COLLABORATIVE RESEARCH: META-ANALYSIS: EVALUATION AND IMPROVEMENT OF AN IMPORTANT SYNTHETIC TOOL

合作研究：荟萃分析：重要合成工具的评估和改进

基本信息

批准号：
1655426
负责人：
Craig Osenberg
金额：
$ 22.1万
依托单位：
University of Georgia Research Foundation Inc
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2017
资助国家：
美国
起止时间：
2017-08-15 至 2021-07-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1655426&HistoricalAwards=false
关键词：
COLLABORATIVE RESEARCH META ANALYSIS EVALUATION

项目摘要

In environmental biology, different studies on the same topic can reach very different conclusions. Sometimes those disagreements are because of different methods or slightly different questions being answered, but other times they're due to true differences between years, places or species being studied. It is important that scientists be able to distinguish between these possibilities and confidently reach conclusions. For example, some studies suggest that growing different crops together results in higher overall yields for farmers, whereas other studies suggest that growing a single type of crop results in the highest yields. Do these conflicting results reflect some randomness in nature, different methods used by scientists in the two types of studies, or some other reason? A statistical approach called meta-analysis has been developed to solve this problem. Meta-analysis has helped many disciplines because it allows scientists to combine results from many studies, taking into account how they differ and providing insights that would otherwise remain hidden. This project will use advanced techniques to improve how meta-analyses are done. Researchers will focus on using the technique in the field of ecology. Early career scientists engaged in this project will be trained in modern ecological methods, including meta-analysis. This project also will develop online materials that will be available publicly, thus helping educate ecologists across the nation in meta-analytic methods and improving the application of meta-analysis to ecological problems.To improve the application of meta-analysis in ecology, this project will: 1) systematically review recent ecological meta-analyses to describe how they are typically performed (i.e., the statistical model and the way the size of an effect is calculated) and the characteristics of the dataset (e.g., the number of studies, the sample sizes of each study, and the magnitude of among-study and within-study variation); 2) use simulations to evaluate the performance of existing meta-analysis models and proposed alternatives (e.g., differing in weighting schemes, using Bayesian vs. frequentist approaches, or using different adjustments for non-independence), under a range of conditions likely to be encountered in ecological datasets (e.g., using sample sizes and the magnitudes of different sources of variation observed in ecology). The results will be useful for improving the nation's ability to bring together and accurately interpret the complicated results of ecological studies.

在环境生物学中，对同一主题的不同研究可以得出非常不同的结论。有时，这些分歧是由于不同的方法或略有不同的问题要回答，但其他时候它们是由于所研究的年份，地点或物种之间的真正差异所致。重要的是，科学家必须能够区分这些可能性并自信得出结论。例如，一些研究表明，种植不同的农作物会导致农民的总体产量更高，而其他研究表明，种植一种单一类型的作物的产量最高。这些相互矛盾的结果是否反映了自然界中的某些随机性，两种研究类型的科学家使用的不同方法，或者其他原因？已经开发了一种称为荟萃分析的统计方法来解决此问题。荟萃分析对许多学科有所帮助，因为它允许科学家将许多研究的结果结合在一起，考虑到它们的差异以及提供原本隐藏的见解。该项目将使用先进的技术来改善荟萃分析的方式。研究人员将专注于在生态学领域使用该技术。从事该项目的早期职业科学家将接受现代生态方法的培训，包括荟萃分析。该项目还将开发在线材料，从而有助于对全国各地的生态学家进行荟萃分析方法的教育，并改善荟萃分析在生态问题中的应用。为了改善荟萃分析在生态学中的应用，该项目将：1）系统地审查了近期的生态元分析，以描述其典型的典型和统计的统计数据，并且是典型的统计数据。数据集的特征（例如，研究的数量，每项研究的样本大小以及研究和研究中的变化中的大小）； 2）使用模拟来评估现有的荟萃分析模型和拟议替代方案的性能（例如，在加权方案，使用贝叶斯与频繁的方法与频繁的方法或对非独立的不同调整），在一系列可能在生态数据集中遇到的范围（例如，使用样本量量和大量的量级），在不同的生态数据集中可能会遇到不同的条件。该结果将有助于提高国家汇集在一起并准确解释生态学研究的复杂结果的能力。