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We focus in this course on cognitive anthropology and cultural domain analysis. CDA involves systematic interviewing to get lists of items that comprise a coherent cognitive domain. (Anything that can be listed is a domain: trees, kinds of music, things to do on vacation, rock singers, foods, medicinal plants, ...) The object is to understand how people--—as individuals and as groups—think about and organize the items in a domain.
The data collection methods include:
- Free lists
- Pile sorts
- Triad tests
- Paired comparisons
- Ratings
- Rankings
The data analysis methods include:
- Multidimensional scaling
- Hierarchical clustering
- PROFIT (property fitting)
- Guttman scaling
- Likert scaling
- Consensus analysis
The methods covered in this course are based on the analysis of profile matrices and similarity matrices. At the first meeting of class, we’ll discuss the theory behind these matrices and how they can be used in many different areas of research, including the analysis of qualitative data (like text and images). We meet each week in a computer lab to maximize hands-on experience.
In Week 2, the class chooses a cultural domain to analyze. Students pool their data and move through the data collection and analysis together. Grades are based on class participation (.25) and on the production of a lab book detailing the work of the worse (.75). Class participation means being prepared each week to discuss assigned readings and assignments involving data collection and analysis. The lab book tracks your work on cultural domain analysis.

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Week 1 - The history of cognitive anthropology
Week 2 - Free lists
Week 3 - Pile sorts
Week 4 - Triad tests
Week 5 -
Ratings, rankings and paired comparisons
Week 6 -
Multidimensional scaling
Week 7 - February 22. No class
Week 8 - Cluster analysis
Week 9 - Consensus analysis
Week 10 - Spring break
Week 11 -
Property Fitting (PROFIT) analysis
Week 12 -
Quadratic assignment procedure (QAP)
Week 13 -
Extending the use of matrix methods
Weeks 14-15 -
Review, discussion, and lab
work

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