- Copyright
- Figures
- Preface
- Approach to Teaching and Learning
- What's New in the Seventh Edition?
- Chapter Organization
- Acknowledgments
- About the Authors
Learn political analysis through guided SPSS menus, annotated screenshots, and practical exercises with real political data. Save commands in syntax files to reproduce your work, interpret results, and present clear tables and graphs. Build SPSS skills 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. Chapter reading links follow the seventh-edition topic mappings in the book's teaching outline.
The book features SPSS 29. The authors advise that readers using release 12 or later can use the book; menu details and output may vary by release. Access to SPSS software is required.
Request an instructor copy from Sage.
Access instructor syntax 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.
Expand a section for its overview, outline, and supporting materials. Numbered chapters have five printed exercises; instructor syntax files, solutions, and additional fillable exercises are accessed through Sage.
Locate the five companion datasets and download the files for this edition. Learn how to combine the book's guided examples, learning objectives, chapter reviews, and tutorial videos to practice SPSS skills and understand the political analysis behind them.
Related reading: Essentials, 7e, Introduction: The Scientific Approach to Politics
Defined terms: Companion datasets, Graphical user interface (GUI)
Navigate Data and Variable Views, set variable-list options, and execute commands through SPSS menus. Select and save output, format academic-style tables, preserve commands in reusable syntax files, and use the help system and keyboard shortcuts.
Related reading: Essentials, 7e, Chapter 1: The Definition and Measurement of Concepts
Defined terms: Academic-style tables, Data Editor, Data View, Keyboard shortcuts, Outline pane, Metadata, Missing values, Syntax file, Table Editor, Value labels, Variable labels, Variable lists, Variable names, Variable View, Viewer
In the book: Learning objectives, Chapter review, 5 printed exercises.
For instructors: Chapter syntax files, Solutions manual, and Additional exercises on fillable PDFs (Sage instructor access required).
Inspect variable coding and levels of measurement, then choose appropriate frequency tables, charts, and summary statistics. Describe central tendency, dispersion, skewness, and kurtosis; apply survey weights, edit graphics, and use Case Summaries to examine individual observations.
Related reading: Essentials, 7e, Chapter 2: Measuring and Describing Variables
Defined terms: Bar chart, Categorical variables, Central tendency, Chart Editor, Coding system, Cumulative percent, Descriptive statistics, Dispersion, Frequency, Frequency distribution table, Histogram, Interquartile range, Interval level, Kurtosis, Level of measurement, Median, Nominal level, Ordinal level, Percent, Properties, Quartiles, Range, Skewness, Standard deviation, Valid percent, Variance, Variation, Weight variable
In the book: Learning objectives, Chapter review, 5 printed exercises.
For instructors: Chapter syntax files, Solutions manual, and Additional exercises on fillable PDFs (Sage instructor access required).
Create dummy variables, edit variable labels, and recode detailed measures into simpler categories. Compare Recode with Visual Binning, center or standardize numeric variables, and use Compute to build an additive index with clear, reproducible coding decisions.
Related reading: Essentials, 7e, Chapter 3: Creating and Transforming Variables
Defined terms: Additive indexes, Cutpoints, Dummy variable, Inequality operators, Logical operators, Math operators, Mean centering, Recoding protocol, Standardize, Variable transformations, Visual binning
In the book: Learning objectives, Chapter review, 5 printed exercises.
For instructors: Chapter syntax files, Solutions manual, and Additional exercises on fillable PDFs (Sage instructor access required).
Use cross-tabulations and column percentages to compare categories, and use mean comparison tables for interval-level outcomes. Add bar and line charts, create graphics from summary tables, and examine relationships with interval-level independent variables.
Related reading: Essentials, 7e, Chapter 4: Proposing Explanations, Framing Hypotheses, and Making Comparisons
Defined terms: Category axis, Column percentages, Cross-tabulations, Dependent variable (DV), Hypothesis, Independent variable (IV), Line chart, Mean comparison analysis, Mean comparison tables, Simple bar chart, Summary axis, Summary data
In the book: Learning objectives, Chapter review, 5 printed exercises.
For instructors: Chapter syntax files, Solutions manual, and Additional exercises on fillable PDFs (Sage instructor access required).
Choose graphs by the dependent and independent variables' levels of measurement. Build simple and clustered bar charts, area and line charts, box plots, and scatterplots; use the Chart Editor to make patterns readable and address overlapping observations.
Related reading: Essentials, 7e, Chapter 5: Graphing Relationships and Describing Patterns
Defined terms: Aggregated data, Area chart, Box plot, Clustered bar chart, Data visualization, Markers, Multiple line chart, Outliers, Overplotting, Scatterplot, Simple line chart, Stacked area chart, Stacked bar chart
In the book: Learning objectives, Chapter review, 5 printed exercises.
