Course Materials

Material status
Required
Optional

# Module 1

Introduction

Learning objectives: Explain what empirical research contributes to public policy and administration; distinguish questions that data can answer from questions it cannot; and set up the tools we will use all semester, including the course website, Canvas, and Google Colab.

Before class

  • Wheelan, Naked Statistics. Introduction and Chapter 1, “What’s the Point?”Optional

# Module 2

Research Design

Learning objectives: Describe how theory, models, and research questions fit together; distinguish descriptive, causal, and normative questions; and turn a broad policy interest into a research question that can actually be answered with evidence.

Before class

  • Remler & Van Ryzin, Chapter 1. Research in the Real World.Required
  • Remler & Van Ryzin, Chapter 2. Theory, Models, and Research Questions. You can skip “Logic Models: Mechanisms of Programs” and “Alternative Perspectives on Theory in Social Research.”Required

# Module 3

Measurement & Central Tendency

Learning objectives: Explain how validity, reliability, measurement error, and levels of measurement affect the quality of policy research; identify appropriate measures of central tendency; and calculate and interpret means, medians, and modes.

Before class

  • Remler & Van Ryzin, Chapter 8. Making Sense of Numbers. You can skip “Relationships Between Categorical Variables,” “Practical Significance,” and “Statistical Software.”Required
  • Remler & Van Ryzin, Chapter 4. Measurement. Focus on “What Is Measurement?,” “Validity” (excluding criterion-related validity), “Measurement Error,” “Reliability,” “Validity and Reliability: Contrasted and Compared,” and “Levels of Measurement.”Optional

# Module 4

Distribution of Data

Learning objectives: Calculate and interpret standard deviation, coefficient of variation, and z-scores; use the standard normal distribution to compare observations measured on different scales; describe the direction and strength of relationships between numerical variables using scatterplots and Pearson’s correlation coefficient; and create simple data subsets based on specified criteria.

Before class

# Module 5

Sampling & Estimation

Learning objectives: Distinguish populations, samples, parameters, and statistics; compare probability and non-probability sampling methods and identify common sources of sampling bias; explain how sampling variability, sampling distributions, and the central limit theorem support statistical inference; and construct and interpret confidence intervals as estimates of population parameters.

Before class

# Module 6

Surveys

Learning objectives:

Before class

    # Module 7

    Hypothesis Testing

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    Before class

      # Module 8

      Means Comparisons

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      Before class

        # Module 9

        Survey Analysis Presentation

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        Before class

          # Module 10

          Regression

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          Before class

            # Module 11

            Policy Brief Presentation

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            Before class