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所在平台: Coursera |
课程主页: https://www.coursera.org/learn/power-sample-size
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课程名称：多层次和纵向研究设计的效能与样本量 课程概述： 《多层次和纵向研究设计的效能与样本量》是一门为期五周的完全在线课程，涵盖了创新的、基于研究的效能和样本量方法，以及适用于多层次和纵向研究的软件。课程中教授的效能和样本量方法以及软件可以用于任何与健康相关或更广泛的社会科学相关的应用（例如，教育研究）。所有课程视频中的示例均来自于真实的行为和社会科学研究，采用多层次和纵向设计。该课程的理念是专注于进行效能和样本量方法所需的概念知识，目标是教授和传播准确的样本量选择方法，并最终为您专业背景中的相关研究制定效能/样本量分析。 在研究中，效能和样本量的选择是研究人员面临的最重要的伦理问题之一。过大的干预研究可能让志愿者面临不必要的伤害，而过小的研究则可能无法达到科学目标，同样对参与者造成潜在的伤害且无法产生相关的知识收益。在观察性研究中，虽然对参与者没有潜在的危害，但适当的效能确保了时间和资金的良好运用。 大多数国家卫生研究院（NIH）的研究小组只有在获得者编写有说服力且准确的效能和样本量分析时才会资助其拨款。美国教育部的教育统计、研究和评估机构（IES）也提供竞争性资助，要求有具有说服力和准确的效能与样本量分析（目标3：有效性和复制，目标4：有效性/扩展）。 课程结束时，学习者将能够： - 使用框架和策略进行研究规划 - 将研究目标写成可测试的假设 - 描述纵向和多层次研究设计 - 编写统计分析计划 - 规划子群体（如种族和民族）的抽样设计 - 演示招募的可行性 - 描述预期的缺失数据和流失 - 编写与计划的统计分析相一致的效能和样本量分析 课程大纲： 第1周：多层次和纵向设计导论 - 介绍课程结构、学习目标及参与者，回顾基本统计概念，并探讨多层次和纵向研究的基础。 第2周：复杂多层次和纵向设计基础 - 深入研究研究设计的各个方面与效能和样本量分析的重要考量。 第3周：模型假设、对齐、缺失数据与流失 - 讨论多元和混合模型的假设及其对效能的影响，以及如何处理缺失数据。 第4周：分析输入、招募可行性与多重目标 - 探讨多种影响效能和样本量分析的输入来源及其对样本大小计算的影响。 第5周：伦理及如何利用效能和样本量分析获得资助 - 介绍样本量分析的伦理问题，如何写作资助申请中的样本量部分以及如何提升获取资助的机会。 该课程为期五周，包含互动式教育视频、软件演示视频、在线阅读、测验和作业。最后的课程项目是设计一个同伴评审的研究，以便进行未来的效能或样本量分析。
Name:Week 1: Introduction to Multilevel and Longitudinal Designs
Description: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.
Name:Week 2: Foundations of Complex Multilevel and Longitudinal Designs
Description: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.
Name:Week 3: Model Assumptions, Alignment, Missing Data, and Dropout
Description: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.
Name:Week 4: Inputs to Analysis, Recruitment Feasibility, and Multiple Aims
Description: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.
Name:Week 5: Ethics and Using Power and Sample Size Analysis to Get Funded
Description: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.
Power and Sample Size for Longitudinal and Multilevel Study Designs, a five-week, fully online course covers innovative, research-based power and sample size methods, and software for multilevel and longitudinal studies. The power and sample size methods and software taught in this course can be used for any health-related, or more generally, social science-related (e.g., educational research) application. All examples in the course videos are from real-world studies on behavioral and social science employing multilevel and longitudinal designs. The course philosophy is to focus on the conceptual knowledge to conduct power and sample size methods. The goal of the course is to teach and disseminate methods for accurate sample size choice, and ultimately, the creation of a power/sample size analysis for a relevant research study in your professional context. Power and sample size selection is one of the most important ethical questions researchers face. Interventional studies that are too large expose human volunteer research participants to possible, and needless, harm from research. Interventional studies that are too small will fail to reach their scientific objective, again bringing possible harm to research participants, without the possibility of concomitant gain from the increase in knowledge. For observational studies in which there are no possible harms to the participants, such as observational studies, proper power ensures good stewardship of both time and money. Most National Institutes of Health (NIH) study sections will only fund a grant if the grantee has written a compelling and accurate power and sample size analysis. The Institute of Education Sciences (IES), the statistics, research, and evaluation arm of the U.S. Department of Education, also offers competitive grants requiring a compelling and accurate power and sample size analysis (Goal 3: Efficacy and Replication and Goal 4: Effectiveness/Scale-Up). At the end of the online course, learners will be able to: • Use a framework and strategy for study planning • Write study aims as testable hypotheses • Describe a longitudinal and multilevel study design • Write a statistical analysis plan • Plan a sampling design for subgroups, e.g. racial and ethnic • Demonstrate the feasibility of recruitment • Describe expected missing data and dropout • Write a power and sample size analysis that is aligned with the planned statistical analysis This is a five-week intensive and interactive online course. We will use a mix of instructional videos, software demonstration videos, online readings, quizzes, and exercise assignments. The final course project is a peer-reviewed research study you design for future power or sample size analysis.
