Course Catalogue

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Students will learn to think more deeply about the theory underlying quantitative data: both how it is measured and how it is used to explore underlying structures. A variety of the most popular and widely used methods for data projection and dimensional reduction will be presented in applied settings that allow students to gain an intuition for their theory, application and the interpretation of the results. These tools will equip students with the ability to explore research questions using both primary and secondary data in a structured and methodologically sound fashion, ultimately enabling them to investigate important substantive questions in areas such as crime, demographics, education, inequality and many others.

Eleven weeks, each with one hour lecture, one hour lecture or lab (depending on the week) - lecture led

2 one hour drop in lab sessions for help with final project during the last two weeks of the semester - GTA led

Must have completed Research Design (SPS4003) and Advanced Regression (SPS4004) at grade D3 or higher.

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The 3000 word summative assessment will be made up of two main parts: a portfolio made up of three formative assessments throughout the semester and a final project.

For the portfolio, students will be asked to apply the models from weeks 3 to weeks 9 to secondary data and report their results as formative assessments with feedback returned. These will then be revised in relation to the feedback given and submitted as a summative portfolio.

The final project will require students to develop a research question related to a nominated theme of interest using the linked areal level datasets from statistics.gov.scot or other suitable data source identified by the student and approved by the lecturer. The proposal will include statement of this research question, the data selected to answer it and the method proposed to apply to the data, along with a defence for why this method is appropriate. This 500 word proposal will be submitted as a formative assessment with feedback given, followed by a report that will be submitted with their work on this project.

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 aims of this course are:

■ To build on the quantitative skills already assimilated from other Q-step courses

■ To encourage students to think more critically about measuring and comparing responses and subjects through quantitative data

■ To allow the students to gain an understanding of dimensionality

■ To give them a grounding in the theory underlying some of the common scaling methods used in surveys, as well as other projection and dimension reduction techniques

■ To enable the students to correctly evaluate which of the unidimensional versus multidimensional scaling methods are appropriate in different contexts

■ To equip students with the analytical skills to evaluate statistical methods appropriate to the data under study and apply these methods using statistical software

■ This course will be more methodological than substantive, seeking to provide students with a methodological toolkit applicable to a wide variety of settings.

By the end of this course students will be able to:

■ Understand & evaluate the foundations of measurement theory and scaling;

■ Assess when classical unidimensional scaling methods are appropriate and when alternatives are required;

■ Differentiate between the different intended objectives of the various methods presented in the course and determine which method is appropriate in given situations;

■ Apply all of the methods to data using the relevant commands from packages in the R statistical software language;

■ Present both graphically and verbally the results of the methods; and discuss their interpretation/implications in the context of the setting under study.

Students must submit at least 75% by weight of the components (including examinations) of the course's summative assessment.