Alternative Strategies for Identifying High-Performing Charter Schools in Texas

The Obama administration's recurring policy emphasis on high-performing charter schools begs the obvious question: how do you identify a high-performing charter school? That is a crucially important policy question because any evaluation strategy that incorrectly identifies charter school performance could have negative effects on the economically and/or academically disadvantaged students who frequently attend charter schools. If low-performing schools are mislabeled and allowed to persist or encouraged to expand, then students may be harmed directly. If high-performing schools are driven from the market by misinformation, then students will lose access to programs and services that can make a difference in their lives. Most of the scholarly analysis to date has focused on comparing the performance of students in charter schools to that of similar students in traditional public schools (TPS). By design, that research measures charter school performance only in relative terms. Charter schools that outperform similarly situated, but low performing, TPSs have positive effects, even if the charter schools are mediocre in an absolute sense. This analysis describes strategies for identifying high-performing charter schools by comparing charter schools with one another. We begin by describing salient characteristics of Texas charter schools. We follow that discussion with a look at how other researchers across the country have compared charter school effectiveness with TPS effectiveness. We then present several metrics that can be used to identify high-performing charter schools. Those metrics are not mutually exclusive—one could easily justify using multiple measures to evaluate school effectiveness—but they are also not equally informative. If the goal is to measure the contributions that schools are making to student knowledge and skills, then a value-added approach like the ones highlighted in this report is clearly superior to a levels-based approach like that taken under the current accountability system.

Behavioral and psychosocial factors associated with sexual risk in a population of crack cocaine users

The purpose of this dissertation was to examine the relationship between key psychosocial and behavioral components of the Transtheoretical Model and the Theory of Reasoned Action for sexual risk reduction in a population of crack cocaine smokers and sex workers, not in drug treatment. ^ The first study examined the results of an analysis of the association between two principal constructs in the Transtheoretical Model, the processes of change and the stages of change for condom use, in a high risk population. In the analysis of variance for all respondents, the overall F-test revealed that people in different stages have different levels of experiential process use, F(3,317) = 17.79, p = 0.0001 and different levels of behavioral process use, F(3,317) = 28.59, p = .0001. For the experiential processes, there was a significant difference between the precontemplation/contemplation stage, and both the action, and maintenance, stages.^ The second study explored the relationship between the Theory of Reasoned Action “beliefs” and the stages-of-change in the same population. In the analysis of variance for all participants, the results indicate that people in different stages did value the positive beliefs differently, F(3,502) = 15.38, p = .0001 but did not value the negative beliefs differently, F(3,502) = 2.08, p = .10. ^ The third study explored differences in stage-of-change by gender, partner type drug use, and HIV status. Three discriminant functions emerged, with a combined χ2(12) = 139.57, p = <.0001. The loading matrix of correlations between predictors and discriminant functions demonstrate that the strongest predictor for distinguishing between the precontemplation/contemplation stage and the preparation, action, and maintenance stages (first function) is partner type (.962). The loadings on the second discriminant function suggest that once partner type has been accounted for, ever having HIV/AIDS (.935) was the best predictor for distinguishing between the first three stages and the maintenance stage. ^ These studies demonstrate that behavioral change theories can contribute important insight to researchers and program planners attempting to alter HIV risk behavior in high-risk populations. ^

Correlation of tuberculosis and human immunodeficiency virus in Harris County, Texas from 2009 through 2010 using Geographic Information Systems

A population based ecological study was conducted to identify areas with a high number of TB and HIV new diagnoses in Harris County, Texas from 2009 through 2010 by applying Geographic Information Systems to determine whether distinguished spatial patterns exist at the census tract level through the use of exploratory mapping. As of 2010, Texas has the fourth highest occurrence of new diagnoses of HIV/AIDS and TB.[31] The Texas Department of State Health Services (DSHS) has identified HIV infected persons as a high risk population for TB in Harris County.[29] In order to explore this relationship further, GIS was utilized to identify spatial trends. ^ The specific aims were to map TB and HIV new diagnoses rates and spatially identify hotspots and high value clusters at the census tract level. The potential association between HIV and TB was analyzed using spatial autocorrelation and linear regression analysis. The spatial statistics used were ArcGIS 9.3 Hotspot Analysis and Cluster and Outlier Analysis. Spatial autocorrelation was determined through Global Moran's I and linear regression analysis. ^ Hotspots and clusters of TB and HIV are located within the same spatial areas of Harris County. The areas with high value clusters and hotspots for each infection are located within the central downtown area of the city of Houston. There is an additional hotspot area of TB located directly north of I-10 and a hotspot area of HIV northeast of Interstate 610. ^ The Moran's I Index of 0.17 (Z score = 3.6 standard deviations, p-value = 0.01) suggests that TB is statistically clustered with a less than 1% chance that this pattern is due to random chance. However, there were a high number of features with no neighbors which may invalidate the statistical properties of the test. Linear regression analysis indicated that HIV new diagnoses rates (β=−0.006, SE=0.147, p=0.970) and census tracts (β=0.000, SE=0.000, p=0.866) were not significant predictors of TB new diagnoses rates. ^ Mapping products indicate that census tracts with overlapping hotspots and high value clusters of TB and HIV should be a targeted focus for prevention efforts, most particularly within central Harris County. While the statistical association was not confirmed, evidence suggests that there is a relationship between HIV and TB within this two year period.^