Tightly Bound: The Relationship of Network Clustering Coefficients and Reincarceration at Three Therapeutic Communities.

OBJECTIVE

Clustering, the tendency of individuals to form closed triads, is ubiquitous in human social networks. Previous research has found that therapeutic community (TC) residents whose social networks include a high degree of clustering are less likely to be reincarcerated following discharge. In this study, we test this finding in a larger number of TCs.

METHOD

We use a temporal network autocorrelation model (TNAM) to analyze clustering in social networks of affirmations exchanged between TC residents as a predictor of the hazard of reincarceration. The networks were drawn from three corrections-based TCs, two of which include both men's and women's units and one of which housed only men.

RESULTS

The findings were inconsistent across facilities. Increased clustering correlates with a reduced hazard of reincarceration for women at both facilities (β = -3.274, 95% CI [-4.299, -2.238]; β = -18.233, 95% CI [-32.370, -4.095]) and for men at two of the facilities (β =-0.910, 95% CI [-1.213, -0.606]; β = -1.393, 95% CI [-1.825, -0.961]). However, clustering increased the hazard of reincarceration for men at one facility (β = 5.558, 95% CI [4.124, 6.993]).

CONCLUSIONS

These results support the idea that the likelihood of reincarceration following discharge from a TC is predicted by clustering, a network structure that occurs at a system level between the individual resident and the entire community. Inconsistency in the direction of the relationship suggests that future research should analyze predictors of prosocial clustering in TCs.