FOUNDATIONS OF QUANTITATIVE AND QUALITATIVE RESEARCH METHODS
Presents introductory research procedures in the social sciences, including the exploration of theoretical foundations and practical use of basic tools and programs needed for quantitative and qualitative data analysis. Examines how different methodologies can complement or compete with the other, and showcases how pertinent quantitative and qualitative methods are applied in the field of learning analytics with particular emphasis on data about - and therefore issues in - learning environments such as classrooms, online courses, apprenticeships and internships, museum exhibits, after school programs, and other formal and informal educational contexts.
Prerequisites & course information
Requisites: Satisfied Quantitative Reasoning (QR) B or graduate/professional standing Course Designation: Grad 50% - Counts toward 50% graduate coursework requirement Repeatable for Credit: No Last Taught: Fall 2026 Learning Outcomes: 1. Draw connections via discovery and analysis of foundational quantitative, qualitative, and mixed methods approaches. Audience: Both Grad & Undergrad 2. Discover the theoretical foundations of regression analysis as well as apply it to real datasets. Audience: Both Grad & Undergrad 3. Analyze data and draw meaningful conclusions with regard to research questions. Audience: Both Grad & Undergrad 4. Critically read and evaluate social science publications that use regression models as analytic tools. Audience: Both Grad & Undergrad 5. Collect and qualitatively analyze thick collections of data. Audience: Both Grad & Undergrad 6. Engage in a rigorous process of documenting methods, assumptions, and conclusions. Audience: Graduate 7. Apply methods to a variety of learning analytics research via written memos and projects. Audience: Graduate Learning Outcomes: 1. Draw connections via discovery and analysis of foundational quantitative, qualitative, and mixed methods approaches. Audience: Both Grad & Undergrad 2. Discover the theoretical foundations of regression analysis as well as apply it to real datasets. Audience: Both Grad & Undergrad 3. Analyze data and draw meaningful conclusions with regard to research questions. Audience: Both Grad & Undergrad 4. Critically read and evaluate social science publications that use regression models as analytic tools. Audience: Both Grad & Undergrad 5. Collect and qualitatively analyze thick collections of data. Audience: Both Grad & Undergrad 6. Engage in a rigorous process of documenting methods, assumptions, and conclusions. Audience: Graduate 7. Apply methods to a variety of learning analytics research via written memos and projects. Audience: Graduate
Sources
- Course catalogue ↗2026-2027