CASA0007 Assessment

This Assessment is worth 100% of the grade for this course.

Assessment Aims

This assessment is designed to test your understanding of the quantitative methods introduced in this course, but also, crucially, your ability to really understand how, if used correctly and applied to appropriate data, these methods can help you tell a powerful story and be the underpinning evidence-base for a relevant local, national or international issue.

Using quantitative methods to contribute to public debates is vital if students, academics and universities are to demonstrate our value to the wider world. We don’t want you to just apply a method and regurgitate unintelligible coefficients. Quantitative methods can only have real value if they are used to support wider debates in ways that everyone should be able to understand.

We are not testing your coding abilities – you may use either of the main coding languages taught this term (R or Python). We are not testing your ability to write a standard piece of academic writing. But we are testing your ability to use quantitative methods appropriately and with a clear connection between the data, methods and outputs, to help others understand a particular issue through a data lens.

Part 1 - Article

Your task is to write a short 800-1000-word piece of ‘public-facing scholarly writing’ in the style of an article that that could appear in The Conversation or the Financial Times in their data section. This piece could relate to a local, national or international topic which could be either serious or frivolous but must employ appropriate quantitative methods learned in this course to derive novel insights from a particular dataset (or range of datasets) associated with a particular topic of your choosing. Data analysis and the use of quantitative methods should be central to your piece, but outputs must be appropriate for a general (non-academic) audience with your piece illuminated by appropriate visual outputs – maps, graphs or other data visualisations. Your piece should contain a range of graphical or tabular elements.

Part 2 - Technical Appendix

Your 800-1000-word article should be accompanied by a max 1000-word technical appendix detailing the analysis you have carried out behind the scenes to allow you to make the observations you have in your article. Here you might want to include additional exploratory visualisations, tabular outputs, interpretations of those outputs, equations etc. and you could also include any observations about the dataset or the validity / statistical significance of any models you employ. The purpose of the appendix is to reassure anyone who wants to delve deeper, that the observations you made in your main article are valid and reliable and your interpretations valid.

Topic

Your topic can be anything you like broadly related to human, urban or social issues, as long as you can find some suitable data to analyse. For inspiration on relevant topics, you might want to review some of the articles that John Burn-Murdoch has written for the Financial Times in recent years - https://www.ft.com/john-burn-murdoch (you can log-in via your UCL credentials) or some of the pieces in the Conversation - https://theconversation.com/.

If you are struggling for inspiration, you are welcome to explore an educational topic using DfE schools data used in class, but you are encouraged to be creative in your data choices (as you are being partially marked on your originality), and you should not repeat analyses carried out on variables in any of the practical sessions. The only topic we will not permit in this assessment is anything related to AirBnB as this is the focus of CASA0013.

Article Content

You will note that most of John Burn-Murdoch’s articles generally contain the sorts of analyses we would describe as exploratory. While your article should contain basic exploratory analysis in the form of visualisations, you should also use an appropriate method from the second half of the course (lectures 5-10) related to either more sophisticated exploratory analysis like multivariate statistical analysis (e.g. dimensionality reduction or cluster analysis) or some explanatory / predictive methods such as ANOVA, linear regression or some of the generalised linear models also introduced.

Style of Briefing

You should write in plain English and avoid the use of jargon or technical language. For tips on how to write in this style, The Conversation has produced a guide: https://socsci.web.ox.ac.uk/files/conversation-writing-public-why-and-how

Types of Data Permissible and Sources

Anything you like, but you should choose carefully so that you are able to demonstrate the appropriate skills. There are many potential sources of data – these could be linked from FT or Conversation articles, or you could try sites like:

https://data.gov/

https://www.data.gov.uk/

https://opendata.nhsbsa.net/

https://tfl.gov.uk/info-for/open-data-users/  

https://data.europa.eu/data/datasets?locale=en

https://data.worldbank.org/

https://data.london.gov.uk/

You can probably find many more!

Referencing

We will not expect standard academic referencing in this piece, however, this doesn’t mean that you shouldn’t include references – you should. In this style of public facing scholarly writing, it is common to use hyperlinks and footnotes and you should make use of these to support your narrative.

