Overcoming the pitfalls of categorizing continuous variables in ecology, evolution, and behavior ...
Many variables in biological research - from body size to life history timing to environmental characteristics - are measured continuously (e.g., body mass in kilograms) but analyzed as categories (e.g., large versus small), which can lower statistical power and change interpretation. We conducted a...
| Main Authors: | , |
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| Format: | Dataset |
| Language: | English |
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Dryad
2023
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| Online Access: | https://dx.doi.org/10.5061/dryad.5x69p8d9r https://datadryad.org/stash/dataset/doi:10.5061/dryad.5x69p8d9r |
| Summary: | Many variables in biological research - from body size to life history timing to environmental characteristics - are measured continuously (e.g., body mass in kilograms) but analyzed as categories (e.g., large versus small), which can lower statistical power and change interpretation. We conducted a mini-review of 72 recent publications in six popular ecology, evolution, and behavior journals to quantify the prevalence of categorization. We then summarized commonly categorized metrics and simulated a dataset to demonstrate the drawbacks of categorization using common variables and realistic examples. We show that categorizing continuous variables is common (31% of publications reviewed). We also underscore that predictor variables can and should be collected and analyzed continuously. Finally, we provide recommendations on how to keep variables continuous throughout the entire scientific process. Together, these pieces comprise an actionable guide to increasing statistical power and facilitating large ... : # Overcoming the pitfalls of categorizing continuous variables in ecology and evolutionary biology [https://doi.org/10.5061/dryad.5x69p8d9r](https://doi.org/10.5061/dryad.5x69p8d9r) We simulated data to quantify the detrimental impact of categorizing continuous variables using various statistical breakpoints and sample sizes (details below). To give the example biological relevance, we created a dataset that illustrates the complexity of life history theory and climate change impacts, and contains a predictor variable that is frequently categorized (Table 2) - reproductive timing in one year and its effect on body size in the following year. A reasonable research question would be: How does timing of reproduction in year t influence body mass at the start of the breeding season in year t+1? For illustrative purposes, let’s say we collected data from individually banded penguins in Antarctica. Based on the mechanistic relationships between seasonally available sea ice and food availability, we hypothesize ... |
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