The Seventh Edition of The Essentials of Political Analysis is now available!
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Table of Contents Click chapter title for description and links to resources
Getting Started with R
This chapter introduces students to the R programming environment, including RStudio, and helps them install necessary packages.
Students learn basic syntax and how to load, inspect, and manipulate political datasets in preparation for analysis.
Students learn to perform essential data analysis tasks using R, including reading in data, selecting variables, and filtering cases.
The chapter emphasizes R functions and commands that are foundational for political data exploration.
This chapter covers descriptive statistics in R, teaching students to calculate measures of central tendency and dispersion.
Students use R functions to summarize political data and interpret distributions using both numeric and visual outputs.
Students practice creating new variables and transforming existing ones using R's vectorized operations.
Topics include recoding variables, computing indices, and applying mathematical transformations in a political analysis context.
This chapter focuses on comparing means or proportions across categories, such as party affiliation or region.
Using R functions, students learn to summarize and contrast political data across relevant groups.
Students will create bar charts, histograms, and scatterplots using R functions.
This chapter emphasizes effective visualization of political relationships and patterns through clean, interpretable plots.
Students simulate random assignment and sampling techniques in R.
This chapter emphasizes research design principles and shows how to generate randomized groups and samples for empirical political studies.
This chapter teaches students how to control for confounding variables using stratified comparisons.
With R, students assess relationships while accounting for rival explanations.
Students are introduced to probability theory and confidence intervals using R simulations and formulas.
They calculate standard errors and construct confidence intervals for political estimates from samples.
This chapter covers one- and two-sample hypothesis tests in R, focusing on t-tests and proportion tests.
Students evaluate statistical significance and interpret p-values in the context of political science research questions.
Students use R to perform chi-square tests for categorical variables and ANOVA for comparing means across groups.
The chapter explains how to interpret these tests to assess relationships in political data.
This chapter explores linear relationships between two variables using correlation and bivariate regression.
Students fit simple regression models in R and interpret coefficients, significance, and scatterplots.
Building on previous chapters, students use R to perform multiple regression analysis with several independent variables.
They evaluate how multiple factors jointly influence a political outcome and assess model performance.
Students learn to examine residuals from regression models to diagnose problems. Using R’s diagnostic plots and statistical tests,
they detect outliers, leverage points, and violations of model assumptions.
This chapter introduces logistic regression in R to model binary outcomes, such as voting.
Students estimate models, interpret odds ratios, and use predicted probabilities to understand categorical political outcomes.
The final chapter guides students through the process of designing and executing an independent political analysis using R.
Emphasis is placed on combining skills from earlier chapters to answer an original research question.
This appendix provides definitions and coding information for all variables used in the datasets throughout the book.
It serves as a reference for students working with R to understand the political data provided.
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