Data is systematically gathered information based around the concepts expressed in your theory. Identifying observable indicators for concepts in your theory and hypotheses greatly assists in achieving valid, reliable measurement and a satisfactory test of your hypotheses. Qualitative data generally derive from two sources, texts and human subjects. Data collection from texts frequently involves archival research, though data sourcing from strictly secondary sources can also be viable. Data from human subjects may involve elite interviewing, mass surveys, or participant observation. Your reference librarians are best positioned to help you locate relevant resources in your library and elsewhere.

This chapter introduces the basic parts of a standard empirical paper – introduction, literature review, theory & hypotheses, analysis, and conclusions. We then home in on the first one that most people write, the literature review. The literature review is a unique genre of writing with its own conventions and expectations. Good literature reviews are multi-stage processes that show evidence of redrafting and that place literature in a context that allows the reader to not only understand the shape of the forest – the entire body of literature – but to locate the author’s claims within in that literature.

This chapter provides a comprehensive introduction to supervised learning techniques for classification problems. It begins with logistic regression for binary classification, explaining the sigmoid function and gradient ascent optimization. The chapter then covers softmax regression for multi-class problems, followed by k-nearest neighbors (kNN) as an intuitive distance-based classifier.

Decision trees are explored in detail, including entropy, information gain, and the ID3 algorithm, along with derived decision rules and association rules. Random forests are presented as an ensemble method that addresses overfitting by combining multiple decision trees.

The chapter covers Naive Bayes classification based on Bayes’ theorem, despite its "naive" independence assumption. Finally, Support Vector Machines (SVMs) are introduced for both linear and non-linear classification using maximum margin hyperplanes.

Each technique includes hands-on R programming examples with real datasets, practical applications, and exercises to reinforce learning concepts.

Good hypotheses identify observable implications of the theory – things we would observe if the theory were correct – and make predictions about relationships between measurable indicators of the theory’s concepts. Measurement thus plays a critical role in the transition from claims to tests. Six types of hypotheses are common in political science. Probabilistic hypotheses include directional, relative, no-effect, and conditional types; these hypotheses make claims that they expect to be true, on average, across many cases. Deterministic hypotheses, on the other hand, make claims that should always hold; these include claims of necessity and/or sufficiency. Preregistration attempts to reduce the incentives to adjust hypotheses to match findings.

This chapter presents some of the basic conventions of writing empirical papers in political science. Abstracts, introductions, and conclusions are formulaic and follow a predictable pattern; they are often among the last parts of a paper to be written. Conventions for reporting quantitative results include indicating significance, goodness of fit, and N in tables, discussing the significance of coefficients rather than of variables, and using baseline and multiple models to support your findings. Conventions for reporting qualitative research vary by research design, but they include careful obfuscation of sources for interview data, clear sequencing and temporality indicators in process tracing, minimizing direct quotations, and providing estimates of uncertainty for all conclusions drawn from qualitative data. Always acknowledge all help from outside sources in your paper.

This chapter explores fundamental analytical techniques in data science, distinguishing between data analysis (backward-looking) and data analytics (forward-looking prediction).

Six key analysis categories are covered:

Descriptive Analysis examines current data through statistical measures (mean, median, mode) and visualizations to understand "what is happening."

Diagnostic Analytics investigates "why something happened" using correlation analysis, emphasizing the distinction between correlation and causation.

Predictive Analytics forecasts future outcomes using historical data and regression analysis.

Prescriptive Analytics determines optimal courses of action by analyzing potential decisions.

Exploratory Analysis discovers unknown relationships through visualization when questions aren’t predetermined.

Mechanistic Analysis examines exact variable changes and their effects.

The chapter emphasizes statistical literacy as essential for data scientists, covering key concepts like variable types, frequency distributions, measures of centrality and dispersion, and regression modeling. Hands-on examples demonstrate applications across business, healthcare, and social sciences.

A framing case study examines North Korea’s nuclear tests. Then the chapter examines how states make international law. The chapter specifically discusses: (1) treaties, including entry into treaties, reservations, interpretation, and exit; (2) customary international law, including state practice, acceptance as law (opinio juris), and conceptual challenges; and (3) other important factors, including general principles, unilateral declarations, and peremptory norms (jus cogens).

This chapter focuses on applying data science and machine learning techniques to real-world problems using Python. It covers four main applications: clinical data analysis, social media data collection and analysis, and large-scale data processing.

The chapter begins with exploring clinical data from a dermatology study, demonstrating visual exploration, gradient descent regression, random forest classification, and k-means clustering techniques. It then transitions to social media analysis, specifically working with Reddit APIs to collect and analyze posts, examining relationships between variables like post length, scores, and upvotes.

The YouTube section covers API authentication and data collection for video statistics analysis. Finally, the Yelp analysis demonstrates big data processing techniques, exploring user behavior patterns through correlation analysis, regression modeling, and clustering of review data.

The chapter emphasizes practical API usage, data visualization, statistical testing, and the importance of understanding both the problem and data before analysis.