Description of the task
In order to write your report, you should find a survey for which the data and a detailed description are accessible. You can use any survey that fits this description in your report. We suggest that you browse the database of the UK Data Service, where the data and documentation of hundreds of open access surveys from the past decades are available. We provide you with further pointers and assistance during the semester to help you choose an adequate survey for the assignment.
Using the survey dataset and documentation you chose, you are asked to
- motivate and formulate a research question about the relationship between two variables of your choice in the dataset (related course material: week 1);
- describe the sampling design of the survey using relevant concepts from the course(weeks 1-5);
- identify three important sources of bias arising from how your chosen variables were measured in the survey questionnaire (weeks 6-7);
- test the relationship between your variables using simple methods in R (weeks 2-5, 8);
- propose two ways to improve the quality of the survey, one focusing on the sampling and the other on the measurement of your variables (week 8);
- discuss what you find the most important ethical issue in this survey (week 9);
- compile a written report on the above steps (week 10).
Structure of the report
We suggest the following section structure for your report (you may choose to structure your report differently):
- Introduction: briefly describe the empirical context of the survey
- Research question: motivate and formulate your research question (task 1)
- Sample: briefly describe the sampling used in the survey (task 2)
- Measurement: present the measurement of the two variables, discuss biases (task 3)
- Analysis: present and interpret your results (task 4)
- Discussion: briefly summarise the main findings, propose ways to improve survey quality, and discuss ethics (task 5-6)
- Appendix: add any important additional figures/tables, include R code (so that your analyses can be reproduced)
20% – Data presentation and research question (sections 1-2 from the above structure)
– Do you present the context of the data (e.g. topic) and the dataset (e.g. types of variables, number of observations you use) clearly?
– Do you formulate your research question clearly?
– Do you explain why you think the research question is interesting and relevant?
– Can you answer your research question with the selected variables?
20% – Description of sampling (section 3)
– Do you provide a clear and correct presentation of the sampling design and procedure used in the survey?
– Do you use the concepts learned in the course to describe the sampling?
20% – Discussion of measurement (section 4)
– Do you present the questionnaire items measuring the two selected variables clearly,using the concepts learned in the course?
– Do you explain the three biases clearly, using the relevant concepts from the course?
– Are your identified biases indeed important in the context of the survey?
20% – Data analysis (section 5, appendix)
– Do you perform the adequate statistical test to answer your research question?
– Do you use the R packages and functions most appropriate for your analysis?
– Do you provide a reproducible R code in the appendix?
– Do you interpret the results and explain what they imply for your research question?
20% – Discussion of quality and ethics (section 4)
– Do you clearly explain your proposal for improving the sampling of the survey?
– Do you clearly explain your proposal for improving the measurement of one or both of your analysed variables?
– Do you identify a relevant ethical issue in this survey?
Additional notes on marking
We will value original and well-motivated research questions.
We will also reward thoughtful ideas about improving survey quality.
Though your R codes will not be assessed, we appreciate it if you provide a clear and understandable code in the appendix.
We will provide you with good examples and further guidelines for the different elements of the report throughout the semester (for example, about finding an adequate dataset or writing clean R codes).
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