Statistical Analysis for Health Research
Posted 4 months 1 day ago by King's College London
Gain an introduction to descriptive statistics
Statistical analysis is hugely important in health research to help gather and analyse data in a meaningful way.
On this three-week course, you’ll develop a firm understanding of the statistical analysis techniques commonly used in health research, arming you with practical skills you can immediately start using.
Beginning with descriptive statistics, you’ll explore the basic diagnostic and epidemiological measures, and gain an understanding of how data is used in health research.
Explore inferential statistics in conflict health research
Next, you’ll look at the specific areas of inferential statistics in conflict health research.
Within this, you’ll discover hypothesis testing, regression analysis, and how to assess the impact of a healthcare intervention. You’ll then unpack real-life scenarios to help you gain practical skills in conflict health research.
Become empowered as a health researcher
To help you master health research analysis, you’ll take part in an online workshop on data analysis tools in conflict-affected settings.
You’ll learn to use Excel and SPSS in data analysis to help you bring meaning to your data, empowering you as a health researcher.
Learn how to choose the right statistical test
Finally, you’ll discover the strategies for choosing appropriate statistical tests based on research questions and data types.
Armed with this knowledge, you’ll understand how to use the many facets of statistical analysis to improve your health research and extract meaningful insights to use in your work.
This course is designed for healthcare professionals, academics, and researchers in the healthcare sector.
This course is designed for healthcare professionals, academics, and researchers in the healthcare sector.
- Understand the importance of statistical analysis in health research.
- Choose appropriate statistical tests based on research questions and data types.
- Perform basic data preprocessing and descriptive statistics.
- Conduct hypothesis testing and interpret p-values and confidence intervals.