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 novel automated integrative complexity system designed specifically from the integrative complexity theoretical framework. This new automated IC system achieved an alpha of .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 (average alpha=.62; average r=.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 differentiation and integration 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.
ASJC Scopus subject areas
- Social Psychology
- Experimental and Cognitive Psychology
- Clinical Psychology
- Sociology and Political Science
- Political Science and International Relations