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.

科学研究的价值，仅在于能否获取并传播相关研究成果，而这一任务需要对数据进行整理、评估、分析，并清晰明确地阐释数据的意义。在此背景下，处理反映自然界复杂性与相互作用关系的生态数据，往往颇具挑战。近年来，针对多维度关联数据的统计分析方法创新，使得获取更精准且更具价值的研究结果成为可能，但在选择适用的分析方法、以及确定科学论文或报告中需呈现的分析内容时，研究者往往会面临繁杂难择的困境。为此，我们提出一套10步流程方案，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，并优化研究过程与结果向读者的传播效果。该方案引导研究者从研究设计与数据整理（包括明确相关研究问题、可视化数据采集过程、开展数据探索、识别数据依赖关系）出发，历经分析实施（包括模型呈现、拟合与验证）与结果输出（以数值与可视化形式呈现），最终通过模拟拓展模型应用。每一步骤均包含明确影响统计分析的数据维度的操作流程，以及书面报告撰写的规范指引。本方案各步骤均辅以已发表文献中的数据案例进行说明。遵循该方案，可将原本繁杂如山的信息整理、分析与呈现工作，拆解为循序渐进且切实可行的可控步骤。同时，本方案还提供了相关规范，用于遴选最优统计工具以评估数据的相关性与显著性，确定需纳入已发表报告的分析内容，并清晰高效地传递研究信息。