THE UNIVERSITY of EDINBURGH

DEGREE REGULATIONS & PROGRAMMES OF STUDY 2020/2021

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: Doing Social Research with Statistics (SSPS08007)

Course Outline
SchoolSchool of Social and Political Science CollegeCollege of Arts, Humanities and Social Sciences
Credit level (Normal year taken)SCQF Level 8 (Year 2 Undergraduate) AvailabilityNot available to visiting students
SCQF Credits20 ECTS Credits10
SummaryThis course is designed to provide intermediate level statistical data analysis skills to students in the ┐with Quantitative Methods┐ degree programs in SPS. This course will have a practical and applied focus.
Course description This course covers regression models for binary, ordinal, nominal and count outcome variables. This will include binomial logistic regression, ordinal logistic regression, multinomial logistic regression and poisson regression.
This course will use the statistical data analysis package, Stata. This course will introduce skills in data management and data analysis using Stata.
The course is delivered via lectures and interactive computer lab sessions.
Entry Requirements (not applicable to Visiting Students)
Pre-requisites Students MUST have passed: Introduction to Statistics for Social Science (SSPS08008) OR Introduction to Statistics for Social Science- Summer School (SSPS08006)
Co-requisites
Prohibited Combinations Other requirements None
Course Delivery Information
Academic year 2020/21, Not available to visiting students (SS1) Quota:  28
Course Start Semester 2
Timetable Timetable
Learning and Teaching activities (Further Info) Total Hours: 200 ( Lecture Hours 22, Seminar/Tutorial Hours 22, Programme Level Learning and Teaching Hours 4, Directed Learning and Independent Learning Hours 152 )
Assessment (Further Info) Written Exam 0 %, Coursework 100 %, Practical Exam 0 %
Additional Information (Assessment) 1) 75% A short research report on a substantive topic containing the application of one of the regression analysis techniques covered in this course. The report should be no more than 2000 words long which includes a substantive introduction, descriptive statistics, a regression model, and a set of substantive conclusions.

2) 25% An annotated Stata .do file which contains all the code required to reproduce the analysis presented in the report. There is no word limit for the .do file, but parsimony is encouraged.
Feedback Students will receive feedback on an analysis project to be submitted in late March.
No Exam Information
Learning Outcomes
On completion of this course, the student will be able to:
  1. Make effective use of regression models for categorical dependent variables, including binomial logistic regression, multinomial logistic regression, and ordinal logistic regression.
  2. Use the statistical data analysis package Stata to estimate logistic regression models.
  3. Effectively interpret estimates from logistic regression models using different approaches.
  4. ake effective use of regression models for count data (e.g. the Poisson model).
  5. Use the statistical data analysis package Stata to estimate regression models for count data, and effectively interpret these models.
Reading List
Indicative Readings

Acock, A.C., 2016. A gentle introduction to Stata., College Station, Texas: Stata Press.

Kohler, U. and Kreuter, F., 2009. Data Analysis Using Stata. College Station Texas: Stata Press.

Long, J.S. 2009. The Workflow of Data Analysis Using Stata. College Station Texas: Stata Press.

Long, J.S. & Freese, J., 2014. Regression models for categorical dependent variables using Stata., College Station, Texas: Stata Press.

Mehmetoglu, M. and Jakobsen, T.G., 2016. Applied statistics using Stata: a guide for the social sciences. London: Sage.

Treiman, D. J., 2009. Quantitative Data Analysis: Doing Social Research to Test Ideas. San Francisco: Jossey-Bass.
Additional Information
Graduate Attributes and Skills Not entered
KeywordsNot entered
Contacts
Course organiserDr Roxanne Connelly
Tel:
Email: Roxanne.Connelly@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