The core topic is program evaluation. Health professionals implement Public health programs at different levels (for example, at individual, community, and national levels), and this often occurs simultaneously. Therefore, the evaluation of such programs is complicated. Ideally, such programs embed evaluation from the start. Outcome evaluation investigates if program goals are met for example increase of knowledge or decrease in health complaints. . Process evaluation investigates how the program is implemented by professionals and participants Process data can help explain the outcomes for example certain goals may not be met because corresponding activities were not implemented at all or did not fulfill participants’ needs. In this module, students work with real-world data and apply statistical analyses they are familiar with (Chi-square tests, t-tests and regression analyses).

Experimental designs (RCTs) are the gold standard for outcome evaluation. However, randomization at individual level is not always feasible or appropriate in public health programs.

In week 1, quasi-experimental designs are introduced as good alternatives for programs at individual level. In addition, students learn how to compensate for design weaknesses within statistical analyses for example combining process data and outcome data or controlling for baseline differences between groups caused by non-randomization. 

In week 2, cluster randomized trials are introduced as good alternatives for multilevel programs that not only promote individual behavior change but also environmental change. The implications for sample size calculation are explained. 

In week 3, participatory action research is introduced as good example of mixed-methods approaches. The term “mixed-methods” refers to the combination of qualitative and quantitative methods. The term “participatory” refers to giving voice to participants throughout all phases of the program that is development, implementation and evaluation.

• Expert:
• Organizes and applies health definitions within the context of health interventions, policies, and research
• Analyses real-world data on health status and inequalities
• Applies techniques to analyse and interpret correlation and causation between variables
• Examines and analyses the effects of confounding and interaction

• Investigator:
• Describes advantages and disadvantages of standard research designs and data collection
• Matches and applies intermediate-level statistical analyses to research data
• Distinguishes among various categories (i.e. types) of public health interventions at community, organization and policy levels
• Matches research methods and data analysis to specific intervention types
• Applies critical academic thinking tools to dilemmas in public health policy, practice, and scientific research
• Tests principles of research ethics and integrity against case studies from practice
• Identifies and contrasts differing targets (aims), & methods of implementing, financing and applying public health research projects

• Professional:
• Engages feedback from staff and students actively
• Behaves in a respectful, professional and reliable manner in tutor groups, practicals and group work
• Contributes actively and positively within tutor groups and training tutorial groups