Learn to analyze political data with Stata through guided commands, annotated screenshots, and exercises with real political data. Build a repeatable workflow with Do-files, interpret results, and produce clear tables and graphs for your own research. Prepare for your first lab or find support for teaching.
Start with Getting Started or explore chapter resources for datasets, tutorial videos, and related reading.
Sage handles purchases and instructor-copy requests; instructor copies are subject to Sage's eligibility requirements.
Pairs with The Essentials of Political Analysis, Seventh Edition or another methods text.
The authors prepared examples with Stata 14, 15, and 17. Companion datasets are saved in Stata 12 format for compatibility with earlier releases. Access to Stata is required.
Request an instructor copy from Sage.
Access adopter Do-files, solutions, and additional fillable exercises through Sage. Instructor materials require Sage instructor access.
Explore sample syllabi for course planning.
Publisher-account and instructor-access questions can be directed to Sage support.
Use the datasets that match your edition. Older files may use different variables and coding.
Simple Random Sampling and the Sample Command
Postestimation with estat commandsExpand a section for its overview, outline, and supporting materials. Numbered chapters have five printed exercises; adopter Do-files, solutions, and additional fillable exercises are accessed through Sage.
Related-reading links here use Essentials, Seventh Edition; printed references in the Stata book use the sixth edition.
Section numbers in these outlines are provided as a reading guide to the chapter hierarchy.
Locate and open the companion datasets, tour the Stata interface, and run describe and codebook to examine variables and their coding. Use the Command, Review, and Variables windows efficiently, and find chapter tutorials and resources before beginning the guided examples.
Related reading: Essentials, 7e, Introduction: The Scientific Approach to Politics
Defined terms: Command language, Comments, Datasets, General syntax, Graphical user interface
Learn command syntax, options, and conditions, and use the graphical interface to discover commands. Write reusable Do-files, print and copy output, customize your display, create log files that preserve commands and results, and use Stata’s help system.
Related reading: Essentials, 7e, Chapter 1: The Definition and Measurement of Concepts
Defined terms: Commands, Conditions, Delimiter, Do-files, Generic usage statement, Graphs, Log file, Options, Stata Manual, Suboptions, Table, Weight observations
In the book: Learning objectives, 5 printed exercises.
For instructors: Chapter Do-files, Solutions manual, and Additional fillable exercises. Do-files require Sage adopter access; other materials require Sage instructor access.
Choose descriptive statistics for nominal, ordinal, and interval-level measures. Read frequency tables, compare weighted and unweighted analysis, describe central tendency and dispersion, create bar charts and histograms, and obtain case-level information for political datasets.
Related reading: Essentials, 7e, Chapter 2: Measuring and Describing Variables
Defined terms: Bar chart, Bins, Cumulative percent, Frequency distribution table, Histogram, Interquartile range, Interval, Kurtosis, Level of measurement, Median, Mode, Nominal, Ordinal, Range, Raw frequencies, Skewness, Standard deviation, Unweighted data, Variance, Weighted data
In the book: Learning objectives, 5 printed exercises.
For instructors: Chapter Do-files, Solutions manual, and Additional fillable exercises. Do-files require Sage adopter access; other materials require Sage instructor access.
Create dummy variables, apply mathematical operators, and manage variable descriptions and value labels using commands and the Variables Manager. Collapse categories, center or standardize numeric measures, and combine related variables into an additive index for later analysis.
Related reading: Essentials, 7e, Chapter 3: Creating and Transforming Variables
Defined terms: Additive index, Centering, Cutpoints, Double equal signs, Dummy variables, Logical operators, Mathematical operators, Per capita, Recoding protocol, Set of dummy variables, Standardize, Value labels, Variable labels, Variables Manager, z-Scores
In the book: Learning objectives, 5 printed exercises.
For instructors: Chapter Do-files, Solutions manual, and Additional fillable exercises. Do-files require Sage adopter access; other materials require Sage instructor access.
Generate and interpret cross-tabulations and mean comparison tables, including weighted observations. Visualize comparisons with simple bar charts and box plots, format reported results, and group interval-level independent variables to examine political differences across categories.
Related reading: Essentials, 7e, Chapter 4: Proposing Explanations, Framing Hypotheses, and Making Comparisons
Defined terms: Box plot, Column percentages, Cross-tabulations, Dependent variable, Frequencies, Independent variable, Mean comparison table, Outliers, Simple bar chart
In the book: Learning objectives, 5 printed exercises.
For instructors: Chapter Do-files, Solutions manual, and Additional fillable exercises. Do-files require Sage adopter access; other materials require Sage instructor access.