For instructors: Chapter syntax files, Solutions manual, and Additional exercises on fillable PDFs (Sage instructor access required).
Assign observations to experimental groups with equal or varying probabilities, assess balance, and evaluate treatment effects. Draw random samples, select cases for most-similar and most-different systems comparisons, and apply research ethics when analyzing political data.
Related reading: Essentials, 7e, Chapter 6: Research Design, Research Ethics, and Evidence of Causation
Defined terms: Clustered random sample, Clusters, Human subject research, Most different systems, Most similar systems, Period, Random assignment, Random sample, Random seed value, Replication, Robustness checks, Simple random sample, Starting value, Stratified random sample, Stratum, Systematic random sample
In the book: Learning objectives, Chapter review, 5 printed exercises.
For instructors: Chapter syntax files, Solutions manual, and Additional exercises on fillable PDFs (Sage instructor access required).
Add a control variable to cross-tabulations and mean comparisons to assess rival explanations. Visualize conditional relationships with multiple lines, clustered bars, and box plots, and consider controlled comparisons when the control variable is measured at the interval level.
Related reading: Essentials, 7e, Chapter 7: Making Controlled Comparisons
Defined terms: Additive relationship, Control variable (Z), Controlled cross-tabulation, Controlled mean comparison table, Declutter, Direction, Interactive relationship, Spurious relationship, Strength
In the book: Learning objectives, Chapter review, 5 printed exercises.
For instructors: Chapter syntax files, Solutions manual, and Additional exercises on fillable PDFs (Sage instructor access required).
Simulate random sampling to see how sample proportions and means vary. Connect sampling distributions to the central limit theorem, standard errors, and the empirical rule, then construct confidence intervals and use critical values from normal and t-distributions.
Related reading: Essentials, 7e, Chapter 8: Foundations of Statistical Inference
Defined terms: Binomial distribution, Central limit theorem, Computer simulations, Confidence intervals, Confidence level, Critical values, Cumulative density function, Degrees of freedom, Empirical rule, Error bars, Error tolerance, Inferential statistics, Margin of error, Point estimate, Population, Population parameters, Probability density function, Sample, Sample statistics, Sampling distributions, Standard error, Standard error of a sample mean, Standard error of a sample proportion, Standard normal distribution, t-Distribution
In the book: Learning objectives, Chapter review, 5 printed exercises.
For instructors: Chapter syntax files, Solutions manual, and Additional exercises on fillable PDFs (Sage instructor access required).
State null and research hypotheses and test one population proportion, differences between two proportions, one population mean, and differences between two means. Interpret significance, select appropriate t-test assumptions, and visualize uncertainty with error-bar graphs.
Related reading: Essentials, 7e, Chapter 9: Hypothesis Tests with One or Two Samples
Defined terms: Alpha (α), Assumptions about variance, Directional hypothesis, Nondirectional hypothesis, Null hypothesis (H0), One-sample hypothesis test, One-tailed, P-value, P-value approach, Paired observations, Research hypothesis (Ha), Significance level, t-statistic, t-test, Two-sample test, Two-tailed, z-statistic
In the book: Learning objectives, Chapter review, 5 printed exercises.
For instructors: Chapter syntax files, Solutions manual, and Additional exercises on fillable PDFs (Sage instructor access required).
Conduct chi-square tests of independence, then assess categorical associations with lambda, Somers's d, and Cramer's V. Extend these analyses to controlled comparisons and use single-factor and two-factor ANOVA to test differences among group means.
Related reading: Essentials, 7e, Chapter 10: Chi-Square Test and Analysis of Variance
Defined terms: Analysis of variance (ANOVA), Asymmetric measure of association, Chi-square distribution, Chi-square test of independence, Chi-square test statistic, Conditional distributions, Cramer’s V, Expected frequencies (fe), F-distribution, F-statistic, Informed prediction errors, Lambda (Λ), Marginal distribution, Measures of association, Naive prediction errors, Observed frequencies (fo), Proportional reduction in error, Single-factor ANOVA, Somers’s d, Sum of squares between groups, Sum of squares within groups, Total sum of squares, Two-factor ANOVA
In the book: Learning objectives, Chapter review, 5 printed exercises.
For instructors: Chapter syntax files, Solutions manual, and Additional exercises on fillable PDFs (Sage instructor access required).
Use correlation to assess linear association and estimate a bivariate regression equation. Interpret coefficients, hypothesis tests, and model-fit statistics; consider weighting, create edited scatterplots, and recognize patterns that do not follow a straight line.
