Core Courses (15 credits; required of all PhD students)
Intellectual History of Public Administration (26:834:617)
This course examines the field of public administration through historical lenses, focusing on the periods of development from "the Orthodoxy" to New Public Management and beyond.
Government Budgeting and Resources Acquisition (26:834:605)
This course addresses the macro and micro aspects of budgeting and finance from both the normative and descriptive views in the fields of public management, political science, and economics.
Study of Public Organizations (26:834:601)
This course covers such topics as public sector organization theory and behavior at the micro and macro levels; networking; interorganizational relations.
Governance and Politics (26:834:603)
This course covers such topics as bureaucratic politics; democratic theory and public sector governance.
Leadership, Equity and Diversity (26:834:618)
This courses addresses governance from a human resources perspective, focusing on such topics as leadership and diversity in the public sector.
Research Methods (12 credits; required of all PhD students)
Quantitative I (26:834:607)
This course covers the design, production and analysis of quantitative data for research in public affairs and administration. It reviews quantitative theory and models, measurement, sampling, and the logic of causal inference. The course will focus attention in particular on multiple regression as a tool for data analysis as well as a framework for answering substantive, causal questions. The course will introduce students to some additional multivariate methods, such as reliability analysis, factor analysis, path analysis, and the basics of structural equation modeling. Emphasis will be on the use of statistical software and the interpretation of results, with applications to substantive research questions.
Quantitative II (26:834:608)
This course covers various advanced, multivariate statistical techniques used in public administration and policy research. It begins with regression models for limited dependent variables, i.e., models for nominal outcomes, ordered outcomes, and count outcomes, using maximum likelihood estimation techniques. The course then covers the basics of panel data analyses and selection models. Throughout, students will be given hand-on training in the use of statistical software, the interpretation of results from real data, and the translation of results into useful summaries through tables and figures. Students are encouraged to apply the methods learned to their own datasets, including data from their on-going projects or dissertation research.
Qualitative I (26:834:609)
The purpose of this course is to introduce doctoral students to the philosophy and methods of qualitative research. Through an examination of the evolution of qualitative methods, the various forms of qualitative research and the ways to conduct qualitative research studies, students will develop the basic skills necessary to develop qualitative research designs and to conduct qualitative research. It will examine the similarities and differences between qualitative and quantitative research design, different approaches to qualitative research, including grounded theory, analytic induction and ethnomethodology, and how these relate to mixed methods design. Students will be introduced to qualitative methods of data collection and analysis, including interviews, observation and participant observation, ethnography, case studies, content analysis, historical and archival methods, action research, and video methods The course will enable students will be able to interpret, evaluate and present qualitative data and to design their own qualitative research proposal.
Mixed Methods (26:834:619)
Although many extol the virtues of mixed methods research, few conduct mixed methods research. The goal of this seminar is to enhance student ability to conduct mixed methods research. Towards that end, the course will build upon earlier courses in qualitative and quantitative methods. The course will examine philosophical assumptions that underlie mixed methods research and review research designs that use both qualitative and quantitative data. Students will learn how to competently implement mixed methods research designs through application oriented exercises.