Why Single Measures Fail: The Case for Systems-Based Analysis in Preclinical Research
Researchers have long relied on single measures and univariate analytical approaches to evaluate animal health, physiology, and behavior as proxies for complex biological states. While clearly useful, a growing body of evidence spanning from early psychometrics to modern neuroscience shows that single measures can underperform and mislead. In this article, we make the case that animal physiology and behavior are complex phenomena that often warrant analysis as integrated systems.
This is not a new principle. In 1959, Campbell and Fiske argued that truly understanding a phenomenon requires measuring not only variables likely related to your response of interest, but also variables likely unrelated to it. They then evaluated all those measures in relation to each other. By analyzing which measures correlated with one another and which did not, they could build corollary evidence that related measures were genuinely capturing the response of interest, while measures that correlated with unrelated variables were likely confounders.
Importantly, this was not a formally validated statistical method but a systematic, rational interrogation of the data. The key contribution was demonstrating that inter-relational analysis improves insight in ways that isolated analysis cannot. You cannot trust a single measurement alone. When multiple measures are analyzed relationally, confounds are exposed and what is actually being captured becomes clearer. In doing so, Campbell and Fiske laid the conceptual foundation for the statistically rigorous relational methods that would follow.
Bringing the Framework to Animal Behavior
Crusio (2001) brought this methodological framework to animal behavioral neuroscience. Applying principal component analysis to mouse behavioral data, Crusio identified two factors underlying mouse exploratory behavior. One represented active exploration and one represented stress and fear. Both factors showed significant genetic correlations with neuroanatomical differences in hippocampal mossy fiber terminal field size. These relationships were invisible at the phenotypical level. They only became visible through multivariate analysis, demonstrating that traditional statistical analysis, like performing multiple ANOVAs of individual measures, can potentially mask underlying biological truth.
Similarly in 2002, Chesler et al demonstrated, through a multi-factor computational analysis that ranked the relative contributions of genetic and environmental variables, that factors driving variability in individual behavioral measures were only identifiable within a broader analytical framework. In this case sources of variation remained invisible to any single-measure approach but were revealed by the relational analysis.
By 2019, Datta and colleagues, in a computational-based review of neuroethology (the study of naturalistic behavior and its neural mechanisms), had gone further. They argued that reliance on single measures is not merely a practical limitation but a fundamental scientific one, and that making sense of complex biological systems will ultimately require measurement and analysis of matching complexity.
Forward-thinking: Toward a Solution
The reproducibility crisis in preclinical research is much discussed, with real and costly impacts. One way to mitigate it is to ground animal studies in a systems-based methodology that treats behavior and physiology as integrated phenomena requiring integrated analysis. Fortunately, emerging technologies are making this approach more feasible. Continuous, high-resolution data collection dramatically increases the number of observations available for multivariate analysis, reduces dependence on identifying the "right" timepoints, and improves statistical power.
The evidence is clear: studying single measures in isolation risks fundamental scientific limitations, and preclinical research stands to benefit immensely from a systems-based approach.