About the Instructors & Workshops
Corey M. Abramson is Associate Professor of Sociology at the University of Arizona. Professor Abramson has over a decade-and-a-half of experience using, teaching, and developing methods of Computer Assisted Qualitative Data Analysis (CAQDA). Abramson has used ATLAS.ti in his own qualitative and mixed-methods projects including his book with Harvard University Press, and recent methodological pieces in Sociological Methodology, and Ethnography. He is also co-editor of (with Neil Gong) of a new volume with Oxford University Press on comparative ethnographic methods. Professor Abramson has had the opportunity to serve as a methodological adviser and consultant for individual and team based projects encompassing a wide range of data types, analytical approaches, and disciplines. He has worked to develop novel training programs for conducting qualitative research in social science and policy disciplines at the undergraduate, graduate, and post-graduate levels. In recent years, his workshops have been commissioned by a range of organizations including: universities, medical centers, think tanks, professional associations, and the ATLAS.ti training center. Professor Abramson received his Ph.D. in sociology from UC Berkeley in 2012 and spent the following year as a post-doctoral fellow at the Philip R. Lee Institute for Health Policy Studies at the University of California, San Francisco.
Martín Sánchez-Jankowski is Professor of Sociology and Director of the Institute for the Study of Societal Issues at the University of California, Berkeley. His research has focused on the sociology of poverty and violence. He has conducted long-term participant-observation field research for 42 years. He has published a number of books and research papers, among them Islands in the Street: Gangs and American Urban Society (University of California Press 1991); Cracks in the Pavement: Social Change and Resilience in Poor Neighborhoods (University of California Press 2008); and Burning Dislike: Ethnic Violence in High Schools ( University of California Press 2016). His current field research focuses on the poverty conditions of rural indigenous people in the United States, Fiji, and India.
James Moody is the Robert O. Keohane professor of sociology at Duke University. He has published extensively in the field of social networks, methods, and social theory. His work has focused theoretically on the network foundations of social cohesion and diffusion, with a particular emphasis on building tools and methods for understanding dynamic social networks. He has used network models to help understand school racial segregation, adolescent health, disease spread, economic development, and the development of scientific disciplines. Moody's work is funded by the National Science Foundation, the National Institutes of Health and the Robert Wood Johnson Foundation and has appeared in top social science, health and medical journals. He is winner of INSNA's (International Network for Social Network Analysis) Freeman Award for scholarly contributions to network analysis, founding director of the Duke Network Analysis Center and editor of the on-line Journal of Social Structure.
Katherine Stovel is Professor and Chair of the Department of Sociology at the University of Washington, and the Faculty Chair of the University's Research Fund. She previously served as the Director of the Center for Statistics and the Social Sciences (CSSS). She is a faculty affiliate of the Center for Research on Demography and Ecology, and a senior fellow at the eScience Institute. Since 2015, she has served at the chair of the Fellowship Selection Committee at the Center for Advanced Study in the Behavioral Science at Stanford University. From January 2013 - December 2016, she served as the (North American) Editor of the British Journal of Sociology. Stovel is a general sociologist whose research addresses basic questions concerning the dynamics of social organization and social relations. Her work, which follows in the tradition of social networks analysis, is motivated by a desire to understand how common social processes are expressed in particular historical contexts, and why these processes occasionally result in new institutional arrangements or new identities for individuals. A distinctive feature of Stovel's work is the arsenal of methods she employs, methods that emphasize the dynamic, sequential, interactive, and multi-level nature of social phenomena. This allows her to tackle questions traditionally asked--often in a much less systematic way--by historical sociologists and others concerned with the dynamic interaction of individuals and their local context. Stovel's published research spans a variety of topics, including the micro-dynamics of brokerage relations, networks and employment segregation, technology and information flows, the emergence of modern career systems, the process of becoming a Nazi, and temporal patterning in lynching in the Southern US. She also has a long-standing interest in how social context affects the health of adolescents. Her 2004 article,"Chains of Affection," a study of the structure of adolescent sexual networks, was awarded the Roger Gould Prize by the American Journal of Sociology. Stovel's research has been supported by the National Science Foundation, the National Institutes of Health, and private foundations. Current NSF funding supports her study of the ways new search technologies impact the practice of academic research. Stovel, who hails from New England, has an A.B. in Political Science from Stanford University, and a Ph.D. in Sociology from the University of North Carolina at Chapel Hill. She spent the 2008-09 academic year as a fellow at the Center for Advanced Study in the Behavioral Sciences at Stanford University. In her extra-professional life, Stovel enjoys engaging in a multitude of outdoor activities, cooking, and learning about early 20th century expressionist art.
