- Copyright
- List of Figures
- Preface
- Workbooks and Chapter Exercises
- Chapter Organization
- Advantages and Disadvantages of Teaching with Excel
- Accompanying Core Text
- Acknowledgments
- About the Authors
A practical guide to political analysis with a familiar spreadsheet. Follow guided examples, work with real political data, and build spreadsheet skills you can use in your own research. Progress from descriptive statistics and comparisons to hypothesis testing, regression, and your own research projects.
Start with Getting Started or explore chapter resources for companion workbooks, 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 research methods text.
Examples use desktop Excel 365, primarily on Windows; Mac menus and shortcuts may differ. The book uses the Data Analysis ToolPak, with Solver and the Real Statistics add-in for later analyses.
Request an instructor copy from Sage.
Access the solutions manual through Sage. Instructor materials require Sage instructor access.
Explore sample syllabi for course planning.
Workbook and publisher-account questions can be directed to Sage support.
Expand a section for its overview, outline, and supporting materials. Numbered chapters include practical exercises; instructor solutions are accessed separately through Sage.
Related-reading links here use Essentials, Seventh Edition; printed references in the Excel book use the sixth edition.
Find and download the companion workbooks, identify the version of Excel used in the book, and locate tutorial videos. Learn which add-ins support later analyses so you can prepare your computer and follow the guided examples.
Related reading: Essentials, 7e, Introduction: The Scientific Approach to Politics
Organize analysis with workbooks and worksheets, activate data analysis add-ins, and distinguish formulas, functions, and tools. Define names for cells and ranges, keep analysis separate from data, format and save tables and figures, and use Excel help to solve problems.
Related reading: Essentials, 7e, Chapter 1: The Definition and Measurement of Concepts
Defined terms: Workbook, Worksheet, Cell references, Field, Ribbon, Add-ins, Formulas, Functions, General syntax, Dialogs, Data Analysis tools, Arguments, Names, Tables, Charts
In the book: Learning objectives, 10 practical exercises.
For instructors: Solutions manual (Sage instructor access required).
Choose descriptive statistics for nominal, ordinal, and interval-level variables. Create frequency distributions and bar charts with pivot tables, compare central tendency and dispersion, use the Data Analysis ToolPak for numeric summaries and histograms, and sort observations to obtain case-level information.
Related reading: Essentials, 7e, Chapter 2: Measuring and Describing Variables
Defined terms: Level of measurement, Frequency distribution table, PivotTable, Bar chart, Skewness, Kurtosis, Histogram
In the book: Learning objectives, 10 practical exercises.
For instructors: Solutions manual (Sage instructor access required).
Use formulas and functions to create variables, generate indicator variables, and recode numeric values into simpler categories. Build a clear recoding protocol, use filter-and-fill and sort-and-fill methods, and center or standardize numeric measures for subsequent analysis.
Related reading: Essentials, 7e, Chapter 3: Creating and Transforming Variables
Defined terms: Indicator variable, Recoding protocol, Filter and fill, Sort and fill, Center, Z scores, Standardize
In the book: Learning objectives, 10 practical exercises.
For instructors: Solutions manual (Sage instructor access required).
Create and interpret cross-tabulations and mean comparison tables with pivot tables. Add counts and standard deviations, group values of interval-level independent variables, and build maps to compare political patterns across states and countries.
Related reading: Essentials, 7e, Chapter 4: Proposing Explanations, Framing Hypotheses, and Making Comparisons
Defined terms: Cross-tabulation, Independent variable, Dependent variable, Column percentages, Mean comparison table
In the book: Learning objectives, 10 practical exercises.
For instructors: Solutions manual (Sage instructor access required).
Select charts to fit binary, nominal, ordinal, and interval-level dependent variables. Build simple and clustered bar charts, line and area charts, stacked displays, box plots, and scatterplots, then edit chart elements to communicate relationships clearly.
Related reading: Essentials, 7e, Chapter 5: Graphing Relationships and Describing Patterns
Defined terms: Simple bar chart, Simple line chart, Area chart, Clustered bar chart, Multiple line chart, Stacked bar chart, Stacked area chart, Transpose, Chart Elements, Box plots, Interquartile range, Outliers, Scatterplot, Markers
In the book: Learning objectives, 10 practical exercises.
For instructors: Solutions manual (Sage instructor access required).
Randomly assign subjects to experimental groups, check assignment, and evaluate treatment effects. Draw samples with or without replacement, explore systematic, clustered, and stratified sampling, select cases for qualitative comparisons, and use validation and forms to enter survey or experimental data.
Related reading: Essentials, 7e, Chapter 6: Research Design, Research Ethics, and Evidence of Causation
Defined terms: Random assignment, Bernoulli distribution, Discrete distribution, Volatile, Random seed value, Simple random sample, Replacement, Systematic random sample, Data validation, Forms
In the book: Learning objectives, 10 practical exercises.
For instructors: Solutions manual (Sage instructor access required).
Use pivot tables and slicers to add controls to cross-tabulations and mean comparisons. Interpret spurious, additive, and interactive relationships, visualize controlled comparisons, and work with interval-level control variables to assess whether a relationship holds within groups.
Related reading: Essentials, 7e, Chapter 7: Making Controlled Comparisons
Defined terms: Control variable, Controlled cross-tabulation, Slicer, Spurious relationship, Additive relationships, Interactive relationship, Controlled mean comparison table
In the book: Learning objectives, 10 practical exercises.
For instructors: Solutions manual (Sage instructor access required).
