Canadian General Social Survey GSS Analysis using R programming

Working in teams of one to four people please use the Canadian General Social Survey GSS and a regression model to analyse some aspect of interest
Depending on your focus and background you may like to use a Bayesian hierarchical model but regardless of the particular model that you use it must be well explained thoroughly justified appropriate to the task at hand and the results must be beautifully described
You may focus on any year aspect or geography that is reasonable given the focus and constraints of the GSS As a reminder the GSS program was designed as a series of independent annual cross sectional surveys each covering one topic in depth So please consider the topic and the year The GSS is available to University of Toronto students via the library In order to use it you need to clean and prepare it Code to do this for one year is being distributed alongside this problem set and was discussed in lectures
You are welcome to simply use this code and this year but the topic of that year will constrain your focus Naturally you are welcome to adapt the code to other years If you use the code exactly as is then you must cite it If you adapt the code then you don t have to cite it as it has a MIT license but it would be appropriate to at least mention and acknowledge it depending on how close your adaption is
Using R Markdown please write a paper about your analysis and compile it into a PDF
Your paper must be well written draw on relevant literature and show your statistical skills by explaining all statistical concepts that you draw on

Your paper must have the following sections
○ title name s and date
○ abstract
○ introduction
○ data
○ model
○ results
○ discussion and
○ references
You are welcome to use appendices for supporting but not critical material Your discussion must include sub sections on weaknesses and next steps
In your report you must provide a link to a GitHub repo that fully contains your analysis Your code must be entirely reproducible documented and readable Your repo must be well organised and appropriately use folders Your graphs and tables must be of an incredibly high standard Graphs and tables should be well formatted and report ready They should be clean and digestible Furthermore you should label and describe each table figure
When you discuss the dataset in the data section you should make sure to discuss at least
○ Its key features strengths and weaknesses generally
○ A discussion of the questionnaire what is good and bad about it
○ A discussion of the methodology including how they find people to take the survey what their population frame and sample were what sampling approach they took and what some of the trade offs may be what they do about non response the cost
○ This is just some of the issues strong submissions will consider Show off your knowledge If this becomes too detailed then you should push some of this to footnotes or an appendix When you discuss your model in the model section you must be extremely careful to spell out the statistical model that you are using defining and explaining each aspect and why it is important For a Bayesian model a discussion of priors and regularization is almost always important You should mention the software that you used to run the model You should be clear about model convergence model checks and diagnostic issues How do the sampling and survey aspects that you discussed assert themselves in the modelling decisions that you make Again if it becomes too detailed then push some of the details to footnotes or an appendix
You should present model results graphs figures etc in the results section This section should strictly relay results Interpretation of these results and conclusions drawn from the results should be left for the discussion section
Your discussion should focus on your model results Interpret them and explain what they mean Put them in context What do we learn about the world having understood your model and its results What caveats could apply To what extent does your model represent the small world and the large world to use the language of McElreath Ch 2 What are some weaknesses and opportunities for future work
Check that you have referenced everything Strong submissions will draw on related literature in the discussion and other sections and would be sure to also reference those The style of references does not matter provided it is consistent
As a team via Quercus submit a PDF of your paper Again in your paper you must have a link to the associated GitHub repo in an appendix And you must include the R Markdown file that produced the PDF in that repo A good way to work as a team would be to split up the work so that one person is doing each section The people doing the sections that rely on data such as the analysis and the graphs could just simulate it while they are waiting for the person putting together the data to finish It is expected that your submission be well written and able to be understood by the average reader of say 53 This means that you are allowed to use mathematical notation but you must be able to explain it all in plain English Similarly you can and hint you should use survey sampling observational and statistical terminology but again you need to explain it Your work should have flow and should be easy to follow and understand To communicate well anyone at the university level should be able to read your report once and relay back the methodology overall results findings weaknesses and next steps without confusion It is recommended that you informally proofread one another s sections
why not exchange papers with another group Everyone in the team receives the same mark
There should be no evidence that this is a class assignment

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