Decision-making, supported by qualitative and quantitative data analysis, is increasingly crucial in the public and nonprofit sectors. Public and nonprofit employees routinely utilize what they have learned in their research methods and analytics classes to facilitate informed decisions, shape policies, evaluate programs, and manage resources. This course will provide the foundational knowledge necessary to become competent managers, administrators, analysts, and coordinators in any public and nonprofit organization.

The focus of this course is on research design and the application of qualitative and quantitative analysis in the realms of public and nonprofit administration, policy, and management. Throughout this course, students will develop both a conceptual and practical understanding of research and analytical methods. Students will also acquire fundamental skills such as conducting literature reviews, collecting and managing qualitative and quantitative data, creating data visualizations, conducting statistical analyses, including univariate, bivariate, and multivariate methods, and designing interview and survey questions.

Class sessions will be divided between lectures, discussions and lab sessions, fostering an interactive learning environment. During the lab session, students will use Google Colab to complete the guided practice. Students will prepare an original research paper that includes data analysis as an integral component of this course.

This course provides a comprehensive exploration of statistical techniques essential for data analysis in public administration and social sciences. Students will develop a mastery of Maximum Likelihood Estimation (MLE) and apply Generalized Linear Models (GLMs) to analyze categorical dependent variables. The course also delves into multilevel models, offering a deep understanding of these complex structures. Students will also review and reinforce foundational concepts, including hypothesis testing, t-tests, ANOVA, linear regression, and chi-square tests. A key component of the course is the effective utilization of publicly-available datasets for real-world applications.

Class sessions are designed to blend lectures, discussions, and hands-on lab work, creating a dynamic and interactive learning environment. In the lab sessions, you'll use R Cloud to engage in guided practice, applying the statistical methods covered in class. Students will prepare an original research paper, integrating data analysis as a core element, to demonstrate your mastery of the course material.

This course is a deep dive into quantitative methods for applied social science research, with a special focus on public policy and management topics. Given the continuously evolving landscape of quantitative analytical methods, the course will emphasize understanding the nature, assumptions, and purposes of each method. Hands-on practice will be a key component, allowing students to use tools developed by statisticians and software developers. By the end of the course, students will have a solid foundation in these methods, understand when and when not to use them, and be capable of executing basic models independently.

By the end of this course, students will be able to

• Perform statistical analyses for causal modeling using techniques like difference-in-differences, regression discontinuity, and instrumental variable analysis.
• Utilize basic machine learning methods, including supervised and unsupervised learning, with approaches such as bagging, boosting, and clustering.
• Implement fundamental natural language processing techniques, including text classification and sentiment analysis.
• Analyze network data with methods such as centrality measures and community detection.
• Conduct factor analysis to uncover underlying relationships between variables and reduce dimensionality.

Public policy affects most aspects of our lives including, but not limited to, public health, education, economy, living environment, quality of life, and safety and security. Addressing and understanding public policy have become increasingly important considering rampant misinformation and mistrust of evidence. This course is designed to give master’s students in public affairs a background in understanding critical issues and techniques used in public policy analysis. This course provides students with core skills and knowledge necessary to understand the causes and consequences of public policies. Students will conduct a systematic analysis of public policy issues by identifying a range of policy alternatives to address the policy issues and comparing the alternatives with respect to various public values including efficiency, feasibility, and social equity and justice. Students will learn using analytical tools, rationales, and knowledge to make sense of policy interventions and will practice effective delivery and communication of evidence, data, and policy recommendations at various levels of government.

This course is also designed to prepare students for their current and future careers in public service. By the end of the semester, students will be able to develop their ideas about questions such as (a) What are the proper roles of the government in society? What are the theoretical justifications for public intervention? (b) What are the political dynamics in defining policy problems and choosing policy alternatives? (c) What are the analytic tools that can be used? (d) How can we evaluate policy outcomes? (e) How do you make convincing arguments for communicating your analysis? (f) What are the limitations of policy analysis?

This course introduces students to how evidence can inform policy and managerial decision-making in various policy, management, and program evaluation scenarios. In this course, students will explore the following questions:

• How do legislative staff, public and nonprofit organization employees, and stakeholders gather and utilize evidence in decision-making?
• How do decision-makers assess the types and the quality of the evidence? What are the implications of differences in the types and quality of the evidence?
• How do decision-makers evaluate policies and programs using evidence from both practice and research?

This course introduces the fundamentals of research design in the social sciences and covers applied qualitative and quantitative analysis techniques. Students will develop a conceptual and practical understanding of research and analytic methods, as well as gain essential skills for literature review, text analysis, data collection and management, data visualization, and statistical analysis including univariate, bivariate, and multivariate analysis. Class sessions will be divided into lectures, discussions, and lab sessions. Students will also work on their own original research projects, with a focus on solving real-world problems and using data for social good.