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https://github.com/carlislerainey/methods

notes, exercises, and data for undergraduate political science research methods
https://github.com/carlislerainey/methods

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notes, exercises, and data for undergraduate political science research methods

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README 

        

This is a set of notes introducing political science research methods.



# Immediate To-Do

- Misc.

   - [x] add `parties` data set

     - [x] data set

     - [x] codebook

     - [x] example

- [ ] Fractions and Percentages

   - [ ] rewrite text

   - [ ] add exercises

     - [x] add exercise for `state-governments` data set

     - [ ] add exercise for `anes` data set

     - [ ] add exercise for `social-pressure` data set

     - [ ] improve data sets and tables with kableExtra

     (https://haozhu233.github.io/kableExtra/awesome_table_in_pdf.pdf)

   - [ ] develop in-class example

   - [ ] add `state-governments` data set

     - [x] create file to clean the data: `data/R/clean-state-governments.R`

     - [x] create general .R file to create a vector of file types: `create-extensions.R`

     - [x] create file to compress data: `data/R/compress-data.R`

     - [x] initiate a .Rmd to describe the data sets: `data/data-sources.Rmd`

     - [x] create a file that re-does all cleaning and compressing: `data/R/clean-compress-all.R`

     - [ ] add quantitative state government ideology variable to data set

     - [ ] codebook

     - [ ] example

   - [ ] add `anes` 

     - [ ] data set

     - [ ] codebook

     - [ ] example

   - [x] add `social-pressure`

     - [x] data set

     - [x] codebook

     - [x] example

    



# Outline

  

## Part 1: Our Approach



- [ ] Concepts

    - [ ] Example: Democracy (Dahl’s concept of Polyarchy)

    - [ ] Engage the Literature: "The Confusing Case of Budgetary Incrementalism"

    - [ ] Review Exercises

- [] Questions

    - [x] Normative

    - [x] Descriptive

    - [x] Causal

    - [ ] Engage the Literature: *On Political Equality*.

    - [ ] Review Exercises

- [ ] Models

    - [ ] Principles of Model-Building

    - [ ] Example 1: Collective Action (Olson)

    - [ ] Example 2: Opinion Formation (Zaller)

    - [ ] Example 3: Electoral Rules and Mobilization (Rainey, 2014)

    - [ ] Example 4: Duverger’s Law (Clark and Golder)

    - [ ] In-Class Exercise: Explaining Vote Choice

- [ ] Measurement

    - [ ] Qualitative and Quantitative Variables

    - [ ] Reliability

    - [ ] Validity

    - [ ] Example 1: Measuring Democracy

    - [ ] Example 2: Measuring Voting

    - [ ] Engage the Literature: "The Myth of the Vanishing Voter" [link]

    - [ ] Exercise: Measuring Democracy

    - [ ] Exercise: "Counting" Marbles

- [ ] Comparisons

    - [ ] Three Ways Compare

        - [ ] Differences in Percentages (Qualitative, Qualitative) - some tables from American voter

        - [ ] Difference in Means (Qualitative, Quantitative) - life expectancy, wealth by democracy

        - [ ] Scatterplot (Quantitative, Quantitative) - gun control versus firearm deaths; health versus ACA opinions

    - [ ]  Four Ways to Get a Correlation

        - [ ] Causation

        - [ ] Reverse Causation

        - [ ] Confounder

        - [ ] Chance

    - [ ] Randomization

    - [ ] Example 1: Campaign Mailers

    - [ ] Example 2: Motivated Reasoning (Taber and Lodge 2006)

    - [ ] Example 3: "Do Politicians Racially Discriminate Against Constituents?"

    - [ ] Engage the Literature: Experiments in International Relations: Lab, Survey, and Field



## Part 2: Describing Lists of Numbers



- [ ] Histograms (Density)

- [ ] Introduction to R

- [ ] Reading Data into R

- [ ] Histograms in R

    - Working with Data: Polarization in Congress. 

- [ ] Average and SD

- [ ] Average and SD in R 

  - mean()

  - sd()

  - group_by() and summarize()

- [ ] Fractions and Percents

- [ ] Normal Approximation



## Part 3: Describing Relationships Between Quantitative Variables



- [ ] Review: Points and Lines

- [ ] Scatterplots in R

    - Working with Data: Health and Public Opinion in the 50 States

- [ ] The Correlation Coefficient

    - [ ] Exercise: Height and Handspan

    - [ ] Working with Data: Gamson's Law

    - [ ] Working with Data: Duverger's Law

- [ ] Prediction Using the Correlation Coefficient

    - Exercise: Height and Handspan, cont.

    - Working with Data: Predicting Presidential Election Using 2nd Quarter GDP

- [ ] Regression

    - [ ] In-Class Exercise: Fitting a Line

    - [ ] The Principle of Least Squares

    - [ ] Finding the Slope and Intercept that Minimizes the Squared Errors

    - [ ] Interpreting the Slope and Intercept

    - [ ] Working with Data: Predicting Presidential Election Using 2nd Quarter GDP

- [ ]  The R.M.S. Error of the Regression

    - Working with Data: Predicting Presidential Election Using 2nd Quarter GDP

- [ ]  Multiple Regression

    - Working with Data: Predicting Presidential Election Using 2nd Quarter GDP and Other Variables

- [ ] Regression in R    



## Part 4: Sample Surveys



- [ ] Calculating the Chances

    - [ ] Listing the Equally Likely Ways

    - [ ] Multiplication Rule and Independence

    - [ ] Addition Rule and Mutually Exclusive

    - [ ] Sampling With and Without Replacement

- [ ] The Urn Model

    - [ ] Chance Processes

    - [ ] Setting up an Urn Model

        - Tickets in the Urn

        - Number of Draws

        - Sum

    - [ ] Characterizing the Sum of the Draws

        - [ ] Expected Value

        - [ ] Standard Error

        - [ ] Normal Approximation

    - [ ] Characterizing the Average and Percentage

    - [ ] In-Class Exercise: Simulating Chance Processes

- [ ] The Design of Surveys

- [ ] Confidence Intervals for Percentages

- [ ] Confidence Intervals for Averages

- [ ] Hypothesis Tests for Percentages and Averages

    - In-Class Exercise: The Lady Tasting Tea

- [ ] Confidence and Hypothesis Tests for Differences