Philanthropy: Historical and Contemporary Approaches (2018 fall, graduate)
  • Course description: This course provides students with a foundational understanding of philanthropy, it’s historical and political development in the United States, the ethics and values expressed through philanthropy and a comparison to international perspectives of philanthropy and nonprofit/nongovernment sector. Students interested in nonprofit management and international development explore the critical role of voluntary action in building society. The course is an opportunity to explore the intersection of how philanthropy is both studied and practiced, with an expectation of improving skills related to development and fundraising, as well as managing organizations or programs that rely on philanthropic support.
  • Requirements & Expectations: Individual student performance will be measured by analysis of readings (15%), plagiarism test (5%) group projects (30%) and a final individual project (50%). The final project may be customized toward academic research or policy development according to student’s interests.
  • Readings: The course is supported by readings compiled from a wide range of books and academic articles that ground theories of philanthropy and nonprofit/nongovernment sector, as well as practice-oriented articles from Stanford Social Innovation Review, Nonprofit Quarterly, and the Chronicle of Philanthropy, et al.
PHST-P 105: Giving and Volunteering in America
  • 2017 fall, 2018 spring, Indiana University, undergraduate.

Data Science-Oriented

Data Management and Research Life Cycle (forthcoming in 2019 spring)
  • Description of the course: This class equips thoughtful thinkers with powerful data science skills. You will learn how to manage and work with complex and big datasets in social science research, particularly in policy and nonprofit studies. You are expected to learn the following skills and respond to “big questions” that have social importance: 1) Understand the structure of data and how to work with big and complex datasets; 2) Understand the workflows of acquiring and managing data; 3) Able to conduct data-intensive and replicable social science research.
  • Each class has two sections: discussion of reading materials and hands-on programming. Programming environment will be JupyterHub using Python 3.
  • Prior programming experience is helpful but not required. You are expected to have knowledge of college-level statistics (e.g., you know what is “mean”, “standard deviation”, “normal distribution”, and you can use Excel to draw line charts). Advanced programming skills and statistics are helpful but not required because they are not the focus of assignments, and the final project can be customized towards individual needs.
  • The tentative syllabus is available: http://jima.me/?page_id=601
  • The class will also have four lunch seminars with guest speakers from the academia, the nonprofit industry, and the intelligence community.
  • Requirements and expectations: You will be graded on 1) plagiarism test (10%), 2) annotation on weekly readings (10%), 3) biweekly assignments (20%), 4) lead discussion and analysis of empirical studies (20%), 5) final paper (40%).
  • Readings: No required textbooks. The course is supported by readings compiled from a wide range of books and academic articles. The expected reading load will be 3-5 journal articles or book chapters (around 60-100 pages) per week.

Social Science-Oriented

Research Topics on Nonprofit and Philanthropic Studies (Draft)

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  • Ma, J. (2017). Bibliographic Records of Nonprofit and Philanthropic Studies (1925-2016). Retrieved from http://bit.ly/ma_open
  • Ma, J., & Konrath, S. (2016). Thirty Years of Nonprofit Research: Scaling the Knowledge of the Field 1986-2015 (SSRN Scholarly Paper No. ID 2834121). Rochester, NY: Social Science Research Network. Retrieved from https://papers.ssrn.com/abstract=2834121
  • Nonprofit management education: A literature pool
Impact Evaluation (draft)
  • Qualitative: Interview and fieldwork design, data collection, causal inference in qualitative studies, conceptualization, data analysis.
  • Quantitative: statistics (fundamentals, correlation, regression), data structure and storage, data visualization, causal inference in quantitative studies, survey design (psychometrics, sampling), valuation.
  • Logic framework, application in nonprofit organizations, impact evaluation and organizational life cycle.

Other Teaching or Workshop

Data visualization
  • Guangzhou, PRC, 5/2016; audience: NPO leaders and program managers.
  • Hefei, PRC, 7/2015; audience: NPO leaders and program managers.
Program evaluation
  • Hefei, PRC, 7/2015; audience: NPO leaders and program managers.
TOEFL iBT and IELTS: Vocabulary and Listening
  • 2009 Summer and Fall, Global Education, Anhui, PRC.