Related reading: Essentials, 7e, Chapter 11: Correlation and Bivariate Regression
Defined terms: Adjusted R-squared, Bivariate regression, Correlation analysis, Correlation coefficients, Correlation matrix, Curvilinear relationship, Linear regression line, Other correlation coefficients, Pearson’s correlation coefficient, Regression analysis, R-squared, Regression coefficient, Regression equation, Scatterplot matrix
In the book: Learning objectives, Chapter review, 5 printed exercises.
For instructors: Chapter syntax files, Solutions manual, and Additional exercises on fillable PDFs (Sage instructor access required).
Estimate and interpret regression models with multiple predictors, dummy variables, and interaction terms. Choose reference categories, interpret standardized coefficients, present regression tables, and visualize multivariable and interaction relationships with bubble plots and other graphs.
Related reading: Essentials, 7e, Chapter 12: Multiple Regression
Defined terms: Base terms, Bubble plots, Interaction term, Multiple regression, Multiple regression equation, Partial effects, Partial regression coefficients, Reference category, Standardized regression coefficients
In the book: Learning objectives, Chapter review, 5 printed exercises.
For instructors: Chapter syntax files, Solutions manual, and Additional exercises on fillable PDFs (Sage instructor access required).
Calculate expected values and residuals, then use squared and standardized residuals to assess a linear model. Examine diagnostic plots, test normality and constant variance, identify influential observations, and consider what to do when model assumptions are doubtful.
Related reading: Essentials, 7e, Chapter 13: Analyzing Regression Residuals
Defined terms: Assumptions of linear regression model, Breusch–Pagan test, Constant variance, Constant variance tests, Cook’s distances, Correct specification, Diagnostic plots, Diagnostic tests, Expected values, Histogram of regression residuals, Influential observations, Kolmogorov–Smirnov test, Leverage, Limitations to analysis, Normality tests, Observed values, P-P plot, Regression residuals, Residuals vs. fitted plot, Shapiro–Wilk test, Squared residuals, Standardized residuals, Sum of squared errors, Table of residuals, Two components of regression model
In the book: Learning objectives, Chapter review, 5 printed exercises.
For instructors: Chapter syntax files, Solutions manual, and Additional exercises on fillable PDFs (Sage instructor access required).
Estimate logistic regression models for binary outcomes and interpret logged odds, odds ratios, and model fit. Save predicted probabilities and graph them with one or multiple predictors, distinguishing full effects, marginal effects at the means, and effects at representative values.
Related reading: Essentials, 7e, Chapter 14: Logistic Regression
Defined terms: −2 Log-likelihood, Classification table, Final log-likelihood, Full effect, Initial log-likelihood, Logged odds, Logistic regression, Logistic regression equation, Marginal effect, Marginal effects at representative values, Marginal effects at the means, Maximum likelihood estimation, Odds, Odds ratio, Percentage change in the odds, Predicted probabilities, Probability, Pseudo-R-squared measures, Wald statistic
In the book: Learning objectives, Chapter review, 5 printed exercises.
For instructors: Chapter syntax files, Solutions manual, and Additional exercises on fillable PDFs (Sage instructor access required).
Develop a feasible research question using existing analyses, classic theories, or emerging political topics. Import SPSS files, other supported formats, Excel worksheets, and web tables; add variables to an existing dataset and organize a written analysis from literature review to conclusions.
Related reading: Essentials, 7e, Chapter 15: Conducting Your Own Political Analysis
Defined terms: Importing data, Literature review, Replication datasets, Research question, Research topic, SPSS-format datasets, SPSS-friendly spreadsheets, Standard outline, Supported file types
In the book: Learning objectives, Chapter review, 5 printed exercises.
For instructors: Chapter syntax files, Solutions manual, and Additional exercises on fillable PDFs (Sage instructor access required).
Find names, descriptions, and coding for the American National Election Studies teaching dataset, including political attitudes, election participation, issue preferences, and respondent characteristics.
Find the variables and coding for the laboratory experiment on the televised 1960 Kennedy-Nixon debate. Consult the descriptions of treatment assignment, participant characteristics, and debate assessments before analyzing experimental results.
Find variable descriptions and coding for the General Social Survey teaching dataset, including social and political attitudes, demographic characteristics, and policy preferences.
Explore state-level measures grouped by topic, from civic culture and demographics to elections, the economy, and public policy. Consult variable descriptions and sources before comparing states.
Explore country-level measures grouped by topic, including government, conflict, elections, development, health, education, and international relations. Use the coding descriptions and sources to select measures for comparative analysis.