DOI: 10.5061/dryad.5x69p8d9r
title: Overcoming the pitfalls of categorizing continuous variables in ecology, evolution, and behavior

description: 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 synthesis studies by simply leaving continuous
 variables alone. Overcoming the pitfalls of categorizing continuous
 variables will allow ecologists, ethologists, and evolutionary biologists
 to continue making trustworthy conclusions about natural processes, along
 with predictions about their responses to climate change and other
 environmental contexts.
description: # 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 that late reproductive timing could negatively impact the abilities of penguins to grow larger before the next breeding season. Let’s say we wander around the penguin colony recording the initiation date of first nest of each banded penguin (reproductive timing, measured as “day of year”, continuous), and then return to Antarctica the following year to weigh those same penguins using a platform scale (body mass, kilograms, continuous). The data have a *Gamma* distribution with a long tail because there are only a few very small breeding penguins. With these data, we will describe simulations to answer the questions: how does the relationship between reproductive timing and body mass change if 1) Reproductive Timing data are categorized using different breakpoints, and 2) the dataset contains different sample sizes. We used R version 4.3.1 (R Core Team, 2022)  for all analyses, including the glm() function (Gamma, link=”log”) for model fitting, the AIC() function for Akaike Information Criterion model comparisons (Akaike, 1998), the emmeans() function to calculate 95% Confidence Intervals (±1.96*SE back-transformed from the log scale), and the cld() function to create compact letter displays. We calculated R<sup>2</sup> using McFaddens' Pseudo-R<sup>2</sup> (McFadden, 1973). Please see manuscript for most up-to-date methods.  ## Description of the data and file structure The code files are used to create data (BodySize, in kilograms, and ReproTime, in days). ## Code/Software Code to reproduce data, analyses, and figures, to be opened in R. The "CatVsCont Fake Data Categorization Methodsv12.R" file simulates datasets to test for the influence of different categorization methods on results. The "CatVsCont Fake Data Sample Sizev12.R" file simulates datasets to test for the influence of sample size on results.