Choose graphs for binary, nominal, ordinal, and interval-level dependent variables. Build bar charts, area charts, multiple line plots, box plots, and scatterplots, using markers, jitter, and other options to communicate relationships and distributional patterns clearly.
Related reading: Essentials, 7e, Chapter 5: Graphing Relationships and Describing Patterns
Defined terms: Aggregated data, Area chart, Box plot, Clustered bar chart, Jitter, Markers, Multiple line chart, Scatterplot, Simple bar chart, Simple line chart, Stacked bar chart, Strip chart
In the book: Learning objectives, 5 printed exercises.
For instructors: Chapter Do-files, Solutions manual, and Additional fillable exercises. Do-files require Sage adopter access; other materials require Sage instructor access.
Create randomized groups, including groups of predetermined size, and assess assignment and treatment effects in an experiment. Draw samples with or without replacement, explore systematic and grouped sampling, select qualitative cases, and apply research ethics and replication Do-file practices.
Related reading: Essentials, 7e, Chapter 6: Research Design, Research Ethics, and Evidence of Causation
Defined terms: Binomial distribution, Clusters, Discrete distribution, Human subjects, Most different systems, Most similar systems, Period, Random assignment, Random seed value, Replacement, Replication, Robustness checks, Simple random sample, Starting value, Stratum, Systematic random sample
In the book: Learning objectives, 5 printed exercises.
For instructors: Chapter Do-files, Solutions manual, and Additional fillable exercises. Do-files require Sage adopter access; other materials require Sage instructor access.
Add control variables to cross-tabulations and mean comparisons using Stata’s grouping tools. Interpret spurious, additive, and interactive relationships, visualize controlled results for categorical and numeric outcomes, and use the if qualifier to examine selected observations.
Related reading: Essentials, 7e, Chapter 7: Making Controlled Comparisons
Defined terms: Additive relationship, Controlled cross-tabulation, Controlled mean comparison table, Control variable, Direction, Interactive relationship, Legend, Spurious relationship, Strength
In the book: Learning objectives, 5 printed exercises.
For instructors: Chapter Do-files, Solutions manual, and Additional fillable exercises. Do-files require Sage adopter access; other materials require Sage instructor access.
Simulate samples to understand estimates of population proportions and means. Explore sampling distributions, the normal distribution, standard errors, and the empirical rule, then construct and interpret confidence intervals, margins of error, and Student’s t-based estimates.
Related reading: Essentials, 7e, Chapter 8: Foundations of Statistical Inference
Defined terms: Confidence intervals, Critical values, Cumulative density function, Degrees of freedom, Inferential statistics, Margin of error, Population, Population parameters, Point estimate, Probability density function, Sample, Sampling distributions, Sample statistics, Standard error, Standard error of a sample proportion, Standard error of a sample mean, Standard normal distribution, t-Distribution
In the book: Learning objectives, 5 printed exercises.
For instructors: Chapter Do-files, Solutions manual, and Additional fillable exercises. Do-files require Sage adopter access; other materials require Sage instructor access.
Translate research questions into null hypotheses and test claims about proportions and means. Compare one sample with a hypothesized value or compare two groups or variables, account for variance assumptions, and work with weighted observations where appropriate.
Related reading: Essentials, 7e, Chapter 9: Hypothesis Tests with One or Two Samples
Defined terms: Alpha, Assumptions about variance, Directional, Nondirectional, Null hypothesis, One-sample test
In the book: Learning objectives, 5 printed exercises.
For instructors: Chapter Do-files, Solutions manual, and Additional fillable exercises. Do-files require Sage adopter access; other materials require Sage instructor access.
Test independence with chi-square, compare observed and expected frequencies, and interpret results with weighted data and control variables. Measure association with lambda, Somers’ d, and Cramer’s V, and use single-factor and two-factor ANOVA to compare group means.
Related reading: Essentials, 7e, Chapter 10: Chi-Square Test and Analysis of Variance
Defined terms: Analysis of variance, Chi-square distribution, Chi-square test statistic, Conditional distributions, Cramer’s V, Expected frequencies, F-distribution, F-statistic, Informed prediction errors, Lambda, Marginal distribution, Measures of association, Naïve prediction errors, Observed frequencies, Single-factor, Somers’ d, Sum of squares between groups, Sum of squares within groups, Total sum of squares, Two-factor
In the book: Learning objectives, 5 printed exercises.
For instructors: Chapter Do-files, Solutions manual, and Additional fillable exercises. Do-files require Sage adopter access; other materials require Sage instructor access.
