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Training on Quantitative Text Analysis for the Social Sciences – September 2023<\/span><\/strong> The workshop was held at Zayed university\u2019s recently inaugurated social sciences lab, established to provide faculty and student accessibility to various software and tools to support their research capacities, thereby promoting integration of quantitative methodologies alongside qualitative approaches. This collaboration marks the third departmental partnership between APSA’s MENA Program and Zayed University. Previously, APSA supported the organization of a training on machine learning as a data analysis tool for the social sciences in 2022 and conducted a series of workshops in 2018 for faculty and MA students, focusing on teaching research methods.<\/p>\n\n\n\n Workshop on Machine Learning for the Social Sciences – March 2022<\/strong><\/span> “Skyler was an excellent teacher. The sessions were very clearly organized into separate modules and he explained all the concepts and processes in a very logical way with a range of concrete examples. I know now much more about machine learning and how it can be used in quantitative-based research”<\/p>\n\u2014 Dr. Michael Bowles<\/cite><\/blockquote>\n\n\n\n “I am indebted to APSA for offering us this opportunity to learn the latest ML techniques in political science research. Dr. Skyler was a fantastic trainer and I have a much better understanding of ML after the workshop and hope to implement it in my own research soon.”<\/p>\n \u2014 Workshop Attendee <\/em> <\/cite><\/blockquote>\n<\/div><\/div>\n\n\n\n
In September 2022, APSA supported the Department of International Affairs and Social Sciences (IASS) at Zayed University in organizing a two-day training workshop on Quantitative Text Analysis for the Social Sciences. At Zayed University\u2019s IASS, where faculty are predominantly qualitative-oriented researchers, a demand emerged for training in quantitative methodology, particularly in the utilization of software for text-based research in political science, such as Critical Discourse Analysis (CDA) and sentiment analysis. Dr. Luwei Ying (University of California, Los Angeles) led the two-day training for faculty, introducing attendees to the fundamentals of quantitative text analysis techniques within the framework of social sciences. The sessions covered key topics, including text pre-processing, dictionary methods, supervised learning techniques (e.g., sentiment analysis, scaling, naive Bayes, SVM, random forest), unsupervised leaning (e.g., topic models), and causal inference with text data. Each method introduced was accompanied by practical applications, providing participants with a tangible understanding of their real-world utility. Beyond the theoretical knowledge, Dr. Ying designed the sessions with the aim of equipping faculty with the skills to design and implement a text analysis project from data collection to interpretation.<\/p>\n\n\n\n![\"\"](\"https:\/\/web.apsanet.org\/mena\/wp-content\/uploads\/sites\/6\/2026\/02\/group-photo_Zayed-University-scaled.jpg\")
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<\/em><\/strong><\/u>APSA MENA was pleased to work with the Social Science Department at the College of Humanities and Social Science (CHSS) at Zayed University to organize a workshop titled “A Basic Orientation to Machine Learning” from March 11-13. Dr. Skyler Cranmer (Ohio State University) led a two-day introductory workshop on machine learning (ML) and its application in social sciences. A theoretical introduction into the logic of ML, the workshop discussed ML fundamentals and tools using case studies in social sciences to demonstrate how it can help make predictions in research. Participants gained a better understanding of how ML could be used in their work, whether they are quantitative or qualitative-oriented researchers. The training also touched on other ML-related concepts and software, such as supervised regressions, unsupervised clustering, and Python and R. More than twenty faculty members attended the training from the International Affairs and Social Sciences Department.<\/p>\n\n\n\n<\/a>\n
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