Description
Report
Objective
To predict the overall popular vote of the next Canadian federal election (tentatively 2025) using a regression
model with post-stratification.
Please note that there is NO requirement on the type of model you use. You can use a standard model (i.e.,
simple or multiple regression), a multilevel model or a Bayesian model (standard or multilevel). The model
choice is up to you. With that being said, the model should still be appropriate (e.g., logistic regression for
binary outcome, or if you assume a prior distribution you should justify the prior in some way).
Description:
In this assignment you will create an “Introduction”, “Data”, “Model” (or “Methods”), “Results” and a
“Conclusions” section of a report, based on a post-stratification analyses. It is recommended that you use
the General Social Survey (GSS) as the “census” data, and data from the CES2019 package as “survey”
data.
The idea is, as a small team (of size 1-4) you will work through the following steps:
1. Load in the sample/survey data (CES data).
2. Build a model (any model is acceptable) on the sample data. Note: any model is acceptable, but some
justification (either practical or statistical) should be given. (Some options: meaningful variables,
p-values, LRTs, AIC, BIC, etc.)
3. Load in the census data (GSS data).
4. Calculate yˆ
P S.
General Social Survey (GSS) – Census Data
You will need to grab the GSS data from the CHASS website (I cannot post it for copyright/privacy reasons).
Instructions for how to access and load in this data are available in the first 30 lines of the gss_cleaning.R
code. Additionally, the gss_cleaning.R document has code that I used to clean the data. You do NOT need
to describe the cleaning included in this R script in your report, you only need to describe any additional
cleaning that YOUR GROUP had done.
CES – Survey Data
Here is a resource for grabbing the CES2019 data: https://awstringer1.github.io/sta238-book/section-shorttutorial-on-pulling-data-for-assignment-1.html#section-canadian-election-study. There is some code available in the Assignment3.Rmd where I go through selecting and grabbing the CES2019phone data set.
Additionally, Paul and Rohan have some more documentation here https://hodgettsp.github.io/cesR/ that
you may find useful.
Report Components
Introduction
The goal of the Introduction section is to introduce the overall “problem” to the reader.
Your Introduction section should include the following:
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• Describe the data and the problem in 2-3 clear sentences.
• Introduce the importance of the analysis.
• Get the reader interested/excited about analysis.
• Provide some background/context explaining the overall relevance of the problem/data/analysis.
• Introduce terminology and prep the reader for the following sections. For example, here you should
explain different political terms if they are niche.
• Introduce research question.
• Introduce any hypotheses (hypotheses should be decided on prior to performing your analysis and
should have some mild justification).
• Inline referencing.
Data
The goal of the Data section is to introduce the reader to the data set, showcase some meaningful aspects
of the data, and get them thinking about potential hypotheses/findings.
Your Data section should include the following:
• A description of the data collection process.
• A summary of the cleaning process (if you cleaned the data). Someone (who is NOT necessarily familiar
with Tidyverse functions) should be able to read this section and reproduce your cleaning process based
off reading your description.
• A description of the important variables.
• Some text (and perhaps graphical summaries) of the variables you will use in your model. This
should help prep the reader in understanding why the subsequent analysis is important/interesting
and whether it is appropriate.
• Some appropriate numerical summaries (at minimum center and spread, but something else may be
more appropriate). If there are a lot, please put them in a well formatted and labelled/numbered table.
• At least 1 aesthetically pleasing plot/graph/figure (No more than 4 plots).
• Text explaining/highlighting each table or figure.
• In line referencing if needed.
• Reference the programming language/software used to complete this section.
Methods
The goal of the Methods section is to introduce the reader to the statistical methods that you will be using
to analyze the data.
Your Methods section should include the following:
• A complete explanation of each methodology you are using. So a thorough explanation of the regression
model and a thorough explanation of poststratification.
• Here you will describe the chosen model (e.g., if you decide to perform linear regression you must write
out the mathematical model, with symbols (not numbers) and describe the parameters and variables
included).
• Give some justification for why this model was selected.
• Here you will also give an explanantion of the poststratification process. I.e., explaining yˆ
P S.
• This should include a description of what poststratification is (in non-statistical language) and a
description on why it is useful.
• As part of the poststratification technique you should also describe the cell/bin splits that you will
display/implement in the Results, based on the sample data. Here you should briefly recall the variables
that you are using to create the cells (again, the full description of these should be in the Data section).
You can briefly justify the choice to include or exclude certain variables when creating the cells/bins.
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(For example, choosing “province” because it is likely to influence voter outcome because of. . . , or not
including “eye colour” because it is not available in the census data).
• Explain any/all assumptions.
• An explanation of the parameters of interest.
• An explanation of the method for a general science reader (i.e., not a statistician).
• A description of why the method is appropriate (based off assumptions, variable types and practical
rationale).
• If you want to include some additional analysis (e.g., standard error, poststratification by province,
etc.) then you should describe your methodology here. Additionally, if you do this be sure to include
any citations/references that may be needed by the reader.
• In line referencing
• In line R code (if needed).
Results
The goal of the Results section is to present the results of the statistical analyses to the reader.
Your Results section should include the following:
• The results of the methodologies included in the report.
• An explanation/interpretation of the results.
• Some commentary on whether or not the results seem reasonable.
• Text explaining/highlighting each table or figure.
• In line referencing.
• In line R code to produce output in text (E.g. The mean is ` r mean(x) `.).
Conclusions
The goal of the Conclusions section is to present the story of your analysis to the reader.
Your Conclusions section should include the following:
• A brief recap of the hypotheses, methods, and results.
• State (or re-iterate) your key results.
• State any reasonable conclusions drawn from the results.
• An explanation/interpretation of the results.
• Some commentary on any drawbacks/limitations.
• Recommendations for Next Steps for future analyses/reports.
Bibliography
A well formatted bibliography, including references in a well formatted list. These should have been referred
to in the text above.
General Notes:
• All tables/figures should be well labelled and clean.
• Everything should be written in full sentences/paragraphs.
• There should be no evidence that this is a class assignment, I should be able to take a copy of this
report and paste it into a newspaper/blog without needing to implement any edits.
• There should be no raw code in the pdf. All output should be nicely formatted/presentable.
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• You will also need a reference/bibliography section. You should reference the data, any outside
code/documentation and any ideas/concepts that are taken outside of the course.
• Note, we are not marking grammar, but we are looking for clarity. If you need help with writing
there are resources posted on the Course Info>Resources page of Quercus. It is important that you
communicate in a clear and professional manner. I.e., no slang or emojis should appear.
• Be specific. Remember, the reader/marker may not be familiar with the topic or specifically what your
team/grooup did. A good principle is to assume that your audience is not aware of the subject matter.
• Remember to end each section with a concluding sentence. This means reiterating the key points from
your writing.
• You are more than welcome to perform a prediction of a different election (e.g., predict the 2024 U.S.A.
election or the outcome of the next British Columbia provincial election) in lieu of the next Canadian
federal election, just be sure to still perform a regression and poststratification (i.e., create a model on
sample data and poststratify on some census data).
• If you end up using other data (i.e., not CES 2019 for sample or not GSS 2016 for census) to perform
the task please include your csv files in order for us to assess reproducibility.
