MORNING WORKSOPS (meet 9-12 daily)
Geographic Information Systems (GIS) for the Social Sciences
Kathryn Freeman Anderson
This workshop is an introduction to the use of GIS in social science methods. A GIS is a data management system for organizing and analyzing data which is tied to physical geography and utilizes geographic coordinates. As a social science application, we will learn how to examine data which is social in nature, but which is also linked to place (U.S. Census data for example). All human activity occurs in some space, and GIS data and methods can provide a powerful tool for both visualizing and analyzing data. In this workshop, we will cover basic data management strategies in a GIS and the principles of cartography for creating effective maps. Specifically, we will use ESRI’s ArcGIS, which is a suite of geospatial processing programs. ArcGIS is the most commonly used GIS program in both academics and industry, though many of the concepts from the workshop could be exported to other GIS programs.
Kathryn Freeman Anderson is an assistant professor in the Department of Sociology at the University of Houston. She earned her Ph.D. in sociology from the University of Arizona in 2016, along with a graduate certificate in Geographic Information Science (GIS) from the School of Natural Resources and the Environment and the School of Geography and Development. Her research lies at the intersection of urban sociology, the sociology of health and illness, and race/ethnicity. In particular, she examines racial residential segregation and health outcomes, with particular attention to how this relationship could be mediated by the presence of health-promoting community organizations in neighborhoods. To accomplish this, she uses a GIS to map out such resources in physical space, and spatial analytic techniques to examine how the relative presence/absence of these resources relates to other social attributes of neighborhoods.
Managing Research Grants, Projects, and Teams
This workshop will examine key aspects of grant, project and team management and is designed to help both young and experienced investigators. Young investigators will benefit from a thorough introduced to key features of grant and project management related to hiring, training, evaluation, time management, and coordination, while more experienced investigators will be able to explore techniques for scaling up projects to accommodate larger research teams, such as scalable training platforms (e.g., video-based training and computerized training modules) and communication and coordination platforms (e.g., project wikis, servers, and efficient collaborative tools in Google and other private providers).
Jennifer Earl is a Professor of Sociology and Government and Public Policy at the University of Arizona, where she studies social movements, information technologies, and the sociology of law. She is the recipient of a National Science Foundation CAREER Award for research from 2006-2011 on Web activism. She is also a member of the MacArthur Research Network on Youth and Participatory Politics, the recipient of a university-award for excellence in undergraduate research mentoring in 2010-2011, and has received over 1.25 million in grant funding post-PhD. She regularly manages teams including a range of positions (faculty collaborators, post-docs, grad students, and/or undergraduates) and ranging in sizes from very small teams to teams of 15-20.
Propensity Score Techniques
Propensity scores have been gaining popularity in social science research as a way to address selection bias by controlling for confounders that would otherwise be a potential source of spuriousness. This workshop will consider the use of propensity scores in different research contexts and the advantages and disadvantages of propensity score approaches. Participants will learn how to estimate propensity scores and then adjust for the propensity score by matching, stratifying, or weighting observations. This is a hands-on workshop, so participants should bring a laptop and install either STATA or SAS prior to arrival. (Familiarity with STATA or SAS is ideal but not required.) Participants are also encouraged to bring datasets to use for class exercises.
Danielle Steffey, PhD, is a Criminologist with 18 years of experience conducting crime and criminal justice research. She has extensive experience conducting multi-site program evaluations and collecting, managing, and analyzing social science data.
AFTERNOON WORKSHOPS (meet 2-5pm daily)
Qualitative Data Analysis With ATLAS.ti [Windows Version 8]
Corey M. Abramson
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. Topics covered include: 1. How to perform the fundamentals of computer aided qualitative data analysis in ATLAS.ti, 2. The specific strategies necessary for dealing with different types of data including ethnographic field notes, in-depth interviews, texts, audio, video, and documents, 3. How to organize your data set, 4. How to construct and deploy a coding scheme for your project, 5. Making the best use of memos, 6. Inductive and deductive strategies for investigating substantive relationships in your data, 7. The query tool, 8. Using ATLAS.ti for team projects, and 9. Advanced functions in ATLAS.ti (e.g. networks, geocoding, quantitative output, co-occurrence functions, tools for inter-coder reliability, etc.). All readings and handouts will be provided at the workshop. All students receive handouts detailing the course materials and templates for various data set structures.
Corey M. Abramson is Assistant Professor of Sociology at the University of Arizona. He 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. 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. 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.
Intro to R
R is a free, open-source statistical analysis program that has taken the world, and the job market, by storm. Participants will learn how to get data into R and conduct simple analyses, including descriptive analyses and data cleaning, t-tests, ANOVAs, correlations, and uni- and multi-variate regressions. We'll learn about cheater packages that turn R into a point-and-click program and we'll even glide over a few graphics packages where R's value really shines! Participants should be familiar with basic statistical concepts, have used some type of analysis program before (e.g., SAS, SPSS, Excel), and should bring laptops with R already loaded (instructions on how to do this will be sent before the workshop).
Dr. Katerina Sinclair has been an enthusiastic R user for over a decade. After initially using it to do things no other program could do, she eventually came to use it to do pretty much everything. She earned a concurrent Masters of Applied Statistics and a PhD in Human Development and Family Studies, with a focus on research methodology and statistics, from the Pennsylvania State University. She has worked in academia and industry and currently works as the Director of Data Science and Advanced Analytics for the Dohmen Company Foundation. Her past work included a stint at the U of A wherein she tried to convince every social science department to implement “R-only” policies. She eats data for breakfast and retains a steadfast commitment to improving people's lives by writing better open-source algorithms.