Undergraduate Course: Introduction to Statistics for Social Science (SSPS08008)
Course Outline
| School | School of Social and Political Science |
College | College of Humanities and Social Science |
| Credit level (Normal year taken) | SCQF Level 8 (Year 1 Undergraduate) |
Availability | Not available to visiting students |
| SCQF Credits | 20 |
ECTS Credits | 10 |
| Summary | Introducing 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.
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| 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
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Entry Requirements (not applicable to Visiting Students)
| Pre-requisites |
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Co-requisites | |
| Prohibited Combinations | |
Other requirements | While entry to this course normally requires a pass at B in Mathematics at SQA Higher or A-level, students with confidence in their level (high school equivalent) of mathematical knowledge will be considered for admission. Please contact the course convenor if would like to join the course but have any concerns about your current Mathematical knowledge being sufficient |
Course Delivery Information
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| Academic year 2015/16, Not available to visiting students (SS1)
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Quota: None |
| Course Start |
Semester 2 |
Timetable |
Timetable |
| Learning and Teaching activities (Further Info) |
Total Hours:
200
(
Lecture Hours 10,
Seminar/Tutorial Hours 20,
Programme Level Learning and Teaching Hours 4,
Directed Learning and Independent Learning Hours
166 )
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| Assessment (Further Info) |
Written Exam
60 %,
Coursework
0 %,
Practical Exam
40 %
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| Additional Information (Assessment) |
40% mid-term exam (comprised of multiple-choice questions). This constitutes a formative feedback event.
60% take home exam (students will conduct a series of analysis tasks and report them).
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| Feedback |
Not entered |
| No Exam Information |
Learning Outcomes
On completion of this course, the student will be able to:
- A basic understanding of secondary data collection, access and management using statistics software package
- A basic understanding of univariate statistics: graphical skills, presenting and communicating data
- A basic understanding of bivariate statistics, including measures of association
- An understanding of inference and the logic of sampling, of the difference between association and causality, and the concept of control
- A basic understanding of multiple linear regression analysis
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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
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Additional Information
| Graduate Attributes and Skills |
Not entered |
| Keywords | Not entered |
Contacts
| Course organiser | Dr Morag Treanor
Tel: (0131 6)50 3918
Email: morag.treanor@ed.ac.uk |
Course secretary | Mr Daniel Jackson
Tel: (0131 6)50 3932
Email: Daniel.Jackson@ed.ac.uk |
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© Copyright 2015 The University of Edinburgh - 18 January 2016 4:53 am
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