Supported by the National Science Foundation

Now in its fourth year, the SCRM offers a program of intensive, five-day courses on research methods in cultural anthropology. The program is directed by H. Russell Bernard and a board of advisors, including Jean Ensminger, Jeffrey Johnson, Carmella Moore, Eric Smith, and Susan Weller, with support from the National Science Foundation. The SCRM courses will be held at the Duke University Marine Laboratories in Beaufort, North Carolina.

Since 1999, the Duke Marine Lab has hosted the NSF-supported Summer Institute on Research Design in Cultural Anthropology (SIRD), for graduate students in cultural anthropology, directed by Jeffrey Johnson and East Carolina University, with Susan Weller and H. Russell Bernard as co-directors. That three-week program on research design, now in its 13th year, is only for Ph.D. students in cultural anthropology.

The SCRM program is for colleagues who already have the Ph.D. and who want to broaden or improve their skills.

Summer 2008 Course Offerings

Text Analysis
July 14– 18, 2008

Network Analysis
July 21– 25, 2008

Systematic Techniques For Gathering And Analyzing Video Data
July 28 – August 1, 2008

Application Information

Costs - The National Science Foundation, through a grant to the University of Florida, pays the costs of instruction, room, board, and instructional materials. Participants pay their transportation to the course, in Beaufort, North Carolina.

Deadline - February 15, 2008

To Apply - Applications for this year's SCRM are complete. Applications for the 2009 SCRM will open Sept. 1, 2008.

Course Overviews

Text Analysis: Systematic Techniques for Analyzing Qualitative Data 
Clarence Gravlee (University of Florida) and Amber Wutich (Arizona State University)

Description: This five-day course for professional anthropologists lays out a broad range of systematic methods for analyzing qualitative data (e.g., text and images) and provides guidance on when the methods should be used. We will cover the basics of qualitative research, including: techniques for identifying themes, tips for developing and using codebooks, and suggestions on how to produce qualitative descriptions, make systematic comparisons, and build and formally test models. The course will concentrate on three major traditions of analysis: grounded theory, content analysis, and semantic analysis.

We will emphasize hands-on data analysis exercises to illustrate the complementary strengths of different methods for analyzing qualitative data. The course is not a workshop in how to use software, but we will show participants how recent advances in hardware and software can facilitate the recording and transcribing of text and how software (including Microsoft Word & Excel, Atlas.ti, MAXqda, EZText, Anthropac, and UCINet) can be used to facilitate the analysis of qualitative data.

Readings will be available on the course web page (participants will be asked to read the materials prior to the course so we can concentrate on hands-on learning). Classes will be divided between lectures and labs where participants will analyze real data. At the end of the course, participants should be able to use the various methods presented in the analysis of their own data and to demonstrate the methods to their students and colleagues.

Who should apply: Only anthropologists who have completed their Ph.D.s will be considered. Preference will be given to those who have are teaching, or plan to teach, courses in research methods.

Network Analysis
Jeffrey C. Johnson (East Carolina University) and Christopher McCarty (University of Florida)

Description: Social Network Analysis facilitates a better understanding of how the structure of human relations is related to a variety of social and behavioral outcomes of interest (e.g., group productivity, individual performance, psychological well-being). The actors are usually people, but they can also be animals, organizations, or nations.  Social networks analysis can be applied to substantive problems that cut across many subjects and disciplines.  Any research problem where there is a possibility of a measurable effect from the pattern of relations among the actors may benefit from social network analysis.

There are two general types of social network analysis.  “Sociocentric” or “whole” networks comprise relations between all actors within a bounded group.  These actors might be members of a club, a classroom of children, a village, the executive board of a Fortune 500 company, or the members of a trading bloc of nations. The focus of sociocentric network studies is the structure within the group and how this structure relates to individual or group behavior or perceptions. “Egocentric” or “personal” networks comprise the relations among the people known by individuals. Typically respondents (called “egos’ in the jargon of the field) are presented with questions or cues that elicit the names of people they know.  These data are analyzed by summarizing the characteristics of the network “alters” for each respondent and correlating aggregate characteristics with the individual characteristics of the respondents themselves.  Data like these enable the researcher to study topics such as the characteristics of social support networks, or the relationship between IV drug use and the transmission of HIV. The egocentric approach fits well within a social survey methodology.

