Undergraduate Course: Doing Social Research with Statistics (SSPS08007)
Course Outline
| School | School of Social and Political Science |
College | College of Humanities and Social Science |
| Credit level (Normal year taken) | SCQF Level 8 (Year 2 Undergraduate) |
Availability | Not available to visiting students |
| SCQF Credits | 20 |
ECTS Credits | 10 |
| Summary | Providing 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.
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| 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.
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Course Delivery Information
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| Academic year 2017/18, Not available to visiting students (SS1)
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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 )
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| Assessment (Further Info) |
Written Exam
0 %,
Coursework
50 %,
Practical Exam
50 %
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| 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).
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| 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:
- Make effective use of regression models for categorical dependent variables, including binomial logistic regression, multinomial logistic regression, and ordinal logistic regression.
- Make effective use of a regression model for limited dependent variables¿namely, tobit regression.
- Use the statistical analysis software package Stata to estimate regression models for categorical and limited dependent variables.
- Interpret estimates from regression models for categorical and limited dependent variables using different approaches.
- Understand alternative specifications of regression models for categorical and limited dependent variables as a latent variable, nonlinear probability, and linear model.
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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,
London.
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.
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Additional Information
| Graduate Attributes and Skills |
Not entered |
| Keywords | Not entered |
Contacts
| Course organiser | Dr Alexander Janus
Tel: (0131 6)51 3965
Email: Alex.Janus@ed.ac.uk |
Course secretary | Mr Daniel Jackson
Tel: (0131 6)50 3932
Email: Daniel.Jackson@ed.ac.uk |
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