Social Inequality in Education SPS4006P
- Academic Session: 2019-20
- School: School of Social and Political Sciences
- Credits: 20
- Level: Level 4 (SCQF level 10)
- Typically Offered: Semester 1
- Available to Visiting Students: No
- Available to Erasmus Students: No
In this course students engage with exciting theories on social mobility and social stratification in education to understand and inform their empirical analysis of contemporary data around inequality in education. In lectures students discuss substantive content on general trends in, and mechanisms underlying, the relationship between social class background, gender or ethnicity and educational outcomes. In tutorials held in labs students are introduced to the statistical technique propensity score matching using R.
The course builds on the statistical and quantitative methods training students have received in the other Q-Step courses. It will encourage to see complex methods as embedded into their understanding of substantive issues.
Eleven weeks, 2 hour sessions covering traditional lecture plus tutorial run by the lecturer
Requirements of Entry
Must have completed Research Design and Advanced Regression at grade D3 or better.
Students will be given one contemporary data-set pertaining to inequality in education. Students must use the data to develop their own research question and then develop a report for submission covering the following sections. Students must develop a proposal (submitted as part of the project, but forming part of their formative learning see box 22) that covers student's intentions in analysing the data including tests they wish to use. The proposal must include a defence of the tests they intend to use. Tests should include at least one of the following: Propensity score matching; linear or logistic regression; hierarchical (also known as nested) regression.
1. Theoretical Background
2. Relevant Literature
4. Analysis or original data
Are reassessment opportunities available for all summative assessments? Not applicable for Honours courses
Reassessments are normally available for all courses, except those which contribute to the Honours classification. For non-Honours courses, students are offered reassessment in all or any of the components of assessment if the satisfactory (threshold) grade for the overall course is not achieved at the first attempt. This is normally grade D3 for undergraduate students and grade C3 for postgraduate students. Exceptionally it may not be possible to offer reassessment of some coursework items, in which case the mark achieved at the first attempt will be counted towards the final course grade. Any such exceptions for this course are described below.
The course aims to build on the strong statistical and quantitative methods training students have received in the other Q-Step core courses. This course continues to develop student's skills around using new statistics techniques by introducing propensity score matching.
The course has more substantive content than the Q-Step courses studied at levels 1-3. This course has a strong theoretical basis commensurate with other honours courses. Students are required to engage with challenging theory in order to help them understand and inform their empirical analysis of contemporary data around inequality in education. The course therefore will encourage students to see complex methods as embedded into their understanding of substantive issues.
The assignments are designed to build up student's research skills and graduate attributes. Students submit two formative assessments: a proposal and a poster/presentation, both of which will produce feedback to inform the summative assessment of a project report on the analysis of a novel dataset.
Intended Learning Outcomes of Course
By the end of this course students will be able to:
1. Critically understand and evaluate the theoretical foundations and relevance of empirical evidence on social inequality in education;
2. Understand appropriate and effective applications of statistical methods to theoretical questions;
3. Understand the basics of propensity score matching.
4. Be able to apply & evaluate the use of advanced statistical analysis to the field of education.
Minimum Requirement for Award of Credits
Students must submit at least 75% by weight of the components (including examinations) of the course's summative assessment.