical methods within the social sciences. It is a practical course in which discussion of statistical concepts is linked to the use of the SPSS statistical software. Specifically, this short course, will focus on the understanding, critiquing and reporting statistical results within the social science. It is likely to be of particular interest to those who might wish to engage with quantitative social science research, rather than those who wish to conduct their own analysis.

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 SPSS 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 SPSS 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.

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.
Entry Requirements (not applicable to Visiting Students)
Pre-requisites Co-requisites
Prohibited Combinations Students MUST NOT also be taking Core quantitative data analysis for social research: part 2 (PGSP11079)
Other requirements None
Information for Visiting Students
High Demand Course? Yes
Course Delivery Information
Not being delivered
Learning Outcomes
On completion of this course, the student will be able to:
  1. Understand the links between theory and method, including the potential and limitations of quantitative evidence
  2. Understand and have a thorough grounding in exploratory and descriptive data analysis
  3. Understand how to use computer software for statistical analysis of large datasets
  4. Understand and apply simple bivariate regression analysis
  5. Communicate statistical evidence through graphs, tables and text
Reading List
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.
Additional Information
Graduate Attributes and Skills Not entered
Additional Class Delivery Information Students intending to take Core Quantitative Data Analysis parts 1 AND 2 should register for the 20 credit course (SCIL11009).
KeywordsNot entered
Course organiserDr Paul Norris
Tel: (0131 6)50 3922
Course secretaryMrs Beth Richardson-Mills
Tel: (0131 6)51 1659
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