Calculate correlations between two or more variables, estimate bivariate regressions, and interpret coefficients and model fit. Create scatterplots with prediction lines, analyze weighted observations, and use graphical layers and academic-style tables to present results clearly.
Related reading: Essentials, 7e, Chapter 11: Correlation and Bivariate Regression
Defined terms: Academic-style table, Adjusted R-squared, Bivariate regression model, Census, Coefficients table, Correlation matrix, Expected value, Linear regression line, Other correlation coefficients, Pearson’s correlation coefficient, R-squared
In the book: Learning objectives, 5 printed exercises.
For instructors: Chapter Do-files, Solutions manual, and Additional fillable exercises. Do-files require Sage adopter access; other materials require Sage instructor access.
Estimate multiple regressions, interpret partial effects and beta coefficients, and visualize results with bubble plots. Work with weighted data, multiple dummy variables and reference categories, then create interaction terms and prediction lines to explain relationships that differ across groups.
Related reading: Essentials, 7e, Chapter 12: Multiple Regression
Defined terms: Base terms, Beta coefficients, Bubble plot, Confidence bar, Interaction term, Multiple regression analysis, Parallel regression lines, Partial effect, Reference category, Variable prefix
In the book: Learning objectives, 5 printed exercises.
For instructors: Chapter Do-files, Solutions manual, and Additional fillable exercises. Do-files require Sage adopter access; other materials require Sage instructor access.
Calculate predicted values and squared and standardized residuals, then examine model assumptions with histograms and diagnostic plots. Test normality and constant variance for bivariate and multiple regression models, consider other diagnostic tests, and address or disclose problems in the analysis.
Related reading: Essentials, 7e, Chapter 13: Analyzing Regression Residuals
Defined terms: Assumptions of linear regression model, Breusch-Pagan test, Histogram of regression residuals, Jarque-Bera test, Limitations to analysis, Observed values, Q-Q plots, Regression residuals, Residuals vs. fitted plot, Squared residuals, Standardized residuals, Sum of squared errors, Table of residuals, Two components of regression model
In the book: Learning objectives, 5 printed exercises.
For instructors: Chapter Do-files, Solutions manual, and Additional fillable exercises. Do-files require Sage adopter access; other materials require Sage instructor access.
Connect binary outcomes with probabilities, odds, and logged odds. Estimate logistic models with one or several predictors, interpret odds ratios and model fit, use weighted observations, and graph predicted probabilities at representative values, predictor means, and combinations of categorical and numeric predictors.
Related reading: Essentials, 7e, Chapter 14: Logistic Regression
Defined terms: Classification table, Full effect, Logged odds, Logistic regression, Marginal effects at representative values, Marginal effects at the means, Odds ratio, Odds, Predicted probability curve, Prediction frame, Probability, Pseudo R-squared measures, ROC curve
In the book: Learning objectives, 5 printed exercises.
For instructors: Chapter Do-files, Solutions manual, and Additional fillable exercises. Do-files require Sage adopter access; other materials require Sage instructor access.
Develop a feasible research project on political attitudes, elections, state policies, religion, or race. Open Stata datasets, import Excel or web-table data, enter new observations, and organize a research report from its question and previous research through analysis, limitations, and conclusions.
Related reading: Essentials, 7e, Chapter 15: Conducting Your Own Political Analysis
Defined terms: Comma separated values, Limitations, Literature review, Metadata, Microsoft Excel, Replication datasets, Stata’s native file formats
In the book: Learning objectives, 5 printed exercises.
For instructors: Chapter Do-files, Solutions manual, and Additional fillable exercises. Do-files require Sage adopter access; other materials require Sage instructor access.
Look up the coding and descriptions of variables from the experiment on the televised 1960 presidential debate, including assignment, participant characteristics, candidate assessments, and perceived outcomes.
Find variable names and coding information for the General Social Survey teaching dataset, including respondent characteristics, political attitudes, and social and policy preferences.
Find variable descriptions and coding for the National Election Study teaching dataset, including political knowledge, campaign and election attitudes, issue preferences, and respondent characteristics.
Explore state-level measures by topic, from demographics, elections, and the economy to courts, civic culture, health, and public policy. Consult coding descriptions and sources before comparing states.
Explore country-level measures of government, conflict, economic conditions, health, education, and development. Use the topic groupings and variable descriptions to identify measures for comparative analysis.
The book ends with an alphabetical reference to the statistical concepts, Stata terminology, and research methods introduced throughout the chapters. Chapter-specific term lists appear above with each chapter's resources.