| Families are frequently described as and referred to as self-organized critical systems. In the field of nonlinear dynamics, self-organized critical systems have been shown to follow the inverse power law. The purpose of this study was to discern which, if any, families in self-perceived economic need taken from a convenient sample were modeled best by the inverse power law. Using the Gaussian model and lognormal model as a benchmark, a time-series analysis of wait times of 31 families seeking assistance from a faith-based community service organization provided the data to measure the fit of the inverse power law model. After selection based on pre-set criteria, the families' wait times between request for assistance were analyzed and put into a distribution using computer software. The inverse power law model and Gaussian or lognormal model were fit to the data. The fit of the model was determined by the greater R2 coefficient and substantive comparisons of histograms. Results indicated that the inverse power law provides the best model when raw data are used. Transforming to logged data changed the pattern within the data and gave unreliable results. Nonlinear dynamics rigorously applied to research in family sciences yields information and insight not available through more traditional research approaches. |
