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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 Humanities and Social Science
Credit level (Normal year taken)SCQF Level 8 (Year 2 Undergraduate) AvailabilityNot available to visiting students
SCQF Credits20 ECTS Credits10
SummaryProviding Intermediate level Statistical Tools for Students in the Quantitative Methods Degrees

This course is designed to allow students in the with Quantitative Methods degree programmes in SPS to move beyond basic statistical techniques into intermediate-level techniques, which will later enable them to learn advanced techniques. Therefore, it aims to lay the foundations for advanced techniques: Considering the ways in which secondary data is produced; Moving beyond linear regression to models based on log-odds to predict categorical results; data reduction; analysis of variance between groups. A well trained analyst should have acquired skills uing a variety of software packages that are commonly used in social research, and as such this course introduces Stata and R, in addition to SPSS. 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.
Course description This course covers regression models for categorical dependent variables (political party affiliation). Estimates from these models are more challenging to interpret than linear regression estimates. We thus pay special attention to interpretation issues. We also examine alternative ways of expressing the same models as a latent variable, nonlinear probability, or linear model to facilitate their practical application, to gain a greater appreciation of the methodological issues these models deal with, and to better understand how these models work.

Through the tutorials students gain practice using these methods to analyse data from the Survey of Heath, Aging and Retirement in Europe (¿SHARE¿), a major European study on aging. Real data is imperfect data¿SHARE included¿and generally does not represent a simple random sample from the population of interest. We thus also cover procedures for dealing with missing data, nonresponse, and complex survey designs.

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 2018/19, Not available to visiting students (SS1) Quota:  26
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 50 %, Practical Exam 50 %
Additional Information (Assessment) 50% mid-term exams (comprised of multiple-choice questions)as a formative feedback event.
50% take home exam (students will conduct a series of analysis tasks and report them).
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. Make effective use of a regression model for limited dependent variables¿namely, tobit regression.
  3. Use the statistical analysis software package Stata to estimate regression models for categorical and limited dependent variables.
  4. Interpret estimates from regression models for categorical and limited dependent variables using different approaches.
  5. Understand alternative specifications of regression models for categorical and limited dependent variables as a latent variable, nonlinear probability, and linear model.
Reading List
The course will suggest that students use both books and on-line resources to facilitate their learning.

Argyrous G (2005). Statistics for research: with a guide to SPSS (2nd edn), Sage,

Pampel, FC (2000). Logistic regression: a primer, Quantitative Applications in the
Social Sciences, 132, Sage University Papers, Sage, London.

de Vaus, D (2013) Surveys in Social Research, 6th ed., London: Routledge.
Additional Information
Graduate Attributes and Skills Not entered
KeywordsNot entered
Course organiserDr Alexander Janus
Tel: (0131 6)51 3965
Course secretaryMr Euan Morse
Tel: 0131 (6)51 1137
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