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Introduction. Scientific Approach to Politics
Download pdf of Introduction Students are introduced to the scientific approach in political analysis, focusing on evidence-based reasoning and critical inquiry. The course begins by discussing how scientific principles are applied to study political phenomena, setting the stage for the course. The Essentials preface and Companion introductions do not include exercises because this is typically an orientation week when students can drop/add classes.
Students will learn how to define political concepts and operationalize them into measurable variables. Skills include formulating clear definitions and understanding the link between theoretical and empirical research using software to create operational definitions.
This chapter covers descriptive statistics and levels of measurement. Students will practice summarizing data using means, medians, modes, and visualizations like bar charts and histograms, applying statistical software to describe datasets effectively.
Students will learn how to create new variables and transform existing ones to fit research needs. Skills include calculating indices, recoding data, and applying transformations in statistical software to enhance data analysis.
4. Proposing Explanations, Framing Hypotheses & Making Comparisons
This chapter emphasizes the development of testable hypotheses and the logic of comparisons. Students will practice crafting hypotheses and performing preliminary analyses to explore relationships using cross-tabulation tools in software.
Students will visualize data relationships using scatterplots and trendlines. Key skills include interpreting patterns (linear, curvilinear) and using graphing functions in statistical software to illustrate findings clearly.
6. Research Design, Research Ethics & Evidence of Causation
This chapter focuses on designing ethical research and establishing causation. Students will learn about experimental and observational designs and use software to simulate experimental setups and analyze causal relationships.
Students will explore techniques for controlling rival explanations and interpreting controlled comparisons. They will practice using statistical software to create and analyze control tables, identifying patterns and relationships.
This chapter introduces students to probability theory and confidence intervals. Students will calculate standard errors and use statistical software to compute and interpret confidence intervals for datasets.
Students will learn hypothesis testing fundamentals, focusing on single and two-sample tests. They will practice performing t-tests in software to assess whether differences in means are statistically significant.
This chapter covers analyzing relationships between categorical variables with chi-square tests and comparing group means using ANOVA. Students will apply these techniques to datasets using statistical software.
Students will study linear relationships between two variables using correlation and regression analysis. Skills include interpreting Pearson’s r and running bivariate regressions in statistical software.
This chapter expands to multiple regression models, emphasizing controlling for multiple variables. Students will practice building and interpreting regression models with multiple predictors in software.
Students will learn to diagnose and improve regression models by analyzing residuals. They will use software to identify outliers, test model assumptions, and refine their analyses.
Students will explore logistic regression to analyze binary outcomes. Key skills include interpreting odds ratios and predicted probabilities, with hands-on software practice for real-world applications.
This chapter focuses on independent research projects. Students will apply the skills learned throughout the course to analyze a political question of their choice using software.
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