The DOI matches its Crossref record; 48 of the 48 references it cites were found in a registry or an index.
A protocol for conducting and presenting results of regression‐type analyses
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.
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Cites
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BackgroundInfluentialA general and simple method for obtaining R 2 from generalized linear mixed‐effects models
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Cited by
A brief introduction to mixed effects modelling and multi-model inference in ecology
Harrison, X. A., Donaldson, L., Correa-Cano, M. E., Evans, J., Fisher, D. N., Goodwin, C. E. D., Robinson, B. S., Hodgson, D. J., & Inger, R. (2018) · PeerJ · doi:10.7717/peerj.4794 · Cited by 2,094
Douma, J. C., & Weedon, J. T. (2019) · Methods in Ecology and Evolution · doi:10.1111/2041-210x.13234 · Cited by 719
LaDue, C. A., Vandercone, R. P. G., Kiso, W. K., & Freeman, E. W. (2022) · Animals · doi:10.3390/ani12091215 · Cited by 26
Metadata
- Authors
- Zuur, A. F., & Ieno, E. N.
- Venue
- Methods in Ecology and Evolution
- Publisher
- Wiley
- Published
- 2016-06
- Volume
- 7
- Issue
- 6
- Pages
- 636-645
- ISSN
- 2041-210X (ISSN-L)
- OpenAlex
- W2419728870
- Cited by
- 834
- References
- 48
- FWCI
- 49.66
- Type
- journal article
- Language
- en
- Open access
- closed
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.