Automated Integrative Complexity

Abstract

Integrative complexity is a conceptually unique and very popular measurement of the complexity of human thought. We believe, however, that it is currently being underutilized because it takes quite a bit of time to score. More time‐efficient computer‐based measurements of complexity that are currently available are correlated with integrative complexity at fairly low levels. To help fill in this gap, we developed a novelautomated integrative complexitysystem designed specifically from the integrative complexity theoretical framework. This new automatedICsystem achieved analphaof .72 on the standard integrative complexity coding test. In addition, across nine datasets covering over 1,300 paragraphs, this new automated system consistently showed modest relationships with human‐scored integrative complexity (averagealpha = .62; averager = .46). Further analyses revealed that this relationship consistently remained significant when controlling for superficial markers of complexity and that the new system accounted for both the differentiationandintegration components of integrative complexity. Although the overlap between the automated and human‐scored systems is only modest (and thus suggests the continued usefulness of human scoring), it nonetheless provides the best automated integrative complexity measurement to date.

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