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

Undergraduate Course: Introduction to Statistics for Social Science (SSPS08008)

Course Outline
SchoolSchool of Social and Political Science CollegeCollege of Humanities and Social Science
Credit level (Normal year taken)SCQF Level 8 (Year 1 Undergraduate) AvailabilityNot available to visiting students
SCQF Credits20 ECTS Credits10
SummaryIntroducing basic statistical tools for Students in the With Quantitative Methods Degrees

This course is the introduction to common quantitative techniques and software used in the social sciences. It is designed to meet the needs of students in the with Quantitative Methods degree programmes in SPS, and to provide them with a broad range of basic concepts and methods, which they will later use as the basis for intermediate and advanced quantitative techniques. The course is aimed at students who also study Sociology, Social Policy, Politics, and International Relations. As such, it will contain examples and applications relevant for all these disciplines. The course, with slight modifications, will be taught both as a first year option for students in the with Quantitative Methods degree programmes, and as a conversion course, aimed to bring students who have finished their first year to the level required to transfer to one of these degree programmes at the end of their first year of studies.
Course description Drawing on real data examples, and incorporating hands-on manipulation of data, this course will follow a path leading students from Univariate analysis exploring the distribution of a single variable through Bivariate analysis examining the association between two variables to the point of conducting basic Multivariate analysis which adds further variables.

In the first part (weeks 1-2), students will be introduced to key concepts and vocabulary used in quantitative methods in the social sciences and to some of the possibilities they offer. Two important tools will be introduced: existing datasets through the UK Data Service, and the SPSS software, which will be used throughout this course.
The second part (weeks 3-5) of the course focuses on associations between categorical variables, mainly focusing on the use of 2-way and 3-way contingency tables. Understanding the concept of control through the use of 3-way contingency tables (and supported by the use of measures of associations used for such tables) provides a visually clear concept of the meaning of control, which will be invaluable for the rest of the with QM degree.
The third part (weeks 6-8) of the course turns to continuous variables, focusing on Pearson¿s r and simple and multiple linear regression. As many intermediate and advanced quantitative methods are based on the principles of regression models, the course will allow students to learn this technique in depth, including solutions for situations in which the independent variables are not continuous, and where independent variables meaningfully interact with one another.
In the fourth and final part (weeks 9-10) students will be encouraged to consider the uses of quantitative methods in a wider social context. The issue of representativeness and inference will be discussed in relation to the process of producing survey knowledge and to sampling in surveys. Finally, the course will look at communicating research findings, including graphic techniques. Week 11 will be conclusion & revision.


1: Introduction

Week 1: Introduction: Why do quantitative methods? And an introduction to secondary data access and management

Week 2: Distribution and introduction to SPSS

2: Associations with categorical variables

Week 3: Contingency tables

Week 4: Measures of association between categorical variables

Week 5: Causality and the concept of control in a 3-way contingency table

Part 3: Associations with continuous variables

Week 6: Bivariate analysis for continuous variables

Week 7: Introduction to multiple linear regression

Week 8: Linear regression continued: Dummy variables and interactions

Part 3: The bigger picture: Inference and communicating research

Week 9: Inference and the logic of sampling

Week 10: Summary: Communicating quantitative analysis

Week 11: Conclusion & revision
Entry Requirements (not applicable to Visiting Students)
Pre-requisites Co-requisites
Prohibited Combinations Other requirements Higher/A level Maths at B required
Course Delivery Information
Not being delivered
Learning Outcomes
At the end of this course, students are expected to have the following skills:
-Basic SPSS skills, including graphical skills and skills of presenting summaries of data clearly
-A basic understanding of secondary data access and management
-An understanding of measures of association
-An understanding of inference and the logic of sampling
-Communication of basic statistics
-Appreciation of the difference between association and causality
-An understanding of the concept of control
-Being able to construct 3 way cross-tabulations
-A basic understanding of regression analysis
Reading List
D.M. Diez, C.D. Barr, & M.C. Etinkaya-Rundel (2013) OpenIntro Statistics (2nd edition),
http://www.openintro.org/stat/textbook.php
J. Pallant (2010 ¿ 4th edition) SPSS Survival Manual, Maidenhead: Open UP
C. Marsh & J. Elliott (2008) Exploring Data (2nd edition), Cambridge: Polity
J. Fielding & N. Gilbert (2006) Understanding Social Statistics (2nd edition), London: Sage
D. Freedman et al. (various editions), Statistics, London: Norton
H. Blalock (various editions), Social Statistics, New York: McGraw-Hill
Additional Information
Graduate Attributes and Skills Not entered
KeywordsNot entered
Contacts
Course organiser Course secretaryMr Edwin Cruden
Tel: (0131 6)51 5197
Email: Edwin.Cruden@ed.ac.uk
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