Data from: A protocol for conducting and presenting results of regression-type analyses

Scientific investigation is of value only insofar as relevant results are obtained and communicated, a task that requires organizing, evaluating, analysing and unambiguously communicating the significance of data. In this context, working with ecological data, reflecting the complexities and interactions of the natural world, can be a challenge. Recent innovations for statistical analysis of multifaceted interrelated data make obtaining more accurate and meaningful results possible, but key decisions of the analyses to use, and which components to present in a scientific paper or report, may be overwhelming. We offer a 10-step protocol to streamline analysis of data that will enhance understanding of the data, the statistical models and the results, and optimize communication with the reader with respect to both the procedure and the outcomes. The protocol takes the investigator from study design and organization of data (formulating relevant questions, visualizing data collection, data exploration, identifying dependency), through conducting analysis (presenting, fitting and validating the model) and presenting output (numerically and visually), to extending the model via simulation. Each step includes procedures to clarify aspects of the data that affect statistical analysis, as well as guidelines for written presentation. Steps are illustrated with examples using data from the literature. Following this protocol will reduce the organization, analysis and presentation of what may be an overwhelming information avalanche into sequential and, more to the point, manageable, steps. It provides guidelines for selecting optimal statistical tools to assess data relevance and significance, for choosing aspects of the analysis to include in a published report and for clearly communicating information.科学研究唯有在获得并传播相关研究成果时才具备价值，而完成这一任务需要对数据进行整理、评估、分析，并清晰明确地阐释其意义。在此背景下，处理能够反映自然世界复杂性与相互作用关系的生态数据，往往是一项颇具挑战的工作。近年来，针对多维度关联数据的统计分析创新手段虽可助力获得更精准且更具意义的研究结果，但在选用何种分析方法、确定科学论文或报告中需呈现哪些分析内容时，这些关键决策往往会令研究者倍感压力。为此，我们提出一套十步流程方案（protocol），以简化数据分析流程，增进对数据、统计模型及分析结果的理解，并优化研究者与读者就分析流程与最终结果的沟通效果。该方案将引导研究者从研究设计与数据整理环节入手，包括明确相关研究问题、可视化数据收集过程、开展数据探索、识别数据依赖性，再历经分析实施环节，包括呈现模型、拟合模型与验证模型，以及结果输出（以数值与可视化形式），最终通过模拟拓展模型应用。每一步骤均包含阐明影响统计分析的数据特征的操作流程，以及书面呈现的相关规范。各步骤均辅以已发表文献中的数据案例进行说明。遵循本方案，可将原本令人应接不暇的海量信息整理、分析与呈现工作，拆解为一系列循序渐进且切实可控的步骤。本方案还提供了相关规范，用于选取最优统计工具以评估数据的相关性与显著性，筛选需纳入已发表报告的分析内容，并清晰准确地传递研究信息。