DATA MANAGEMENT FOR SOCIAL SCIENCE RESEARCH

Prerequisites & course information

Requisites: C&E SOC/SOC 360 , PSYCH 210 , STAT 301 , ECON 310 , or MATH/STAT 310 Course Designation: Core GenEd - Social & Behavioral Science Level - Advanced L&S Credit - L&S Liberal Arts and Science Repeatable for Credit: No Last Taught: Spring 2026 Learning Outcomes: 1. Identify common problems that make social scientific data "messy" and apply techniques to prepare data for use in research using various techniques for data cleaning, cross-checking, addressing missing data. Audience: Undergraduate 2. Identify and locate appropriate data sources for applications to real-world research questions. Audience: Undergraduate 3. Employ statistical software independently to analyze quantitative data, including writing programs to automate tasks appropriately. Audience: Undergraduate 4. Document and communicate about data accurately and professionally; build a portfolio of code and research writing. Audience: Undergraduate 5. Apply fundamental data analysis skills, including making tables and visualizing variable distributions. Audience: Undergraduate 6. Incorporate open science practices of transparency and reproducibility by creating and managing research projects in standard, replicable formats. Audience: Undergraduate Learning Outcomes: 1. Identify common problems that make social scientific data "messy" and apply techniques to prepare data for use in research using various techniques for data cleaning, cross-checking, addressing missing data. Audience: Undergraduate 2. Identify and locate appropriate data sources for applications to real-world research questions. Audience: Undergraduate 3. Employ statistical software independently to analyze quantitative data, including writing programs to automate tasks appropriately. Audience: Undergraduate 4. Document and communicate about data accurately and professionally; build a portfolio of code and research writing. Audience: Undergraduate 5. Apply fundamental data analysis skills, including making tables and visualizing variable distributions. Audience: Undergraduate 6. Incorporate open science practices of transparency and reproducibility by creating and managing research projects in standard, replicable formats. Audience: Undergraduate