Introduction to Statistical Methods for Data Analysis
Full course description
Year one of the new BEPH curriculum concludes with the statistics module, which builds the foundation of statistical methodology and hypothesis testing. The module consists of three themes: (1) Summarizing and describing research data; (2) Testing concept, generalization of results obtained from sample; (3) Introduction to basic statistical techniques. The first theme explores various methods for summarizing and visualizing data collected within a specific research context. Students learn about typology of variables (quantitative vs qualitative), central tendencies and dispersion, and graphical tools like histogram and boxplot. In addition, they study measures of association between two variables such as Pearson correlation, relative risk and odds ratio. An important focal point is the difference between correlation and causation. Theme two of the module is devoted to inferential statistics implying the degree to which conclusions obtained from a sample (of persons) can be generalized to a much larger group (i.e., population). A distinction is made between population, sample and sampling distribution. The latter eventually leads to the concept of confidence intervals for testing. Statements about the population are translated statistically as a null hypothesis and alternative hypothesis and concepts like significant level, p-value, type I and type II errors, and power are discussed in detail. In theme 3, students are introduced to basic statistical techniques for testing a hypothesis, such as the t-test for one sample, two (paired and unpaired) samples, and post-hoc comparisons for more than two samples. Finally, the module ends with simple statistical methods for studying relationships between two variables like the chi-square or linear regression analysis.
Course objectives
Expert
By the end of the module, students should be able to:
- Recall and name basic public health measures of health status
- Distinguishes the concepts of correlation and causality
- Recognizes scientific evidence establishing correlation and causality of investigated factors with health status
Investigator
By the end of the module, students should be able to:
- Explains basic forms of (qualitative and) quantitative research methods and data collection
- Matches and applies basic statistical analyses to research data
- Define science, scientific thinking and scientific knowledge
- Assess scientific research and publications at a basic level under close supervision
- Recall fundamental principles of research ethics and integrity
- Reads selectively in terms of both quantity and quality of reading material
Communicator
By the end of the module, students should be able (on a basic level) to:
- Presents on public health topics for peers and teachers
- Discuss topics and findings in English (aiming for level B2)
- Demonstrate understanding of feedback from teachers and peers
- Produce limited feedback for peers under supervision
Professional
By the end of the module, students should be able to:
- Accept and reflect on feedback from staff and students passively
- Behave in a respectful, professional and reliable manner in tutor groups, practicals and group work.
- Contribute actively and positively in tutor groups and training groups
- Understand, describe and apply the problem-based learning approach
- Positively engages the challenges and opportunities of intercultural diversity within tutorial groups