Simulate samples to understand estimates of population proportions and means. Explore sampling distributions, the normal distribution, standard errors, and the empirical rule; use companion worksheets and Excel functions to construct confidence intervals, margins of error, and t-based estimates.
Related reading: Essentials, 7e, Chapter 8: Foundations of Statistical Inference
Defined terms: Inferential statistics, Population, Sample, Population parameters, Standard error, Sampling distributions, Standard error of a sample proportion, Probability density function, Cumulative density function, Standard error of a sample mean, Standard normal distribution, Confidence intervals, Critical values, Margin of error, t-Distribution, Degrees of freedom
In the book: Learning objectives, 10 practical exercises.
For instructors: Solutions manual (Sage instructor access required).
Test claims about a single proportion or mean and differences between two proportions or means. Use confidence intervals and p-values, work with the Significance Testing Workbook, and conduct difference-of-means tests with the Data Analysis ToolPak and T.TEST function.
Related reading: Essentials, 7e, Chapter 9: Hypothesis Tests with One or Two Samples
Defined terms: Null hypothesis, Research hypothesis, P-value, Alpha, P-value approach, Directional hypothesis, z-Statistic, Pooled proportion, t-Statistic, Assumptions about variance, Two-tail
In the book: Learning objectives, 10 practical exercises.
For instructors: Solutions manual (Sage instructor access required).
Test independence with chi-square by constructing observed and expected frequency tables. Assess association using lambda, Somers’ d, and Cramer’s V, then use analysis of variance to compare group means and interpret statistical significance alongside substantive differences.
Related reading: Essentials, 7e, Chapter 10: Chi-Square Test and Analysis of Variance
Defined terms: Conditional distributions, Chi-square test statistic, Expected frequencies, Marginal distribution, Absolute cell references, Relative cell references, Measures of association, Lambda, Naïve prediction errors, Informed prediction errors, Somers’ d, Cramer’s V, Total sum of squares, Sum of squares between groups, Sum of squares within groups, F-statistic
In the book: Learning objectives, 10 practical exercises.
For instructors: Solutions manual (Sage instructor access required).
Calculate and interpret correlations with worksheet functions and the Data Analysis ToolPak. Prepare data for bivariate regression, address missing values, read coefficients and model-fit output, and add regression lines to scatterplots for interpretation and prediction.
Related reading: Essentials, 7e, Chapter 11: Correlation and Bivariate Regression
Defined terms: Pearson’s correlation coefficient, Correlation matrix, Bivariate regression model, Coefficients table, ANOVA table, Regression statistics table, Linear regression line
In the book: Learning objectives, 10 practical exercises.
For instructors: Solutions manual (Sage instructor access required).
Estimate multiple regression models with the Data Analysis ToolPak and interpret relationships while controlling for other predictors. Create multiple dummy variables and interaction terms, choose reference categories, and use bubble plots and distinct regression lines to visualize model results.
Related reading: Essentials, 7e, Chapter 12: Multiple Regression
Defined terms: Multiple regression analysis, Partial effect, Reference category, Interaction term, Base terms, Bubble plot, Scale size
In the book: Learning objectives, 10 practical exercises.
For instructors: Solutions manual (Sage instructor access required).
Calculate observed, predicted, squared, and standardized residuals and relate them to model fit. Inspect residual tables, histograms, scatterplots, and Q-Q plots; test normality and constant variance; and decide how to address or disclose problems revealed by the diagnostics.
Related reading: Essentials, 7e, Chapter 13: Analyzing Regression Residuals
Defined terms: Observed values, Predicted values, Regression residuals, Squared residuals, Sum of squared errors, Table of residuals, Standardized residuals, Residual Plots option, Histogram of standardized residuals, Residuals against expected values, Q-Q plots, Jarque-Barre test, Breusch-Pagan test, Chi-square goodness of fit test
In the book: Learning objectives, 10 practical exercises.
For instructors: Solutions manual (Sage instructor access required).
Connect binary outcomes with probabilities, odds, and logged odds. Use the Real Statistics add-in to estimate logistic models with one or several predictors, interpret coefficients, odds ratios, and model fit, and graph predicted probabilities at representative values and at predictor means.
Related reading: Essentials, 7e, Chapter 14: Logistic Regression
Defined terms: Probability, Odds, Logged odds, Wald statistic, Odds ratio, Pseudo R-squared measures, Classification table, Predicted probability curves, Full effect, Marginal effects at representative values, Marginal effects at the means
In the book: Learning objectives, 10 practical exercises.
For instructors: Solutions manual (Sage instructor access required).
Develop a feasible research project using examples on elections, turnout, religion, race, and women in politics, or replicate a published paper. Import and prepare Excel, web-table, and other data formats, then organize a research report from the question and literature through analysis and conclusions.
Related reading: Essentials, 7e, Chapter 15: Conducting Your Own Political Analysis
Defined terms: Replication datasets, Delimiter, Literature review, Limitations
In the book: Learning objectives, 10 practical exercises.
For instructors: Solutions manual (Sage instructor access required).
Look up the names and coding of variables from the presidential debate experiment, including assignment to television or radio conditions, participant characteristics, and assessments of the candidates.
Find variable descriptions for U.S. election outcomes, presidential approval, turnout, economic conditions, and changes in congressional representation. Use the coding and source notes when preparing election-related analyses.
Explore state-level variables by topic, from demographics, elections, and the economy to courts, education, health, and public policy. Consult coding descriptions and sources before comparing states.
Explore country-level variables covering demographics, economic conditions, government, education, health, and development. Use topic groupings, variable descriptions, and source notes to identify suitable measures for comparative analysis.