This course is designed for those who want to extend their data analytic skills beyond a basic knowledge of multiple regression analysis and who want to communicate their findings clearly to audiences of researchers, scholars, and policymakers. S-052 contributes directly to the diverse data analytic toolkit that the well-equipped empirical researcher must possess in order to perform sensible analyses of complex educational, psychological, and social data. The course begins with general linear models and continues with generalized linear models, survival analysis, multilevel models, multivariate methods, causal inference, and measurement. Specific methods exemplifying each of these topics include regression, discrete-time survival analysis, fixed- and random-effects models, principal components analysis, instrumental variables, and reliability, respectively. S-052 is an applied course. It offers conceptual explanations of statistical techniques and provides many opportunities to examine, implement, and practice these techniques using real data. Students will learn to produce readable and sensible code to enable others to replicate and extend their analyses. Attendance at weekly sections is required.
Prerequisites: Successful completion of S-040 (B+ or better allowed, A- or A recommended) or an equivalent course or courses that include 12 or more full hours of class time on multiple regression and its direct extensions. Students who have not passed S-40 must discuss their previous training before or at the first class meeting. Students who do not meet the prerequisite should consider S-030. See the syllabus at the instructor’s website for more details.
The course is designed to develop and extend the data-analytic skills acquired in earlier courses and to help students learn to communicate findings clearly to audiences of other empirical researchers, scholars, policy-makers, practitioners, students, and parents. We have designed S-052 to contribute to the diverse data-analytic toolkit that you will need to perform sensible and believable analyses of complex educational, psychological, and social data.
This course supports careers that require data-analytic literacy and data-analytic fluency. Literacy goals include asking critical questions of current educational, social science, and health science research reports and peer-reviewed publications. Fluency goals include productive contribution to quantitative research teams and written analyses. Common next steps include doctoral research trajectories, research and policy think-tanks, governmental organizations, data journalism, and the wide array of for-profit and not-for-profit organizations that value data-analytic skills.