Timetable information in the Course Catalogue may be subject to change.

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)
Prohibited Combinations Other requirements None
Course Delivery Information
Academic year 2023/24, Not available to visiting students (SS1) Quota:  24
Course Start Semester 2
Timetable Timetable
Learning and Teaching activities (Further Info) Total Hours: 200 ( Lecture Hours 11, Seminar/Tutorial Hours 22, Programme Level Learning and Teaching Hours 4, Directed Learning and Independent Learning Hours 163 )
Assessment (Further Info) Written Exam 0 %, Coursework 100 %, Practical Exam 0 %
Additional Information (Assessment) 1) 70% A short research report on a substantive topic containing the application of binary logistic regression analysis. The report should be no more than 2000 words long.

2) 20% A short take home practical exercise.

3) 10% 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. Use the statistical data analysis package Stata to effectively and efficiently analyse social science data resources.
  2. Appropriately undertake analyses using regression models for linear and binary dependent variables (e.g. linear regression and binary logistic regression).
  3. Appreciate the core differences between linear regression models and binary logistic regression models.
  4. Accurately interpret analyses of regression models.
  5. Effectively report analyses of regression 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
Course organiserDr Roxanne Connelly
Course secretaryMr Ian McClory
Tel: (0131 6)50 3932
Help & Information
Search DPTs and Courses
Degree Programmes
Browse DPTs
Humanities and Social Science
Science and Engineering
Medicine and Veterinary Medicine
Other Information
Combined Course Timetable
Important Information