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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 1 and 2 (SCIL11009)

Course Outline
SchoolSchool of Social and Political Science CollegeCollege of Humanities and Social Science
Credit level (Normal year taken)SCQF Level 11 (Postgraduate) AvailabilityAvailable to all students
SCQF Credits20 ECTS Credits10
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 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 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 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; correlation and regression; and patterns of causality in three or more variables. The second half of the course explores principles of inference and the logic of obtaining empirical evidence about populations from samples; confidence intervals; hypothesis formulation and testing; elementary multivariate analysis; and linear and logistic regression.
Course description 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 SPSS 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 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.

Outline content

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.
Entry Requirements (not applicable to Visiting Students)
Pre-requisites Co-requisites
Prohibited Combinations Other requirements None
Information for Visiting Students
High Demand Course? Yes
Course Delivery Information
Academic year 2018/19, Available to all students (SV1) Quota:  100
Course Start Semester 1
Timetable Timetable
Learning and Teaching activities (Further Info) Total Hours: 200 ( Seminar/Tutorial Hours 20, Programme Level Learning and Teaching Hours 4, Directed Learning and Independent Learning Hours 176 )
Additional Information (Learning and Teaching) "50/50
Assessment (Further Info) Written Exam 0 %, Coursework 75 %, Practical Exam 25 %
Additional Information (Assessment) 1) A multiple choice exam (typically taken in class in week 6) weighted 25% of the course grade
2) A practical assignment (coursework completed at the end of the course) weighted 75% of the course grade
Feedback 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.,

At the conclusion of the multiple choice exam the correct answers, and the reasons for them, will be discussed in class.

Written feedback will be provided to all students with regards to their final coursework submission. This will be returned within 15 working days.
Exam Information
Exam Diet Paper Name Hours & Minutes
Outwith Standard Exam Diets OctoberMultiple Choice Test1:00
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 and multivariate regression analyses with continuous and discrete data
  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 Lectures in alternate weeks 1-11, plus weekly computer-based workshops. These will be supported by on-line materials to complement each week and drop-in tutorials.
Keywordsquantitative methods exploration description inference
Course organiserDr Paul Norris
Tel: (0131 6)50 3922
Course secretaryMr Jack Smith
Tel: (0131 6)51 1485
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