This course will review the fundamental principles of multilevel analysis and discuss how it differs from other analytical approaches. The type of questions for which multilevel analysis is most useful will be discussed. Students will learn how to specify, fit, and interpret multilevel models.

Prior knowledge of linear regression is required. Emphasis will be on conceptual understanding, application and interpretation. Examples from urban health research will be discussed. Conceptual and methodological challenges in using multilevel analysis will be reviewed. This course may be of interest to public health professionals, researchers, and students who conduct or interpret quantitative research and have some familiarity using statistical software to conduct and interpret simple analyses.

This course will provide students with a solid foundation in acquisition, manipulation, and presentation of spatial data using geographic information system (GIS). The course emphasizes hands-on use of data from Philadelphia to gain practical experience using GIS software (ArcGIS for Desktop) to explore spatial patterns in health in cities.

Topics covered in this course include: acquisition of spatial data, data management, geocoding, symbolizing features, coordinate and projection systems, making maps for presentation, and introduction to spatial analyses. Classes will consist of a daily mini-lecture to orient students to key topics. The bulk of each session will then be spent on applied labs and projects.

Bayesian methods combine information from various sources and are increasingly used in biomedical and public health settings to accommodate complex data and produce readily interpretable output. This course will introduce students to Bayesian methods, emphasizing the basic methodological framework, real-world applications, and practical computing. Special consideration will be given to methods for spatial data analysis.

An introductory course for public health professionals and others working in community-based organizations and other settings.

How do we know if our programs are effective? Are they getting us closer to our organizational mission? And how do we show this to our funders? Through a series of readings, collective discussions, and presentations, this 2-day course will orient students to the basic components and steps of program evaluation.

The first day of the course will cover logic models and developing evaluation plans for formative, process, and outcomes evaluations. The second day of the course will provide a foundation for the data needed for these evaluations. The fundamentals of writing and assembling an evaluation plan/report will conclude the course.

Students will gain hands-on experience in developing logic models, crafting surveys and data management through this course.

Community-Based System Dynamics (CBSD) is a participatory method for engaging and working with communities and organizations to address complex issues. Developed from group model building and system dynamics, CBSD is now being used around the world, both in research and practice, to engage diverse communities and organizations to address a variety of complex issues from engaging youth in addressing youth homelessness, identifying effective policies to improve health in urban areas, and coordinating NGO efforts in planning maternal-child health interventions.

This intensive 2.5-day workshop provides an introduction to CBSD, with opportunities for participants to receive feedback on preliminary designs for their own CBSD work. Participants will gain knowledge of basic concepts in CBSD, including systems thinking, skills on identifying and selecting issues appropriate for CBSD, assessing readiness, convening a core modeling team, workshop design and facilitation.

While no prior training or experience in group facilitation, group model building or system dynamics is required, participants currently working with or planning to work with groups (e.g., classrooms, community meetings, focus groups, coalitions, and workgroups) will find the content more relevant.