COMMUNICATION NETWORK ANALYSIS IN PUBLIC HEALTH PROGRAMS Syllabus
Course Learning Objectives
Upon successfully completing this course, students will be able to:
- Define essential terms related to social network concepts and analytic approaches
- Describe methods for measuring the properties of social networks
- Interpret books and articles that incorporate social network terminology, concepts, and analytic approaches
- Apply social network concepts and analytic approaches in their own research
Course DescriptionIntroduces the theory and method of network analysis, its application to public health, emphasizing the dissemination of public health information and the transmission of disease, and the influence of networks on health-related behavior.
Methods of Assessment
Student evaluation based on four problem sets and class participation.
Additional Faculty Notes:
Assignment One: Analyze and interpret personal network data using Stata
Handout: 9/17 at the start of Labtime
Due: 9/19 at 11:59 pm
Assignment Two: Analysis of network structure with Ucinet and Netdraw
Handout: 10/1 at the start of Labtime
Due: 10/3 at 11:59 pm
Assignment Three: Analysis of group membership using Ucinet
Handout: 10/15 at the start of Labtime
Due: 10/17 at 11:59 pm
Assignment Four: Analysis of node centrality using Ucinet
Handout: 10/22 at the start of Labtime
Due: 10/24 at 11:59 pm
Dataset for Part B will be distributed on 10/15
Due: 10/26 at 11:59 pm
Description of the Assignments
These assignments are intended to provide an opportunity to develop knowledge and skills using computer software packages to analyze social network data. The first 30 minutes will be spent demonstrating the use of the software package – either Stata or Ucinet – to conduct the analysis for the assignment. Students will then have the remaining 50 minutes of the class time to work on the assignment for that session, although I will able to stay in the lab until 1pm to help with students who would like or need to continue working on it. Since these are practice sessions, the students are encouraged to ask questions and are free to work with and learn from their fellow students. The dataset will be uploaded to the Courseplus site the evening prior to the session and the assignment will be handed out after the 30-minute demonstration. Remember to put the data on a flashdrive (and bring it with you) prior to showing up in the lab!
The Final Assignment is intended to provide an opportunity for students to demonstrate what they have learned during the course. NOTE: In contrast to Assignments 1-4, Assignment 5 is to be completed independently.
The assignment will consist of two parts:
- Students will choose a health topic of interest and will provide a brief review of the literature about this topic and, informed by this literature review, a rationale for how 3 network-informed theoretical concepts may contribute to an understanding of this topic. They will then describe: 1) what type of network data they would need to measure these concepts and (briefly) how they would collect it, 2) the specific network measures they would use to operationalize these concepts, and 3) how they would incorporate these measures into a larger analysis exploring the importance of these concepts to the health topic. This section should not exceed 10 double-spaced pages.
- Using a network dataset provided by the instructor, the students will be asked to respond to questions based on an analysis of these data.
Additional Faculty Notes:
Valente TW. (2010). Social Networks and Health: Models, Methods, and Applications. New York: Oxford University Press. [Electronic version available on JHU library catalog]
Please see the course Session for a full list of dates and items for this course.
Academic Ethics Code
Students enrolled in the Bloomberg School of Public Health of The Johns Hopkins University assume an obligation to conduct themselves in a manner appropriate to the University's mission as an institution of higher education. A student is obligated to refrain from acts which he or she knows, or under the circumstances has reason to know, impair the academic integrity of the University. Violations of academic integrity include, but are not limited to: cheating; plagiarism; knowingly furnishing false information to any agent of the University for inclusion in the academic record; violation of the rights and welfare of animal or human subjects in research; and misconduct as a member of either School or University committees or recognized groups or organizations.
Contact Information(from old syllabus)
Marc Boulay, PhD
Office: Candler Building and HH 710
Disability Support ServicesIf you are a student with a documented disability who requires an academic accommodation, please contact Betty H. Addison in the Office of Student Life Services: firstname.lastname@example.org, 410-955-3034, or 2017 E. Monument Street.