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DEGREE REGULATIONS & PROGRAMMES OF STUDY 2015/2016

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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 free-standing, separately assessed 10-credit parts, although most students take the entire 20-credit course in one semester. Part 1 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; correlation and regression; and patterns of causality in three or more variables. Part 2 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 Introduction to quantitative data analysis; Levels of measurement; Discrete and continuous variables
Summarising data: Measures of spread and central tendency; Presenting data in table and charts
Relationships between variables: correlation, association and causation; simple linear regression
Measures of association; Modelling nominal and ordinal variables
Relationships between more than two variables: controlling for a third variable
Probability; The normal distribution; Sampling and inference
Hypothesis formulation and testing for categorical variables
Multiple linear regression
Binary logistic regression
Entry Requirements (not applicable to Visiting Students)
Pre-requisites Co-requisites
Prohibited Combinations Other requirements None
Information for Visiting Students
Pre-requisitesNone
High Demand Course? Yes
Course Delivery Information
Academic year 2015/16, Available to all students (SV1) Quota:  100
Course Start Semester 1
Course Start Date 21/09/2015
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 60 %, Practical Exam 40 %
Additional Information (Assessment) Part 1 is assessed by means of a multiple choice test. Part 2 is assessed by means of a take home exercise that requires students to analyse quantitative data from a variety of sources and report their findings. For those students taking both parts 1 and 2, the assessment marks in each will be aggregated to provide one overall mark.
Feedback Not entered
Exam Information
Exam Diet Paper Name Hours & Minutes
Outwith Standard Exam Diets October1:00
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
Contacts
Course organiserProf Andrew Thompson
Tel: (0131 6)51 1562
Email: Andrew.Thompson@ed.ac.uk
Course secretaryMr Andrew Macaulay
Tel: (0131 6)51 5067
Email: Andrew.Macaulay@ed.ac.uk
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