Premium
How important is the statistical approach for analyzing categorical data? A critique using artificial nests
Author(s) -
Lewis Keith P.
Publication year - 2004
Publication title -
oikos
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.672
H-Index - 179
eISSN - 1600-0706
pISSN - 0030-1299
DOI - 10.1111/j.0030-1299.2004.12636.x
Subject(s) - categorical variable , logistic regression , statistical power , statistical hypothesis testing , statistics , statistical model , computer science , data set , binary data , type i and type ii errors , data mining , artificial intelligence , mathematics , binary number , arithmetic
Ecologists rely heavily upon statistics to make inferences concerning ecological phenomena and to make management recommendations. It is therefore important to use statistical tests that are most appropriate for a given data‐set. However, inappropriate statistical tests are often used in the analysis of studies with categorical data (i.e. count data or binary data). Since many types of statistical tests have been used in artificial nests studies, a review and comparison of these tests provides an opportunity to demonstrate the importance of choosing the most appropriate statistical approach for conceptual reasons as well as type I and type II errors. Artificial nests have routinely been used to study the influences of habitat fragmentation, and habitat edges on nest predation. I review the variety of statistical tests used to analyze artificial nest data within the framework of the generalized linear model and argue that logistic regression is the most appropriate and flexible statistical test for analyzing binary data‐sets. Using artificial nest data from my own studies and an independent data set from the medical literature as examples, I tested equivalent data using a variety of statistical methods. I then compared the p‐values and the statistical power of these tests. Results vary greatly among statistical methods. Methods inappropriate for analyzing binary data often fail to yield significant results even when differences between study groups appear large, while logistic regression finds these differences statistically significant. Statistical power is is 2–3 times higher for logistic regression than for other tests. I recommend that logistic regression be used to analyze artificial nest data and other data‐sets with binary data.