Power and Sample Size for Multilevel and Longitudinal Study Designs

University of Florida via Coursera

Go to Course: https://www.coursera.org/learn/power-sample-size

Introduction

**Course Review and Recommendation: Power and Sample Size for Multilevel and Longitudinal Study Designs** In the realm of research—especially in the health and social sciences—the importance of adequately estimating power and sample size cannot be overstated. The Coursera course titled **Power and Sample Size for Multilevel and Longitudinal Study Designs** offers a comprehensive and practical approach to mastering these essential skills over the span of five weeks. As someone who values strong methodologies in research, I found this course both enlightening and highly applicable to various research scenarios. ### Course Overview The course is designed to equip learners with innovative, research-based methods for conducting power and sample size analyses specifically tailored to multilevel and longitudinal study designs. It is fully online and structured to allow participants to engage with real-world studies, making it relevant and practical. ### Week-by-Week Breakdown **Week 1: Introduction to Multilevel and Longitudinal Designs** The course kicks off with an introduction not only to the course structure but also to fundamental statistical concepts essential for understanding multilevel and longitudinal studies. The module effectively establishes a strong foundation for learners, introducing GLIMMPSE software for conducting power analyses, which will be a recurring tool throughout the course. **Week 2: Foundations of Complex Multilevel and Longitudinal Designs** This week dives deeper into research design facets, considering factors like types of errors and correlation structures that can impact study outcomes. The guided exercises help illustrate complex concepts, ensuring students can apply theoretical knowledge in practical scenarios. **Week 3: Model Assumptions, Alignment, Missing Data, and Dropout** A critical aspect of conducting robust analyses is understanding model assumptions. This module addresses the implications of these assumptions on power analysis, and also tackles the often-overlooked issue of missing data. Equipping learners with strategies to handle missing data adds significant value to the course. **Week 4: Inputs to Analysis, Recruitment Feasibility, and Multiple Aims** Focusing on practical considerations, this week discusses how empirical literature and pilot studies can inform sample size calculations. Moreover, the module highlights factors influencing recruitment feasibility, which is crucial for planning successful studies. **Week 5: Ethics and Using Power and Sample Size Analysis to Get Funded** The final week is particularly insightful, discussing the ethical implications of power analysis and how to effectively present analyses in grant proposals. It underscores the importance of responsible research design and provides participants with strategies to enhance their chances of securing funding. ### Learning Outcomes and Recommendations By the course's end, learners gain a solid understanding of not only the technical aspects of power and sample size analysis but also the ethical and practical considerations that accompany these methodologies. The interactive assignments and peer reviews foster collaboration and deeper learning, making it a rewarding experience. **Why I Recommend This Course:** 1. **Strong Practical Application**: The course emphasizes real-world studies and examples, making theoretical concepts easier to grasp and apply. 2. **Expert Instruction**: Participants benefit from knowledgeable instructors who guide them through complex material clearly and effectively. 3. **Community Engagement**: Peer reviews and discussions enhance learning and provide diverse perspectives on the same topics. 4. **User-Friendly Software**: Familiarization with GLIMMPSE software equips learners with analytical tools necessary for modern research environments. 5. **Ethical Consideration Focus**: Incorporating ethics into the analysis process teaches researchers to approach their work responsibly. Both novice researchers and seasoned professionals in health and social sciences will find tremendous benefit in taking this course. It not only sharpens analytical skills but also prepares participants to tackle the challenges of designing rigorous and ethical studies. In conclusion, **Power and Sample Size for Multilevel and Longitudinal Study Designs** is a course I wholeheartedly recommend for anyone serious about advancing their research capabilities.

Syllabus

Week 1: Introduction to Multilevel and Longitudinal Designs

This first module introduces all course participants to the online course, its structure, its learning objectives, and your peers within the course. As noted, the course is composed of multiple activities to reach the learning objectives. Next, we review basic statistical concepts (e.g., hypothesis testing), and explore the fundamentals of both multi-level and longitudinal studies. Conceptual knowledge is covered to provide a framework for analyzing and synthesizing research study designs. This module lays a foundation for subsequent learning. The module concludes with an introduction to the GLIMMPSE software for conducting your own power and sample size analyses. You will walk through a fully guided exercise problem to solve for power for a single level cluster design.

Week 2: Foundations of Complex Multilevel and Longitudinal Designs

In the second module, we are going to dive into the many facets of research design, and important considerations related to power and sample size analysis. Specifically, we will examine between, within, and interactions; type 1 error, type 2 error, and power; and standard deviation, variance, and correlation structure. We will explore the appropriate statistical tests for use in specific models, criteria for evaluating these different tests, and how to choose an appropriate test for a data analysis problem. Finally, we will note how clusters of observations or multivariate designs can induce correlation. This module provides the details for specifying research designs, and the beginning steps in aligning the research design to sample size and power analysis. The module concludes with summarizing research designs for GLIMMPSE software. You will walk through a guided exercise problem to solve for sample size analysis for a longitudinal study.

Week 3: Model Assumptions, Alignment, Missing Data, and Dropout

The third module includes a wide variety of topics related to power and sample size analysis. First, we examine multivariate and mixed models, their assumptions, and how this assumption impact power. After we focus on aligning the features of data analysis and power analysis as well as the consequences of misalignment. Then we focus on missing data from sources like participant drop-out, machine failures or data entry errors; and how to account for missing data by adjusting your sample size. This module highlights several important features to consider in power and sample size analysis. To conclude the module, you will walk through an exercise problem to solve for power for a multilevel study independently.

Week 4: Inputs to Analysis, Recruitment Feasibility, and Multiple Aims

Our emphasis in the fourth module includes the many sources of inputs for power and sample size analysis from the empirical literature, internal pilot studies, planned pilot studies, and computer simulations. Each of these approaches is discussed in detail in relation to power and sample size analysis, including the overall benefits and challenges associated with each approach. Next we talk about recruitment feasibility and its critical importance to sample size calculations by discussing some key factors such as health, socioeconomic, and demographic factors that can be predictive of recruitment difficulty. Next, we deal with research studies that address multiple aims (e.g., hypotheses) and how to address this situation in your sample size analysis. Lastly, you will walk through a fully independent exercise problem to solve for sample size analysis for a multilevel study with longitudinal repeated measures.

Week 5: Ethics and Using Power and Sample Size Analysis to Get Funded

The fifth and final module first introduces the ethics of sample size analysis, including overpowered and underpowered research studies and the importance of early planning. Next, we walk through the process of structuring a sample size section of a proposal in a grant application. Then we dive into power curves again and discuss how to decide to incorporate a graphic to best tell your story. After, we explore subgroup analyses such as gender or race, and how to incorporate these design features into a power and sample size analysis. We close our last lecture on searching and applying for funding opportunities, and how a clear design and analysis plan improves your chances for funding. As this is our last module, you will walk through a fully independent exercise problem to solve for sample size analysis for a planned subgroup analysis. You will review at minimum two of your peers’ research design and sample size analyses documents, and finally, complete the final exam in the course.