| Unit name | Advanced Quantitative Methods for Social and Policy Research |
|---|---|
| Unit code | POLIM0021 |
| Credit points | 20 |
| Level of study | M/7 |
| Teaching block(s) |
Teaching Block 2 (weeks 13 - 24) |
| Unit director | Dr. Mircea Popa |
| Open unit status | Not open |
| Units you must take before you take this one (pre-requisite units) |
Prior training in quantitative methods at the undergraduate or postgraduate levels, which should include a solid knowledge of linear regression. For example, training in SOCIM0011 (Introduction to Quantitative Research Methods) meets this prerequisite |
| Units you must take alongside this one (co-requisite units) |
None |
| Units you may not take alongside this one |
None |
| School/department | School of Sociology, Politics and International Studies |
| Faculty | Faculty of Social Sciences and Law |
Why is this unit important?
Data analysis skills are becoming crucial for careers in the private, public, and non-profit sectors, as well as for academic research in the social sciences. This unit will introduce students to a variety of novel or relatively more advanced methods for quantitative data analysis and data science. The choice of topics is guided by the latest developments both in and outside academia. Topics include logistic and multinomial regression, panel data, nonparametric methods, quasi-experimental methods, unsupervised and supervised learning, and text-as-data. We will be using the software Stata for much of the unit, but an introduction to R will also be provided. Assessment is based on a portfolio of data analysis using at least two of the methods taught in the unit, on datasets to be chosen by the student. Students will be provided with guidance on how to develop the portfolio, including through formative work.
How does this unit fit into your programme of study?
This unit assumes that you have already received training in quantitative methods, including in linear regression. One way to meet this prerequisite is by enrolling in SOCIM0011 (Introduction to Quantitative Research Methods). The unit provides you with skills that could be used to write a dissertation including quantitative research. The methods we study are becoming a standard toolkit for quantitative PhD researchers, so those students intending to pursue a PhD are encouraged to enrol. The unit is mandatory for students enrolled in the “with Quantitative Research Methods” MSc programmes.
An overview of content:
The first part of the unit deals with methods which are widely used in contemporary social science research. These include models in which the dependent variable is not continuous, models in which we predict the distribution rather than the mean of an outcome, and models which allow a flexible relationship between the dependent and independent variables. The second part focuses on methods that aim to establish causality using observational data – panel data, instrumental variables, and regression discontinuity designs. The third part focuses on methods inspired by the statistical learning approach, including clustering, supervised learning, and text-as-data. Students can complete the assessment using only the Stata software, but R will also be introduced, and can be used.
How will students, personally, be different as a result of the unit:
Students will become competent in a wide range of methods for data analysis which are widely used in and outside academia. Students will be more confident in pursuing careers which involve data analysis, and will be able to pursue quantitative academic research projects. Students will also become competent in two widely used statistical software packages.
Learning Outcomes:
At the end of this unit a successful student will:
This is a hands-on, applied unit, in which your engagement with the software on a week-by-week basis will be crucial. Seminars will contain presentations of the methods, hands-on exercises, and discussion. Each week there will be a workbook (Stata or R script) with activities to be completed in class and at home. You will devote some time every week to developing your portfolio, which will similarly entail hands-on work using statistical software. The methods will be taught with a strong emphasis on application rather than theory, and the best way to become competent in them is to apply them yourself on topics and using data sources that are relevant to your interests.
Tasks which help you learn and prepare you for summative tasks:
You will submit a preliminary version of your portfolio, for which you will receive specific feedback on how to improve. You will receive guidance on what should be included in the summative assessment, and you will have the opportunity to discuss your plans in seminars throughout the term.
Tasks which count towards your unit mark (summative):
The summative assessment consists of a 4000-word data analysis portfolio. There are two parts, each drawing from two halves of the unit. You can choose one or two data sources to be used for your project, and in case you do not know what data could be appropriate, you will receive guidance and suggestions. The assessment has to evidence a solid understanding of at least two of the methods taught in the unit. The assessment is marked according to the requirements for any postgraduatelevel essay, and the marks for the two components are averaged.
When assessment does not go to plan:
If you are not able to take or to pass the summative assessment, you will have the opportunity to re-submit it in the summer exam period. The requirements for the re-submission are the same as for the initial submission. You will receive extensive guidance on what went wrong and on the steps you need to take to improve. You can meet with your tutor and discuss your approach for re-submission.
If this unit has a Resource List, you will normally find a link to it in the Blackboard area for the unit. Sometimes there will be a separate link for each weekly topic.
If you are unable to access a list through Blackboard, you can also find it via the Resource Lists homepage. Search for the list by the unit name or code (e.g. POLIM0021).
How much time the unit requires
Each credit equates to 10 hours of total student input. For example a 20 credit unit will take you 200 hours
of study to complete. Your total learning time is made up of contact time, directed learning tasks,
independent learning and assessment activity.
See the University Workload statement relating to this unit for more information.
Assessment
The Board of Examiners will consider all cases where students have failed or not completed the assessments required for credit.
The Board considers each student's outcomes across all the units which contribute to each year's programme of study. For appropriate assessments, if you have self-certificated your absence, you will normally be required to complete it the next time it runs (for assessments at the end of TB1 and TB2 this is usually in the next re-assessment period).
The Board of Examiners will take into account any exceptional circumstances and operates
within the Regulations and Code of Practice for Taught Programmes.
