As the course is about teaching you how to analyse quantitative data, the summative assessment will test your ability to analyse quantitative data by developing your own research project. You will identify a research question, choose a suitable dataset, select a de

Overview
As the course is about teaching you how to analyse quantitative data, the summative assessment will test your ability to analyse quantitative data by developing your own research project. You will identify a research question, choose a suitable dataset, select a dependent variable and independent variables, and apply the skills you have acquired in the course. After this, you will write up your results into a paper. You should also include a reflective statement about how you think you performed.
The summative assessment will be submitted through Moodle and is due in by 12 noon on 13-January-2025.
Format
You should produce a paper with a maximum of 4,000 words. Students are required to keep to within an additional 10 per cent of the word limit given for an assessment – there are penalties on assessment that are longer than this. Submissions that go 10-14% over the word limit on an assessment will be subject to a 1 point deduction; 15-19% over a 2 points deduction; 20-24% over a 3 points deduction and 25% or more over will be awarded a fail (zero) and required to resubmit as a second attempt. The submission should not exceed 35 pages of A4. Anything past page 35 will not be marked.
You are required to submit a paper which is produced in R Studio and knitted using R Markdown. You will submit both the source file (a .Rmd file) and the output (I recommend html although Word or pdf are also acceptable). Any code included in the output, along with tables and references will not count towards the word limit. If you prefer, you may also exclude your reflective statement. You can find out more about R Markdown here: Using R Markdown for Class Reports} and/or R Markdown for R Studio. There is also a Datacamp course on using R Markdown. Having an R Markdown cheat sheet to hand will also be helpful. We will be using R Markdown during most of the tutorials. Writing the document in this way follows best practice for quantitative research. It will make it easier to manage your analysis and write-up. It also aids reproducibility.
Note that credit will not be given for Rmd files which are not submitted along with a successfully knitted version.
To count words in an R Markdown document you will have to use this add in. First, check you have the devtools package installed. You may also need to install R Tools. Once this is installed, you can run:
devtools::install_github(“benmarwick/wordcountaddin”, type = “source”, dependencies = TRUE)
You can then get a word count by clicking on the relevant option on the Addins menu in R Studio. Words are counted using two different methods. You can select either. If you have problems getting the add in to work, you can count using another method e.g., pasting the text into Microsoft Word.
You should not use your name anywhere in the summative submission. You may use your student number. Please save the file name as your student ID number and the title of the course (e.g. 1234567_URBAN5127.html).
The summative submission must be written in English. Submissions not in English will be awarded a grade of H.
Please check you have submitted the correct files and that those are the ones you want marked. Changes made to submissions after the deadline will be counted as late and will be subject to late penalties. It is your responsibility to check.
For more information about assessment at the University of Glasgow, please consult the Code of Assessment. You can read the University’s degree programme regulations here. Please also take time to familiarise yourself with the University’s policy on plagiarism. Finally, please note the University’s position on AI and the Student Learning Development’s guidance on AI.
Content
The paper should include:
• An introduction providing a research question, a rationale, and some supporting literature
• Summary statistics
• Data visualisations
• Linear regression models
• Tests of linear regression model specification
• A discussion of the linear regression assumptions and whether they are met
• A summary of your findings
• A reflective statement on what grade you think the work should be awarded.
Note that the summative assessment is not about mindlessly churning out R output. It is about your ability to select appropriate data and methods to address your research question, and to critically interpret the output of the tests/methods. Strong emphasis should be put on the linear regression model. It is mentioned explicitly in the course’s learning outcomes, and will therefore carry significant weight in the marking.
The reflective statement should clearly state which grade you think your work should be awarded and why you think that. You may also include additional reflections on the process of doing the summative assessment. The marking criteria are outlined below. You can read about the possible grades and their descriptions here. The idea of this is to help you look critically at your own work through the eyes of a marker.
Marking
As usual, the purpose of the assignment is to assess the extent of your attainment of the course’s intended learning outcomes. The intended learning outcomes of this course are:
1. Manage, visualise, summarise, and present univariate and bivariate data.
2. Construct a robust linear regression model.
3. Test hypotheses involving data measured at different levels e.g., interval, binary and categorical.
4. Use the statistical software R for quantitative analysis of univariate variables, bivariate associations and linear regression analysis.
5. Critically evaluate theories, test hypotheses, and answer substantive research questions using quantitative approaches with available data from a social science perspective beyond the examples given in the course.
6. Describe quantitative methods, and to interpret and write up the results of quantitative analyses, clearly and concisely.
For more information about assessment at the University of Glasgow, please consult the Code of Assessment. Please also take time to familiarise yourself with the University’s policy on plagiarism. Finally, please note the University’s position on AI and the Student Learning Development’s guidance on AI.
Data
You have the option of choosing which data you want to work with. However, be careful with your choice as a poor choice may make it difficult to complete the assessment to a high standard. Here are some points to consider:
• Is the data a match for your research question?
• See whether you have the data to run a linear regression model e.g., you will need a continuous dependent variable.
• Be cautious in using a count variable (e.g. the number of times an event occurs in a given time period) for your dependent variable, as linear regression requires some assumptions and count data do not necessarily meet them.
• Do you have enough observations? Probably at least 200 will be needed but this will be discussed during the course.
• Is the quality good enough? Is the data well documented?
• Is the data cross-sectional? We only consider cross-sectional analysis in this course, so that’s what you should use for the assessment.
• Do not use longitudinal data
• Do not use data from Kaggle.com
I recommend selecting a cross-sectional dataset from the UK Data Service. The following datasets would be good choices:
• Scottish Household Survey
• Health Survey for England
• Scottish Health Survey
• National Energy Efficiency Data-Framework
• Survey of English Housing
• Living Costs and Food Survey
• National Travel Survey
• Understanding Society
Note that Understanding Society is a longitudinal data source. In this case I suggest you identify a dataset from a specific wave which can be used for cross-sectional analysis.
You may use another dataset, though it is best to discuss this with your tutor to make sure it is suitable. I strongly advise against using the (Quarterly) Labour Force Survey, Inside Airbnb data, or doing any analysis about income or wage differences. Note that the learning outcomes explicitly mention an ability to go beyond the examples from the lecture. The more similar your work is to the lectures and tutorials the less well you will perform.
Choosing something you are interested in will help you maintain motivation during the process. Note that you will not be able to submit the same work for another course’s assessment. Bear this in mind when selecting a research question and data. Your work may, however, be used to inform other work.