Undergraduate Course: Understanding Data for Law and Legal Studies (LAWS10260)
Course Outline
| School | School of Law |
College | College of Arts, Humanities and Social Sciences |
| Credit level (Normal year taken) | SCQF Level 10 (Year 4 Undergraduate) |
Availability | Available to all students |
| SCQF Credits | 20 |
ECTS Credits | 10 |
| Summary | Understanding how data, and particularly quantitative data, are created and used has become important for the legal profession and research in law. This course is focused on key concepts and ideas in quantitative research and requires minimal prior experience in mathematics and statistics, while providing students with skills and knowledge that will allow them to critically assess quantitative analyses and to undertake small data-driven research projects of their own. This course is intended to help students develop quantitative reasoning skills and as such takes a very concept-oriented approach, building up skills in mathematics and statistics as the semester progresses. |
| Course description |
Finding areas of academic study and professional practice in which there is no reliance on quantitative data analysis is becoming increasingly difficult, as large-scale datasets are collected on voters, consumers, legislation, companies, impact of policy, international commerce, criminal activity, to name but a few, and analysed with the express purpose of guiding individual and collective decisions. With public policy and private decision-making increasingly relying on the findings of such analyses, understanding their provenance and implications is becoming more and more important for the legal profession and research in law. This course provides students in the LLB programmes at the University of Edinburgh Law School with an understanding of core concepts in social and legal research statistics and equips them with the ability to critically assess the data and data analyses they may encounter in their studies and in their professional life. The course starts with an overview of the process of data creation, types of quantitative and quantifiable data (with a focus on law and legal research), methods through which data can be summarised, appropriate interpretation of summary statistics, measures of uncertainty, and common mistakes in how these measures are understood. In Weeks 3 and 4, students will become acquainted with the tenets of probability theory, and the empirical and theoretical issues in identifying relationships of correlation and causation using quantitative data. Weeks 5 and 6 will be practical: students and the CO will meet in a computer lab, where they will access Microsoft Excel, gain insight into tidy data structures, and practically apply the knowledge from Weeks 1-4. Students will learn basic techniques for accessing, summarising, and visualising quantitative data, with CO-chosen data related to socio-legal research. These two lab sessions are also a preparation for the students¿ group presentations, which will be assigned at the end of the second lab session. The three sessions that follow cover more advanced issues in quantitative data analysis, addressing the problems of the possibility and limits of predictive power of quantitative analysis, ethical concerns in the use of primary and secondary data sources, providing an overview of recent innovative work in empirical legal studies, and putting forward a set of guidelines for successfully reading and understanding quantitative analyses in academic work and policy reports. The final session will be reserved for short presentations of students¿ group projects, in which they will select one of the provided data sources, assess its provenance and typical use, interrogate any ethical concerns in re-use of data, and provide key insights into the content of the data using techniques they learned in the lab sessions.
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Entry Requirements (not applicable to Visiting Students)
| Pre-requisites |
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Co-requisites | |
| Prohibited Combinations | |
Other requirements | None |
Information for Visiting Students
| Pre-requisites | None |
Course Delivery Information
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| Academic year 2024/25, Available to all students (SV1)
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Quota: 0 |
| Course Start |
Semester 2 |
Timetable |
Timetable |
| Learning and Teaching activities (Further Info) |
Total Hours:
200
(
Seminar/Tutorial Hours 20,
Programme Level Learning and Teaching Hours 4,
Directed Learning and Independent Learning Hours
176 )
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| Assessment (Further Info) |
Written Exam
15 %,
Coursework
85 %,
Practical Exam
0 %
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| Additional Information (Assessment) |
Short online, open-book, multiple-choice exams relating to materials from Weeks 2-4, and Weeks 7-8 (15%)«br /»
In-class group presentation in which students present key insights from a provided dataset, using tools learned in lab sessions (15%)«br /»
Written Assignment (70%) |
| Feedback |
Students will be submitting a 500-word outline of their final research report by Week 7, for which they will receive written feedback.
Additionally, students will have the opportunity to submit and receive written feedback on a 1500-word draft of their final research report, if submitted three weeks in advance of the report deadline. The second round of summative assessment will be fully optional. |
| No Exam Information |
Learning Outcomes
On completion of this course, the student will be able to:
- Apply and further develop knowledge and critical understanding of uses of quantitative data in empirical legal research.
- Use widely available software to view, summarise, interpret, and visualize quantitative datasets to support their own research in substantive areas of law.
- Present the results of their data assessment and analysis to a group of peers.
- Critically review and consolidate knowledge, skills, practices related to empirical legal research that uses quantitative data for description and inference.
- Demonstrate and discuss core concepts in probability theory and methods of descriptive statistical inference.
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Reading List
Kees van den Bos. 2020. Empirical Legal Research: A Primer. Edward Elgar. (Not available in UoE Library)
Ethan Bueno de Mesquita and Anthony Fowler. 2022. Thinking Clearly with Data, A guide to Quantitative Reasoning and Analysis. Princeton University Press. (Not available in UoE Library)
Andrew Dilnot and Michael Blastland. 2008. The Tiger That Isn¿t: Seeing Through a World of Numbers. Profile Books. (Online access through UoE Library)
Jordan Ellenberg. 2014. How Not to be Wrong: the Hidden Maths of Everyday Life. London: Allen Lane. (One physical copy available at UoE Library)
Judea Pearl and Dana MacKenzie. 2018. The Book of Why. London: Allen Lane. (Physical copies available at UoE Library)
David Spiegelhalter. 2019. The Art of Statistics: Learning from Data. UK: Pelican. (Physical copies available at UoE Library) |
Additional Information
| Graduate Attributes and Skills |
1) Research and Enquiry
- Analytical thinking: students will be able to consider, critically and methodically, the ways in which quantitative data are used in social and legal research
- Knowledge integration for research: students will be able to bring together the knowledge from substantive legal studies and the core ideas in quantitative data analysis to gain a more granular insight into areas of study that they are interested in pursuing.
- Digital literacy and numeracy: students will gain a general awareness of how data are used in social and legal research and will be able to conduct basic quantitative research tasks themselves, using widely available software.
2) Personal and Intellectual Autonomy
- Self-awareness and resilience: students will develop these qualities as they are faced with a new kind of material and a different approach to understanding law in society.
- Independent thinking: the group presentation and the final written assignment will give the students an opportunity to think creatively, to develop numerical reasoning, and to use these skills to critically assess academic outputs and public discourse.
3) Personal effectiveness
- Planning, organizing, time management: for a period of the final three to four weeks of the course, students will be managing the preparation of their group presentation and the final written assignment.
- Team working: students will prepare a group presentation, which will give them an opportunity to work with colleagues, some of which may be from different backgrounds and with different experiences; they will also have to manage their work as a group, and each student will be developing their skills in persuasion and negotiation.
4) Communication
- Verbal communication and presentation: students will develop skills in effectively and efficiently presenting complex ideas and analyses.
- Written communication: students will be able to produce clear and structured assessment of quantitative data resources and existing analyses. |
| Keywords | Quantitative reasoning,data analysis,law,statistical literacy,data literacy,data,methodology |
Contacts
| Course organiser | Ms Sanja Badanjak
Tel:
Email: Sanja.Badanjak@ed.ac.uk |
Course secretary | Mr Ryan McGuire
Tel: (0131 6)50 2386
Email: Ryan.Mcguire@ed.ac.uk |
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