Enroll Course: https://www.coursera.org/learn/power-sample-size
For anyone involved in research, particularly in health and social sciences, understanding how to properly calculate power and sample size is absolutely critical. It’s the bedrock of a well-designed study, ensuring your findings are robust and your efforts are not in vain. Recently, I completed Coursera’s “Power and Sample Size for Multilevel and Longitudinal Study Designs,” and I can confidently say it’s an invaluable resource for any researcher looking to up their game.
This five-week, fully online course is a deep dive into the often-complex world of power and sample size calculations for multilevel and longitudinal studies. These study designs are prevalent in fields like behavioral and social sciences, as well as health-related research, making the skills taught here broadly applicable.
The course is structured logically, starting with the fundamentals. Week 1 introduces you to the online learning environment, basic statistical concepts like hypothesis testing, and the core principles of multilevel and longitudinal studies. It’s a gentle, yet thorough, introduction that sets a strong foundation. The module concludes with a practical introduction to the GLIMMPSE software, guiding you through an exercise to solve for power in a single-level cluster design. This hands-on approach right from the start is excellent.
Week 2 builds on this foundation, delving into the intricacies of research design. You’ll explore concepts like between-subject vs. within-subject factors, Type I and Type II errors, and the crucial role of variance and correlation structures. The course effectively explains how to choose appropriate statistical tests and how to align these design choices with power and sample size analysis. The guided exercise for longitudinal study sample size analysis is particularly helpful here.
As you move into Week 3, the focus shifts to model assumptions, alignment between analysis and power calculations, and the ever-important topics of missing data and participant dropout. Understanding how these factors impact power and sample size is vital for real-world research, and the course provides clear explanations and practical advice. The independent exercise for multilevel study power analysis allows you to apply these concepts yourself.
Week 4 tackles the practicalities of sourcing inputs for your calculations, including empirical literature, pilot studies, and simulations. It also addresses the critical aspect of recruitment feasibility, discussing factors that can influence your ability to reach your target sample size. Furthermore, it guides you through handling studies with multiple research aims, a common scenario in many research projects. The independent exercise here, focusing on multilevel studies with longitudinal repeated measures, is a challenging but rewarding application of the learned material.
Finally, Week 5 brings it all together by exploring the ethical considerations of sample size analysis, the importance of early planning, and how to effectively present your sample size calculations in grant proposals. The discussion on power curves and subgroup analyses is particularly insightful for grant writing. The course concludes with advice on funding opportunities and a final independent exercise on planned subgroup analysis, peer review, and a final exam.
Throughout the course, the use of real-world examples from behavioral and social science studies makes the concepts relatable and the learning process more engaging. The GLIMMPSE software, a free and powerful tool, is integrated seamlessly, allowing you to practice what you learn immediately.
**Recommendation:**
I highly recommend “Power and Sample Size for Multilevel and Longitudinal Study Designs” to graduate students, early-career researchers, and even seasoned professionals who need to conduct or understand studies involving complex data structures. It demystifies a complex topic, equips you with practical skills using accessible software, and ultimately enhances the rigor and impact of your research. If you’re serious about designing studies that yield meaningful results, this course is a must-take.
Enroll Course: https://www.coursera.org/learn/power-sample-size