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QST DS 906: Fundamentals of Research and the Philosophies of Science (previously MK912)
This course introduces students to research. The class provides a brief introduction to the philosophy of science and debates about the nature of theory before diving thoroughly into different research methods. Students are exposed to research methods from their own and adjacent fields ranging from causal inference and experiments to qualitative research methods. The last part of the class introduces students to issues around diversity, ethics, and equity in research. As part of the class students will complete the introductory ethics modules that are required by the university. Students will be graded on their class participation, a research proposal which is due at the end of the class, and their feedback to other students on their research proposals.
QST DS 911: Seminar in Macro Organizational Theory
This doctoral-level course is an introduction to the major theoretical approaches and ongoing debates in organizational theory, an inter- disciplinary subject area that draws on several traditions, including economics, political science, psychology, and sociology. Organization theory aims to explain the origins, persistence, and disappearance of the organizations that are central to our society and daily life (e.g., firms, markets, governments, occupations, non-profit organizations, and more). We will start with the classics and then trace the history of ideas as the field has evolved to its present state. The purpose of this course is to provide a roadmap to navigate the terrain of organizational theory and guide students as they generate original research ideas. (Cross-listed as GRS SO716).
QST DS 913: Experimental Design and Methods
This course provides an introduction to research methodology applicable to marketing and other related fields. The course will survey the major research methodologies used in marketing and social psychology, and will focus on both theoretical and practical considerations of research methods. This is not a statistics course (though an introduction to basic principles is part of the course). The purpose of the course is to give students the background to choose the methods that are most appropriate for their area of study, helping them to anticipate the shortcomings and problems they will encounter executing their chosen methodologies, and to defend their methodological choices against criticism in their interactions with investigators from allied and not-so-allied disciplines.
QST DS 919: Machine Learning Method for Social Science Research
This course aims to introduce PhD students in Management to Machine Learning methods with an emphasis on their application in social science research. The first half of the course discusses popular predictive models (regression models, SVM, tree-based methods, etc) and related concepts. The second half discusses graphical models to develop and estimate probabilistic models. The course will have a set of programming/estimation assignments based on recent relevant papers and one final exam. By the end of the course, students will be equipped to spot a machine learning problem in their line of research, specify a model for it, and estimate and evaluate it.
QST DS 925: Methods for Causal Inference in Strategy Research
(Formerly SI 915) This course reviews tools and methods for drawing causal inferences from non-experimental data. The class emphasizes conceptual difficulties associated with establishing causality in observational settings, the strengths and weaknesses of statistical methods based on so-called natural experiments, and the practical problems that arise in the application of these tools. This course is designed to complement a traditional two-semester graduate sequence in econometrics.
QST DS 999: Doctoral Dissertation Study
This 2-credit course is a requirement to maintain doctoral student status during the completion of your Comprehensive Exam, Dissertation Proposal Defense and ultimately, Dissertation Defense. Each department has its own section which are as follows: Accounting (A1); Finance/Economics (B1); Information Systems (C1); Strategy and Policy (D1); Marketing (E1); Operations and Technology Management (F1); Organizational Behavior (G1); and Mathematical Finance (M1).