Commentary on "Alternative Strategies for Identifying High-Performing Charter Schools in Texas"

In the last few years policy makers and practitioners nationally have shown much interest in identifying, recognizing, and replicating successful charter schools, many of which are showing that they can educate low-income and otherwise at-risk students remarkably well. However past efforts to identify high performing schools have been problematic. Using these systematic, rigorous value-added methods, the authors identify 44 Open Enrollment charter schools that merit a “high-performer” rating. Nearly all of those campuses identified serve a disadvantaged student population. The article also finds that most of those high performers are highly cost-effective, earning high ratings on the cost-efficiency measures. The authors argue for more widespread use of value-added modeling in the state accountability system. The approach taken to identifying high-performers is sensible and fair, but any formulaic approach to school labels comes with some limitations.

Intersystem Collaboration: A Statewide Initiative to Support Families

The study described in this paper utilized a qualitative case study method to assess the processes involved in inter-system collaboration in the context of one state's system change initiative. The collaborative experience is described from the perspective of participating service system professionals and family members. The major themes of collaboration that emerged from the study included changes in communication across systems, changes in inter-system relationships, changes in attitudes, changes in interactions with families, and changes in the ways services are delivered. Lessons learned and practice implications of each theme are discussed.

NONLINEAR TIME SERIES/SYSTEM ANALYSIS (STATE-SPACE, DYNAMICS, INTERVENTION, RESILIENCE, KALMAN)

A discussion of nonlinear dynamics, demonstrated by the familiar automobile, is followed by the development of a systematic method of analysis of a possibly nonlinear time series using difference equations in the general state-space format. This format allows recursive state-dependent parameter estimation after each observation thereby revealing the dynamics inherent in the system in combination with random external perturbations.^ The one-step ahead prediction errors at each time period, transformed to have constant variance, and the estimated parametric sequences provide the information to (1) formally test whether time series observations y(,t) are some linear function of random errors (ELEM)(,s), for some t and s, or whether the series would more appropriately be described by a nonlinear model such as bilinear, exponential, threshold, etc., (2) formally test whether a statistically significant change has occurred in structure/level either historically or as it occurs, (3) forecast nonlinear system with a new and innovative (but very old numerical) technique utilizing rational functions to extrapolate individual parameters as smooth functions of time which are then combined to obtain the forecast of y and (4) suggest a measure of resilience, i.e. how much perturbation a structure/level can tolerate, whether internal or external to the system, and remain statistically unchanged. Although similar to one-step control, this provides a less rigid way to think about changes affecting social systems.^ Applications consisting of the analysis of some familiar and some simulated series demonstrate the procedure. Empirical results suggest that this state-space or modified augmented Kalman filter may provide interesting ways to identify particular kinds of nonlinearities as they occur in structural change via the state trajectory.^ A computational flow-chart detailing computations and software input and output is provided in the body of the text. IBM Advanced BASIC program listings to accomplish most of the analysis are provided in the appendix. ^