Jeremy Freese is interested broadly in the relationship between social differences and individual differences, and between social advantage and embodied advantage. This includes work differences in physical health, cognitive functioning, health behaviors, and the role of differential utilization of knowledge and innovations toward producing differences. He is the co-leader of the Health Disparities Working Group for the Stanford Center for Population Health Sciences. Jeremy Freese is part of ongoing efforts to better integrate biological and social science thinking. He has worked on how behavioral and molecular genetic information can be used to complement and elaborate our understanding of the consequences of social environments. He has also done work on the application of evolutionary biological reasoning to human behavior and social arrangements. Jeremy Freese is the co-author of a book on discrete-choice-type models, which is now in its third edition and is used in many graduate classes in the social sciences. For this book, Jeremy is also the co-author of a widely-used suite of add-on commands to the software package Stata that facilitates the interpretation of model results. He teaches statistics and data analysis to graduate students here at Stanford. Jeremy Freese also does other work on improving the practice and conceptualization of social research. This includes projects on best practices for survey research, how to improve participation in social surveys, and how to think more clearly about complex causal processes. Jeremy Freese is also involved in some science studies research regarding the rise of meta-analysis and open science. Jeremy Freese is co-PI of three major social science “public goods” projects. Time-Sharing Experiments in the Social Sciences (NSF) promotes experimentation in the social sciences by soliciting proposals for survey experiments and fielding selected proposals for free using an Internet-based nationally representative sample. General Social Survey (NSF) is the most-used survey to study trends in social characteristics and attitudes in the United States, and, as part of the International Social Survey Programme, it is also the most-used survey for comparing US public opinion to other countries. Wisconsin Longitudinal Study (NIA) is one of the longest-running population-based survey studies in the US, and I am a central member of the team that is integrating Genome-Wide Association Study (GWAS) data into this study.
Jeffrey Oliver provides bioinformatic support to life science researchers, especially in data analysis and visualization. He is a specialist in the intersection of population genetics and phylogenetics and have programming expertise in R, Java, and python. He earned his Ph.D. at the University of Arizona studying phyloinformatics, then spent five years at Yale University as a bioinformatic and molecular biology post-doc, where he developed open-source bioinformatic software packages. He then went on to a post-doctoral position at Oregon State University, where he continued developing bioinformatic software and platforms for genomic data management. You can find his publications on Google Scholar and PubMed.
Keaton Wilson is passionate about leveraging novel data sources to understand how the natural world works. His work integrates physiology, ecology & evolution with a data science toolkit that includes traditional statistical tools and machine-learning algorithms. Keaton Wilson's teaching philosophy focuses on three themes: connecting science to the real world, critical thinking & exposure to data, and creativity & communication. Keaton Wilson teaches data science, biology and environmental science in an inclusive classroom environment.
Data Analysis in ATLAS.ti
This workshop will provide both a conceptual background and practical experience in computer assisted qualitative data analysis (CAQDA) using ATLAS.ti. The workshop begins by examining core elements of CAQDA, regardless of methodological orientation, discipline/profession, or platform. After instruction in the fundamental aspects of CAQDA, the course turns to the logic of the ATLAS.ti program, and how it can function as a tool for CAQDA. The workshop consists of both instruction and hands-on exercises in ATLAS.ti. By the end of the course, participants will have all the conceptual and practical tools necessary to employ ATLAS.ti in their current or future projects involving qualitative data.
Social Network Analysis
This workshop combines a conceptual background in social network analysis, with hands-on practice, and discussion.
Introduction to Sequence Analysis
This workshop combines a conceptual background in Sequence Analysis, with hands-on practice, and discussion.
Observing and Analyzing Everyday Behavior
This workshop combines a conceptual background with training in the concrete techniques necessary to successfully employ the field methods used by social scientists for observing analyzing behavior in real world settings (e.g. participant observation and ethnography). The workshop includes discussions of the practical challenges, logistics, and potential pitfalls of conducting field research as well as discussions of how the techniques being taught can be applied to the participants’ own projects.
Practices for Producing Transparent and Reproducible Research
This workshop describes different practices for social scientists interested in doing more transparent and reproducible research. While transparency and reproducibility have gained enthusiasm as ways to make research more credible, these practices also often make work more efficient as well, especially for projects involving many collaborators or intermittent work. The practices presented will range from tweaks to workflow to proposals for novel types of collaboration or documentation. Broad topics include pre-specification, literate programming, data sharing, version management, and dynamic document generation. The workshop will provide concrete examples in which basic principles of reproducible work are implemented as research practice
Introduction to R
In an increasingly complex world full of large and challenging data it is important that researchers be able to effectively explore, analyze and communicate data using the most state-of-the-art tools available. The R Statistical Software is a free, open-source programming language that has been collaboratively developed among the international community, and whose versatility and power allow unmatched freedom and the ability to tackle a wider range of problems than most other platforms. The goal of this workshop is to use active-learning, interactive pedagogical methods in order to help participants learn the basics of R and equip them with resources to continue learning on their own after the meeting.
The workshop will be taught by Dr. Jeff Oliver (Data Science Specialist) and Dr. Keaton Wilson (Research Scientist) from the University of Arizona. The workshop will be based on principles from Data and Software Carpentries, and modules cover both the basic syntax needed to operate R for complete beginners as well as how to leverage the power of the R language to simplify complex analyses and produce stunning data visualizations. Sample code for a variety of data visualizations and statistical procedures will be provided to participants so that they can continue leveraging the power of R when they return to their home institutions, as well as a roadmap that includes tutorials, recommended readings, and websites for participants to continue their own journey with this software.