Build confidence with guided examples, current political datasets, and scripts you can adapt for your own research. This practical companion for political science research methods courses takes you from descriptive statistics and visualization to hypothesis tests and regression. Find support for course planning, your first lab, or your next chapter.
Pair it with The Essentials of Political Analysis, Seventh Edition or another methods text. Start with Getting Started or explore chapter resources.

Expand a chapter on this page to explore its topics and find links to supporting materials.
Use the chapter outlines to choose a sequence and supply assignments suited to your course.
Sample syllabi: explore course planning examples, including courses using R.
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Tutorial links open the third-edition collection; commands and datasets may differ from RCPA4. Chapter-code links are coming soon.
Set up R, install and load RCPA4, and check your installation with welcomeC(). Tour the R environment, add RStudio, and work through common setup problems so you are ready to follow the book's examples.
Related reading: Essentials, 7e, Introduction: The Scientific Approach to Politics
Defined terms: base version of R, Comprehensive R Archive Network (CRAN), datasets, integrated development environment (IDE), missing package problem, package dependency, packages, R console, R session, RCPA4
Build a working foundation in R: enter commands, create and access objects, use function arguments, and save reusable scripts. Practice with the Widget Factory, manage graphical and text output, export tables and figures with printC, and find help when a command does not work.
Related reading: Essentials, 7e, Chapter 1: The Definition and Measurement of Concepts
Defined terms: assign, assignment operator, arguments, character string, charts, companion functions, data frame, function, generic syntax, global environment, indexing, keyword matching, list, matrices, missing value (NA), object, open source, optional arguments, packages, positional matching, R console, R session, R documentation, R scripts, required arguments, scalars, subset, tables, vector, working directory, workspace
Choose descriptive statistics that fit nominal, ordinal, and interval-level variables. Read frequency tables, summarize central tendency and dispersion, customize plots, distinguish weighted from unweighted results, and inspect individual cases to understand the patterns behind a summary.
Related reading: Essentials, 7e, Chapter 2: Measuring and Describing Variables
Defined terms: bar chart, binary variables, bins, categorical variable, central tendency, continuous variables, cumulative percentage, discrete variables, dispersion, factor, frequency distribution table, histogram, interquartile range, interval-level, kurtosis, level of measurement, logical values, nominal-level, numeric, ordered factors, ordinal-level, range, ratio variables, sampling weight, skewness, standard deviation, unordered factors, unweighted data, variance, weighted data
Create variables with mathematical and logical operators, build dummy variables, and combine categories without losing track of their meaning. Modify variable attributes, center and standardize numeric measures, create rankings, and construct an additive index for later analysis.
Related reading: Essentials, 7e, Chapter 3: Creating and Transforming Variables
Defined terms: additive index, attributes, center, class attribute, concatenate, cutpoints, dummy variables, factor, interval notation, levels, logical operators, logical values, mathematical operators, metadata, missing value (NA), numeric, NULL, per capita, recoding protocol, standardize, variable labels, $z$ scores
Compare political outcomes using cross-tabulations and mean comparisons, including comparisons with interval-level independent variables. Create plots from summary data, interpret differences across groups, and practice debugging techniques that help you diagnose and correct problems in R code.
Related reading: Essentials, 7e, Chapter 4: Proposing Explanations, Framing Hypotheses, and Making Comparisons
Defined terms: checking your code, column percentages, counts, cross-tabulations, debugging, dependent variable, factor and numeric variables, independent variable, mean comparison analysis, mean comparison table, observation-level data, summary data
Choose graphs to suit binary, nominal, ordinal, and interval-level dependent variables. Use bar charts, line plots, boxplots, and scatterplots to reveal relationships, then customize labels, colors, and other graphical elements to communicate political patterns clearly.
Related reading: Essentials, 7e, Chapter 5: Graphing Relationships and Describing Patterns
Defined terms: aggregated data, base R graphics, box plots, clustered bar chart, formula syntax, graphical parameters, grammar of graphics, graphics device, interquartile range, jitter, layers, markers, mosaic plot, multiple line chart, outliers, overplotting, scatterplot, simple bar chart, simple line chart, strip chart
Use random assignment to create experimental groups and analyze the results of an experiment. Draw random samples, select cases for qualitative research, and connect these practical tools with ethical analysis, clear reporting, replication, and transparency.
Related reading: Essentials, 7e, Chapter 6: Research Design, Research Ethics, and Evidence of Causation
Defined terms: cluster random sample, clusters, comments, discrete distribution, human subjects, period, population, random assignment, random seed value, replication, robustness checks, sample, sampling with replacement, simple random sample, starting value, stratified random sample, stratum, systematic random sample
Add control variables to cross-tabulations and mean comparisons to examine whether an observed relationship holds within groups. Interpret controlled tables and graphs, work with interval-level controls, and customize legends to make more complex comparisons readable.
Related reading: Essentials, 7e, Chapter 7: Making Controlled Comparisons
Defined terms: additive relationships, control variable, controlled comparison, controlled cross-tabulation, controlled mean comparison table, direction, factor, interactive relationship, legend, numeric, spurious relationship, strength
Use R to explore sampling distributions, standard errors, and the uncertainty surrounding sample estimates. Work with normal and Student's t distributions, construct confidence intervals and margins of error, and interpret what these quantities tell you about population proportions and means.