Format of the Piece

Examples – Here is just one example of the kind of article you might produce (minus the technical appendix), but read widely around the data journalism sites linked from publications like the Financial Times, New York Times, Guardian.

https://theconversation.com/constituency-level-data-reveals-which-parties-are-most-threatened-by-reform-264422

Deadline and Handing In

The Deadline for the assessment is Tuesday, 13 January 2026 @ 10:00.

Your report and technical appendix should be uploaded to Moodle as a single PDF document.

Mark Scheme

This is how we will mark your work - take note of them

Criterion	80-100%	70-79% (A)	60-69% (B)	50-59% (C)	40-49% (D)	1-39% (E)
Exploratory Analysis (25%)	Outstanding selection of exploratory statistics and/or sophisticated data visualisations which illuminate the underlying data, revealing distributions / trends / relationships and associations in the data with absolute clarity. Graphics are labelled such that readers are in no doubt about what is being shown. Data may have been transformed, normalised or standardised in some way to reveal otherwise hidden patterns and justified impeccably.	Excellent selection of exploratory statistics and/or sophisticated data visualisations which illuminate the underlying data, revealing distributions / trends / relationships and associations in the data. Graphics are labelled such that readers are in no doubt about what is being shown. Data may have been transformed, normalised or standardised in some way to reveal otherwise hidden patterns and justified.	Good selection of exploratory statistics and/or data visualisations which illuminate the underlying data, revealing distributions / trends / relationships and associations. Graphics are labelled such that readers are able to interpret the plots with ease.	Adequate selection of exploratory statistics and/or data visualisations which illuminate some of the underlying data, revealing some distributions / trends / relationships and associations. Graphics are labelled, but may lack clarity. Data may not be transformed, normalised or standardised in a way to reveal patterns.	Inadequate selection of statistics or visualisations. Graphics are poorly labelled or unclear, and do not illuminate the underlying data. Data transformations are incorrect or missing.	Negligible use of statistics or visualisations. Any visualisations are irrelevant, inaccurate, or inaccessible. The analysis shows no grasp of the underlying data.
Use of multivariate statistical analysis or explanatory / predictive methods (25%)	Exceptional selection of data / variables entirely appropriate for the chosen article topic. Masterful understanding of the nuances related to the careful pruning and selection of appropriate variables. Outstanding understanding of the methods employed and their interaction with the data to hand with masterful understanding of outputs produced	Excellent selection of data / variables entirely appropriate for the chosen article topic. Highly competent understanding of the nuances related to the careful pruning and selection of appropriate variables. Highly competent understanding of the methods employed and their interaction with the data to hand.	Good selection of data / variables appropriate for the chosen article topic. Competent understanding of the methods employed and their interaction with the data.	Adequate selection of data / variables for the chosen article topic. Basic understanding of the methods employed and their interaction with the data. May get some of the nuances in the outputs, but may also ignore some key features in the data	Inadequate selection of data / variables for the chosen article topic. Little to no understanding of the methods employed or how they interact with the data.	Negligible or irrelevant selection of data / variables. No grasp of the methods or their application to the data.
Originality, article narrative and communication (25%)	Highly original topic selection, or of exceptional relevance to a contemporary debate in the society, the media or politics at a local, national or international level with broad interest. Article narrative shows flare or originality which draws the reader in and reveals something entirely new. Writing style is highly accessible - clear, concise and creative and the reader is left without query or misunderstanding.	Excellent topic selection, of high relevance to a contemporary debate. The narrative is engaging and written with excellent clarity, flowing well from one section to the next. The work is of a very high standard. Writing style clear and concise with few wasted words.	Good topic selection, of relevance to a contemporary debate. The narrative is clear and well-structured. The work shows some evidence of originality. The narrative is good and the message emerging from the analysis is conveyed well.	Adequate topic selection, but may lack relevance or wider interest. The narrative is satisfactory but may lack clarity or logical flow – ideas appearing slightly disorganised. The reader can understand the piece but may have to work hard to derive meaning from it.	Inadequate topic selection – perhaps dated or totally irrelevant to the degree programme (i.e. not even a human topic). The narrative is lacking in clarity and is difficult to follow. Deriving meaning from the work is a challenge.	The work has no clear topic or narrative. The communication is confused, unclear, or inaccessible.
Conceptual understanding and Critical Reflection (25%)	Both the Article and Technical Appendix show exemplary understanding of the topic / wider issues associated with it and of the methods employed to interrogate the data. A clear understanding of any data / methodological shortcomings / issues / challenges is presented with a highly sophisticated degree of critical reflection in relation to the substantive topic and / or methods employed is demonstrated.	Article and Technical Appendix show excellent and highly competent conceptual understanding of key concepts and theories related to both the topic and methods employed. The work demonstrates a thorough understanding of the chosen example and recognises and reflects lucidly on any shortcomings and / or the wider significance of the findings in a way that is not contrived or formulaic but shows a sophisticated level of insight.	Article and Technical Appendix show good understanding of key concepts and theories related to both the topic and methods employed. The work demonstrates a sound understanding of the chosen example and recognises and reflects lucidly on any shortcomings and / or the wider significance of the findings in a way that is not contrived or formulaic but shows a sophisticated level of insight.	Article and Technical Appendix show basic understanding of key concepts and theories related to both the topic and methods employed. The work demonstrates a rudimentary understanding of the chosen example and recognises and may offer only some reflection on the shortcomings of the work or not at all / contrived at the bottom end.	Inadequate and insufficient conceptual understanding of key concepts and theories. The work demonstrates an invalid or lack of understanding of the concepts introduced. Any analysis attempted fails to support the observations made.	Negligible or no conceptual understanding of key concepts and theories. The work demonstrates an irrelevant, inaccurate, confused, unclear, or inaccessible understanding of the concepts.

