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Course Catalog


Department: International Health
Term: 2nd term
Credits: 4 credits
Contact: Peter Winch
Academic Year: 2014 - 2015
Course Instructor:

Discusses methods for collecting and analyzing qualitative data; quantifying ethnomedical beliefs; and integrating qualitative and quantitative methods. Topics include cultural consensus analysis, scale development and testing, multi-dimensional scaling, analysis of structured observation data, development of manuals for qualitative data collection, and the use of social science data in the design of public health interventions.

Learning Objective(s):
Upon successfully completing this course, students will be able to:
Describe the evolution in the concepts and methods of cognitive anthropology over the past 50 years
Explain the implications of key concepts in cognitive anthropology (e.g. prototypicality, marked and unmarked terms) for the design, analysis and interpretation of quantitative data collected with instruments informed by the findings of qualitative studie
Describe the problem of intracultural variation, and its implication for public health and list different types of intracultural variation
Describe types of research questions for which structured observation would be appropriate
List different ways of conducting structured observation, and select the form of structured observation that would be appropriate for different research questions
Identify approaches to data analysis that would be appropriate for different kinds of structured observation data, and list the steps in conducting each analysis approach
List the assumptions made in cultural consensus analysis, the types of data for which cultural consensus analysis should always be conducted, the steps in conducting cultural consensus analysis, and the strengths and weaknesses of this type of analysis
Distinguish between cultural consensus theory and cultural schema theory
Describe the steps in conducting free-listing, and common threats to the validity of free-listing data; analyze and interpret a set of free-listing data
List the characteristics of proximity data, and describe different ways proximity data can be collected and analyzed; list common threats to the validity proximity data collected through pile sorting (card sorting)
Describe the steps in conducting Guttman scaling, situations where Guttman scaling may be appropriate, and the strengths and weaknesses of Guttman scaling

Methods of Assessment: Students will be evaluated on the basis of the final examination (30%), class participation (20%) and written critiques of articles (50%) during each term.
Location: East Baltimore
Class Times:
  • Tuesday 1:30 - 3:20
  • Thursday 1:30 - 3:20
Enrollment Minimum: 5
Enrollment Maximum: 15
Instructor Consent: Consent required for all students
For consent, contact:

224.690 and 224.691 or equivalent

Auditors Allowed: No
Grading Restriction: Pass/Fail