PubMed · 14744688
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J R Crosby, E A Hesketh. 2004. Developing the teaching instinct. 11: Small group learning.. https://doi.org/10.1080/01421590310001648122
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Scholars have characterized academic communities of faculty, administration, and students in U.S. universities as "organized anarchies." In contrast, we offer evidence that the community structures of two representative public university systems are notably systematic by applying empirical phase-diagram techniques from the nonlinear dynamics literature to reconstruct low-dimensional deterministic behavior from historic data on the coevolution of faculty, administration, and student populations in each system. Ecological community models, fit with population data for each university, reproduce the essence of this behavior. The models offer novel explanations of how university resources obtained from enrollments and other sources are systematically partitioned among faculty, administration, and student populations interacting in shifting and well-defined community roles. This work offers empirical evidence that ecological principles, typically reserved for characterizing nonhuman interactions in biological systems, can shed light on human interactions in social systems.
Growth mixture models are often used to determine if subgroups exist within the population that follow qualitatively distinct developmental trajectories. However, statistical theory developed for finite normal mixture models suggests that latent trajectory classes can be estimated even in the absence of population heterogeneity if the distribution of the repeated measures is nonnormal. By drawing on this theory, this article demonstrates that multiple trajectory classes can be estimated and appear optimal for nonnormal data even when only 1 group exists in the population. Further, the within-class parameter estimates obtained from these models are largely uninterpretable. Significant predictive relationships may be obscured or spurious relationships identified. The implications of these results for applied research are highlighted, and future directions for quantitative developments are suggested.