### An evaluation of the log‐transformed strategy for count data in ecological studies

#### Abstract

Count data are found in Ecological studies. The log-transformed strategy is commonly used in the count or rate data. The rate data are defined as the count data divided by a scale variable such as population at risk or an expected count. The log-transformed strategy is used to satisfy the parametric approach and simplify the model estimation. However, this strategy is not correct. The parameter estimation based on the log-transformed strategy could produce a biased estimate with a high standard error estimate. In this study, we are interested in evaluating the bias of parameter estimates based on the log-transformed strategy on the linear regression model. The generalized linear models have better performance in dealing with count data. However, some practitioners who are more familiar with the linear regression model prefer to use a log-transformed strategy and handle the zero cases by adding small values to zero observations. Simulation data from a Poisson distribution were used to compare the Poisson regression model and the linear regression model combined with the log-transformed strategy. The models were evaluated based on the bias and the root-mean-squared error statistics. We found that the linear regression with log-transformation strategy provided a high bias and a small value of root-mean-squared error, especially for small sample size and a small value of the count data. We also use real data set to explore more detail the uses of log-transformed strategy and compare it with Poisson regression.

**Published:**2021-05-04

**How to Cite this Article:**Anna Chadidjah, I.G.N.M. Jaya, An evaluation of the log‐transformed strategy for count data in ecological studies, Commun. Math. Biol. Neurosci., 2021 (2021), Article ID 41 Copyright © 2021 Anna Chadidjah, I.G.N.M. Jaya. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Commun. Math. Biol. Neurosci.

ISSN 2052-2541

Editorial Office: [email protected]

Copyright ©2024 CMBN