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.

After completing this course, participants will be able to:

• Understand the fundamentals of multilevel studies and multilevel analysis and their differences with other study designs and analytical approaches.
• Fit multilevel models and interpret estimates derived from them.
• Be familiar with applications of multilevel analysis in urban health research.
• Understand the strengths and limitations of multilevel analysis for urban health research

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. This 5-day course provides an introduction to CBSD, with opportunities for participants to receive feedback on preliminary designs for their own CBSD work. Participants will build capabilities relevant to CBSD, including qualitative causal mapping skills, exposure to system dynamics simulation models, skills on identifying issues appropriate for CBSD, assessing community 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.

After completing this course, participants will be able to:

• Understand what CBSD is and how it fits into the larger space of systems science, systems thinking, and system dynamics approaches.
• Learn how to identify and structure problems in CBSD.
• Develop skills for reflecting the structures underlying complex issues using system dynamics diagramming conventions.
• Understand the role of simulation models in CBSD projects.
• Learn best practices in CBSD workshop planning and facilitation and where to access relevant resources for additional practice.
• Practice facilitation skills including presenting model structure and incorporating revisions from others.
• Learn about meaningful ways to engage communities and researchers in the process of CBSD.

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.

After completing this course, participants will be able to:

• Understand the fundamentals of Bayesian inference and the differences between Bayesian and frequentist (classical) methods.
• Be able to formulate research questions and develop Bayesian approaches to address these questions.
• Become familiar with the available software for implementing Bayesian methods.
• Understand advanced Bayesian methods used in the scientific literature.

Improving the quality of data visualizations can help in communicating results more clearly and transparently to other scientists, stakeholders and the general public. This course will use a workshop format to help students build their own data visualization projects using R, with Urban Health examples. The five sessions will include: (1) a general introduction to basic data visualization concepts; (2) best practices to build static figures + a workshop on different types of static figures (bar, line-points, etc.); (3) basic concepts regarding maps and GIS + a workshop on how to map your geographic data in R; (4) interactive web-plots and their utility + a workshop on interactive web plotting in R using Shiny; (5) a plotting project, where students will bring their own plotting needs, present a basic sketch to other students and then work in the code needed to get a production-quality figure with help from the code from previous sessions. 

This course will provide all of the required code, so participants do not need to have much prior knowledge of R to get something out of it. However, this is a programming course, so students with some knowledge of R will benefit. For example, some of the code may be useful to apply to your own visualizations with some adjustments. Thus participants with a stronger R background coming into the course will be able to create more complex visualizations, but again, you do not need to have advanced pre-knowledge of R to take this course. If you are interested in an introduction to the material that will be covered in this course, please check out one of the Coursera courses in R.

After completing this course, participants will be able to:

• Understand the basic principles of data visualization
• Perform basic data management to get data ready for visualization using R
  
• Use the ggplot2 R package to plot high-quality figures
• Create interactive web-plots using R and the shiny R package
• Plot spatial data using R

Community-based participatory research (CBPR) is an orientation to research that begins with community research interests and seeks to use the knowledge gained to inform action for community health improvements and social change. Although this approach to research is widely adopted in urban health and generally familiar to both community-based organizations and researchers, many still struggle with the lack of knowledge or experience with applying practical CBPR approaches to a pressing community health issue or potential research partnership.

This course will review the history and principles of CBPR, and introduce participants to practical approaches and tools for equitable and authentic community-researcher engagement in all phases of research: from the creation of research questions, to study design, data collection, data analysis, and dissemination. Participants will also learn strategies for participatory grant-writing, budgeting, and opportunities for workforce development and capacity building for community residents and leaders in research. This course welcomes researchers and community-based organizations as participants.

After completing this course, participants will:

• Know the history and principles of CBPR, and what distinguishes it from community-placed research.
• Understand the importance of and practical approaches for community-researcher engagement in all phases of a research process.
• Be able to apply CBPR principles to research partnership development and grant-seeking processes.

The goal of this course is to familiarize students with the applications of Geographic Information Systems (GIS) to assess or evaluate urban health challenges, and potential solutions, with a focus on the Philadelphia context. Students are expected to have some prior experience with GIS and be familiar with acquiring spatial data, data management, symbolizing features and making maps, and data coordinate and projection systems. Through hands-on exercises based on local case studies, students will gain practice in assessing the relationship between aspects of our environments (e.g. housing, transportation, food/nutrition, crime, chemical pollution, parks, and vegetation) and health in Philadelphia. Students will gain practice in using tools such as spatial joins, buffer analyses, spatial overlays and interpolation with both vector and raster data, in combination with basic statistics to answer urban health research questions.

After completing this course, participants will be able to:

• Identify, acquire and use spatial data relevant to urban health, such as from the census, satellite imagery, administrative records, or street address geocoding.
• Work with various spatial object representations, such as raster and vector data. 
• Answer spatial research questions using data produced from spatial operations in combination with basic statistics.
• Develop maps that effectively communicate spatial data and analysis results.