Related reading: Essentials, 7e, Chapter 8: Foundations of Statistical Inference
Defined terms: binomial distribution, confidence intervals, critical values, cumulative density function, degrees of freedom, empirical rule, inferential statistics, margin of error, normal distribution, point estimate, population, population parameters, probability, probability density function, probability distribution, quantile function, sample, sampling distributions, simulation, standard deviation, standard error, standard error of a sample mean, standard error of a sample proportion, standard normal distribution, $t$ distribution, $z$ scores
Translate research questions into null hypotheses and test claims about one proportion, one mean, or differences between two proportions or means. Interpret test statistics and p-values, distinguish independent from paired samples, and choose the procedure that fits your comparison.
Related reading: Essentials, 7e, Chapter 9: Hypothesis Tests with One or Two Samples
Defined terms: alpha, alternative hypothesis, assumptions about variance, confidence intervals, critical values, directional hypothesis, null hypothesis, null hypothesis significance testing (NHST), one-tailed and two-tailed $P$-values, one-tailed test, one-sample test, $P$-value, $P$-value approach, paired observations, pooled variance, research hypothesis, standard error, statistical significance, $t$ statistic, two-sample test, Type I and Type II errors, $z$ statistic
Test relationships between categorical variables with chi-square and assess their strength with measures of association, including controlled comparisons. Use single-factor and two-factor analysis of variance to compare means across groups and interpret the evidence alongside substantive differences and graphical results.
Related reading: Essentials, 7e, Chapter 10: Chi-Square Test and Analysis of Variance
Defined terms: analysis of variance (ANOVA), asymmetrical measure, chi-square distribution, chi-square test statistic, concordant, discordant, and tied pairs, conditional distributions, Cramer’s $V$, degrees of freedom, expected frequencies, $F$ distribution, $F$ statistic, lambda, marginal distribution, measures of association, null hypothesis, observed frequencies, omnibus test, $P$-value, proportional reduction in error (PRE), single-factor, Somers’ $d$, statistical independence, statistical significance, sum of squares between groups, sum of squares within groups, symmetrical measure, total sum of squares, two-factor
Measure linear association with correlation and estimate bivariate regression models. Interpret coefficients and model fit, visualize fitted relationships and predictions, account for weighted observations, and create tables that communicate regression results.
Related reading: Essentials, 7e, Chapter 11: Correlation and Bivariate Regression
Defined terms: academic-style table, adjusted R-squared, bivariate regression model, coefficients table, correlation matrix, expected or fitted values, intercept and slope coefficients, linear regression line, model object, ordinary least squares (OLS), other correlation coefficients, Pearson’s correlation coefficient, regression coefficient, R-squared, scatterplot
Estimate multiple regression models to assess relationships while holding other predictors constant. Work with categorical predictors and multiple dummy variables, interpret interaction effects, and visualize model predictions to explain how relationships vary across groups and values.
Related reading: Essentials, 7e, Chapter 12: Multiple Regression
Defined terms: additive regression model, base terms, bubble plot, character expansion, confidence bar, dummy variables, interaction term, model object, multiple regression analysis, parallel regression lines, partial effect, prediction frame, reference category, regression plane, three-dimensional figures
Compare observed and fitted values, calculate squared and standardized residuals, and use diagnostic plots to examine regression assumptions. Test normality and constant variance, investigate unusual observations, and decide how to address or disclose problems revealed by your diagnostics.
Related reading: Essentials, 7e, Chapter 13: Analyzing Regression Residuals
Defined terms: assumptions of linear regression model, Breusch-Pagan test, fitted values, histogram of regression residuals, homoscedasticity and heteroscedasticity, influential observation, Jarque-Bera test, leverage, model object, normal distribution, observed values, outliers, Q-Q plots, regression residuals, residual standard error, residuals vs. fitted plot, squared residuals, standardized residuals, sum of squared errors, two components of regression model
Model binary outcomes by connecting probabilities, odds, and logged odds. Estimate logistic regressions with one or several predictors, interpret coefficients and odds ratios, and plot predicted probabilities for categorical and interval-level predictors, separately and in combination.
Related reading: Essentials, 7e, Chapter 14: Logistic Regression
Defined terms: binary variables, classification table, deviance, full effect, instantaneous effect, inverse logit, logged odds, logistic regression, marginal effect, marginal effects at representative values, marginal effects at the means, model object, odds, odds ratio, predicted probability curves, prediction frame, probability, pseudo R-squared measures, $z$ statistic
Turn an original research question into a feasible political analysis using practical research designs. Find and load R datasets, import Excel files and HTML tables or SPSS and Stata data, check imported values, and organize a research report from its question and prior research through results and conclusions.
Related reading: Essentials, 7e, Chapter 15: Conducting Your Own Political Analysis
Defined terms: comma separated values, codebook, data import, limitations, literature review, metadata, Microsoft Excel, package dependencies, .rda or .RData file, .rds file, rectangular data, replication datasets, SPSS, Stata, working directory
Definitions and coding information for variables in the ANES 2024 dataset, covering respondents, elections, political attitudes, and policy preferences.
Definitions and coding information for variables in the presidential debate experiment dataset, including subject characteristics, experimental assignment, and measured outcomes.
Definitions and coding information for variables in the GSS 2024 dataset, covering respondents, social attitudes, political participation, and public policy preferences.
Definitions and coding information for variables in the States dataset, organized by topics such as demographics, elections, the economy, and public policy.
Definitions and coding information for variables in the World dataset, organized by topics such as government, economic conditions, health, and development.