Research Methods & Data AnalysisRepresentative program

Data Science Using R: Applications of Public Datasets in Social Science Research

Turn public datasets into reproducible analyses using R, research design, regression, and introductory measurement methods.

Overview

This program connects substantive social-science questions with quantitative analysis. Learners use public datasets to practice data cleaning, survey-variable interpretation, regression, and an introduction to factor modeling. Work culminates in a documented analytical report rather than unsupported causal claims.

Learning goals and possible work

Proposed learning outcomes for this program example:

  • Translate a substantive question into measurable variables and an analysis plan.
  • Clean and analyze a public dataset using a documented R workflow.
  • Explain findings, uncertainty, measurement limits, and study limitations.

Illustrative learning sequence

The sequence below illustrates how this program’s content can be organized. Topics, pacing, and project depth are adapted for each offering. This is not an archived record of a specific cohort’s weekly syllabus.

  1. 01Research questions, study designs, and public-data access
  2. 02Getting started with R and reproducible project organization
  3. 03Importing data; codebooks, survey items, and variable definitions
  4. 04Cleaning, recoding, and examining missing data
  5. 05Descriptive statistics and data visualization
  6. 06Statistical inference and bivariate relationships
  7. 07Regression models and interpretation
  8. 08Introduction to factor models and measurement
  9. 09Model checks, sensitivity analysis, and limits to causal interpretation
  10. 10Writing and presenting a reproducible analytical report
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