EDUC 152
Quantitative Research in Education: Regression Analysis
Description
Lecture, two hours; discussion, two hours. Requisite: course 150 or 151. Preparation: basic familiarity with programming language R. Introduction to statistical modeling techniques that go beyond simple correlations among variables to provide better answers to important questions of education policy and practice. Application of tools such as regression to analyze educational data. Students learn to appropriately interpret the results, with consideration to the nature, assumptions, and limitations of these tools; and learn the kinds of questions they can and cannot answer related to association, prediction, and causal inference. Particular attention is given to understanding the use of data to interrogate disparities in educational opportunities and outcomes rather than reify biased assumptions and concepts. Students learn to apply basic regression analysis and related analytical tools using various education datasets. Letter grading.
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