Undergraduate Course: Introduction to Statistics for Social Science- Summer School (SSPS08006)
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 is a 2 week conversion course covering the same course content as the standard semester length version.
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.
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| Course description |
Introduction: Why do quantitative methods? And an introduction to secondary data access and management
Distribution and introduction to SPSS
2: Associations with categorical variables
Contingency tables
Measures of association between categorical variables
Causality and the concept of control in a 3-way contingency table
Part 3: Associations with continuous variables
Bivariate analysis for continuous variables
Introduction to multiple linear regression
Linear regression continued: Dummy variables and interactions
Part 3: The bigger picture: Inference and communicating research
Inference and the logic of sampling
Summary: Communicating quantitative analysis
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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 |
Block 5 (Sem 2) and beyond |
Timetable |
Timetable |
| Learning and Teaching activities (Further Info) |
Total Hours:
200
(
Programme Level Learning and Teaching Hours 4,
Directed Learning and Independent Learning Hours
196 )
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| Assessment (Further Info) |
Written Exam
0 %,
Coursework
100 %,
Practical Exam
0 %
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| Additional Information (Assessment) |
40% mid-course exam (comprised of multiple-choice questions) end of week 1. This constitutes a formative feedback event.
60% continuous assessment in week 2 (students will conduct a series of analysis tasks and report them each morning based on the previous days material).
This assessment would provide the necessary concession for transfer into year 2 of the with QM degree programmes.
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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 |
| Special Arrangements |
Students are expected to attend the whole 2 weeks, which covers the same material as the semester length version. 1 day is roughly equivalent to 1 week, with half the day given over to supervised lab sessions in place of the independent learning that would be expected during the 11 week version. |
| 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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