The definition and measurement of heterogeneity
By Abraham Nunes, Thomas P. Trappenberg, M. Alda. 2020
63 citations recorded by the source corpus.
Heterogeneity is an important concept in psychiatric research and science more broadly. It negatively impacts effect size estimates under case–control paradigms, and it exposes important flaws in our existing categorical nosology. Yet, our field has no precise definition of heterogeneity proper. We tend to quantify heterogeneity by measuring associated correlates such as entropy or variance: practices which are akin to accepting the radius of a sphere as a measure of its volume. Under a definition of heterogeneity as the degree to which a system deviates from perfect conformity, this paper argues that its proper measure roughly corresponds to the size of a system’s event/sample space, and has units known as numbers equivalent. We arrive at this conclusion through focused review of more than 100 years of (re)discoveries of indices by ecologists, economists, statistical physicists, and others. In parallel, we review psychiatric approaches for quantifying heterogeneity, including but not limited to studies of symptom heterogeneity, microbiome biodiversity, cluster-counting, and time-series analyses. We argue that using numbers equivalent heterogeneity measures could improve the interpretability and synthesis of psychiatric research on heterogeneity. However, significant limitations must be overcome for these measures—largely developed for economic and ecological research—to be useful in modern translational psychiatric science.
Subfields: Cognitive Neuroscience, Experimental and Cognitive Psychology, Global and Planetary Change, Statistics and Probability
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Citations in the corpus
- Measuring Data (2022, background): “Diversity is also referred to as heterogeneity in many sciences (Rényi et al., 1961; Nunes et al., 2020) which have their own approaches to measure it, Hill numbers in ecology (Hill, 1973), Hannah–Kay indices in economics (Hannah and Kay, 1977).”