Undergraduate Course: Introduction to Statistics for Social Science (SSPS08008)
Course Outline
| School | School of Social and Political Science |
College | College of Arts, Humanities and Social Sciences |
| Credit level (Normal year taken) | SCQF Level 8 (Year 1 Undergraduate) |
Availability | Not available to visiting students |
| SCQF Credits | 20 |
ECTS Credits | 10 |
| Summary | This course introduces fundamental statistical concepts for social science data analysis for students undertaking 'with Quantitative Methods' degrees. |
| Course description |
The course is an introduction to statistically orientated data analysis for social science research. It is designed for students who also study Sociology, Social Policy, Politics, and International Relations.
The course will introduce a broad range of key statistical concepts for social science research. These concepts include research designs and sampling; large-scale datasets; the variable by case matrix; variables and measures; measure of central tendency and dispersion; bivariate relationships; statistical tests; statistical inference; presenting and communicating results. The course will also introduce the concept of the statistical data analysis workflow, and students will be encouraged to develop a rudimentary understanding of transparent and reproducible statistically orientated social science data analysis. Students will be introduced to the high powered, general purpose statistical software package Stata.
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Entry Requirements (not applicable to Visiting Students)
| Pre-requisites |
Students MUST have passed:
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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 2022/23, Not available to visiting students (SS1)
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Quota: 60 |
| Course Start |
Semester 2 |
Timetable |
Timetable |
| Learning and Teaching activities (Further Info) |
Total Hours:
200
(
Lecture Hours 10,
Seminar/Tutorial Hours 20,
Formative Assessment Hours 1,
Programme Level Learning and Teaching Hours 4,
Directed Learning and Independent Learning Hours
165 )
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| Assessment (Further Info) |
Written Exam
0 %,
Coursework
0 %,
Practical Exam
100 %
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| Additional Information (Assessment) |
40% On-line Multiple Choice Test
60% Data Analysis Practical Assessment
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| Feedback |
Not entered |
| No Exam Information |
Learning Outcomes
On completion of this course, the student will be able to:
- understand a broad range of key statistical concepts for social science research. These concepts include research designs and sampling; large-scale datasets; the variable by case matrix; variables and measures; measure of central tendency and dispersion; bivariate relationships; statistical tests; statistical inference; presenting and communicating results.
- demonstrate that they have developed foundational skills and expertise in statistically orientated social science data analysis.
- demonstrate that they have been introduced to statistical data analysis software.
- have an understanding of the statistical data analysis workflow.
- have a rudimentary understanding of transparent and reproducible statistically orientated social science data analysis.
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Reading List
Main text:
Bittmann, F., 2019. Stata. De Gruyter Oldenbourg.
Other texts:
Kohler, U. and Kreuter, F., 2012. Data analysis using Stata. Stata press.
Mehmetoglu, M. and Jakobsen, T.G., 2016. Applied statistics using Stata: a guide for the social sciences. Sage.
Treiman, D.J., 2014. Quantitative data analysis: Doing social research to test ideas. John Wiley & Sons.
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Additional Information
| Graduate Attributes and Skills |
Not entered |
| Keywords | Statistics,Data Analysis,Stata |
Contacts
| Course organiser | Prof Vernon Gayle
Tel: (0131 6)50 4069
Email: |
Course secretary | Mr Ethan Alexander
Tel: (0131 6)50 4001
Email: |
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