The purpose of this assessment is to test your understanding of the various methods introduced in this course and your ability to apply them appropriately to a topic of your choice. One of the differentiators at Masters level is the ability to think both creatively and critically while showing an awareness or knowledge of contemporary issues either in relation to your specific discipline of study or more widely. Being able to demonstrate how established techniques of research and enquiry can be used to create and interpret knowledge is at the core of Masters level thinking and this assessment piece. The ability to demonstrate self-direction (in choosing an appropriate topic for this assessment) and to think autonomously in designing your own article.

Mark Scheme – Explained

Level 7 Descriptors - https://www.qaa.ac.uk/docs/qaa/quality-code/the-frameworks-for-higher-education-qualifications-of-uk-degree-awarding-bodies-2024.pdf - p26

Master’s degree

The descriptor provided for this level of the Frameworks is for any master’s degree which should meet the descriptor in full. This qualification descriptor should also be used as a reference point for other qualifications at Level 7 on the FHEQ/SCQF Level 11 on the FQHEIS, including postgraduate certificates and postgraduate diplomas.

Master’s degrees are awarded to students who have demonstrated:

•         a systematic understanding of knowledge, and a critical awareness of current problems and/or new insights, much of which is at, or informed by, the forefront of their academic discipline, field of study or area of professional practice

•         a comprehensive understanding of techniques applicable to their own research or advanced scholarship

•         originality in the application of knowledge, together with a practical understanding of how established techniques of research and enquiry are used to create and interpret knowledge in the discipline

•         conceptual understanding that enables the student:

•         to evaluate critically current research and advanced scholarship in the discipline

•         to evaluate methodologies and develop critiques of them and, where appropriate, to propose new hypotheses.

Typically, holders of the qualification will be able to:

•         deal with complex issues - both systematically and creatively, make sound judgements in the absence of complete data, and communicate their conclusions clearly to specialist and non-specialist audiences

•         demonstrate self-direction and originality in tackling and solving problems, and act autonomously in planning and implementing tasks at a professional or equivalent level

•         continue to advance their knowledge and understanding, and to develop new skills to a high level. 

And holders will have:

•         the qualities and transferable skills necessary for employment requiring:  

•         the exercise of initiative and personal responsibility

•         decision-making in complex and unpredictable situations

•         the independent learning ability required for continuing professional development.