| Unit name | Advanced Quantitative Research |
|---|---|
| Unit code | POLIM0064 |
| Credit points | 20 |
| Level of study | M/7 |
| Teaching block(s) |
Teaching Block 2 (weeks 13 - 24) |
| Unit director | Dr. Whillans |
| Open unit status | Not open |
| Units you must take before you take this one (pre-requisite units) |
SOCIM0011 or equivalent prior training in quantitative methods at the undergraduate or postgraduate level. Please inquire with unit owner if unsure about your preparation for the unit. |
| 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 increasingly vital across careers in the private, public, and non-profit sectors, as well as for academic research in the social sciences. This unit equips you with advanced quantitative data analysis and data science methods. Key topics include multivariate linear regression, logistic and multinomial regression, generalised linear models, and panel data analysis. You will also engage with diverse data sources and develop practical skills using statistical software. These tools provide a strong foundation for conducting more advanced quantitative research and developing transferable analytical skills.
How does this unit fit into your programme of study?
This unit assumes that you have already received basic training in quantitative methods. One way to meet this prerequisite is by enrolling in SOCIM0011 (Introduction to Quantitative Research Methods). Students who have received equivalent basic training in quantitative methods elsewhere are also welcome.
This unit builds on foundational training in quantitative methods, with SOCIM0011 or equivalent basic training a prerequisite. It provides essential skills for dissertations involving quantitative research. The methods you will study form a standard toolkit for quantitative PhD researchers, so students intending to pursue a PhD are encouraged to enrol.
An overview of content:
The first part of the unit focuses on methods widely used in contemporary social science research, including multivariate linear regression and models where the dependent variable is not continuous. The second part introduces more advanced methods, such as those allowing for flexible relationships between dependent and independent variables, and approaches for establishing causality using observational data. These include panel data, instrumental variables, and regression discontinuity designs. 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 will students, personally, be different as a result of the unit:
By the end of this unit, you will be competent in a range of data analysis methods commonly used within and beyond academia. You will gain confidence in pursuing careers involving data analysis and be well-prepared to undertake quantitative academic research projects. Additionally, you will develop proficiency in at least one widely-used statistical software package.
Learning Outcomes:
At the end of this unit a successful student will be able to:
1. Design and implement advanced statistical analyses to address specific research questions,
2. Critically evaluate the appropriateness and limitations of statistical methods introduced in the unit.
3. Employ statistical software to conduct statistical analyses using a variety of data sources.
4. Critically evaluate applied quantitative research in the social sciences.
How you will learn:
This is a hands-on, applied unit in which your weekly engagement with the software is essential. Seminars will include presentations of methods, hands-on exercises, and discussions. Each week, you will receive a workbook with activities to complete both in class and complete at home. You will also spend time each week developing your portfolio, which will involve practical work using statistical software. The methods will be taught with a strong emphasis on application rather than theory. The best way to develop competence is to apply these methods yourself, using topics and data sources relevant to your interests.
Tasks which help you learn and prepare you for summative tasks:
Throughout the unit, you will be offered the opportunity to complete 'Independent Progress Checks'—short tasks designed to help you determine whether you have understood newly introduced analytical methods and to identify areas where further learning is needed. You will also be offered the option to review this work with peers and/or in one-to-one sessions with your tutor.
Guidance will be provided on what to include in the summative assessment, and you will be offered the opportunity to discuss your plans during seminars throughout the term.
Tasks which count towards your unit mark (summative):
The summative assessment consists of a 4,000-word data analysis portfolio, divided into two parts, each corresponding to one half of the unit. You are encouraged to select your own data sources for the project. If you are unsure which data would be appropriate, guidance and suggestions will be provided. The assessment must demonstrate a solid understanding of the concepts covered in both halves of the unit. It will be marked according to postgraduate-level standards, with the final mark being the average of the two components. (ILOs 1,2,3,4)
When assessment does not go to plan:
If you are unable to complete or pass the summative assessment, you will have the opportunity to resubmit it during the summer exam period. The requirements for the resubmission will be the same as those for the initial submission. You will receive guidance on what went wrong and the steps needed to improve.
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. POLIM0064).
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 assessment methods listed in this unit specification are designed to enable students to demonstrate the named learning outcomes (LOs). Where a disability prevents a student from undertaking a specific method of assessment, schools will make reasonable adjustments to support a student to demonstrate the LO by an alternative method or with additional resources.
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.
