THE UNIVERSITY of EDINBURGH

DEGREE REGULATIONS & PROGRAMMES OF STUDY 2021/2022

Information in the Degree Programme Tables may still be subject to change in response to Covid-19

University Homepage
DRPS Homepage
DRPS Search
DRPS Contact
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 Arts, Humanities and Social Sciences
Credit level (Normal year taken)SCQF Level 8 (Year 1 Undergraduate) AvailabilityNot available to visiting students
SCQF Credits20 ECTS Credits10
SummaryThis 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.
Entry Requirements (not applicable to Visiting Students)
Pre-requisites Students MUST have passed:
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
Academic year 2021/22, Not available to visiting students (SS1) Quota:  61
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 )
Assessment (Further Info) Written Exam 0 %, Coursework 0 %, Practical Exam 100 %
Additional Information (Assessment) 40% On-line Multiple Choice Test
60% Data Analysis Practical Assessment
Feedback Not entered
No Exam Information
Learning Outcomes
On completion of this course, the student will be able to:
  1. 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.
  2. On completion of this course, the student will be able to demonstrate that they have developed foundational skills and expertise in statistically orientated social science data analysis.
  3. On completion of this course, the student will be able to demonstrate that they have been introduced to statistical data analysis software.
  4. On completion of this course, the student will have an understanding of the statistical data analysis workflow.
  5. On completion of this course, the student will have a rudimentary understanding of transparent and reproducible statistically orientated social science data analysis.
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.
Additional Information
Graduate Attributes and Skills Not entered
KeywordsStatistics,Data Analysis,Stata
Contacts
Course organiserProf Vernon Gayle
Tel: (0131 6)50 4069
Email: Vernon.Gayle@ed.ac.uk
Course secretaryMr Daniel Jackson
Tel: (0131 6)50 8253
Email: Daniel.Jackson@ed.ac.uk
Navigation
Help & Information
Home
Introduction
Glossary
Search DPTs and Courses
Regulations
Regulations
Degree Programmes
Introduction
Browse DPTs
Courses
Introduction
Humanities and Social Science
Science and Engineering
Medicine and Veterinary Medicine
Other Information
Combined Course Timetable
Prospectuses
Important Information