Participants in this course are introduced to the research methods and theoretical approaches used in both types of social network analysis.  The course begins with an overview of social network analysis, including fundamental concepts such as cohesion, bridging, directed versus undirected ties, strength of tie, structural holes, one mode and two mode data.  Participants learn about specific types of social network metrics that are used to describe these concepts and test them against various outcome measures. We also cover social network visualization, which allows us to quickly identify patterns that are difficult to find using metrics alone. Social network visualization is one of the fastest growing and most exciting areas of social network analysis. 

There are several theoretically important concepts that emerge from social network analysis that will be applicable to a variety of anthropological problems.  Examples of these include social capital, social homophily, preferential attachment, innovation adoption and diffusion, cognitive social networks and network evolution and dynamics. We will focus on how these methods and concepts can be applied to your specific research interests. 

The remainder of the course involves hands-on data collection and analysis using social network tools. These tools include Ucinet for whole network analysis and Egonet for personal network analysis. We focus on how to construct a social network questionnaire and how to collect whole network data from existing data (such as e-mail, bibliographic citations or behavioral observation during fieldwork). To facilitate quick learning of techniques for data collection and analysis, we collect network data in class –  both the whole network of the class and the personal networks of class participants.

Who should apply: Only anthropologists who have completed their Ph.D.s will be considered. Preference will be given to those who are teaching, or plan to teach, courses in research methods.

Course Requirements. Prior to the course participants must have read Social Network Analysis: A Handbook by John Scott, London: Sage (2000), and Introduction to Social Network Methods by Robert A. Hanneman and Mark Riddle Riverside, CA: University of California, Riverside (2005) (published in digital form at http://faculty.ucr.edu/~hanneman/). These books provide the basics on whole (sociocentric) network data collection and an overview of the structural metrics.  For personal (egocentric) networks you will be provided a draft of a book titled Personal Network Analysis that outlines the methods of personal network data collection.

Systematic Techniques For Gathering And Analyzing Video Data
Elizabeth Cartwright (Idaho State University) and Jerome Crowder (University of Houston)

Description: Video recordings provide an exceptional way to capture qualitative data for use in research. Using a hands-on approach, this class will prepare participants to collect and analyze anthropological data gathered through video recording.  In order to successfully carry out research using video equipment, participants must first master the technical side of this kind of data gathering.  During the first two days of the course, participants will learn to use high quality video and audio recording equipment and will learn the basics of video interviewing, location of audio and lighting, camera handling, and scene composition.  Opportunities for filming will be arranged for the participants in the local communities. Working in small groups, participants will generate video footage that will then be brought back to the classroom and downloaded into participants’ computers for coding and analysis. 

During the next three days in the five-day course, participants will learn to tag and code images, create relational databases and create data matrices for statistical analysis. Video footage gives the researcher a unique ability to understand dynamic events within their spatio-temporal context. Participants will learn how to partition their footage into meaningful sequences that can be coded and analyzed for audio and visual content.  At the most basic level the footage can be analyzed thematically for the naturally occurring speech surrounding the event.  Secondly, the analysis of the movements, interactions, facial expressions, and other visually observable events will be added into the timeline generated by the video footage and the already-coded speech acts. Finally, the two levels of analysis are checked against each other by playing back the video footage, along with the written commentaries/analysis/codes that the researcher has written into the timeline and that now appear alongside the video. This triangulation between what was said and what was done provides an important corrective to relying solely on a textual analysis of dialogue.

Another form of triangulation that will be explored by class participants will entail taking still images from video footage and using those stills both as stop-action reference material to understand proxemics between informants and as means for sharing with the informants in order to elicit their descriptions and interpretations of the scene. With these digital stills, researchers can measure and more accurately observe subtleties in body language and facial expressions (which may not be detected during full-speed play back) as these change over short periods of time. Furthermore, eliciting informants’ reactions to still images provides contextual data that may not be captured by the lens or inherent within the dialogue of the video.