A protocol for conducting and presenting results of regression‐type analyses

TLDR

Scientific investigation is valuable only when relevant results are obtained and communicated, which requires organizing, evaluating, analyzing, and clearly presenting data; ecological data add complexity, but recent statistical innovations improve accuracy, though selecting analyses and presentation components can be overwhelming. The authors present a 10‑step protocol that guides investigators in selecting optimal statistical tools, structuring analyses, and communicating results to enhance understanding and clarity. The protocol guides investigators from study design and data organization through analysis, model fitting and validation, to output presentation and model extension via simulation, providing procedures to clarify data aspects and guidelines for written presentation, illustrated with literature examples. Adopting the protocol transforms an overwhelming information avalanche into sequential, manageable steps for organization, analysis, and presentation.

Abstract

Summary 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.