The analysis of bridging constructs with hierarchical clustering methods: An application to identity

When analyzing psychometric surveys, some design and sample size limitations challenge existing approaches. Hierarchical clustering, with its graphics (heat maps, dendrograms, means plots), provides a nonparametric method for analyzing factorially-designed survey data, and small samples data. In the present study, we demonstrated the advantages of using hierarchical clustering (HC) for the analysis of non-higher-order measures, comparing the results of HC against those of exploratory factor analysis. As a factorially-designed survey, we used the Identity Labels and Life Contexts Questionnaire (ILLCQ), a novel measure to assess identity as a bridging construct for the intersection of identity domains and life contexts. Results suggest that, when used to validate factorially-designed measures, HC and its graphics are more stable and consistent compared to EFA

MAXIMAL CLIQUE AND GRAPH COLORING HEURISTICS FOR OVERLAPPING COMMUNITY DETECTION IN NETWORK PSYCHOMETRICS

Community detection is an important aspect of network psychometrics. An inherent limitation of popular methods such as the walktrap and spin glass algorithms is that they do not allow vertices (e.g., symptoms) to have membership in more than one community. Clique percolation remedies this limitation by allowing overlapping communities. However, clique percolation does not necessarily produce solutions in accordance with the standard definition of ‘community’ (i.e., a dense subgraph of the network), often fails to assign all vertices to at least one community, and presents formidable model selection challenges. In this paper, we present two new heuristics for overlapping community detection that are less prone to these problems. The first heuristic assembles communities using maximal cliques, whereas the second produces the communities using cliques identified from colorings of the network graph. Four psychological networks from the literature are used to demonstrate the new heuristics. The results indicate that both heuristics produce communities of dense subgraphs in accordance with the traditional definition of a community and tend to assign all (or almost all) vertices to a community

Walktrap Using Kmeans Clustering

This zip file contains the MATLAB files required to replicate the simullation results in the paper: Improving the Walktrap Algorithm Using K-means Clustering</p

MATLAB OCD Files

These MATLAB files can be used to produce the simulation results in the paper titled: A Mexima0-Clique-Based Set-Covering Approach to Overlapping Community Detection".</p

Does crude measurement contribute to observed unidimensionality of psychological constructs? An example with DSM-5 alcohol use disorder

Mental disorders are complex, multifaceted phenomena that are associated with profound heterogeneity and comorbidity. Despite the heterogeneity of mental disorder, most are generally considered unitary dimensions. We argue that certain measurement practices, especially using too few indicators per construct, preclude the detection of meaningful multidimensionality. We demonstrate the implications of crude measurement for detecting construct multidimensionality with alcohol use disorder (AUD). To do so, we used a large sample of college heavy drinkers (N=909) for whom AUD symptomology was thoroughly assessed (87 items) and a blend of confirmatory factor analysis, exploratory factor analysis, and hierarchical clustering. A unidimensional AUD model with one item per symptom criterion fit the data well, whereas a unidimensional model with all items fit the data poorly. Starting with an 11- item AUD model, model fit decreased and the variability in factor loadings increased as additional items were added to the model. Additionally, multidimensional models outperformed unidimensional ones in terms of variance explained in theoretically-relevant external criteria. All told, we converged on a hierarchically-organized model of AUD with three broad, transcriterial dimensions that reflected Tolerance, Withdrawal, and Loss of Control. In addition to introducing a hierarchical model of AUD, we propose that thorough assessment of psychological constructs paired with serious consideration of alternative, multidimensional structures can move past the deadlock of their unidimensional representations